ArticleEuropean radiology2022
The radiomic-clinical model using the SHAP method for assessing the treatment response of whole-brain radiotherapy: a multicentric study.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 1 of them a synthesis that pooled it.
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Who cites it
67 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of artificial intelligence for brain tumor segmentation, treatment planning, and outcome prediction: a comprehensive systematic review and meta-analysis.Journal of neuro-oncology · 2025Pooled it
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- Diagnosing breast masses in BI-RADS 4: construction and interpretation of an intralesional and perilesional radiomics nomogram based on digital breast tomosynthesis.BMC medical imaging · 2026Article
- Machine Learning-Based Prediction of Brain Metastasis at Initial Diagnosis in Small-Cell Lung Cancer: Model Development and SHAP Interpretation Study.Cancer reports (Hoboken, N.J.) · 2026Article
- An MRI-based radiomics model for precision subtyping of hereditary spastic paraplegia: discriminating SPG4 from SPG5.Neuroradiology · 2026Article
- Integrating handcrafted and deep learning MRI signatures: an interpretable framework for predicting chemotherapy benefit in glioma.NPJ precision oncology · 2026Article
- Preoperative prediction of cervical cancer recurrence using explainable MRI radiomics: a SHAP-Guided machine learning study.Abdominal radiology (New York) · 2026Article
- Assessment of rotational dose perturbations in SBRT: A radiomic-dosiomic predictive model based on the structural similarity index measure.Journal of applied clinical medical physics · 2026Article
- Quality evaluation of ultrasound images for fetal crown-rump length measurement at 11 to 14 weeks' gestation: A fusion model can be interpreted using SHAP method.The ultrasound journal · 2026Article
- Habitat-Derived Radiomic Features of Planning Target Volume to Determine the Local Recurrence After Radiotherapy in Patients with Gliomas: A Feasibility Study.Journal of imaging informatics in medicine · 2026Article
- Machine Learning-Based Risk Assessment of Myasthenia Gravis Onset in Thymoma Patients and Analysis of Their Correlations and Causal Relationships.Journal of imaging informatics in medicine · 2026Article
- Interpretable deep learning radiomics fromBMC medical imaging · 2026Article
- An interpretable radiomics model based on contrast‑enhanced pancreatic computed tomography for predicting the prognosis of post-acute pancreatitis diabetes mellitus.BMC medical imaging · 2026Article
- An explainable radiomics model based on multiparametric magnetic resonance for differentiating benign and malignant orbital tumors.BMC cancer · 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
- Habitat-based MRI radiomics for enhanced parkinson's diagnosis.Scientific reports · 2026Article
- A SHAP-interpretable XGBoost model: MRI-based intratumoral perfusion heterogeneity predicts HER2-zero, -low, and -positive ternary expression status in breast cancer.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Integrating deep learning with multimodal MRI habitat radiomics: toward personalized prediction of risk stratification and androgen deprivation therapy outcomes in prostate cancer.Insights into imaging · 2026Article
- Integrative single-cell and spatial transcriptomics with explainable AI reveal lethal prognostic axis in prostate cancer.NPJ digital medicine · 2026Article
7 more citing papers are in PubMed but not listed here.
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12 authors.
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Abstract
objectiveTo develop and validate a pretreatment magnetic resonance imaging (MRI)-based radiomic-clinical model to assess the treatment response of whole-brain radiotherapy (WBRT) by using SHapley Additive exPlanations (SHAP), which is derived from game theory, and can explain the output of different machine learning models.
methodsWe retrospectively enrolled 228 patients with brain metastases from two medical centers (184 in the training cohort and 44 in the validation cohort). Treatment responses of patients were categorized as a non-responding group vs. a responding group according to the Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) criteria. For each tumor, 960 features were extracted from the MRI sequence. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. A support vector machine (SVM) model incorporating clinical factors and radiomic features wase used to construct the radiomic-clinical model. SHAP method explained the SVM model by prioritizing the importance of features, in terms of assessment contribution.
resultsThree radiomic features and three clinical factors were identified to build the model. Radiomic-clinical model yielded AUCs of 0.928 (95%CI 0.901-0.949) and 0.851 (95%CI 0.816-0.886) for assessing the treatment response in the training cohort and validation cohort, respectively. SHAP summary plot illustrated the feature's value affected the feature's impact attributed to model, and SHAP force plot showed the integration of features' impact attributed to individual response.
conclusionThe radiomic-clinical model with the SHAP method can be useful for assessing the treatment response of WBRT and may assist clinicians in directing personalized WBRT strategies in an understandable manner. KEY POINTS: • Radiomic-clinical model can be useful for assessing the treatment response of WBRT. • SHAP could explain and visualize radiomic-clinical machine learning model in a clinician-friendly way.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.