Cancer Research and Treatment

Original Articles

A Machine Learning Risk Prediction Model for Gastric Cancer with SHapley Additive exPlanations: Bomi Park, Chung Ho Kim, Jae Kwan Jun, Mina Suh, Kui Son Choi, Il Ju Choi, Hyun Jin Oh; Received August 29, 2024 Accepted December 15, 2024 Published online December 16, 2024; DOI: https://doi.org/10.4143/crt.2024.843 [Epub ahead of print]

Abstract PDF Supplementary Material PubReader ePub

Purpose
Gastric cancer (GC) prediction models hold potential for enhancing early detection by enabling the identification of high-risk individuals, facilitating personalized risk-based screening, and optimizing the allocation of healthcare resources.
Materials and Methods
In this study, we developed a machine learning-based GC prediction model utilizing data from the Korean National Health Insurance Service, encompassing 10,515,949 adults who had not been diagnosed with GC and underwent GC screening during 2013-2014, with a follow-up period of 5 years. The cohort was divided into training and test datasets at an 8:2 ratio, and class imbalance was mitigated through random oversampling.
Results
Among various models, logistic regression demonstrated the highest predictive performance, with an area under the receiver operating characteristic curve (AUC) of 0.708, which was consistent with the AUC obtained in external validation (0.669). Importantly, the outcomes were robust to missing data imputation and variable selection. The SHapley Additive exPlanations (SHAP) algorithm enhanced the explainability of the model, identifying advancing age, being male, Helicobacter pylori infection, current smoking, and a family history of GC as key predictors of elevated risk.
Conclusion
This predictive model could significantly contribute to the early identification of individuals at elevated risk for GC, thereby enabling the implementation of targeted preventive strategies. Furthermore, the integration of noninvasive and cost-effective predictors enhances the clinical utility of the model, supporting its potential application in routine healthcare settings.

Citations

Citations to this article as recorded by

Development and validation of a prediction model for myelosuppression in lung cancer patients after platinum-based doublet chemotherapy: a multifactorial analysis approach
Xueyan Li
American Journal of Cancer Research.2025; 15(2): 470. CrossRef

871 View
100 Download
1 Crossref

Machine Learning–Based Prognostic Gene Signature for Early Triple-Negative Breast Cancer: Ju Won Kim, Jonghyun Lee, Sung Hak Lee, Sangjeong Ahn, Kyong Hwa Park; Received September 26, 2024 Accepted November 18, 2024 Published online November 19, 2024; DOI: https://doi.org/10.4143/crt.2024.937 [Epub ahead of print]

Abstract PDF Supplementary Material PubReader ePub: Purpose
This study aimed to develop a machine learning–based approach to identify prognostic gene signatures for early-stage triple-negative breast cancer (TNBC) using next-generation sequencing data from Asian populations.
Materials and Methods
We utilized next-generation sequencing data to analyze gene expression profiles and identify potential biomarkers. Our methodology involved integrating various machine learning techniques, including feature selection and model optimization. We employed logistic regression, Kaplan-Meier survival analysis, and receiver operating characteristic (ROC) curves to validate the identified gene signatures.
Results
We identified a gene signature significantly associated with relapse in TNBC patients. The predictive model demonstrated robustness and accuracy, with an area under the ROC curve of 0.9087, sensitivity of 0.8750, and specificity of 0.9231. The Kaplan-Meier survival analysis revealed a strong association between the gene signature and patient relapse, further validated by logistic regression analysis.
Conclusion
This study presents a novel machine learning-based prognostic tool for TNBC, offering significant implications for early detection and personalized treatment. The identified gene signature provides a promising approach for improving the management of TNBC, contributing to the advancement of precision oncology.

883 View
67 Download

Lung and Thoracic cancer

Enhancing Identification of High-Risk cN0 Lung Adenocarcinoma Patients Using MRI-Based Radiomic Features: Harim Kim, Jonghoon Kim, Soohyun Hwang, You Jin Oh, Joong Hyun Ahn, Min-Ji Kim, Tae Hee Hong, Sung Goo Park, Joon Young Choi, Hong Kwan Kim, Jhingook Kim, Sumin Shin, Ho Yun Lee; Cancer Res Treat. 2025;57(1):57-69. Published online June 26, 2024; DOI: https://doi.org/10.4143/crt.2024.251

Abstract PDF Supplementary Material PubReader ePub: Purpose
This study aimed to develop a magnetic resonance imaging (MRI)–based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns.
Materials and Methods
As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features.
Results
Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)–eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively).
Conclusion
Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

2,457 View
144 Download

Gastrointestinal cancer

Development and Validation of Models to Predict Lymph Node Metastasis in Early Gastric Cancer Using Logistic Regression and Gradient Boosting Machine Methods: Hae Dong Lee, Kyung Han Nam, Cheol Min Shin, Hye Seung Lee, Young Hoon Chang, Hyuk Yoon, Young Soo Park, Nayoung Kim, Dong Ho Lee, Sang-Hoon Ahn, Hyung-Ho Kim; Cancer Res Treat. 2023;55(4):1240-1249. Published online March 21, 2023; DOI: https://doi.org/10.4143/crt.2022.1330

Abstract PDF Supplementary Material PubReader ePub

Purpose
To identify important features of lymph node metastasis (LNM) and develop a prediction model for early gastric cancer (EGC) using a gradient boosting machine (GBM) method.
Materials and Methods
The clinicopathologic data of 2556 patients with EGC who underwent gastrectomy were used as training set and the internal validation set (set 1) at a ratio of 8:2. Additionally, 548 patients with EGC who underwent endoscopic submucosal dissection (ESD) as the initial treatment were included in the external validation set (set 2). The GBM model was constructed, and its performance was compared with that of the Japanese guidelines.
Results
LNM was identified in 12.6% (321/2556) of the gastrectomy group (training set & set 1) and 4.3% (24/548) of the ESD group (set 2). In the GBM analysis, the top five features that most affected LNM were lymphovascular invasion, depth, differentiation, size, and location. The accuracy, sensitivity, specificity, and the area under the receiver operating characteristics of set 1 were 0.566, 0.922, 0.516, and 0.867, while those of set 2 were 0.810, 0.958, 0.803, and 0.944, respectively. When the sensitivity of GBM was adjusted to that of Japanese guidelines (beyond the expanded criteria in set 1 [0.922] and eCuraC-2 in set 2 [0.958]), the specificities of GBM in sets 1 and 2 were 0.516 (95% confidence interval, 0.502-0.523) and 0.803 (0.795-0.805), while those of the Japanese guidelines were 0.502 (0.488-0.509) and 0.788 (0.780-0.790), respectively.
Conclusion
The GBM model showed good performance comparable with the eCura system in predicting LNM risk in EGCs.

