Cancer Research and Treatment

Original Articles

Novel Breast Cancer Risk Assessment Tools for Pre- and Post-Menopausal Asian Women: Development and Validation in a Nationwide Mammographic Screening Cohort: Wonyoung Jung, Yong-Moon Mark Park, Sang Hyun Park, Kyungdo Han, Junhee Park, Yohwan Yeo, Jung Kwon Lee, Dale P. Sandler, Dong Wook Shin; Received September 4, 2024 Accepted January 30, 2025 Published online January 31, 2025; DOI: https://doi.org/10.4143/crt.2024.861 [Accepted]

Abstract PDF: Purpose
Widely used breast cancer risk-prediction tools are based on data from Western countries, but risk factors may differ for Asian women. Hence, we aimed to develop a risk assessment tool for breast cancer in Asian women using a nationwide, population-based mammographic screening cohort.
Materials and Methods
Women aged ≥40 years who underwent breast cancer screening and general health examination in 2009 were included. Age, body mass index (BMI), breast density, lifestyle and reproductive factors, and comorbidities were used to develop 5-year breast cancer risk-prediction models for premenopausal (n=771,856) and postmenopausal (n=1,108,047) women at baseline. The best-fit risk prediction model was constructed using backward stepwise selection in a Cox proportional hazards model and was transformed into a risk score nomogram. The performance was assessed by discrimination and calibration.
Results
In premenopausal women, high BMI, low parity, short breastfeeding period, early age at menarche, high breast density, a history of benign breast masses, and family history of breast cancer contributed to the risk prediction of breast cancer. In postmenopausal women, age, diabetes mellitus, dyslipidemia, late-onset menopause, and hormone replacement therapy use were additional risk predictors of breast cancer. Our risk-prediction model showed a concordant statistic of 0.58 (0.57–0.59) for premenopausal women and 0.64 (0.63–0.65) for postmenopausal women. The calibration plot demonstrated good correlations for both models.
Conclusion
Our breast cancer risk-prediction model demonstrated performance comparable to that of Western countries, especially among postmenopausal women. This provides a foundation for implementing risk-based screening recommendations in Asian women.

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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 [Accepted]

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 at least five 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 gastric cancer, 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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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

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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; Received June 2, 2024 Accepted December 13, 2024 Published online December 16, 2024; DOI: https://doi.org/10.4143/crt.2024.521 [Epub ahead of print]

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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Predictive Value of the nProfiler 1 Assay for the Efficacy of Adjuvant S-1–Based Doublet Chemotherapy in Stage III Gastric Cancer: A Post-Hoc Analysis of a Randomized Phase III Trial: Dong Ki Lee, Choong-kun Lee, Hyo Song Kim, Sun Jin Sym, Dae Young Zang, Ki Hyang Kim, Joo Han Lim, Hae Su Kim, Kyung Hee Lee, Heon Yung Gee, Sun Young Rha, Hyunki Kim, Minkyu Jung; Received July 25, 2024 Accepted November 9, 2024 Published online November 12, 2024; DOI: https://doi.org/10.4143/crt.2024.705 [Epub ahead of print]

Abstract PDF Supplementary Material PubReader ePub: Purpose
The nProfiler 1 Stomach Cancer Assay (nProfiler1), designed to predict responses to fluorouracil-based adjuvant chemotherapy, measures the expression of four gastric cancer target genes (GZMB, WARS, SFRP4, and CDX1). The randomized phase III POST trial aimed to compare the efficacies of two adjuvant S-1-based doublet chemotherapies: S-1 plus cisplatin (SP) and S-1 plus docetaxel (DS). This study aimed to validate the nProfiler1 assay using a distinct cohort from the POST trial.
Materials and Methods
The nProfiler1 assay stratifies patients into three groups (low-risk, intermediate-risk, and high-risk) using the prognostic single-patient classifier and two groups (chemotherapy-benefit and no-benefit) using the predictive single-patient classifier. The nProfiler1 assay was applied to formalin-fixed paraffin-embedded slides obtained from the POST trial. Disease-free survival (DFS) and overall survival (OS), including 5-year survival rates, were calculated for the enrolled patients.
Results
Of the 153 patients in the POST trial, 118 were included in the post-hoc analysis. With a median follow-up of 57.9 months, no significant difference in DFS or OS was observed between the SP and DS groups. The prognostic single-patient classifier predicted the OS in the SP group (p=0.043) but not in the DS group (p=0.594). The chemotherapy-benefit group exhibited numerically longer DFS than the no-benefit group in the SP and DS groups.
Conclusion
The nProfiler1 assay offers valuable insights into the prognosis and efficacy of adjuvant chemotherapy based on fluorouracil plus platinum doublet regimens but not docetaxel-containing regimens. Further validation with larger patient cohorts and different regimens is warranted.

