Cancer Research and Treatment

Original Articles

Noninvasive Assessment of Ki-67 Expression in Breast Cancer Using Ultrasound Radiomics: A Multi-Institutional Study: Sijie Mo, Zhibin Huang, Jing Zheng, Huaiyu Wu, Shuzhen Tang, Mengyun Wang, Jinfeng Xu, Hongtian Tian, Xiaoli Huang, Fajin Dong; Received May 30, 2025 Accepted September 2, 2025 Published online September 5, 2025; DOI: https://doi.org/10.4143/crt.2025.581 [Accepted]

Abstract PDF: Purpose
This study aimed to develop and rigorously evaluate machine learning models using ultrasound (US) breast cancer (BC) images to predict Ki-67 expression. Additionally, the study sought to identify independent factors influencing Ki-67 expression, with further test conducted through external datasets.
Materials and Methods
This study analyzed US images of BC from 347 patients (training set: n = 230; external test set: n=117) from Shenzhen People’s Hospital and Guangxi Academy of Medical Sciences. Radiomic features were extracted using manual region-of-interest (ROI) delineation and the Pyradiomics package. Feature selection was performed using LASSO and decision tree analysis, resulting in 16 features. Machine learning models-logistic regression (LR), support vector machine (SVM), and multilayer perceptron (MLP)-were developed, and their performance was assessed using the area under the receiver operating characteristic curve (ROC), accuracy, sensitivity, specificity, and decision curve analysis. Statistical analysis included univariate and multivariate logistic regression.
Results
Three machine learning models (LR, SVM, MLP) were developed to predict Ki-67 expression from US images. The LR model demonstrated the best diagnostic performance, with an AUC of 0.800 in external test set. Key predictors of Ki-67 expression included ill-defined mass maximum Maximum diameter and HER2 expression, along with other significant clinical variables.
Conclusion
This dual-center study demonstrates the potential of radiomics models based on US BC images to predict Ki-67 expression accurately. As a non-invasive diagnostic tool, this approach offers valuable support for clinical decision-making and personalized treatment planning in BC patients.

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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

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

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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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Breast cancer

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; Cancer Res Treat. 2025;57(3):731-740. Published online November 19, 2024; DOI: https://doi.org/10.4143/crt.2024.937

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.

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Machine Learning in Clinical Decision Making: Applications, Data Limitations and Multidisciplinary Perspectives
Augusta Raţiu, Emilia-Loredana Pop
Applied Sciences.2026; 16(2): 785. CrossRef

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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.

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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.

Citations

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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
Investigating the relationship between tongue diagnosis features and gastric cancer: A machine learning-based prediction model
Pengfei Tian, Zonglin Chen, Biaojie Fang, Xintian Wang, Xin Yu, Mingdian Lu
European Journal of Surgical Oncology.2025; 51(10): 110352. CrossRef
Artificial Intelligence and Bioinformatics in the Malignant Progression of Gastric Cancer
Tasuku Matsuoka, Masakazu Yashiro
Applied Sciences.2025; 15(20): 11092. CrossRef
Risk prediction models for lymph node metastasis in early gastric cancer patients: a systematic review and meta-analysis
Meng Duan, Min Li, Lei He, Shiming Dai, Lang Zhou, Zhiqun Liu, Jie Yang, Lingyuan Chen, Xiang Liu, Zhaoshu Wu
BMC Gastroenterology.2025;[Epub] CrossRef
Interpretable machine learning analysis of clinicopathological and immunonutritional biomarkers for predicting lymph node metastasis in gastric cancer
Feifei Ding, Binjie Huang, Jie Liu, Zhijun Feng, Yumin Li
Scientific Reports.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

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14 Web of Science
14 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.

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Clinicopathologic, genetic and immune cell infiltration analysis of colorectal signet ring cell carcinoma with comparison to conventional adenocarcinoma
Yang An, Jiaolin Zhou, Lan Su, Lin Cong, Xinxin Mao, Bo Chen, Yuhua Gong, Yaping Xu, Han Chen, Chentong Wang, Guole Lin, Huanwen Wu
Journal of the National Cancer Center.2026; 6(1): 30. CrossRef
Macrophage-associated immune-stromal crosstalks correlate with the tumor microenvironment in microsatellite-stable mucinous colorectal cancer
Yinjun He, Tianneng Zhu, Mingyu Zheng, Qingxin Yu, Siqin Lei, Chaoyi Chen, Dianhe Yu, Zhiyong Liang, Honghe Zhang
Pathology - Research and Practice.2026; 279: 156371. CrossRef
Checkpoint Inhibition in Gestational Trophoblastic Neoplasia: A Narrative Review on the Reawakening of Antitumor Immunity
Marcio Bezerra Barcellos, Antônio Braga, Raphael Alevato, Sully Turon, Gustavo Yano Callado, Solange Artimos, Sue Yazaki Sun, Jorge Rezende-Filho, Edward Araujo Júnior, Andreia Cristina de Melo, Ross S. Berkowitz, Neil S. Horowitz, Roberta Granese
Advances in Therapy.2026;[Epub] CrossRef
Senescence-associated and immune-related 9p21.3 locus genes in colorectal cancer: epigenetic architecture, molecular landscape and therapeutic possibilities
Darya A. Lisitsa, Vadim V. Shindyapin, Artem R. Nurislamov, Oleg N. Demidov, Daria A. Bogdanova
Frontiers in Cell and Developmental Biology.2026;[Epub] CrossRef
Enhancing Immunotherapy in Lung Cancer: The Promise of dMMR/MSI‐H Tumors and Immune Checkpoint Inhibitors
Md Mohiuddin
Health Science Reports.2026;[Epub] CrossRef
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
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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
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Roghaye Keshavarz Sadegh, Kiarash Saleki, Nima Rezaei
International Immunopharmacology.2025; 159: 114837. CrossRef
The Role of Next-Generation Probiotics in Colorectal Cancer Pathways: Mechanisms and Therapeutic Potential
Sheikh Saba Naz, Josep Sabate Ortega
Probiotics and Antimicrobial Proteins.2025;[Epub] 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
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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
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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
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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
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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
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Norah A. Alturki
Journal of Clinical Medicine.2023; 12(13): 4301. CrossRef
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Nian-Nian Zhong, Han-Qi Wang, Xin-Yue Huang, Zi-Zhan Li, Lei-Ming Cao, Fang-Yi Huo, Bing Liu, Lin-Lin Bu
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Yuhan Yang, Yunuo Zhao, Xici Liu, Juan Huang
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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