Citations

Citations to this article as recorded by

Establishment of a machine learning model for predicting splenic hilar lymph node metastasis
Kenichi Ishizu, Satoshi Takahashi, Nobuji Kouno, Ken Takasawa, Katsuji Takeda, Kota Matsui, Masashi Nishino, Tsutomu Hayashi, Yukinori Yamagata, Shigeyuki Matsui, Takaki Yoshikawa, Ryuji Hamamoto
npj Digital Medicine.2025;[Epub] CrossRef
The artificial intelligence revolution in gastric cancer management: clinical applications
Runze Li, Jingfan Li, Yuman Wang, Xiaoyu Liu, Weichao Xu, Runxue Sun, Binqing Xue, Xinqian Zhang, Yikun Ai, Yanru Du, Jianming Jiang
Cancer Cell International.2025;[Epub] CrossRef
Machine learning models for prediction of lymph node metastasis in patients with gastric cancer: a Chinese single-centre study with external validation in an Asian American population
Qian Li, Shangcheng Yan, Weiran Yang, Zhuan Du, Ming Cheng, Renwei Chen, Qiankun Shao, Yuan Tian, Mengchao Sheng, Wei Peng, Yongyou Wu
BMJ Open.2025; 15(3): e098476. CrossRef
Combining biomarkers to construct a novel predictive model for predicting preoperative lymph node metastasis in early gastric cancer
Yujian He, Xiaoli Xie, Bingxue Yang, Xiaoxu Jin, Zhijie Feng
Frontiers in Oncology.2025;[Epub] CrossRef
Intratumoural and peritumoural CT-based radiomics for diagnosing lepidic-predominant adenocarcinoma in patients with pure ground-glass nodules: a machine learning approach
Y. Zou, Q. Mao, Z. Zhao, X. Zhou, Y. Pan, Z. Zuo, W. Zhang
Clinical Radiology.2024; 79(2): e211. CrossRef
eCura and W-eCura: different scores, different populations, same goal
Rui Morais, Diogo Libanio, João Santos-Antunes
Gut.2024; 73(11): e29. CrossRef
A machine learning model for predicting the lymph node metastasis of early gastric cancer not meeting the endoscopic curability criteria
Minoru Kato, Yoshito Hayashi, Ryotaro Uema, Takashi Kanesaka, Shinjiro Yamaguchi, Akira Maekawa, Takuya Yamada, Masashi Yamamoto, Shinji Kitamura, Takuya Inoue, Shunsuke Yamamoto, Takashi Kizu, Risato Takeda, Hideharu Ogiyama, Katsumi Yamamoto, Kenji Aoi,
Gastric Cancer.2024; 27(5): 1069. CrossRef
The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide
Amin Zadeh Shirazi, Morteza Tofighi, Alireza Gharavi, Guillermo A. Gomez
Technology in Cancer Research & Treatment.2024;[Epub] CrossRef
Computed Tomography-Based Radiomics Analysis of Different Machine Learning Approaches for Differentiating Pulmonary Sarcomatoid Carcinoma and Pulmonary Inflammatory Pseudotumor
An-Lin Zhang, Yan-Mei Fu, Zhi-Yang He
Iranian Journal of Radiology.2024;[Epub] CrossRef
Screening of gastric cancer diagnostic biomarkers in the homologous recombination signaling pathway and assessment of their clinical and radiomic correlations
Ahao Wu, Tengcheng Hu, Chao Lai, Qingwen Zeng, Lianghua Luo, Xufeng Shu, Pan Huang, Zhonghao Wang, Zongfeng Feng, Yanyan Zhu, Yi Cao, Zhengrong Li
Cancer Medicine.2024;[Epub] CrossRef

5,593 View
225 Download
9 Web of Science
10 Crossref

Histopathologic and Molecular Biomarkers of PD-1/PD-L1 Inhibitor Treatment Response among Patients with Microsatellite Instability‒High Colon Cancer: Jaewon Hyung, Eun Jeong Cho, Jihun Kim, Jwa Hoon Kim, Jeong Eun Kim, Yong Sang Hong, Tae Won Kim, Chang Ohk Sung, Sun Young Kim; Cancer Res Treat. 2022;54(4):1175-1190. Published online January 12, 2022; DOI: https://doi.org/10.4143/crt.2021.1133

Abstract PDF Supplementary Material PubReader ePub

Purpose
Recent clinical trials have reported response rates < 50% among patients treated with programmed death-1 (PD-1)/programmed death-ligand 1 (PD-L1) inhibitors for microsatellite instability‒high (MSI-H) colorectal cancer (CRC), and factors predicting treatment response have not been fully identified. This study aimed to identify potential biomarkers of PD-1/PD-L1 inhibitor treatment response among patients with MSI-H CRC.
Materials and Methods
MSI-H CRC patients enrolled in three clinical trials of PD-1/PD-L1 blockade at Asan Medical Center (Seoul, Republic of Korea) were screened and classified into two groups according to treatment response. Their histopathologic features and expression of 730 immune-related genes from the NanoString platform were evaluated, and a machine learning–based classification model was built to predict treatment response among MSI-H CRCs patients.
Results
A total of 27 patients (15 responders, 12 non-responders) were included. A high degree of lymphocytic/neutrophilic infiltration and an expansile tumor border were associated with treatment response and prolonged progression-free survival (PFS), while mucinous/signet-ring cell carcinoma was associated with a lack of treatment response and short PFS. Gene expression profiles revealed that the interferon-γ response pathway was enriched in the responder group. Of the top eight differentially expressed immune-related genes, PRAME had the highest fold change in the responder group. Higher expression of PRAME was independently associated with better PFS along with histologic subtypes in the multivariate analysis. The classification model using these genes showed good performance for predicting treatment response.
Conclusion
We identified histologic and immune-related gene expression characteristics associated with treatment response in MSI-H CRC, which may contribute to optimal patient stratification.

Citations

Citations to this article as recorded by

The Relationship of PRAME Expression with Clinicopathologic Parameters and Immunologic Markers in Melanomas: In Silico Analysis
Yasemin Cakir, Banu Lebe
Applied Immunohistochemistry & Molecular Morphology.2025; 33(2): 117. CrossRef
Exploration of the regulatory mechanism of norcantharidin on sine oculis homeobox homolog 4 in colon cancer using transcriptome sequencing and bioinformatic
Fanqin Zhang, Chao Wu, Jingyuan Zhang, Zhihong Huang, Antony Stalin, Yiyan Zhai, Shuqi Liu, Jiarui Wu
Journal of Traditional Chinese Medical Sciences.2025; 12(2): 259. CrossRef
Biomarkers to predict efficacy of immune checkpoint inhibitors in colorectal cancer patients: a systematic review and meta-analysis
Hang Yu, Qingquan Liu, Keting Wu, Shuang Tang
Clinical and Experimental Medicine.2024;[Epub] CrossRef
An Insight into the Peculiarities of Signet-Ring Cell Carcinoma of the Colon – a Narrative Review
Loredana Farcaș, Diana Voskuil-Galoș
Journal of Medical and Radiation Oncology.2024; 4(7): 1. CrossRef
High serum IL-6 correlates with reduced clinical benefit of atezolizumab and bevacizumab in unresectable hepatocellular carcinoma
Hannah Yang, Beodeul Kang, Yeonjung Ha, Sung Hwan Lee, Ilhwan Kim, Hyeyeong Kim, Won Suk Lee, Gwangil Kim, Sanghoon Jung, Sun Young Rha, Vincent E. Gaillard, Jaekyung Cheon, Chan Kim, Hong Jae Chon
JHEP Reports.2023; 5(4): 100672. CrossRef
Identification of ZBTB4 as an immunological biomarker that can inhibit the proliferation and invasion of pancreatic cancer
Zhe Yang, Feiran Chen, Feng Wang, Xiubing Chen, Biaolin Zheng, Xiaomin Liao, Zhejun Deng, Xianxian Ruan, Jing Ning, Qing Li, Haixing Jiang, Shanyu Qin
BMC Cancer.2023;[Epub] CrossRef
PD-L1 Expression in Colorectal Carcinoma: A Comparison of 3 Scoring Methods in a Cohort of Jordanian Patients
Heyam A. Awad, Maher A. Sughayer, Jumana M. Obeid, Yaqoot N. Heilat, Ahmad S. Alhesa, Reda M. Yousef, Nabil M. Hasasna, Shafiq A. Masoud, Tareq Saleh
Applied Immunohistochemistry & Molecular Morphology.2023; 31(6): 379. CrossRef
Systemic Delivery of a STING Agonist‐Loaded Positively Charged Liposome Selectively Targets Tumor Immune Microenvironment and Suppresses Tumor Angiogenesis
Eun‐Jin Go, Hannah Yang, Wooram Park, Seung Joon Lee, Jun‐Hyeok Han, So Jung Kong, Won Suk Lee, Dong Keun Han, Hong Jae Chon, Chan Kim
Small.2023;[Epub] CrossRef
Review of the Immune Checkpoint Inhibitors in the Context of Cancer Treatment
Norah A. Alturki
Journal of Clinical Medicine.2023; 12(13): 4301. CrossRef
Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives
Nian-Nian Zhong, Han-Qi Wang, Xin-Yue Huang, Zi-Zhan Li, Lei-Ming Cao, Fang-Yi Huo, Bing Liu, Lin-Lin Bu
Seminars in Cancer Biology.2023; 95: 52. CrossRef
Artificial intelligence for prediction of response to cancer immunotherapy
Yuhan Yang, Yunuo Zhao, Xici Liu, Juan Huang
Seminars in Cancer Biology.2022; 87: 137. CrossRef

7,042 View
267 Download
8 Web of Science
11 Crossref

Machine Learning Model for Predicting Postoperative Survival of Patients with Colorectal Cancer: Mohamed Hosny Osman, Reham Hosny Mohamed, Hossam Mohamed Sarhan, Eun Jung Park, Seung Hyuk Baik, Kang Young Lee, Jeonghyun Kang; Cancer Res Treat. 2022;54(2):517-524. Published online June 15, 2021; DOI: https://doi.org/10.4143/crt.2021.206

