Cancer Research and Treatment

Original Articles

Gastrointestinal cancer

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; Cancer Res Treat. 2025;57(3):821-829. Published online December 16, 2024; DOI: https://doi.org/10.4143/crt.2024.843

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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.

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Liver cancer risk stratification using deep learning on nationwide longitudinal health screening data: a retrospective cohort study
Yewon Choi, Sungmin Cho, Changdai Gu, Chungho Kim, Bomi Park, Hwiyoung Kim
BMC Medical Informatics and Decision Making.2026;[Epub] CrossRef
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
Development and Validation of the Early Gastric Carcinoma Prediction Model in Post-Eradication Patients with Intestinal Metaplasia
Wulian Lin, Guanpo Zhang, Hong Chen, Weidong Huang, Guilin Xu, Yunmeng Zheng, Chao Gao, Jin Zheng, Dazhou Li, Wen Wang
Cancers.2025; 17(13): 2158. CrossRef
Field-Scale Maize Yield Estimation Using Remote Sensing with the Integration of Agronomic Traits
Shuai Bao, Yiang Wang, Shinai Ma, Huanjun Liu, Xiyu Xue, Yuxin Ma, Mingcong Zhang, Dianyao Wang
Agriculture.2025; 15(17): 1834. CrossRef
Ensemble Transfer Learning for Gastric Cancer Prediction Using Electronic Health Records in a Data-Scarce Single-Hospital Setting
Hyon Hee Kim, Ji Yeon Han, Yae Bin Lim, Young Seo Lim, Seung-In Seo, Kyung Joo Lee, Woon Geon Shin
Applied Sciences.2025; 15(23): 12428. CrossRef
Cross-Cancer Transfer Learning for Gastric Cancer Risk Prediction from Electronic Health Records
Daeyoung Hong, Jiung Kim, Jiyong Jung
Diagnostics.2025; 15(24): 3175. CrossRef

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Relationships between the Microbiome and Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: Hye In Lee, Bum-Sup Jang, Ji Hyun Chang, Eunji Kim, Tae Hoon Lee, Jeong Hwan Park, Eui Kyu Chie; Cancer Res Treat. 2025;57(3):840-851. Published online December 16, 2024; DOI: https://doi.org/10.4143/crt.2024.521

Abstract PDF Supplementary Material PubReader ePub

Purpose
This study aimed to investigate the dynamic changes in the microbiome of patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy (nCRT), focusing on the relationship between the microbiome and response to nCRT.
Materials and Methods
We conducted a longitudinal study involving 103 samples from 26 patients with LARC. Samples were collected from both the tumor and normal rectal tissues before and after nCRT. Diversity, taxonomic, and network analyses were performed to compare the microbiome profiles across different tissue types, pre- and post-nCRT time-points, and nCRT responses.
Results
Between the tumor and normal tissue samples, no differences in microbial diversity and composition were observed. However, when pre- and post-nCRT samples were compared, there was a significant decrease in diversity, along with notable changes in composition. Non-responders exhibited more extensive changes in their microbiome composition during nCRT, characterized by an increase in pathogenic microbes. Meanwhile, responders had relatively stable microbiome communities with more enriched butyrate-producing bacteria. Network analysis revealed distinct patterns of microbial interactions between responders and non-responders, where butyrate-producing bacteria formed strong networks in responders, while opportunistic pathogens formed strong networks in non-responders. A Bayesian network model for predicting the nCRT response was established, with butyrate-producing bacteria playing a major predictive role.
Conclusion
Our study demonstrated a significant association between the microbiome and nCRT response in LARC patients, leading to the development of a microbiome-based response-prediction model. These findings suggest potential applications of microbiome signatures for predicting and optimizing nCRT treatment in LARC patients.