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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.

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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
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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
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Amin Zadeh Shirazi, Morteza Tofighi, Alireza Gharavi, Guillermo A. Gomez
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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

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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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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
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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
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Esra KEKİLLİ, Ebru ATASEVER AKKAŞ, Serab UYAR, Emre YEKEDÜZ
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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
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Wei‐Xiang Qi, Xiaoyan Wang, Chengqiang Li, Shuyan Li, Huan Li, Feifei Xu, Jiayi Chen, Shengguang Zhao, Hecheng Li
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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
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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
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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
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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,
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Jifeng Feng, Liang Wang, Xun Yang, Qixun Chen, Xiangdong Cheng
Journal of Inflammation Research.2022; Volume 15: 3783. CrossRef
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Xiaoyan Sun, Yingnan Feng, Bin Zhang, Wuhao Huang, Xiaoliang Zhao, Hua Zhang, Dongsheng Yue, Changli Wang
Cancer Research and Treatment.2022; 54(4): 1017. CrossRef
The predictive value of peripheral blood cells and lymphocyte subsets in oesophageal squamous cell cancer patients with neoadjuvant chemoradiotherapy
Jin Zhou, Hai-Ping Lin, Xin Xu, Xiao-Hang Wang, Ling Rong, Yao Zhang, Lei Shen, Lei Xu, Wei-Ting Qin, Qing Ye, Xiu-Mei Ma, Yong-Rui Bai
Frontiers in Immunology.2022;[Epub] CrossRef

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A Nomogram for Predicting the Oncotype DX Recurrence Score in Women with T1-3N0-1miM0 Hormone Receptor‒Positive, Human Epidermal Growth Factor 2 (HER2)‒Negative Breast Cancer: Sae Byul Lee, Junetae Kim, Guiyun Sohn, Jisun Kim, Il Yong Chung, Hee Jeong Kim, Beom Seok Ko, Byung Ho Son, Sei-Hyun Ahn, Jong Won Lee, Kyung Hae Jung; Cancer Res Treat. 2019;51(3):1073-1085. Published online November 1, 2018; DOI: https://doi.org/10.4143/crt.2018.357

Abstract PDF Supplementary Material PubReader ePub

Purpose
This preliminary study was conducted to evaluate the association between Oncotype DX (ODX) recurrence score and traditional prognostic factors. We also developed a nomogram to predict subgroups with low ODX recurrence scores (less than 25) and to avoid additional chemotherapy treatments for those patients.
Materials and Methods
Clinicopathological and immunohistochemical variables were retrospectively retrieved and analyzed from a series of 485 T1-3N0-1miM0 hormone receptor-positive, human epidermal growth factor 2‒negative breast cancer patients with available ODX test results at Asan Medical Center from 2010 to 2016. One hundred twenty-seven patients (26%) had positive axillary lymph node micrometastases, and 408 (84%) had ODX recurrence scores of ≤25. Logistic regression was performed to build a nomogram for predicting a low-risk subgroup of the ODX assay.
Results
Multivariate analysis revealed that estrogen receptor (ER) score, progesterone receptor (PR) score, histologic grade, lymphovascular invasion (LVI), and Ki-67 had a statistically significant association with the low-risk subgroup. With these variables, we developed a nomogram to predict the low-risk subgroup with ODX recurrence scores of ≤25. The area under the receiver operating characteristic curve was 0.90 (95% confidence interval [CI], 0.85 to 0.96). When applied to the validation group the nomogram was accurate with an area under the curve = 0.88 (95% CI, 0.83 to 0.95).
Conclusion
The low ODX recurrence score subgroup can be predicted by a nomogram incorporating five traditional prognostic factors: ER, PR, histologic grade, LVI, and Ki-67. Our nomogram, which predicts a low-risk ODX recurrence score, will be a useful tool to help select patients who may or may not need additional ODX testing.