Abstract PDF Supplementary Material PubReader ePub

Purpose
Machine learning (ML) is a strong candidate for making accurate predictions, as we can use large amount of data with powerful computational algorithms. We developed a ML based model to predict survival of patients with colorectal cancer (CRC) using data from two independent datasets.
Materials and Methods
A total of 364,316 and 1,572 CRC patients were included from the Surveillance, Epidemiology, and End Results (SEER) and a Korean dataset, respectively. As SEER combines data from 18 cancer registries, internal validation was done using 18-Fold-Cross-Validation then external validation was performed by testing the trained model on the Korean dataset. Performance was evaluated using area under the receiver operating characteristic curve (AUROC), sensitivity and positive predictive values.
Results
Clinicopathological characteristics were significantly different between the two datasets and the SEER showed a significant lower 5-year survival rate compared to the Korean dataset (60.1% vs. 75.3%, p < 0.001). The ML-based model using the Light gradient boosting algorithm achieved a better performance in predicting 5-year-survival compared to American Joint Committee on Cancer stage (AUROC, 0.804 vs. 0.736; p < 0.001). The most important features which influenced model performance were age, number of examined lymph nodes, and tumor size. Sensitivity and positive predictive values of predicting 5-year-survival for classes including dead or alive were reported as 68.14%, 77.51% and 49.88%, 88.1% respectively in the validation set. Survival probability can be checked using the web-based survival predictor (http://colorectalcancer.pythonanywhere.com).
Conclusion
ML-based model achieved a much better performance compared to staging in individualized estimation of survival of patients with CRC.

Citations

Citations to this article as recorded by

Development and validation of a biomarker-based prediction model for metastasis in patients with colorectal cancer: Application of machine learning algorithms
Erfan Ayubi, Sajjad Farashi, Leili Tapak, Saeid Afshar
Heliyon.2025; 11(1): e41443. CrossRef
Predicting Factors Affecting Survival Rate in Patients Undergoing On‐Pump Coronary Artery Bypass Graft Surgery Using Machine Learning Methods: A Systematic Review
Alireza Jafarkhani, Behzad Imani, Soheila Saeedi, Amir Shams
Health Science Reports.2025;[Epub] CrossRef
Externally validated and clinically useful machine learning algorithms to support patient-related decision-making in oncology: a scoping review
Catarina Sousa Santos, Mário Amorim-Lopes
BMC Medical Research Methodology.2025;[Epub] CrossRef
New perspectives on cancer clinical research in the era of big data and machine learning
Shujun Li, Hang Yi, Qihao Leng, You Wu, Yousheng Mao
Surgical Oncology.2024; 52: 102009. CrossRef
SAGL: A self-attention-based graph learning framework for predicting survival of colorectal cancer patients
Ping Yang, Hang Qiu, Xulin Yang, Liya Wang, Xiaodong Wang
Computer Methods and Programs in Biomedicine.2024; 249: 108159. CrossRef
The development and validation of automated machine learning models for predicting lymph node metastasis in Siewert type II T1 adenocarcinoma of the esophagogastric junction
Chenghao Lu, Lu Liu, Minyue Yin, Jiaxi Lin, Shiqi Zhu, Jingwen Gao, Shuting Qu, Guoting Xu, Lihe Liu, Jinzhou Zhu, Chunfang Xu
Frontiers in Medicine.2024;[Epub] CrossRef
Machine learning for predicting colon cancer recurrence
Erkan Kayikcioglu, Arif Hakan Onder, Burcu Bacak, Tekin Ahmet Serel
Surgical Oncology.2024; 54: 102079. CrossRef
Precision prognosis of colorectal cancer: a multi-tiered model integrating microsatellite instability genes and clinical parameters
Yonghong Wang, Ke Liu, Wanbin He, Jie Dan, Mingjie Zhu, Lei Chen, Wenjie Zhou, Ming Li, Jiangpeng Li
Frontiers in Oncology.2024;[Epub] CrossRef
Light gradient boost tree classifier predictions on appendicitis with periodontal disease from biochemical and clinical parameters
Pradeep Kumar Yadalam, Prathiksha Vedhavalli Thirukkumaran, Prabhu Manickam Natarajan, Carlos M. Ardila
Frontiers in Oral Health.2024;[Epub] CrossRef
Enhanced prediction of preoperative mesenteric lymph node metastasis in colorectal cancer using machine learning with CT‐based data
Lanni Zhou, Lizhu Ouyang, Baoliang Guo, Xiyi Huang, Shaomin Yang, Jialing Pan, Liwen Wang, Ming Chen, Fan Xie, Yunjing Li, Yongxing Du, Xinjie Chen, Qiugen Hu, Fusheng Ouyang
MedComm – Future Medicine.2024;[Epub] CrossRef
An interactive web-based tool for predicting and exploring brain cancer survivability
Gopal Nath, Austin Coursey, Yang Li, Srikanth Prabhu, Harish Garg, Shaymal C. Halder, Saptarshi Sengupta
Healthcare Analytics.2023; 3: 100132. CrossRef
Statistical and machine learning approaches to predict the necessity for computed tomography in children with mild traumatic brain injury
Tadashi Miyagawa, Marina Saga, Minami Sasaki, Miyuki Shimizu, Akira Yamaura, Alvan Ukachukwu
PLOS ONE.2023; 18(1): e0278562. CrossRef
Machine learning for prediction of in-hospital mortality in lung cancer patients admitted to intensive care unit
Tianzhi Huang, Dejin Le, Lili Yuan, Shoujia Xu, Xiulan Peng, Samuele Ceruti
PLOS ONE.2023; 18(1): e0280606. CrossRef
Application of Artificial Intelligence in Gastrointestinal Cancer
洪铭崔
Advances in Clinical Medicine.2023; 13(03): 3942. CrossRef
Clinicopathologic and prognostic factors of patients with T3/T4 colorectal signet ring cell carcinoma: a population-based study
Fan Zhang, Boqi Xu, Yao Peng, Zhongqi Mao
Journal of Cancer Research and Clinical Oncology.2023; 149(12): 9747. CrossRef
Using machine learning algorithms to predict 28-day mortality in critically ill elderly patients with colorectal cancer
Chunxia Guo, Jun Pan, Shan Tian, Yuanjun Gao
Journal of International Medical Research.2023;[Epub] CrossRef

9,982 View
336 Download
13 Web of Science
16 Crossref

Gastrointestinal Cancer

LASSO-Based Machine Learning Algorithm for Prediction of Lymph Node Metastasis in T1 Colorectal Cancer: Jeonghyun Kang, Yoon Jung Choi, Im-kyung Kim, Hye Sun Lee, Hogeun Kim, Seung Hyuk Baik, Nam Kyu Kim, Kang Young Lee; Cancer Res Treat. 2021;53(3):773-783. Published online December 29, 2020; DOI: https://doi.org/10.4143/crt.2020.974

Abstract PDF Supplementary Material PubReader ePub

Purpose
The role of tumor-infiltrating lymphocytes (TILs) in predicting lymph node metastasis (LNM) in patients with T1 colorectal cancer (CRC) remains unclear. Furthermore, clinical utility of a machine learning–based approach has not been widely studied.
Materials and Methods
Immunohistochemistry for TILs against CD3, CD8, and forkhead box P3 in both center and invasive margin of the tumor were performed using surgically resected T1 CRC slides. Three hundred and sixteen patients were enrolled and categorized into training (n=221) and validation (n=95) sets via random sampling. Using clinicopathologic variables including TILs, the least absolute shrinkage and selection operator (LASSO) regression model was applied for variable selection and predictive signature building in the training set. The predictive accuracy of our model and the Japanese criteria were compared using area under the receiver operating characteristic (AUROC), net reclassification improvement (NRI)/integrated discrimination improvement (IDI), and decision curve analysis (DCA) in the validation set.
Results
LNM was detected in 29 (13.1%) and 12 (12.6%) patients in training and validation sets, respectively. Nine variables were selected and used to generate the LASSO model. Its performance was similar in training and validation sets (AUROC, 0.795 vs. 0.765; p=0.747). In the validation set, the LASSO model showed better outcomes in predicting LNM than Japanese criteria, as measured by AUROC (0.765 vs. 0.518, p=0.003) and NRI (0.447, p=0.039)/IDI (0.121, p=0.034). DCA showed positive net benefits in using our model.
Conclusion
Our LASSO model incorporating histopathologic parameters and TILs showed superior performance compared to conventional Japanese criteria in predicting LNM in patients with T1 CRC.

