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Original Articles
- 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
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Abstract
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Supplementary Material
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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. -
Citations
Citations to this article as recorded by
- Machine Learning in Clinical Decision Making: Applications, Data Limitations and Multidisciplinary Perspectives
Augusta Raţiu, Emilia-Loredana Pop
Applied Sciences.2026; 16(2): 785. CrossRef - Development of predictive models for the prognosis of triple-negative breast cancer using multiple transcriptomic analyses
Suhyun Hwangbo, Yoojin Choi, Jae Yong Ryu, Julie Decock
PLOS One.2026; 21(5): e0348414. CrossRef - A systems biology approach to unveil shared therapeutic targets and pathological pathways across major human cancers
Aftab Alam, Mohd Faizan Siddiqui, Rifat Hamoudi, Uday Kishore, Maria J. Fernandez-Cabezudo, Basel K. Al-Ramadi
Computational and Structural Biotechnology Journal.2025; 27: 5459. CrossRef
- Machine Learning in Clinical Decision Making: Applications, Data Limitations and Multidisciplinary Perspectives
- 5,454 View
- 163 Download
- 7 Web of Science
- 3 Crossref
- Molecular Classification of Breast Cancer Using Weakly Supervised Learning
- Wooyoung Jang, Jonghyun Lee, Kyong Hwa Park, Aeree Kim, Sung Hak Lee, Sangjeong Ahn
- Cancer Res Treat. 2025;57(1):116-125. Published online June 25, 2024
- DOI: https://doi.org/10.4143/crt.2024.113
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Abstract
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- Purpose
The molecular classification of breast cancer is crucial for effective treatment. The emergence of digital pathology has ushered in a new era in which weakly supervised learning leveraging whole-slide images has gained prominence in developing deep learning models because this approach alleviates the need for extensive manual annotation. Weakly supervised learning was employed to classify the molecular subtypes of breast cancer.
Materials and Methods
Our approach capitalizes on two whole-slide image datasets: one consisting of breast cancer cases from the Korea University Guro Hospital (KG) and the other originating from The Cancer Genomic Atlas dataset (TCGA). Furthermore, we visualized the inferred results using an attention-based heat map and reviewed the histomorphological features of the most attentive patches.
Results
The KG+TCGA-trained model achieved an area under the receiver operating characteristics value of 0.749. An inherent challenge lies in the imbalance among subtypes. Additionally, discrepancies between the two datasets resulted in different molecular subtype proportions. To mitigate this imbalance, we merged the two datasets, and the resulting model exhibited improved performance. The attentive patches correlated well with widely recognized histomorphologic features. The triple-negative subtype has a high incidence of high-grade nuclei, tumor necrosis, and intratumoral tumor-infiltrating lymphocytes. The luminal A subtype showed a high incidence of collagen fibers.
Conclusion
The artificial intelligence (AI) model based on weakly supervised learning showed promising performance. A review of the most attentive patches provided insights into the predictions of the AI model. AI models can become invaluable screening tools that reduce costs and workloads in practice. -
Citations
Citations to this article as recorded by- Computational Methods for Breast Cancer Molecular Profiling using Routine Histopathology: A Review
Suchithra Kunhoth, Somaya Al-maadeed, Younes Akbari, Rafif Mahmood Al Saady
Archives of Computational Methods in Engineering.2026; 33(2): 2299. CrossRef - Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities
Qingmiao Shi, Na Lou, Chen Xue
Frontiers in Cell and Developmental Biology.2026;[Epub] CrossRef - Domain generalisation challenges in breast cancer molecular classification using foundation models: a cross-cohort exploratory study
Jesus Fernandez-Romero, Pablo Ramos-Berciano, Manuel Perez-Perez, David Benavides, Antonio Robles-Frias, Jorge Garcia-Gutierrez, Laura Macias-Garcia
Medical & Biological Engineering & Computing.2026; 64(6): 2321. CrossRef - Artificial intelligence for triple-negative breast cancer from imaging to multi-omics
Xing Peng, Xinyu Zhou, Xin Feng, Nimin Fang, Xiaoya Dong, Wanjing Hong, Tianli Li, Renxing Li, Mohammad Faidzul Nasrudin
Frontiers in Oncology.2026;[Epub] CrossRef - Development and validation of an artificial intelligence system for triple-negative breast cancer identification and prognosis prediction: a multicentre retrospective study
Xiu-Ming Zhang, Hua-Jun Zhou, Qing Chen, Xi Wang, Yu-Juan Fu, Cheng Jin, Feng-Tao Zhou, Jing-Ping Wang, Qiu-Yu Cai, Ji-Li Wang, Bo Luo, Mao-Tong Hu, Cai-Yun Yao, Xia Yang, Ya-Li Xu, Jing Zhang, Hao Chen
eClinicalMedicine.2025; 89: 103557. CrossRef - An interpretable hybrid deep learning framework for gastric cancer diagnosis using histopathological imaging
Tengfei Ren, Vijay Govindarajan, Sami Bourouis, Xiangkun Wang, Shanbao Ke
Scientific Reports.2025;[Epub] CrossRef - Computational pathology in breast cancer: optimizing molecular prediction through task-oriented AI models
Chiara Frascarelli, Konstantinos Venetis, Antonio Marra, Alberto Concardi, Marianna D’Ercole, Elisa Mangione, Mariachiara Negrelli, Francesca Maria Porta, Sakshi Keswani, Giuseppe Curigliano, Elena Guerini-Rocco, Nicola Fusco
npj Breast Cancer.2025;[Epub] CrossRef
- Computational Methods for Breast Cancer Molecular Profiling using Routine Histopathology: A Review
- 6,786 View
- 289 Download
- 7 Web of Science
- 7 Crossref
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