ArticleScientific reports2021
MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 1 of them a synthesis that pooled it.
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Who cites it
59 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- KRAS status predicted by pretreatment MRI radiomics was associated with lung metastasis in locally advanced rectal cancer patients.BMC medical imaging · 2023Trial
- Integrated Multi-Time-PointMedComm · 2026Article
- Article
- Comparative analysis of tumor and mesorectum radiomics in predicting neoadjuvant chemoradiotherapy response in locally advanced rectal cancer.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Radiologic exposomics: imaging the environmental imprint on cancer for precision oncology.La Radiologia medica · 2026Review
- MRI Based Radiomics as an Imaging Biomarker for Locally Advanced Carcinoma Rectum: Predicting Tumor Response Following Neoadjuvant Chemoradiotherapy as an Organ Preservation Strategy.Cancer reports (Hoboken, N.J.) · 2026Article
- Inclusion of tumor periphery in radiomics analysis of magnetic resonance images does not improve predictions of preoperative therapy response in patients with rectal cancer.Abdominal radiology (New York) · 2026Article
- MRI-based habitat analysis for pathologic response prediction after neoadjuvant chemoradiotherapy in rectal cancer: a multicenter study.European radiology · 2026Article
- Prediction of lymphovascular invasion in rectal cancer based on multimodal magnetic resonance imaging radiomics model.World journal of gastrointestinal surgery · 2026Article
- Multi-omics predicts radiotherapy response in small cell lung cancer patients receiving whole brain irradiation.Journal of applied clinical medical physics · 2026Article
- Research Progress of MRI-based Radiomics in Rectal CancerCurrent medical imaging · 2026Review
- MRI-based radiomics nomogram to predict complete response to definitive chemoradiation in patients with anal cancer.The British journal of radiology · 2025Article
- MRI Radiomics Signatures of 21-Gene Recurrence Score for Predicting Survival in ER+/HER2- Breast Cancer.Cancer medicine · 2025Article
- A novel structural modeling magnitude and orientation radiomic descriptor for evaluating response to neoadjuvant therapy in rectal cancers via MRI.NPJ precision oncology · 2025Article
- Review
- Computed tomography-based radiomics modeling to predict patient overall survival in cervical cancer with intensity-modulated radiotherapy combined with concurrent chemotherapy.The Journal of international medical research · 2025Observational
- Beyond the tumor region: Peritumoral radiomics enhances prognostic accuracy in locally advanced rectal cancer.World journal of gastroenterology · 2025Article
- The Role of Predictive and Prognostic MRI-Based Biomarkers in the Era of Total Neoadjuvant Treatment in Rectal Cancer.Cancers · 2024Review
- Predictive value of flexible proctosigmoidoscopy and laboratory findings for complete clinical responses after neoadjuvant chemoradiotherapy in patients with locally advanced primary rectal cancer: a retrospective cohort study.International journal of colorectal disease · 2024Article
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20 authors.
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Abstract
Neoadjuvant chemo-radiotherapy (CRT) followed by total mesorectal excision (TME) represents the standard treatment for patients with locally advanced (≥ T3 or N+) rectal cancer (LARC). Approximately 15% of patients with LARC shows a complete response after CRT. The use of pre-treatment MRI as predictive biomarker could help to increase the chance of organ preservation by tailoring the neoadjuvant treatment. We present a novel machine learning model combining pre-treatment MRI-based clinical and radiomic features for the early prediction of treatment response in LARC patients. MRI scans (3.0 T, T2-weighted) of 72 patients with LARC were included. Two readers independently segmented each tumor. Radiomic features were extracted from both the "tumor core" (TC) and the "tumor border" (TB). Partial least square (PLS) regression was used as the multivariate, machine learning, algorithm of choice and leave-one-out nested cross-validation was used to optimize hyperparameters of the PLS. The MRI-Based "clinical-radiomic" machine learning model properly predicted the treatment response (AUC = 0.793, p = 5.6 × 10
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