Citations

Citations to this article as recorded by

Assessing prospective molecular biomarkers and functional pathways in severe asthma based on a machine learning method and bioinformatics analyses
Ya-Da Zhang, Yi-Ren Chen, Wei Zhang, Bin-Qing Tang
Journal of Asthma.2025; 62(3): 465. CrossRef
A web-based tool for cancer risk prediction for middle-aged and elderly adults using machine learning algorithms and self-reported questions
Xingjian Xiao, Xiaohan Yi, Nyi Nyi Soe, Phyu Mon Latt, Luotao Lin, Xuefen Chen, Hualing Song, Bo Sun, Hailei Zhao, Xianglong Xu
Annals of Epidemiology.2025; 101: 27. CrossRef
Risk stratification scores for lymph node metastases in T1 colorectal cancer—A systematic review
Rakesh Quinn, Giuleta Jamsari, Ewan MacDermid
Colorectal Disease.2025;[Epub] CrossRef
Artificial Intelligence in Lymph Node Metastasis Prediction for T1 Colorectal Cancer: Promise and Challenges
Jung Ho Bae
Gut and Liver.2025; 19(1): 3. CrossRef
Utilizing Ensemble Learning and Dimension Reduction in Predicting Stock Prices: A Transparent Methodology with Insights from Explainable AI
Nabanita Das, Bikash Sadhukhan, Chayan Ghosh, Avigyan Chowdhury, Satyajit Chakrabarti
SN Computer Science.2025;[Epub] CrossRef
Bioinformatics approach reveals the critical role of inflammation-related genes in age-related hearing loss
Xi Gu, Chenyu Chen, Yuqing Chen, Chaojun Zeng, Yanchun Lin, Ruosi Guo, Shujin Xu, Chang Lin
Scientific Reports.2025;[Epub] CrossRef
Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.)
Zhu Yang, Wenjie Kan, Ziqi Wang, Caiguo Tang, Yuan Cheng, Dacheng Wang, Yameng Gao, Lifang Wu
Frontiers in Plant Science.2025;[Epub] CrossRef
Following intravenous thrombolysis, the outcome of diabetes mellitus associated with acute ischemic stroke was predicted via machine learning
Xiaoqing Liu, Miaoran Wang, Rui Wen, Haoyue Zhu, Ying Xiao, Qian He, Yangdi Shi, Zhe Hong, Bing Xu
Frontiers in Pharmacology.2025;[Epub] CrossRef
An early lung cancer diagnosis model for non-smokers incorporating ct imaging analysis and circulating genetically abnormal cells (CACs)
Ran Ni, Yongjie Huang, Lei Wang, Hongjie Chen, Guorui Zhang, Yali Yu, Yinglan Kuang, Yuyan Tang, Xing Lu, Hong Liu
BMC Cancer.2025;[Epub] CrossRef
Identification and validation of immune-related biomarkers and polarization types of macrophages in keloid based on bulk RNA-seq and single-cell RNA-seq analysis
Yuzhu Zhang, Chenglong Fang, Lizhong Zhang, Fengyu Ma, Meihong Sun, Ning Zhang, Nan Bai, Jun Wu
Burns.2025; 51(3): 107413. CrossRef
Comprehensive analysis of mitochondrial solute carrier family 25 (SLC25) identifies member 19 (SLC25A19) as a regulatory factor in hepatocellular carcinoma
Xueke Gao, Yangtao Xu, Xinyao Hu, Jiayu Chen, Daoming Zhang, Ximing Xu
Gene.2025; 944: 149299. CrossRef
A dynamic nomogram predicting nosocomial infections in patients after colon cancer surgery
Xue Yao, Shuhui Wang, Anning Lu, Yun Xu, Na Li
Frontiers in Oncology.2025;[Epub] CrossRef
Machine learning models to predict 6-month mortality risk in home-based hospice patients with advanced cancer
Wan Cheng, Jianwei Zheng, Yuanfeng Lu, Guojuan Chen, Zheng Zhu, Hong Wu, Yitao Wei, Huimin Xiao
Asia-Pacific Journal of Oncology Nursing.2025; 12: 100679. CrossRef
Interpretable Machine Learning Model for Predicting Depression in Middle-aged and Elderly Chinese Arthritis Patients: A Nationwide Prospective Cohort Study
Xiao Xu, Hai-Yan Huang, Shu-yun Wang, Shen-Yee Tan, Hong-Hui Chen, Min-Min Zhou, Mei-juan Qian
Archives of Gerontology and Geriatrics.2025; : 105810. CrossRef
Identification of nitric oxide-related key genes in pulmonary hypertension via bioinformatics and in vitro validation for therapeutic target discovery
Zhuyang Chu, Yiming Li, Jianjun Ge
Computer Methods in Biomechanics and Biomedical Engineering.2025; : 1. CrossRef
Prediction of Lymph Node Metastasis in Colorectal Cancer Using Intraoperative Fluorescence Multi-Modal Imaging
Xiaobo Zhu, He Sun, Yuhan Wang, Gang Hu, Lizhi Shao, Song Zhang, Fucheng Liu, Chongwei Chi, Kunshan He, Jianqiang Tang, Yu An, Jie Tian, Zhenyu Liu
IEEE Transactions on Medical Imaging.2025; 44(3): 1568. CrossRef
Additional staining for lymphovascular invasion is associated with increased estimation of lymph node metastasis in patients with T1 colorectal cancer: Systematic review and meta‐analysis
Jun Watanabe, Katsuro Ichimasa, Yuki Kataoka, Atsushi Miki, Hidehiro Someko, Munenori Honda, Makiko Tahara, Takeshi Yamashina, Khay Guan Yeoh, Shigeo Kawai, Kazuhiko Kotani, Naohiro Sata
Digestive Endoscopy.2024; 36(5): 533. CrossRef
Risk assessment in pT1 colorectal cancer
Emma Jane Norton, Adrian C Bateman
Journal of Clinical Pathology.2024; 77(4): 225. CrossRef
Identification and Analysis of Immune Microenvironment-Related Genes for Keloid Risk Prediction and Their Effects on Keloid Proliferation and Migration
Yongyan Pei, Yikai Wu, Mengqi Zhang, Xuemin Su, Hua Cao, Jiaji Zhao
Biochemical Genetics.2024; 62(4): 3174. CrossRef
Multiple machine-learning tools identifying prognostic biomarkers for acute Myeloid Leukemia
Yujing Cheng, Xin Yang, Ying Wang, Qi Li, Wanlu Chen, Run Dai, Chan Zhang
BMC Medical Informatics and Decision Making.2024;[Epub] CrossRef
Artificial Intelligence Applications in the Treatment of Colorectal Cancer: A Narrative Review
Jiaqing Yang, Jing Huang, Deqian Han, Xuelei Ma
Clinical Medicine Insights: Oncology.2024;[Epub] CrossRef
Transcriptomic and machine learning analyses identify hub genes of metabolism and host immune response that are associated with the progression of breast capsular contracture
Yukun Mao, Xueying Hou, Su Fu, Jie Luan
Genes & Diseases.2024; 11(3): 101087. CrossRef
Bioinformatics and Machine Learning Methods Identified MGST1 and QPCT as Novel Biomarkers for Severe Acute Pancreatitis
Yang Sun, Jingjun Xie, Jun Zhu, Yadong Yuan
Molecular Biotechnology.2024; 66(5): 1246. CrossRef
Multi-omics reveals the role of ENO1 in bladder cancer and constructs an epithelial-related prognostic model to predict prognosis and efficacy
Zhixiong Su, Lijie You, Yufang He, Jingbo Chen, Guifeng Zhang, Zhenhua Liu
Scientific Reports.2024;[Epub] CrossRef
Use of artificial intelligence in the management of T1 colorectal cancer: a new tool in the arsenal or is deep learning out of its depth?
James Weiquan Li, Lai Mun Wang, Katsuro Ichimasa, Kenneth Weicong Lin, James Chi-Yong Ngu, Tiing Leong Ang
Clinical Endoscopy.2024; 57(1): 24. CrossRef
SLCO4A1, as a novel prognostic biomarker of non‑small cell lung cancer, promotes cell proliferation and migration
Shihao Li, Zihao Li, Lan Huang, Zhenyang Geng, Feng Li, Bin Wu, Yinliang Sheng, Yifan Xu, Bowen Li, Yiming Xu, Zhuoyu Gu, Yu Qi
International Journal of Oncology.2024;[Epub] CrossRef
Overexpression of circulating CD38+ NK cells in colorectal cancer was associated with lymph node metastasis and poor prognosis
Xueling Wang, Haoran Li, Huixian Chen, Kehua Fang, Xiaotian Chang
Frontiers in Oncology.2024;[Epub] CrossRef
Management after non-curative endoscopic resection of T1 rectal cancer
Hao Dang, Daan A. Verhoeven, Jurjen J. Boonstra, Monique E. van Leerdam
Best Practice & Research Clinical Gastroenterology.2024; 68: 101895. CrossRef
Prediction of Lymph Node Metastasis in T1 Colorectal Cancer Using Artificial Intelligence with Hematoxylin and Eosin-Stained Whole-Slide-Images of Endoscopic and Surgical Resection Specimens
Joo Hye Song, Eun Ran Kim, Yiyu Hong, Insuk Sohn, Soomin Ahn, Seok-Hyung Kim, Kee-Taek Jang
Cancers.2024; 16(10): 1900. CrossRef
