ArticleFrontiers in oncology2023
MRI-based pre-Radiomics and delta-Radiomics models accurately predict the post-treatment response of rectal adenocarcinoma to neoadjuvant chemoradiotherapy.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 25 citations in OpenAlex.
- Deep learning algorithms for predicting pathological complete response in MRI of rectal cancer patients undergoing neoadjuvant chemoradiotherapy: a systematic review.International journal of colorectal disease · 2025Pooled it
- Delta radiomics: an updated systematic review.La Radiologia medica · 2024Pooled it
- Predicting chemotherapy response in pediatric lymphoma using pre-treatment contrast-enhanced CT radiomics: A hypothesis-generating study.Abdominal radiology (New York) · 2026Article
- Rectal Cancer Radiotherapy Response Prediction: Retrospective Study of Development of a Deep Learning-Based Radiomics Model.JMIR medical informatics · 2026Article
- Machine learning model based on dual-layer detector spectral CT radiomics features for differentiating luminal and non-luminal breast cancer.Frontiers in oncology · 2026Article
- Radiation enteritis associated with temporal sequencing of total neoadjuvant therapy in locally advanced rectal cancer: a preliminary study.Radiation oncology (London, England) · 2025Article
- Revolutionizing gastroenterology and hepatology with artificial intelligence: From precision diagnosis to equitable healthcare through interdisciplinary practice.World journal of gastroenterology · 2025Review
- Radiomics-clinical nomogram for preoperative tumor-node-metastasis staging prediction in breast cancer patients using dynamic enhanced magnetic resonance imaging.Translational cancer research · 2025Article
- MRI delta radiomics during chemoradiotherapy for prognostication in locally advanced cervical cancer.BMC cancer · 2025Article
- Combined transrectal ultrasound and radiomics model for evaluating the therapeutic effects of neoadjuvant chemoradiotherapy in locally advanced rectal cancer.International journal of colorectal disease · 2025Article
- Preoperative CT-based radiomics nomogram for progression-free survival prediction in pediatric posterior mediastinal malignancies.Frontiers in oncology · 2025Article
- Personalized prediction model for scar response after radionuclide therapy: development and validation in a Chinese cohort.Frontiers in medicine · 2025Article
- CT-based delta-radiomics nomogram to predict pathological complete response after neoadjuvant chemoradiotherapy in esophageal squamous cell carcinoma patients.Journal of translational medicine · 2024Article
- The effect of data resampling methods in radiomics.Scientific reports · 2024Article
- Prognostic Value of Radiomic Analysis Using Pre- and Post-TreatmentJournal of personalized medicine · 2024Article
- Article
- Prognostic prediction of left ventricular myocardial noncompaction using machine learning and cardiac magnetic resonance radiomics.Quantitative imaging in medicine and surgery · 2023Article
- MRI radiomics-based decision support tool for a personalized classification of cervical disc degeneration: a two-center study.Frontiers in physiology · 2023Article
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Authors and funding
11 authors at 8 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: To develop and validate magnetic resonance imaging (MRI)-based pre-Radiomics and delta-Radiomics models for predicting the treatment response of local advanced rectal cancer (LARC) to neoadjuvant chemoradiotherapy (NCRT). Methods: Between October 2017 and August 2022, 105 LARC NCRT-naïve patients were enrolled in this study. After careful evaluation, data for 84 patients that met the inclusion criteria were used to develop and validate the NCRT response models. All patients received NCRT, and the post-treatment response was evaluated by pathological assessment. We manual segmented the volume of tumors and 105 radiomics features were extracted from three-dimensional MRIs. Then, the eXtreme Gradient Boosting algorithm was implemented for evaluating and incorporating important tumor features. The predictive performance of MRI sequences and Synthetic Minority Oversampling Technique (SMOTE) for NCRT response were compared. Finally, the optimal pre-Radiomics and delta-Radiomics models were established respectively. The predictive performance of the radionics model was confirmed using 5-fold cross-validation, 10-fold cross-validation, leave-one-out validation, and independent validation. The predictive accuracy of the model was based on the area under the receiver operator characteristic (ROC) curve (AUC). Results: There was no significant difference in clinical factors between patients with good and poor reactions. Integrating different MRI modes and the SMOTE method improved the performance of the radiomics model. The pre-Radiomics model (train AUC: 0.93 ± 0.06; test AUC: 0.79) and delta-Radiomcis model (train AUC: 0.96 ± 0.03; test AUC: 0.83) all have high NCRT response prediction performance by LARC. Overall, the delta-Radiomics model was superior to the pre-Radiomics model. Conclusion: MRI-based pre-Radiomics model and delta-Radiomics model all have good potential to predict the post-treatment response of LARC to NCRT. Delta-Radiomics analysis has a huge potential for clinical application in facilitating the provision of personalized therapy.
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