ArticleQuantitative imaging in medicine and surgery2024
Multiparametric magnetic resonance imaging (MRI)-based radiomics model explained by the Shapley Additive exPlanations (SHAP) method for predicting complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicenter retrospective study.
Article in Quantitative imaging in medicine and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomic Analysis of MRI for Assessing Response to Neoadjuvant Chemoradiotherapy in Rectal Adenocarcinoma: A Systematic Review and Metaanalysis.Molecular imagingPooled it
- Explainable Machine Learning for Coma Outcome Prediction Based on Structural and Functional Brain MRI.Critical care medicine · 2026Article
- Multiparametric MRI-derived biomarkers for preoperative prediction of recurrence and/or metastasis after neoadjuvant chemoradiotherapy in locally advanced rectal cancer.Quantitative imaging in medicine and surgery · 2026Article
- Influence of image preprocessing on reproducibility and longitudinal repeatability analysis of radiomics features in magnetic resonance image-guided accelerator imaging.Quantitative imaging in medicine and surgery · 2026Article
- MRI-based habitat analysis for pathologic response prediction after neoadjuvant chemoradiotherapy in rectal cancer: a multicenter study.European radiology · 2026Article
- Contrast-enhanced CT-based radiomics model explained by the Shapley Additive exPlanations (SHAP) method for predicting preoperative diagnosis of pheochromocytoma and adrenal adenoma.BMC medical imaging · 2026Article
- Interpretable habitat radiomics model based on multi-sequence MRI for risk prediction of metachronous liver metastasis in rectal cancer: a multicenter study.Japanese journal of radiology · 2026Article
- Interpretable machine learning model for predicting 5-Year postoperative recurrence risk in patients with stage III colon cancer using preoperative laboratory tests: a two-centre study.BMC gastroenterology · 2026Article
- Research Progress of MRI-based Radiomics in Rectal CancerCurrent medical imaging · 2026Review
- Article
- Enhancing the role of MRI in rectal cancer: advances from staging to prognosis prediction.European radiology · 2025Review
- Immunophenotype-guided interpretable radiomics model for predicting neoadjuvant anti-PD-1 response in stage III-IV d-MMR/MSI-H colorectal cancer.Journal for immunotherapy of cancer · 2025Article
- A Multimodal MRI-Based Model for Colorectal Liver Metastasis Prediction: Integrating Radiomics, Deep Learning, and Clinical Features with SHAP Interpretation.Current oncology (Toronto, Ont.) · 2025Article
- A novel computed tomography-based multi-parameter decision tree algorithm model for preoperatively predicting the risk of lymph node metastasis in surgically resectable synchronous multiple primary lung cancer.Quantitative imaging in medicine and surgery · 2025Article
- Article
- Development of clinical inflammatory models to predict the efficacy of neoadjuvant chemoradiotherapy and survival in patients with locally advanced rectal cancer: a retrospective study.International journal of colorectal disease · 2025Article
- Machine learning models using multimodal data accurately predict chemotherapy-induced cardiotoxicity in breast cancer.Frontiers in cardiovascular medicine · 2025Article
- Development and interpretation of a machine learning model for predicting body mass index in Chinese adolescents: a prospective cohort study.Frontiers in public health · 2025Article
- Development and validation of multiple machine learning algorithms for differentiating primary central nervous system lymphoma from adult-type diffuse glioma: an interpretable and multicenter study.Frontiers in oncology · 2025Article
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Abstract
Background: Predicting the response to neoadjuvant chemoradiotherapy (nCRT) before initiating treatment is essential for tailoring therapeutic strategies and monitoring prognosis in locally advanced rectal cancer (LARC). In this study, we aimed to develop and validate radiomic-based models to predict clinical and pathological complete responses (cCR and pCR, respectively) by incorporating the Shapley Additive exPlanations (SHAP) method for model interpretation. Methods: A total of 285 patients with complete pretreatment clinical characteristics and T1-weighted (T1W) and T2-weighted (T2W) magnetic resonance imaging (MRI) at 3 centers were retrospectively recruited. The features of tumor lesions were extracted by PyRadiomics and selected using least absolute shrinkage and selection operator (LASSO) algorithm. The selected features were used to build multilayer perceptron (MLP) models alone or combined with clinical features. Area under the receiver operating characteristic curve (AUC), decision curve, and calibration curve were applied to evaluate performance of models. The SHAP method was adopted to explain the prediction models. Results: The radiomic-based models all showed better performances than clinical models. The clinical-radiomic models showed the best differentiation on cCR and pCR with mean AUCs of 0.718 and 0.810 in the validation set, respectively. The decision curves of the clinical-radiomic models showed its values in clinical application. The SHAP method powerfully interpreted the prediction models both at a holistic and individual levels. Conclusions: Our study highlights that the radiomic-based prediction models have more excellent abilities than clinical models and can effectively predict treatment response and optimize therapeutic strategies for patients with LARCs.
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