Identification of immune characteristic biomarkers and therapeutic targets in cuproptosis for sepsis by integrated bioinformatics analysis and single-cell RNA sequencing analysis
Tianfeng Wang, Xiaowei Fang, Ximei Sheng, Meng Li, Yulin Mei, Qing Mei, Aijun Pan
Heliyon.2024; 10(5): e27379. CrossRef
Possibilities and prospects of artificial intelligence in the treatment of colorectal cancer (review)
A. Yu. Kravchenko, E. V. Semina, V. V. Kakotkin, M. A. Agapov
Koloproktologia.2024; 23(2): 184. CrossRef
A risk prediction nomogram for resistant hypertension in patients with obstructive sleep apnea
Hongze Lin, Chen Zhou, Jiaying Li, Xiuqin Ma, Yan Yang, Taofeng Zhu
Scientific Reports.2024;[Epub] CrossRef
Unveiling the best predictive models for early‑onset metastatic cancer: Insights and innovations (Review)
Liqing Yu, Zhenjun Huang, Ziqi Xiao, Xiaofu Tang, Ziqiang Zeng, Xiaoli Tang, Wenhao Ouyang
Oncology Reports.2024;[Epub] CrossRef
Exploration of the molecular biological mechanisms and review of postoperative radiotherapy cases in tenosynovial giant cell tumors
Tianwei Zhang, Bin Zeng, Ke Liu, Qin Zeng, Na Wang, Ling Peng, Hongbo Qiu, Xiaomei Chen, Lin Wang
Frontiers in Oncology.2024;[Epub] CrossRef
Radiomics-based machine learning in the differentiation of benign and malignant bowel wall thickening
Hande Melike Bülbül, Gülen Burakgazi, Uğur Kesimal, Esat Kaba
Japanese Journal of Radiology.2024; 42(8): 872. CrossRef
Potential Mechanism of Tibetan Medicine Liuwei Muxiang Pills against Colorectal Cancer: Network Pharmacology and Bioinformatics Analyses
Shaochong Qi, Xinyu Liang, Zijing Wang, Haoran Jin, Liqun Zou, Jinlin Yang
Pharmaceuticals.2024; 17(4): 429. CrossRef
A new clinical model for predicting lymph node metastasis in T1 colorectal cancer
Kai Wang, Hui He, Yanyun Lin, Yanhong Zhang, Junguo Chen, Jiancong Hu, Xiaosheng He
International Journal of Colorectal Disease.2024;[Epub] CrossRef
Comprehensive analysis of the interaction of antigen presentation during anti‐tumour immunity and establishment of AIDPS systems in ovarian cancer
Wenhuizi Sun, Ping Xu, Kefei Gao, Wenqin Lian, Xiang Sun
Journal of Cellular and Molecular Medicine.2024;[Epub] CrossRef
Use of artificial intelligence for the prediction of lymph node metastases in early-stage colorectal cancer: systematic review
Nasya Thompson, Arthur Morley-Bunker, Jared McLauchlan, Tamara Glyn, Tim Eglinton
BJS Open.2024;[Epub] CrossRef
Machine learning for predicting colon cancer recurrence
Erkan Kayikcioglu, Arif Hakan Onder, Burcu Bacak, Tekin Ahmet Serel
Surgical Oncology.2024; 54: 102079. CrossRef
A machine learning screening model for identifying the risk of high-frequency hearing impairment in a general population
Yi Wang, Xinmeng Yao, Dahui Wang, Chengyin Ye, Liangwen Xu
BMC Public Health.2024;[Epub] CrossRef
Potential clinical value of fibrinogen-like protein 1 as a serum biomarker for the identification of diabetic cardiomyopathy
Yao Liu, Min Wang, Jia-Bao Su, Xiao Fu, Guan-Li Zheng, Shan Guo, Li-Juan Zhang, Qing-Bo Lu
Scientific Reports.2024;[Epub] CrossRef
CXCR4-mediated neutrophil dynamics in periodontitis
Xuanwen Xu, Tiange Li, Jingqi Tang, Danlei Wang, Yi Zhou, Huiqing Gou, Lu Li, Yan Xu
Cellular Signalling.2024; 120: 111212. CrossRef
Hspb1 and Lgals3 in spinal neurons are closely associated with autophagy following excitotoxicity based on machine learning algorithms
Lei Yan, Zihao Li, Chuanbo Li, Jingyu Chen, Xun Zhou, Jiaming Cui, Peng Liu, Chong Shen, Chu Chen, Hongxiang Hong, Guanhua Xu, Zhiming Cui, Suyan Tian
PLOS ONE.2024; 19(5): e0303235. CrossRef
Impact of artificial intelligence in the management of esophageal, gastric and colorectal malignancies
Ayrton Bangolo, Nikita Wadhwani, Vignesh K Nagesh, Shraboni Dey, Hadrian Hoang-Vu Tran, Izage Kianifar Aguilar, Auda Auda, Aman Sidiqui, Aiswarya Menon, Deborah Daoud, James Liu, Sai Priyanka Pulipaka, Blessy George, Flor Furman, Nareeman Khan, Adewale Pl
Artificial Intelligence in Gastrointestinal Endoscopy.2024;[Epub] CrossRef
Identification and Validation of Nicotinamide Metabolism-Related Gene Signatures as a Novel Prognostic Model for Hepatocellular Carcinoma
Sijia Yang, Ang Li, Lihong Lv, Jinxin Duan, Zhihua Zheng, Wenfeng Zhuo, Jun Min, Jinxing Wei
OncoTargets and Therapy.2024; Volume 17: 423. CrossRef
Machine learning algorithms integrate bulk and single-cell RNA data to unveil oxidative stress following intracerebral hemorrhage
Chaonan Du, Cong Wang, Zhiwei Liu, Wenxuan Xin, Qizhe Zhang, Alleyar Ali, Xinrui Zeng, Zhenxing Li, Chiyuan Ma
International Immunopharmacology.2024; 137: 112449. CrossRef
Single-cell analysis and machine learning identify psoriasis-associated CD8+ T cells serve as biomarker for psoriasis
Sijia He, Lyuye Liu, Xiaoyan Long, Man Ge, Menghan Cai, Junling Zhang
Frontiers in Genetics.2024;[Epub] CrossRef
DcR3-associated risk score: correlating better prognosis and enhanced predictive power in colorectal cancer
Ying Duan, Hangrong Fang, Juanhong Wang, Banlai Ruan, Juan Yang, Jie Liu, Siqi Gou, Yijie Li, Zhengyi Cheng
Discover Oncology.2024;[Epub] CrossRef
Lymph node metastasis detection using artificial intelligence in T1 colorectal cancer: A comprehensive systematic review
Xiaoyan Yao, Zhiyong Zhou, Shengxun Mao, Jiaqing Cao, Huizi Li
Journal of Surgical Oncology.2024; 130(3): 637. CrossRef
Development and Validation of an Interpretable Machine Learning Model for Early Prognosis Prediction in ICU Patients with Malignant Tumors and Hyperkalemia
Zhi-Jun Bu, Nan Jiang, Ke-Cheng Li, Zhi-Lin Lu, Nan Zhang, Shao-Shuai Yan, Zhi-Lin Chen, Yu-Han Hao, Yu-Huan Zhang, Run-Bing Xu, Han-Wei Chi, Zu-Yi Chen, Jian-Ping Liu, Dan Wang, Feng Xu, Zhao-Lan Liu
Medicine.2024; 103(30): e38747. CrossRef
Machine learning-based model to predict composite thromboembolic events among Chinese elderly patients with atrial fibrillation
Jiefeng Ren, Haijun Wang, Song Lai, Yi Shao, Hebin Che, Zaiyao Xue, Xinlian Qi, Sha Zhang, Jinkun Dai, Sai Wang, Kunlian Li, Wei Gan, Quanjin Si
BMC Cardiovascular Disorders.2024;[Epub] CrossRef
Identifying and validating necroptosis‐associated features to predict clinical outcome and immunotherapy response in patients with glioblastoma
Qinghua Yuan, Weida Gao, Mian Guo, Bo Liu
Environmental Toxicology.2024; 39(10): 4729. CrossRef
CuPCA: a web server for pan-cancer association analysis of large-scale cuproptosis-related genes
Yishu Xu, Zhenshu Ma, Yajie Wang, Long Zhang, Jiaming Ye, Yuan Chen, Zhengrong Yuan
Database.2024;[Epub] CrossRef
Transcriptomic analysis reveals oxidative stress-related signature and molecular subtypes in cholangio carcinoma
Zichao Wu
Molecular Genetics and Genomics.2024;[Epub] CrossRef
The application value of support vector machine model based on multimodal MRI in predicting IDH-1mutation and Ki-67 expression in glioma
He-Xin Liang, Zong-Ying Wang, Yao Li, An-Ning Ren, Zhi-Feng Chen, Xi-Zhen Wang, Xi-Ming Wang, Zhen-Guo Yuan
BMC Medical Imaging.2024;[Epub] CrossRef
The AC010247.2/miR-125b-5p axis triggers the malignant progression of acute myelocytic leukemia by IL-6R
Fang Xie, Jialu Xu, Lina Yan, Xia Xiao, Liang Liu
Heliyon.2024; 10(18): e37715. CrossRef
Application of machine learning for predicting lymph node metastasis in T1 colorectal cancer: a systematic review and meta-analysis
Chinock Cheong, Na Won Kim, Hye Sun Lee, Jeonghyun Kang
Langenbeck's Archives of Surgery.2024;[Epub] CrossRef
Short-term PV energy yield predictions within city neighborhoods for optimum grid management
Stefani Peratikou, Alexandros G. Charalambides
