ArticleJournal of cancer research and clinical oncology2024
Predicting pathological complete response to neoadjuvant chemotherapy in breast cancer patients: use of MRI radiomics data from three regions with multiple machine learning algorithms.
Article in Journal of cancer research and clinical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A Practical Machine Learning Model for Predicting Neoadjuvant Response in HER2-Positive Breast Cancer.Diagnostics (Basel, Switzerland) · 2026Article
- Longitudinal Breast MRI for Early Treatment-Response Modeling: A Comparative Study of Handcrafted Radiomics and Frozen Deep Image Embeddings for pCR Prediction.Diagnostics (Basel, Switzerland) · 2026Article
- APASA: adaptive selection of informative peritumoral regions for improved automated cancer lesion analysis.Scientific reports · 2026Article
- Ultrasound and Clinicopathological Features-Based Machine Learning Model for Predicting Neoadjuvant Therapy Efficacy in Breast Cancer.Cancer reports (Hoboken, N.J.) · 2026Article
- Development and multicenter validation of an immunoinflammatory marker-MRI model for pathological complete response after neoadjuvant chemotherapy in invasive breast cancer.Translational cancer research · 2026Article
- A multicenter study of a predictive model for pathological complete response after neoadjuvant therapy in breast cancer using multimodal digital biomarkers.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2025Article
- Predicting breast cancer response to neoadjuvant therapy by integrating radiomic and deep-learning features from early-and-peak phases of DCE-MRI.BMC cancer · 2025Article
- Prediction of lymphovascular invasion in invasive breast cancer via intratumoral and peritumoral multiparametric magnetic resonance imaging machine learning-based radiomics with Shapley additive explanations interpretability analysis.Quantitative imaging in medicine and surgery · 2025Article
- Predicting relative efficacy of anthracyclines and taxanes in breast cancer neoadjuvant AC-T chemotherapy using longitudinal MRI radiomic model.Frontiers in oncology · 2025Article
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10 authors.
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Abstract
objectiveTo construct a multi-region MRI radiomics model for predicting pathological complete response (pCR) in breast cancer (BCa) patients who received neoadjuvant chemotherapy (NACT) and provide a theoretical basis for the peritumoral microenvironment affecting the efficacy of NACT.
methodsA total of 133 BCa patients who received NACT, including 49 with confirmed pCR, were retrospectively analyzed. The radiomics features of the intratumoral region, peritumoral region, and background parenchymal enhancement (BPE) were extracted, and the most relevant features were obtained after dimensional reduction. Then, combining different areas, multivariate logistic regression analysis was used to select the optimal feature set, and six different machine learning models were used to predict pCR. The optimal model was selected, and its performance was evaluated using receiver operating characteristic (ROC) analysis. SHAP analysis was used to examine the relationship between the features of the model and pCR.
resultsFor signatures constructed using three individual regions, BPE provided the best predictions of pCR, and the diagnostic performance of the intratumoral and peritumoral regions improved after adding the BPE signature. The radiomics signature from the combination of all the three regions with the XGBoost machine learning algorithm provided the best predictions of pCR based on AUC (training set: 0.891, validation set: 0.861), sensitivity (training set: 0.882, validation set: 0.800), and specificity (training set: 0.847, validation set: 0.84). SHAP analysis demonstrated that LZ_log.sigma.2.0.mm.3D_glcm_ClusterShade_T12 made the greatest contribution to the predictions of this model.
conclusionThe addition of the BPE MRI signature improved the prediction of pCR in BCa patients who received NACT. These results suggest that the features of the peritumoral microenvironment are related to the efficacy of NACT.
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