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The Mediating Role of the Gut Microbiome in the Nutritional Prevention of Cancer
Priyanka Chambial, Neelam Thakur, Umesh Kumar, Saurabh Gupta
The Journal of Nutrition.2026; 156(2): 101301. CrossRef
Gut-Microbiome Signatures Predicting Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer: A Systematic Review
Ielmina Domilescu, Bogdan Miutescu, Florin George Horhat, Alina Popescu, Camelia Nica, Ana Maria Ghiuchici, Eyad Gadour, Ioan Sîrbu, Delia Hutanu
Metabolites.2025; 15(6): 412. CrossRef

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Prediction of Pathologic Response to Neoadjuvant Chemoradiotherapy in Patients with Esophageal Squamous Cell Carcinoma Incorporating Hematological Biomarkers: Yingjia Wu, Jinbin Chen, Lei Zhao, Qiaoqiao Li, Jinhan Zhu, Hong Yang, Suping Guo, Mian Xi; Cancer Res Treat. 2021;53(1):172-183. Published online September 4, 2020; DOI: https://doi.org/10.4143/crt.2020.594

Abstract PDF Supplementary Material PubReader ePub

Purpose
This study aimed to develop a nomogram for predicting pathologic complete response (pCR) after neoadjuvant chemoradiotherapy (CRT) in patients with esophageal squamous cell carcinoma (ESCC) by integrating hematological biomarkers and clinicopathological characteristics.
Materials and Methods
Between 2003 and 2017, 306 ESCC patients who underwent neoadjuvant CRT followed by esophagectomy were analyzed. Besides clinicopathological factors, hematological parameters before, during, and after CRT were collected. Univariate and multivariate logistic regression analyses were performed to identify predictive factors for pCR. A nomogram model was built and internally validated.
Results
Absolute lymphocyte count (ALC), lymphocyte to monocyte ratio, albumin, hemoglobin, white blood cell, neutrophil, and platelet count generally declined, whereas neutrophil to lymphocyte ratio (NLR) and platelet to lymphocyte ratio (PLR) increased significantly following neoadjuvant CRT. After surgery, 124 patients (40.5%) achieved a pCR. The pCR group demonstrated significantly more favorable survival than the non-pCR group. On multivariate analysis, significant factors associated with pCR included sex, chemotherapy regimen, post-CRT endoscopic finding, pre-CRT NLR, ALC nadir during CRT, and post-CRT PLR, which were incorporated into the prediction model. The nomogram indicated good accuracy in predicting pCR, with a C-index of 0.75 (95% confidence interval, 0.71 to 0.78).
Conclusion
Female, chemotherapy regimen of cisplatin/vinorelbine, negative post-CRT endoscopic finding, pre-CRT NLR (≤ 2.1), ALC nadir during CRT (> 0.35 ×10⁹/L), and post-CRT PLR (≤ 83.0) were significantly associated with pCR in ESCC patients treated with neoadjuvant CRT. A nomogram incorporating hematological biomarkers to predict pCR was developed and internally validated, showing good predictive performance.