Energy and Buildings.2024; 323: 114773. CrossRef
Integrating microarray-based spatial transcriptomics and RNA-seq reveals tissue architecture in colorectal cancer
Zheng Li, Xiaojie Zhang, Chongyuan Sun, Zefeng Li, He Fei, Dongbing Zhao
Journal of Big Data.2024;[Epub] CrossRef
Construction and validation of a nomogram prediction model for the catheter-related thrombosis risk of central venous access devices in patients with cancer: a prospective machine learning study
Guiyuan Ma, Shujie Chen, Sha Peng, Nian Yao, Jiaji Hu, Letian Xu, Tingyin Chen, Jiaan Wang, Xin Huang, Jinghui Zhang
Journal of Thrombosis and Thrombolysis.2024; 58(2): 220. CrossRef
Association of coexposure to perfluoroalkyl and polyfluoroalkyl compounds and heavy metals with pregnancy loss and reproductive lifespan: The mediating role of cholesterol
Hua Fang, Dai Lin, Ziqi Zhang, Haoting Chen, Zixin Zheng, Dongdong Jiang, Wenxiang Wang
Ecotoxicology and Environmental Safety.2024; 286: 117160. CrossRef
Identification of novel diagnostic biomarkers associated with liver metastasis in colon adenocarcinoma by machine learning
Long Yang, Ye Tian, Xiaofei Cao, Jiawei Wang, Baoyang Luo
Discover Oncology.2024;[Epub] CrossRef
Identification of a disulfidptosis-related genes signature for diagnostic and immune infiltration characteristics in endometriosis
Xiangyu Chang, Jinwei Miao
Scientific Reports.2024;[Epub] CrossRef
Machine learning model based on SERPING1, C1QB, and C1QC: A novel diagnostic approach for latent tuberculosis infection
Linsheng Li, Li Zhuang, Ling Yang, Zhaoyang Ye, Ruizi Ni, Yajing An, Weiguo Zhao, Wenping Gong
iLABMED.2024; 2(4): 248. CrossRef
Development of machine learning models for predicting depressive symptoms in knee osteoarthritis patients
Dan Li, Han Lu, Junhui Wu, Hongbo Chen, Meidi Shen, Beibei Tong, Wen Zeng, Weixuan Wang, Shaomei Shang
Scientific Reports.2024;[Epub] CrossRef
Integrated analysis of single-cell, spatial and bulk RNA-sequencing identifies a cell-death signature for predicting the outcomes of head and neck cancer
Yue Pan, Lei Fei, Shihua Wang, Hua Chen, Changqing Jiang, Hong Li, Changsong Wang, Yao Yang, Qinggao Zhang, Yongwen Chen
Frontiers in Immunology.2024;[Epub] CrossRef
Screening the Best Risk Model and Susceptibility SNPs for Chronic Obstructive Pulmonary Disease (COPD) Based on Machine Learning Algorithms
Zehua Yang, Yamei Zheng, Lei Zhang, Jie Zhao, Wenya Xu, Haihong Wu, Tian Xie, Yipeng Ding
International Journal of Chronic Obstructive Pulmonary Disease.2024; Volume 19: 2397. CrossRef
Identification of biomarkers related to Escherichia coli infection for the diagnosis of gastrointestinal tumors applying machine learning methods
Tingting Ge, Wei Wang, Dandan Zhang, Xubo Le, Lumei Shi
Heliyon.2024; 10(23): e40491. CrossRef
Immune Microenvironment Alterations and Identification of Key Diagnostic Biomarkers in Chronic Kidney Disease Using Integrated Bioinformatics and Machine Learning
Jinbao Shi, Aliang Xu, Liuying Huang, Shaojie Liu, Binxuan Wu, Zuhong Zhang
Pharmacogenomics and Personalized Medicine.2024; Volume 17: 497. CrossRef
Identification of alternative lengthening of telomeres-related genes prognosis model in hepatocellular carcinoma
FanLin Zeng, YuLiang Chen, Jie Lin
BMC Cancer.2024;[Epub] CrossRef
Bioinformatics Analysis Identifies PLA2G7 as a Key Antigen-Presenting Prognostic Related Gene Promoting Hepatocellular Carcinoma through the STAT1/PD-L1 Axis
Sihang Guo, Qinhe Yang
Frontiers in Bioscience-Landmark.2024;[Epub] CrossRef
Risk of intraoperative hemorrhage during cesarean scar ectopic pregnancy surgery: development and validation of an interpretable machine learning prediction model
Xinli Chen, Huan Zhang, Dongxia Guo, Siyuan Yang, Bao Liu, Yiping Hao, Qingqing Liu, Teng Zhang, Fanrong Meng, Longyun Sun, Xinlin Jiao, Wenjing Zhang, Yanli Ban, Yugang Chi, Guowei Tao, Baoxia Cui
eClinicalMedicine.2024; 78: 102969. CrossRef
TRIM16 and PRC1 Are Involved in Pancreatic Cancer Progression and Targeted by Delphinidin
Donghua Wang, Long Lv, Jinghu Du, Kui Tian, Yu Chen, Manyu Chen
Chemical Biology & Drug Design.2024;[Epub] CrossRef
Identification of Ferroptosis‐Related Gene in Age‐Related Macular Degeneration Using Machine Learning
Meijiang Zhu, Jing Yu
Immunity, Inflammation and Disease.2024;[Epub] CrossRef
Integration of bioinformatics analysis reveals ZNF248 as a potential prognostic and immunotherapeutic biomarker for LIHC: machine learning and experimental evidence
Lifang Weng
American Journal of Cancer Research.2024; 14(11): 5230. CrossRef
Development and Validation of a Nomogram for Predicting Suicidal Ideation Among Rural Adolescents in China
Yunjiao Luo, Yuhao Wang, Yingxue Wang, Yihan Wang, Na Yan, Blen Shiferaw, Louisa Mackay, Ziyang Zhang, Caiyi Zhang, Wei Wang
Psychology Research and Behavior Management.2024; Volume 17: 4413. CrossRef
High serum mannose in colorectal cancer: a novel biomarker of lymph node metastasis and poor prognosis
Xueling Wang, Haoran Li, Xiaotian Chang, Zibin Tian
Frontiers in Oncology.2023;[Epub] CrossRef
Development of a nomogram for predicting nosocomial infections among patients after cardiac valve replacement surgery
Xue Yao, Na Li, Ranran Lu, Xujing Wang, Yujun Zhang, Shuhui Wang
Journal of Clinical Nursing.2023; 32(7-8): 1466. CrossRef
Machine learning-based warning model for chronic kidney disease in individuals over 40 years old in underprivileged areas, Shanxi Province
Wenzhu Song, Yanfeng Liu, Lixia Qiu, Jianbo Qing, Aizhong Li, Yan Zhao, Yafeng Li, Rongshan Li, Xiaoshuang Zhou
Frontiers in Medicine.2023;[Epub] CrossRef
Artificial intelligence in colorectal surgery: an AI-powered systematic review
A. Spinelli, F. M. Carrano, M. E. Laino, M. Andreozzi, G. Koleth, C. Hassan, A. Repici, M. Chand, V. Savevski, G. Pellino
Techniques in Coloproctology.2023; 27(8): 615. CrossRef
Prognostic significance of neutrophil count on in-hospital mortality in patients with acute type A aortic dissection
Weiqi Feng, Huili Li, Qiuji Wang, Chenxi Li, Jinlin Wu, Jue Yang, Ruixin Fan
Frontiers in Cardiovascular Medicine.2023;[Epub] CrossRef
Machine learning algorithms assisted identification of post-stroke depression associated biological features
Xintong Zhang, Xiangyu Wang, Shuwei Wang, Yingjie Zhang, Zeyu Wang, Qingyan Yang, Song Wang, Risheng Cao, Binbin Yu, Yu Zheng, Yini Dang
Frontiers in Neuroscience.2023;[Epub] CrossRef
Risk prediction of bronchopulmonary dysplasia in preterm infants by the nomogram model
Yang Gao, Dongyun Liu, Yingmeng Guo, Menghan Cao
Frontiers in Pediatrics.2023;[Epub] CrossRef
LncRNA model predicts liver cancer drug resistance and validate in vitro experiments
Qiushi Yin, Xiaolong Huang, Qiuxi Yang, Shibu Lin, Qifeng Song, Weiqiang Fan, Wang Li, Zhongyi Li, Lianghui Gao
Frontiers in Cell and Developmental Biology.2023;[Epub] CrossRef
Artificial intelligence–assisted treatment strategy for T1 colorectal cancer after endoscopic resection
Katsuro Ichimasa, Shin-ei Kudo, Jonathan Wei Jie Lee, Tetsuo Nemoto, Khay Guan Yeoh
Gastrointestinal Endoscopy.2023; 97(6): 1148. CrossRef
A risk prediction model for type 2 diabetes mellitus complicated with retinopathy based on machine learning and its application in health management
Hong Pan, Jijia Sun, Xin Luo, Heling Ai, Jing Zeng, Rong Shi, An Zhang
Frontiers in Medicine.2023;[Epub] CrossRef
LASSO-based machine learning algorithm to predict the incidence of diabetes in different stages
Qianying Ou, Wei Jin, Leweihua Lin, Danhong Lin, Kaining Chen, Huibiao Quan
The Aging Male.2023;[Epub] CrossRef