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Víctor Macarrón, Itsaso Losantos-García, Alberto Peláez-García, Laura Yébenes, Alberto Berjón, Laura Frías, Covadonga Martí, Pilar Zamora, José Ignacio Sánchez-Méndez, David Hardisson
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Jiayin Song, Lin Yang, Zhengqi Feng, Liyu Jiang
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Asim Dhungana, Augustin Vannier, Fangyuan Zhao, Jincong Q. Freeman, Poornima Saha, Megan Sullivan, Katharine Yao, Elbio M. Flores, Olufunmilayo I. Olopade, Alexander T. Pearson, Dezheng Huo, Frederick M. Howard
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Ji Min Kim, Eun Yoon Cho
Journal of Breast Cancer.2024; 27(3): 201. CrossRef
Development of a nomogram to predict recurrence scores obtained using Oncotype DX in Japanese patients with breast cancer
Akio Shibata, Nobuko Tamura, Keiichi Kinowaki, Aya Nishikawa, Kiyo Tanaka, Yoko Kobayashi, Takuya Ogura, Yuko Tanabe, Hidetaka Kawabata
Breast Cancer.2024; 31(6): 1018. CrossRef
Shear-wave elastography-based nomograms predicting 21-gene recurrence score for adjuvant chemotherapy decisions in patients with breast cancer
Ji Hyun Youk, Eun Ju Son, Joon Jeong, Hye Mi Gweon, Na Lae Eun, Jeong-Ah Kim
European Journal of Radiology.2023; 158: 110638. CrossRef
Prediction of Oncotype DX Recurrence Score Using Clinicopathological Variables in Estrogen Receptor-Positive/Human Epidermal Growth Factor Receptor 2-Negative Breast Cancer
Min Chong Kim, Sun Young Kwon, Jung Eun Choi, Su Hwan Kang, Young Kyung Bae
Journal of Breast Cancer.2023; 26(2): 105. CrossRef
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Ran Song, Dong-Eun Lee, Eun-Gyeong Lee, Seeyoun Lee, Han-Sung Kang, Jai Hong Han, Keun Seok Lee, Sung Hoon Sim, Heejung Chae, Youngmee Kwon, Jaeyeon Woo, So-Youn Jung
Cancers.2023; 15(18): 4451. CrossRef
A simplified risk scoring system for predicting high-risk groups in gene expression tests for patients with estrogen receptor-positive, human epidermal growth factor receptor 2-negative, and node-positive breast cancer
Kwang Hyun Yoon, Suk Jun Lee, Yujin Kim, Jee Hyun Ahn, Jee Ye Kim, Hyung Seok Park, Seung Il Kim, Seho Park
Annals of Surgical Treatment and Research.2023; 105(6): 360. CrossRef
Deep Learning-Based Pathology Image Analysis Enhances Magee Feature Correlation With Oncotype DX Breast Recurrence Score
Hongxiao Li, Jigang Wang, Zaibo Li, Melad Dababneh, Fusheng Wang, Peng Zhao, Geoffrey H. Smith, George Teodoro, Meijie Li, Jun Kong, Xiaoxian Li
Frontiers in Medicine.2022;[Epub] CrossRef
A Novel Surrogate Nomogram Capable of Predicting OncotypeDX Recurrence Score©
Matthew G. Davey, Amirhossein Jalali, Éanna J. Ryan, Ray P. McLaughlin, Karl J. Sweeney, Michael K. Barry, Carmel M. Malone, Maccon M. Keane, Aoife J. Lowery, Nicola Miller, Michael J. Kerin
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Austin D. Williams, Kate R. Pawloski, Hannah Y. Wen, Varadan Sevilimedu, Donna Thompson, Monica Morrow, Mahmoud El-Tamer
Breast Cancer Research and Treatment.2022; 196(3): 565. CrossRef
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Jaime A. Pardo, Betty Fan, Alessandra Mele, Stephanie Serres, Monica G. Valero, Isha Emhoff, Amulya Alapati, Ted A. James
Annals of Surgical Oncology.2021; 28(3): 1320. CrossRef
A nomogram to predict the high-risk RS in HR+/HER2-breast cancer patients older than 50 years of age
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Prediction of Cancer Incidence and Mortality in Korea, 2012: Kyu-Won Jung, Sohee Park, Young-Joo Won, Hyun-Joo Kong, Joo Young Lee, Hong Gwan Seo, Jin-Soo Lee; Cancer Res Treat. 2012;44(1):25-31. Published online March 31, 2012; DOI: https://doi.org/10.4143/crt.2012.44.1.25

