ArticleBMC cancer2022
Machine learning-based multiparametric MRI radiomics for predicting poor responders after neoadjuvant chemoradiotherapy in rectal Cancer patients.
Article in BMC cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 25 citations in OpenAlex.
- Article
- Macro Habitat-Based T2-Weighted MRI Radiomics and Deep Learning Fusion for Predicting Treatment Response and Prognosis After Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.Cancer 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
- MRI-based qualitative, quantitative, and radiomics/deep learning methods for assessing treatment response after neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer.Precision radiation oncology · 2026Review
- Research Progress of MRI-based Radiomics in Rectal CancerCurrent medical imaging · 2026Review
- Review
- Multi-parametric MRI combined with radiomics for the diagnosis and grading of endometrial fibrosis.Abdominal radiology (New York) · 2025Article
- Radiation enteritis associated with temporal sequencing of total neoadjuvant therapy in locally advanced rectal cancer: a preliminary study.Radiation oncology (London, England) · 2025Article
- 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
- Development and validation of multi-sequence magnetic resonance imaging radiomics models for predicting tumor response to radiotherapy in locally advanced non-small cell lung cancer.Journal of thoracic disease · 2025Article
- Construction of an oligometastatic prediction model for nasopharyngeal carcinoma patients based on pathomics features and dynamic multi-swarm particle swarm optimization support vector machine.Frontiers in oncology · 2025Article
- LLM-based feature selection and counterfactual explanations applied to functional connectivity analysis in schizophrenia.Frontiers in neuroscience · 2025Article
- Radiomics-based model for prediction of TGF-β1 expression in head and neck squamous cell carcinoma.American journal of nuclear medicine and molecular imaging · 2024Article
- Article
- Computed Tomography Scan and Clinical-based Complete Response Prediction in Locally Advanced Rectal Cancer after Neoadjuvant Chemoradiotherapy: A Machine Learning Approach.Journal of medical signals and sensors · 2024Article
- MRI-based pre-Radiomics and delta-Radiomics models accurately predict the post-treatment response of rectal adenocarcinoma to neoadjuvant chemoradiotherapy.Frontiers in oncology · 2023Article
- Role of radiomics in the diagnosis and treatment of gastrointestinal cancer.World journal of gastroenterology · 2022Review
- Dual-center validation of using magnetic resonance imaging radiomics to predict stereotactic radiosurgery outcomes.Neuro-oncology advancesArticle
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe purpose of this study was to investigate and validate multiparametric magnetic resonance imaging (MRI)-based machine learning classifiers for early identification of poor responders after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC).
methodsPatients with LARC who underwent nCRT were included in this retrospective study (207 patients). After preprocessing of multiparametric MRI, radiomics features were extracted and four feature selection methods were used to select robust features. The selected features were used to build five machine learning classifiers, and 20 (four feature selection methods × five machine learning classifiers) predictive models for the screening of poor responders were constructed. The predictive models were evaluated according to the area under the curve (AUC), F1 score, accuracy, sensitivity, and specificity.
resultsEighty percent of all predictive models constructed achieved an AUC of more than 0.70. A predictive model using a support vector machine classifier with the minimum redundancy maximum relevance (mRMR) selection method followed by the least absolute shrinkage and selection operator (LASSO) selection method showed superior prediction performance, with an AUC of 0.923, an F1 score of 88.14%, and accuracy of 91.03%. The predictive performance of the constructed models was not improved by ComBat compensation.
conclusionsIn rectal cancer patients who underwent neoadjuvant chemoradiotherapy, machine learning classifiers with radiomics features extracted from multiparametric MRI were able to accurately discriminate poor responders from good responders. The techniques should provide additional information to guide patient-tailored treatment.
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