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A composite immune-nutritional and tumor marker score predicts outcomes in LA-ESCC treated with neoadjuvant chemoimmunotherapy and surgery
Yiwei Fan, Jiang Zhou, Xiaotian Liu, Qianhe Ren, Shichun Lu, Weiguo Jin
Discover Oncology.2026;[Epub] CrossRef
Development of a predictive model for pathological complete response following neoadjuvant immunotherapy and chemotherapy in locally advanced resectable esophageal squamous cell carcinoma
Yilei Zhang, Hounai Xie, Deguo Zhang, Yujuan Liu, Shuaiming Fu, Yongkui Yu, Wei Ma
World Journal of Surgical Oncology.2026;[Epub] CrossRef
The Predictive Value of Red Cell Distribution Width in End-Stage Colorectal Cancers’ 6-Month Palliative Chemotherapy Response—A Single Center’s Experience
Maciej Jankowski, Krystyna Bratos, Joanna Wawer, Tomasz Urbanowicz
Journal of Personalized Medicine.2025; 15(8): 359. CrossRef
Perioperative outcomes of neoadjuvant chemotherapy plus camrelizumab versus neoadjuvant chemotherapy plus tislelizumab for locally advanced esophageal squamous cell cancer: a real-world retrospective study
Qi Zhao, Yusen Yuan, Tongxin Xu, Ningning Yan, Fei Li, Juntao Lu, Ming He, Zhaoyang Yan
Frontiers in Immunology.2025;[Epub] CrossRef
Prognostic value of neutrophil to lymphocyte ratio in patients with esophagus cancer receiving neoadjuvant therapy: a systematic review and meta-analysis
Longwei Ma, Jiaxing He, Ping Li, Long Ma, He Wang, Yanchao Deng
Frontiers in Immunology.2025;[Epub] CrossRef
Pan-immune-inflammation value as a novel predictor of pathological response to neoadjuvant chemotherapy combined with anti-PD-1 therapy in esophageal squamous cell carcinoma: a multicenter real-world retrospective clinical study
Jiang-shan Huang, Qi-hong Zhong, Gang Wang, Zi-lu Tang, Bing-Lin Shen, Wei-nan Liu, Fei-long Guo, Jing-yu Wu, Zhen-yang Zhang, Jiang-bo Lin
Therapeutic Advances in Medical Oncology.2025;[Epub] CrossRef
CT-based deep learning radiomics and hematological biomarkers in the assessment of pathological complete response to neoadjuvant chemoradiotherapy in patients with esophageal squamous cell carcinoma: A two-center study
Meng Zhang, Yukun Lu, Hongfu Sun, Chuanke Hou, Zichun Zhou, Xiao Liu, Qichao Zhou, Zhenjiang Li, Yong Yin
Translational Oncology.2024; 39: 101804. CrossRef
A machine learning approach using 18F-FDG PET and enhanced CT scan-based radiomics combined with clinical model to predict pathological complete response in ESCC patients after neoadjuvant chemoradiotherapy and anti-PD-1 inhibitors
Wei-Xiang Qi, Shuyan Li, Jifeng Xiao, Huan Li, Jiayi Chen, Shengguang Zhao
Frontiers in Immunology.2024;[Epub] CrossRef
Neoadjuvant PD-1 Plus Chemotherapy for Locally Advanced Esophageal Squamous Cell Carcinoma
Ting Qian, Delin Liu, Guochun Cao, Zhipeng Chen, Qin Zhang
Technology in Cancer Research & Treatment.2024;[Epub] CrossRef
Nomogram for predicting pathologic complete response to neoadjuvant chemoradiotherapy in patients with esophageal squamous cell carcinoma
Guihong Liu, Tao Chen, Xin Zhang, Binbin Hu, Jiayun Yu
Cancer Medicine.2024;[Epub] CrossRef
Pathological response to neoadjuvant chemoradiotherapy for oesophageal squamous cell carcinoma in Eastern versus Western countries: meta-analysis
Xing Gao, Hidde C G Overtoom, Ben M Eyck, Shi-Han Huang, Daan Nieboer, Pieter C van der Sluis, Sjoerd M Lagarde, Bas P L Wijnhoven, Yin-Kai Chao, Jan J B van Lanschot
British Journal of Surgery.2024;[Epub] CrossRef
Integrating MR radiomics and dynamic hematological factors predicts pathological response to neoadjuvant chemoradiotherapy in esophageal cancer
Yunsong Liu, Zeliang Ma, Yongxing Bao, Xin Wang, Yu Men, Xujie Sun, Feng Ye, Kuo Men, Jianjun Qin, Nan Bi, Liyan Xue, Zhouguang Hui
Heliyon.2024; 10(13): e33702. CrossRef
Preoperative neutrophil–to–lymphocyte ratio after chemoradiotherapy for esophageal squamous cell carcinoma associates with postoperative pulmonary complications following radical esophagectomy
Chien-Ming Lo, Hung-I. Lu, Yu-Ming Wang, Yen-Hao Chen, Yu Chen, Li-Chun Chen, Shau-Hsuan Li
Perioperative Medicine.2024;[Epub] CrossRef