Abstract PDF PubReader ePub

PURPOSE
To estimate the current cancer burden in Korea, cancer incidence and mortality rates were projected for the year 2012.
MATERIALS AND METHODS
The cancer incidence data from 1999 to 2009 were obtained from the Korea National Cancer Incidence Database, and the cancer mortality data from 1993 to 2010 were obtained from Statistics Korea. Cancer incidence in 2012 was projected by fitting a linear regression model on observed age-specific cancer incidence rates against observed years, then multiplying the projected age-specific rates by the age-specific population. For cancer mortality, a similar procedure was applied, except that a Joinpoint regression model was used to determine at which year the linear trend significantly changed.
RESULTS
A total of 234,727 new cancer cases and 73,313 cancer deaths are projected to occur in Korea in 2012. For all sites combined, the crude incidence rates are projected to be 465.6 and 459.7, and the age-standardized incidences to be 345.1 and 300.9 per 100,000 respectively for males and females.
CONCLUSION
Cancer has become an important public health concern in Korea, and as the Korean population ages, the cancer burden will continue to increase.

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Prediction of Cancer Incidence and Mortality in Korea, 2011: Kyu-Won Jung, Sohee Park, Young-Joo Won, Hyun-Joo Kong, Joo Young Lee, Eun-Cheol Park, Jin-Soo Lee; Cancer Res Treat. 2011;43(1):12-18. Published online March 31, 2011; DOI: https://doi.org/10.4143/crt.2011.43.1.12

Abstract PDF PubReader ePub

PURPOSE
To estimate the current cancer burden in Korea, cancer incidence and mortality were projected for the year 2011.
MATERIALS AND METHODS
The cancer incidence data from 1999-2008 were obtained from the Korea National Cancer Incidence Database, and the cancer mortality data from 1993-2009 were obtained from the Korea National Statistics Office. Cancer incident cases and rates in 2011 were projected from fitting a linear regression model on observed age-specific cancer incidence rates against observed years, then multiplying the projected age-specific rates by the age-specific population. For cancer mortality, a similar procedure was applied for projection except that a Joinpoint regression model was used to determine at which year the linear trend significantly changed.
RESULTS
A total of 216,809 new cancer cases and 71,036 cancer deaths are projected to occur in Korea in 2011. For all sites combined, the crude incidence rates are projected to be 437.9 and 420.5 and the age-standardized incidence rates are projected to be 336.5 and 279.7 per 100,000 for men and women, respectively.
CONCLUSION
Cancer has become an important public health concern in Korea, and as Korea becomes an aged society, the cancer burden will continue to increase.

Citations

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Future Prediction of Cancer Deaths in Japan: 종회 김; J Korean Cancer Assoc. 1989;21(2):454-463.

Abstract PDF: The number of cancer deaths and age-adjusted cancer death rates up to 2000 in Japan were predicted based on cancer death rates in 1972-1986. A Simple linear regression model (y=a+bt, where t is calender year and y is cancer death rate per 100,000 population) was fitted to the sex-age specific cancer mortality rates from 1972 to 1986 and cancer death rates in 1990, 1995 and 2000 were predicted by extrapolation method. The number of future cancer deaths was estimated after taking into account future population in Japan. The age-adjusted cancer ratues up to 2000 were also estimated. The present study revealed that the numbers of deaths from stomach cancer (both sexes), uterine cancer and esophageal cancer (females) would keep declining, while all other cancers would increase in the future. The total number of cancer deaths in 2000 was estimated to be about 310,000 which is 1.62 times that in 2000, lung cancer would rank top accounting for 22.2% of all cancer deaths, followed by cancers of the large intestine, liver, stomach, pancreas, biliary tract, leukemic, lymphoma and breast.

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Establishment of SNU Cell Lines in Growth Factor and Growth hormone Supplemented Medium: Jae Gahb Park, Nam Sook Kwon, Dong Young Noh, In Gyu Hong, Seung Keun Oh, Kuhn Uk Lee, Yong Hyun Park, Sung Kuk Hong, Kuk Jin Choe, Jin Pok Kim, Soo Tae Kim, Dae Seog Heo, Yung Jue Bang, Seon Yang Pa; J Korean Cancer Assoc. 1990;22(1):1-14.

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