Prediction model based on radiomics and clinical features for preoperative lymphovascular invasion in gastric cancer patients
Ping Wang, Kaige Chen, Ying Han, Min Zhao, Nanding Abiyasi, Haiyong Peng, Shaolei Yan, Jiming Shang, Naijian Shang, Wei Meng
Future Oncology.2023; 19(23): 1613. CrossRef
Liang-Ge-San: a classic traditional Chinese medicine formula, attenuates acute inflammation via targeting GSK3β
Liling Yang, Lijun Yan, Weifu Tan, Xiangjun Zhou, Guangli Yang, Jingtao Yu, Zibin Lu, Yong Liu, Liyi Zou, Wei Li, Linzhong Yu
Frontiers in Pharmacology.2023;[Epub] CrossRef
Synergistic inhibition of NUDT21 by secretory S100A11 and exosomal miR‐487a‐5p promotes melanoma oligo‐ to poly‐metastatic progression
Bin Zeng, Yuting Chen, Hao Chen, Qiting Zhao, Zhiwei Sun, Doudou Liu, Xiaoshuang Li, Yuhan Zhang, Jianyu Wang, H. Rosie Xing
Molecular Oncology.2023; 17(12): 2743. CrossRef
Identification of immune-related lncRNA in sepsis by construction of ceRNA network and integrating bioinformatic analysis
Tianfeng Wang, Si Xu, Lei Zhang, Tianjun Yang, Xiaoqin Fan, Chunyan Zhu, Yinzhong Wang, Fei Tong, Qing Mei, Aijun Pan
BMC Genomics.2023;[Epub] CrossRef
Applying machine learning algorithms to develop a survival prediction model for lung adenocarcinoma based on genes related to fatty acid metabolism
Dan Cong, Yanan Zhao, Wenlong Zhang, Jun Li, Yuansong Bai
Frontiers in Pharmacology.2023;[Epub] CrossRef
Identification of raffinose family oligosaccharides in processed Rehmannia glutinosa Libosch using matrix‐assisted laser desorption/ionization mass spectrometry image combined with machine learning
Huizhi Li, Shishan Zhang, Yanfang Zhao, Jixiang He, Xiangfeng Chen
Rapid Communications in Mass Spectrometry.2023;[Epub] CrossRef
Development and validation of a LASSO-based prediction model for immunosuppressive medication nonadherence in kidney transplant recipients
Lei Dong, Xiao Zhu, Hongyu Zhao, Qin Zhao, Shan Liu, Jia Liu, Lina Gong
Renal Failure.2023;[Epub] CrossRef
An artificial intelligence prediction model outperforms conventional guidelines in predicting lymph node metastasis of T1 colorectal cancer
Zheng Hua Piao, Rong Ge, Lu Lu
Frontiers in Oncology.2023;[Epub] CrossRef
Silicon versus Superbug: Assessing Machine Learning’s Role in the Fight against Antimicrobial Resistance
Tallon Coxe, Rajeev K. Azad
Antibiotics.2023; 12(11): 1604. CrossRef
Predictive model for early death risk in pediatric hemophagocytic lymphohistiocytosis patients based on machine learning
Li Xiao, Yang Zhang, Ximing Xu, Ying Dou, Xianmin Guan, Yuxia Guo, Xianhao Wen, Yan Meng, Meiling Liao, Qinshi Hu, Jie Yu
Heliyon.2023; 9(11): e22202. CrossRef
Multimodal and multi-omics-based deep learning model for screening of optic neuropathy
Ye-ting Lin, Qiong Zhou, Jian Tan, Yulin Tao
Heliyon.2023; 9(12): e22244. CrossRef
Exploration and validation of key genes associated with early lymph node metastasis in thyroid carcinoma using weighted gene co-expression network analysis and machine learning
Yanyan Liu, Zhenglang Yin, Yao Wang, Haohao Chen
Frontiers in Endocrinology.2023;[Epub] CrossRef
Current problems and perspectives of pathological risk factors for lymph node metastasis in T1 colorectal cancer: Systematic review
Katsuro Ichimasa, Shin‐ei Kudo, Hideyuki Miyachi, Yuta Kouyama, Kenichi Mochizuki, Yuki Takashina, Yasuharu Maeda, Yuichi Mori, Toyoki Kudo, Yuki Miyata, Yoshika Akimoto, Yuki Kataoka, Takafumi Kubota, Tetsuo Nemoto, Fumio Ishida, Masashi Misawa
Digestive Endoscopy.2022; 34(5): 901. CrossRef
Tumor Location as a Prognostic Factor in T1 Colorectal Cancer
Katsuro Ichimasa, Shin-ei Kudo, Yuta Kouyama, Kenichi Mochizuki, Yuki Takashina, Masashi Misawa, Yuichi Mori, Takemasa Hayashi, Kunihiko Wakamura, Hideyuki Miyachi
Journal of the Anus, Rectum and Colon.2022; 6(1): 9. CrossRef
The Importance of Being “That” Colorectal pT1: A Combined Clinico-Pathological Predictive Score to Improve Nodal Risk Stratification
Alessandro Gambella, Enrico Costantino Falco, Giacomo Benazzo, Simona Osella-Abate, Rebecca Senetta, Isabella Castellano, Luca Bertero, Paola Cassoni
Frontiers in Medicine.2022;[Epub] CrossRef
A Predictive Model for Qualitative Evaluation of PG-SGA in Tumor Patients Through Machine Learning
Xiangliang Liu, Yuguang Li, Wei Ji, Kaiwen Zheng, Jin Lu, Yixin Zhao, Wenxin Zhang, Mingyang Liu, Jiuwei Cui, Wei Li
Cancer Management and Research.2022; Volume 14: 1431. CrossRef
Deep Submucosal Invasion Is Not an Independent Risk Factor for Lymph Node Metastasis in T1 Colorectal Cancer: A Meta-Analysis
Liselotte W. Zwager, Barbara A.J. Bastiaansen, Nahid S.M. Montazeri, Roel Hompes, Valeria Barresi, Katsuro Ichimasa, Hiroshi Kawachi, Isidro Machado, Tadahiko Masaki, Weiqi Sheng, Shinji Tanaka, Kazutomo Togashi, Chihiro Yasue, Paul Fockens, Leon M.G. Moo
Gastroenterology.2022; 163(1): 174. CrossRef
LASSO ‐derived nomogram predicting new‐onset diabetes mellitus in patients with kidney disease receiving immunosuppressive drugs
Lina Shao, Chuxuan Luo, Chaoyun Yuan, Xiaolan Ye, Yuqun Zeng, Yan Ren, Binxian Ye, Yiwen Li, Juan Jin, Qiang He, Xiaogang Shen
Journal of Clinical Pharmacy and Therapeutics.2022; 47(10): 1627. CrossRef
Using random forest algorithm for glomerular and tubular injury diagnosis
Wenzhu Song, Xiaoshuang Zhou, Qi Duan, Qian Wang, Yaheng Li, Aizhong Li, Wenjing Zhou, Lin Sun, Lixia Qiu, Rongshan Li, Yafeng Li
Frontiers in Medicine.2022;[Epub] CrossRef
Identification of diagnostic signatures associated with immune infiltration in Alzheimer’s disease by integrating bioinformatic analysis and machine-learning strategies
Yu Tian, Yaoheng Lu, Yuze Cao, Chun Dang, Na Wang, Kuo Tian, Qiqi Luo, Erliang Guo, Shanshun Luo, Lihua Wang, Qian Li
Frontiers in Aging Neuroscience.2022;[Epub] CrossRef
Development and validation of a predictive model combining clinical, radiomics, and deep transfer learning features for lymph node metastasis in early gastric cancer
Qingwen Zeng, Hong Li, Yanyan Zhu, Zongfeng Feng, Xufeng Shu, Ahao Wu, Lianghua Luo, Yi Cao, Yi Tu, Jianbo Xiong, Fuqing Zhou, Zhengrong Li
Frontiers in Medicine.2022;[Epub] CrossRef
Identification of Drug-Induced Liver Injury Biomarkers from Multiple Microarrays Based on Machine Learning and Bioinformatics Analysis
Kaiyue Wang, Lin Zhang, Lixia Li, Yi Wang, Xinqin Zhong, Chunyu Hou, Yuqi Zhang, Congying Sun, Qian Zhou, Xiaoying Wang
International Journal of Molecular Sciences.2022; 23(19): 11945. CrossRef
Development and validation of a machine learning model to predict the risk of lymph node metastasis in renal carcinoma
Xiaowei Feng, Tao Hong, Wencai Liu, Chan Xu, Wanying Li, Bing Yang, Yang Song, Ting Li, Wenle Li, Hui Zhou, Chengliang Yin
Frontiers in Endocrinology.2022;[Epub] CrossRef
Preoperative prediction of lymph node status in patients with colorectal cancer. Developing a predictive model using machine learning
Morten Hartwig, Karoline Bendix Bräuner, Rasmus Vogelsang, Ismail Gögenur
International Journal of Colorectal Disease.2022; 37(12): 2517. CrossRef
A retrospective analysis based on multiple machine learning models to predict lymph node metastasis in early gastric cancer
Tao Yang, Javier Martinez-Useros, JingWen Liu, Isaias Alarcón, Chao Li, WeiYao Li, Yuanxun Xiao, Xiang Ji, YanDong Zhao, Lei Wang, Salvador Morales-Conde, Zuli Yang
Frontiers in Oncology.2022;[Epub] CrossRef
Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology
Chrysanthos D Christou, Georgios Tsoulfas
World Journal of Gastroenterology.2021; 27(37): 6191. CrossRef