Prognostic Impact of Inflammation-Based Factors in Patients with Esophageal Squamous Cell Carcinoma Achieving Pathological Complete Response After Neoadjuvant Chemoradiotherapy Followed by Surgery
Ji Yong Kim, Jae Kwang Yun, Yong-Hee Kim, Seung-il Park, Jeong Hoon Lee, Hwoon-Yong Jung, Gin Hyug Lee, Ho June Song, Do Hoon Kim, Kee Don Choi, Ji Yong Ahn, Sung-Bae Kim, Kyung-Ja Cho, Jin-Sook Ryu, Jong Hoon Kim, Jihoon Kang, Sook Ryun Park, Hyeong Ryul
Annals of Surgical Oncology.2024; 31(10): 6662. CrossRef
The relationship between systemic immune-inflammation indexes and treatment response in locally advanced esophageal cancer
Esra KEKİLLİ, Ebru ATASEVER AKKAŞ, Serab UYAR, Emre YEKEDÜZ
Anatolian Current Medical Journal.2023; 5(1): 53. CrossRef
A Novel Predictor of Pathologic Complete Response for Neoadjuvant Immunochemotherapy in Resectable Locally Advanced Esophageal Squamous Cell Carcinoma
Yalan Yang, Dao Xin, Huike Wang, Lulu Guan, Xiangrui Meng, Taiying Lu, Xiwen Bai, Feng Wang
Journal of Inflammation Research.2023; Volume 16: 1443. CrossRef
Gut microbiome can predict chemoradiotherapy efficacy in patients with esophageal squamous cell carcinoma
Takuma Sasaki, Yasunori Matsumoto, Kentaro Murakami, Satoshi Endo, Takeshi Toyozumi, Ryota Otsuka, Kazuya Kinoshita, Jie Hu, Shinichiro Iida, Hiroki Morishita, Yuri Nishioka, Akira Nakano, Masaya Uesato, Hisahiro Matsubara
Esophagus.2023; 20(4): 691. CrossRef
Pretreatment absolute lymphocyte count is an independent predictor for survival outcomes for esophageal squamous cell carcinoma patients treated with neoadjuvant chemoradiotherapy and pembrolizumab: An analysis from a prospective cohort
Wei‐Xiang Qi, Xiaoyan Wang, Chengqiang Li, Shuyan Li, Huan Li, Feifei Xu, Jiayi Chen, Shengguang Zhao, Hecheng Li
Thoracic Cancer.2023; 14(17): 1556. CrossRef
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Lian-Qiang Han, Ting-Ting Cui, Nian-Jun Xiao, Wen Li
Journal of Cancer Research and Clinical Oncology.2023; 149(17): 15413. CrossRef
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Dan He, Shulan Du, Songyuan He, Hao Song, Bo Pu, Guojun Zhang, Chuan Yang
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Nomogram for predicting pathologic complete response following preoperative chemoradiotherapy in patients with esophageal squamous cell carcinoma
Young Seob Shin, Jeong Yun Jang, Ye Jin Yoo, Jesang Yu, Kye Jin Song, Yoon Young Jo, Sung-Bae Kim, Sook Ryun Park, Ho June Song, Yong-Hee Kim, Hyeong Ryul Kim, Jong Hoon Kim
Gastroenterology Report.2023;[Epub] CrossRef
Impact of Platelets to Lymphocytes Ratio and Lymphocytes during Radical Concurrent Radiotherapy and Chemotherapy on Patients with Nonmetastatic Esophageal Squamous Cell Carcinoma
Yaotian Zhang, Ning Han, Xue Zeng, Chaonan Sun, Shichen Sun, Xinchi Ma, Yanyu Zhang, Zhuang Liu, Zilan Qin, Hong Guo, Yubing Li, Na Zhang, Bruno Vincenzi
Journal of Oncology.2022; 2022: 1. CrossRef
Serum Anti-BRAT1 is a Common Molecular Biomarker for Gastrointestinal Cancers and Atherosclerosis
Liubing Hu, Jiyue Liu, Hideaki Shimada, Masaaki Ito, Kazuo Sugimoto, Takaki Hiwasa, Qinghua Zhou, Jianshuang Li, Si Shen, Hao Wang
Frontiers in Oncology.2022;[Epub] CrossRef
Impact of radiotherapy on the immune landscape in oesophageal adenocarcinoma
Noel E Donlon, Maria Davern, Fiona O’Connell, Andrew Sheppard, Aisling Heeran, Anshul Bhardwaj, Christine Butler, Ravi Narayanasamy, Claire Donohoe, James J Phelan, Niamh Lynam-Lennon, Margaret R Dunne, Stephen Maher, Jacintha O’Sullivan, John V Reynolds,
World Journal of Gastroenterology.2022; 28(21): 2302. CrossRef
Pathologic Complete Response Prediction to Neoadjuvant Immunotherapy Combined with Chemotherapy in Resectable Locally Advanced Esophageal Squamous Cell Carcinoma: Real-World Evidence from Integrative Inflammatory and Nutritional Scores
Jifeng Feng, Liang Wang, Xun Yang, Qixun Chen, Xiangdong Cheng
Journal of Inflammation Research.2022; Volume 15: 3783. CrossRef
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Frontiers in Immunology.2022;[Epub] CrossRef

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