Clinical prediction of pathological complete response in breast cancer: a machine learning study
Abstract Background This study aimed to develop and validate machine learning models to predict pathological complete response (pCR) after neoadjuvant therapy in patients with breast cancer patients. Methods Clinical and pathological data from 1143 patients were analyzed, encompassing variables such...
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| Format: | Article |
| Language: | English |
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BMC
2025-05-01
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| Series: | BMC Cancer |
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| Online Access: | https://doi.org/10.1186/s12885-025-14335-1 |
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| author | Chongwu He Tenghua Yu Liu Yang Longbo He Jin Zhu Jing Chen |
| author_facet | Chongwu He Tenghua Yu Liu Yang Longbo He Jin Zhu Jing Chen |
| author_sort | Chongwu He |
| collection | DOAJ |
| description | Abstract Background This study aimed to develop and validate machine learning models to predict pathological complete response (pCR) after neoadjuvant therapy in patients with breast cancer patients. Methods Clinical and pathological data from 1143 patients were analyzed, encompassing variables such as age, gender, marital status, histologic grade, T stage, N stage, months from diagnosis to treatment, molecular subtype, and response to neoadjuvant therapy. Seven machine learning models were trained and validated using both internal and external datasets. Model performance was evaluated using multiple metrics, and interpretability analysis was conducted to assess feature importance. Results Key variables influencing pCR included grade, N stage, months from diagnosis to treatment, and molecular subtype. The Naive Bayes model emerged as the most effective, with accuracy (0.746), sensitivity (0.699), specificity (0.808), and F1 score (0.759) surpassing other models. Both internal and external validation confirmed the model’s robust predictive power. A web tool was developed for clinical use, aiding in personalized treatment planning. Interpretability analysis further elucidated the contribution of features to pCR prediction, enhancing clinical applicability. Conclusion The Naive Bayes model provides a robust tool for personalized treatment decisions in patients with breast cancer undergoing neoadjuvant therapy. By accurately predicting pCR rates, it enables clinicians to tailor treatment strategies, potentially improving outcomes. |
| format | Article |
| id | doaj-art-24f54e67bae44cb08c897b2bcc47b9b4 |
| institution | Kabale University |
| issn | 1471-2407 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj-art-24f54e67bae44cb08c897b2bcc47b9b42025-08-20T03:48:18ZengBMCBMC Cancer1471-24072025-05-0125111210.1186/s12885-025-14335-1Clinical prediction of pathological complete response in breast cancer: a machine learning studyChongwu He0Tenghua Yu1Liu Yang2Longbo He3Jin Zhu4Jing Chen5Department of Breast Surgery, The Second Affiliated Hospital of Nanchang Medical College, Jiangxi Cancer HospitalDepartment of Breast Surgery, The Second Affiliated Hospital of Nanchang Medical College, Jiangxi Cancer HospitalDepartment of Pathology, Nanchang People’s HospitalDepartment of Breast Surgery, The Second Affiliated Hospital of Nanchang Medical College, Jiangxi Cancer HospitalDepartment of Breast Surgery, The Second Affiliated Hospital of Nanchang Medical College, Jiangxi Cancer HospitalDepartment of Nursing, Nanchang Medical CollegeAbstract Background This study aimed to develop and validate machine learning models to predict pathological complete response (pCR) after neoadjuvant therapy in patients with breast cancer patients. Methods Clinical and pathological data from 1143 patients were analyzed, encompassing variables such as age, gender, marital status, histologic grade, T stage, N stage, months from diagnosis to treatment, molecular subtype, and response to neoadjuvant therapy. Seven machine learning models were trained and validated using both internal and external datasets. Model performance was evaluated using multiple metrics, and interpretability analysis was conducted to assess feature importance. Results Key variables influencing pCR included grade, N stage, months from diagnosis to treatment, and molecular subtype. The Naive Bayes model emerged as the most effective, with accuracy (0.746), sensitivity (0.699), specificity (0.808), and F1 score (0.759) surpassing other models. Both internal and external validation confirmed the model’s robust predictive power. A web tool was developed for clinical use, aiding in personalized treatment planning. Interpretability analysis further elucidated the contribution of features to pCR prediction, enhancing clinical applicability. Conclusion The Naive Bayes model provides a robust tool for personalized treatment decisions in patients with breast cancer undergoing neoadjuvant therapy. By accurately predicting pCR rates, it enables clinicians to tailor treatment strategies, potentially improving outcomes.https://doi.org/10.1186/s12885-025-14335-1Breast cancerMachine learningPathological complete responsePCRPrediction |
| spellingShingle | Chongwu He Tenghua Yu Liu Yang Longbo He Jin Zhu Jing Chen Clinical prediction of pathological complete response in breast cancer: a machine learning study BMC Cancer Breast cancer Machine learning Pathological complete response PCR Prediction |
| title | Clinical prediction of pathological complete response in breast cancer: a machine learning study |
| title_full | Clinical prediction of pathological complete response in breast cancer: a machine learning study |
| title_fullStr | Clinical prediction of pathological complete response in breast cancer: a machine learning study |
| title_full_unstemmed | Clinical prediction of pathological complete response in breast cancer: a machine learning study |
| title_short | Clinical prediction of pathological complete response in breast cancer: a machine learning study |
| title_sort | clinical prediction of pathological complete response in breast cancer a machine learning study |
| topic | Breast cancer Machine learning Pathological complete response PCR Prediction |
| url | https://doi.org/10.1186/s12885-025-14335-1 |
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