13,672 View
373 Download
127 Web of Science
114 Crossref

Prediction of Acquired Taxane Resistance Using a Personalized Pathway-Based Machine Learning Method: Young Rae Kim, Dongha Kim, Sung Young Kim; Cancer Res Treat. 2019;51(2):672-684. Published online August 10, 2018; DOI: https://doi.org/10.4143/crt.2018.137

Abstract PDF Supplementary Material PubReader ePub

Purpose
This study was conducted to develop and validate an individualized prediction model for automated detection of acquired taxane resistance (ATR).
Materials and Methods
Penalized regression, combinedwith an individualized pathway score algorithm,was applied to construct a predictive model using publically available genomic cohorts of ATR and intrinsic taxane resistance (ITR). To develop a model with enhanced generalizability, we merged multiple ATR studies then updated the learning parameter via robust cross-study validation.
Results
For internal cross-study validation, the ATR model produced a perfect performance with an overall area under the receiver operating curve (AUROC) of 1.000 with an area under the precision-recall curve (AUPRC) of 1.000, a Brier score of 0.007, a sensitivity and a specificity of 100%. The model showed an excellent performance on two independent blind ATR cohorts (overall AUROC of 0.940, AUPRC of 0.940, a Brier score of 0.127). When we applied our algorithm to two large-scale pharmacogenomic resources for ITR, the Cancer Genome Project (CGP) and the Cancer Cell Line Encyclopedia (CCLE), an overall ITR cross-study AUROC was 0.70, which is a far better accuracy than an almost random level reported by previous studies. Furthermore, this model had a high transferability on blind ATR cohorts with an AUROC of 0.69, suggesting that general predictive features may be at work across both ITR and ATR.
Conclusion
We successfully constructed a multi-study–derived personalized prediction model for ATR with excellent accuracy, generalizability, and transferability.

Citations

Citations to this article as recorded by

The impact and future of artificial intelligence in medical genetics and molecular medicine: an ongoing revolution
Firat Ozcelik, Mehmet Sait Dundar, A. Baki Yildirim, Gary Henehan, Oscar Vicente, José A. Sánchez-Alcázar, Nuriye Gokce, Duygu T. Yildirim, Nurdeniz Nalbant Bingol, Dijana Plaseska Karanfilska, Matteo Bertelli, Lejla Pojskic, Mehmet Ercan, Miklos Kellerma
Functional & Integrative Genomics.2024;[Epub] CrossRef
Network biology and artificial intelligence drive the understanding of the multidrug resistance phenotype in cancer
Beatriz Bueschbell, Ana Beatriz Caniceiro, Pedro M.S. Suzano, Miguel Machuqueiro, Nícia Rosário-Ferreira, Irina S. Moreira
Drug Resistance Updates.2022; 60: 100811. CrossRef
Multiparametric quantitative phase imaging for real-time, single cell, drug screening in breast cancer
Edward R. Polanco, Tarek E. Moustafa, Andrew Butterfield, Sandra D. Scherer, Emilio Cortes-Sanchez, Tyler Bodily, Benjamin T. Spike, Bryan E. Welm, Philip S. Bernard, Thomas A. Zangle
Communications Biology.2022;[Epub] CrossRef
Machine Learning: An Overview and Applications in Pharmacogenetics
Giovanna Cilluffo, Salvatore Fasola, Giuliana Ferrante, Velia Malizia, Laura Montalbano, Stefania La Grutta
Genes.2021; 12(10): 1511. CrossRef
Computational approaches in cancer multidrug resistance research: Identification of potential biomarkers, drug targets and drug-target interactions
A. Tolios, J. De Las Rivas, E. Hovig, P. Trouillas, A. Scorilas, T. Mohr
Drug Resistance Updates.2020; 48: 100662. CrossRef
FLOating-Window Projective Separator (FloWPS): A Data Trimming Tool for Support Vector Machines (SVM) to Improve Robustness of the Classifier
Victor Tkachev, Maxim Sorokin, Artem Mescheryakov, Alexander Simonov, Andrew Garazha, Anton Buzdin, Ilya Muchnik, Nicolas Borisov
Frontiers in Genetics.2019;[Epub] CrossRef
New Paradigm of Machine Learning (ML) in Personalized Oncology: Data Trimming for Squeezing More Biomarkers From Clinical Datasets
Nicolas Borisov, Anton Buzdin
Frontiers in Oncology.2019;[Epub] CrossRef

9,382 View
165 Download
6 Web of Science
7 Crossref

First
Prev
Page of 1
Next
Last

Search