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Ionut Negoi
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Feng Cui, Xiangji Dang, Daiyun Peng, Yuanhua She, Yubin Wang, Ruifeng Yang, Zhiyao Han, Yan Liu, Hanteng Yang
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Yongjie Zhou, Jinhong Zhao, Fei Zou, Yongming Tan, Wei Zeng, Jiahui Jiang, Jiale Hu, Qiao Zeng, Lianggeng Gong, Lan Liu, Linhua Zhong
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Jakob Woerner, Yonghyun Nam, Sang-Hyuk Jung, Manu Shivakumar, Matthew Lee, Eun Kyung Choe, Min Jung Kim, Rumi Shin, Seung-Bum Ryoo, Seung-Yong Jeong, Kyu Joo Park, Sung Chan Park, Dae Kyung Sohn, Jae Hwan Oh, Dokyoon Kim, Ji Won Park
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Semra Demirli Atici, Aras Emre Canda, Mustafa Cem Terzi
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R. Sh. Abdulaeva, V. I. Pavlova, T. G. Gevorkyan, Y. V. Belenkaya, M. Sh. Manukyan, S. S. Gordeev
Koloproktologia.2025; 24(4): 125. CrossRef
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Yansong Liu, Pramit Khetrapal, Ronnie Strafford, Adamos Hadjivasilou, Nikhil Vasdev, Philip Charlesworth, Muhamaad Shamim Khan, Ahmed Abdulaal, Zhaoyan Dong, James W.F. Catto, Yukun Zhou, John D. Kelly, Ivana Drobnjak
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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
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The development and validation of automated machine learning models for predicting lymph node metastasis in Siewert type II T1 adenocarcinoma of the esophagogastric junction
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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.

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Association of coexposure to perfluoroalkyl and polyfluoroalkyl compounds and heavy metals with pregnancy loss and reproductive lifespan: The mediating role of cholesterol
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Identification of biomarkers related to Escherichia coli infection for the diagnosis of gastrointestinal tumors applying machine learning methods
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Bioinformatics Analysis Identifies PLA2G7 as a Key Antigen-Presenting Prognostic Related Gene Promoting Hepatocellular Carcinoma through the STAT1/PD-L1 Axis
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TRIM16 and PRC1 Are Involved in Pancreatic Cancer Progression and Targeted by Delphinidin
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Machine learning algorithms assisted identification of post-stroke depression associated biological features
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Liang-Ge-San: a classic traditional Chinese medicine formula, attenuates acute inflammation via targeting GSK3β
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Synergistic inhibition of NUDT21 by secretory S100A11 and exosomal miR‐487a‐5p promotes melanoma oligo‐ to poly‐metastatic progression
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Identification of immune-related lncRNA in sepsis by construction of ceRNA network and integrating bioinformatic analysis
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Identification of raffinose family oligosaccharides in processed Rehmannia glutinosa Libosch using matrix‐assisted laser desorption/ionization mass spectrometry image combined with machine learning
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Development and validation of a LASSO-based prediction model for immunosuppressive medication nonadherence in kidney transplant recipients
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An artificial intelligence prediction model outperforms conventional guidelines in predicting lymph node metastasis of T1 colorectal cancer
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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
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The Importance of Being “That” Colorectal pT1: A Combined Clinico-Pathological Predictive Score to Improve Nodal Risk Stratification
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A Predictive Model for Qualitative Evaluation of PG-SGA in Tumor Patients Through Machine Learning
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Deep Submucosal Invasion Is Not an Independent Risk Factor for Lymph Node Metastasis in T1 Colorectal Cancer: A Meta-Analysis
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Using random forest algorithm for glomerular and tubular injury diagnosis
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Identification of Drug-Induced Liver Injury Biomarkers from Multiple Microarrays Based on Machine Learning and Bioinformatics Analysis
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Development and validation of a machine learning model to predict the risk of lymph node metastasis in renal carcinoma
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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

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