Observational studyWorld journal of gastroenterology2020
Multi-modal radiomics model to predict treatment response to neoadjuvant chemotherapy for locally advanced rectal cancer.
Observational study in World journal of gastroenterology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
- Multimodal prediction of metachronous liver metastasis in stage I-III colorectal cancer patients: multicenter cohort study employing machine learning.Abdominal radiology (New York) · 2026Article
- Integrating CT-Based Radiomics Analysis With Serum CEA Level to Predict Neoadjuvant Chemoradiotherapy Response in Colorectal Cancer.The Kaohsiung journal of medical sciences · 2026Article
- MRI-Based Radiomics to Predict Response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer: A Retrospective Study.Journal of personalized 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
- MRI-based habitat analysis for pathologic response prediction after neoadjuvant chemoradiotherapy in rectal cancer: a multicenter study.European radiology · 2026Article
- Article
- Preoperative magnetic resonance imaging-based radiomics nomogram model for predicting postoperative anastomotic leakage in colorectal cancer.Annals of coloproctology · 2025Article
- Progress of MRI-based radiomics and deep learning for predicting the prognosis of locally advanced rectal cancer (Review).Oncology letters · 2025Review
- Multi-modal radiomics features to predict overall survival of locally advanced esophageal cancer after definitive chemoradiotherapy.BMC cancer · 2025Article
- Prevalence of CT-detected extramural vascular invasion in gastric adenocarcinoma and its correlation with other known prognostic factors.Japanese journal of radiology · 2025Article
- Deep Learning of radiology-genomics integration for computational oncology: A mini review.Computational and structural biotechnology journal · 2024Review
- Automatic segmentation-based multi-modal radiomics analysis of US and MRI for predicting disease-free survival of breast cancer: a multicenter study.Breast cancer research : BCR · 2024Article
- Prognostic value of FDX1, the cuprotosis key gene, and its prediction models across imaging modalities and histology.BMC cancer · 2024Article
- Risk stratification and overall survival prediction in extensive stage small cell lung cancer after chemotherapy with immunotherapy based on CT radiomics.Scientific reports · 2024Article
- Article
- MR radiomics predicts pathological complete response of esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy: a multicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Radiomics in rectal cancer: current status of use and advances in research.Frontiers in oncology · 2024Review
- Applications of image-guided locoregional transarterial chemotherapy in patients with inoperable colorectal cancer: a review.Frontiers in oncology · 2024Review
- Article
- The Role of Radiomics in Rectal Cancer.Journal of gastrointestinal cancer · 2023Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNeoadjuvant chemotherapy is currently recommended as preoperative treatment for locally advanced rectal cancer (LARC); however, evaluation of treatment response to neoadjuvant chemotherapy is still challenging.
aimTo create a multi-modal radiomics model to assess therapeutic response after neoadjuvant chemotherapy for LARC.
methodsThis retrospective study consecutively included 118 patients with LARC who underwent both computed tomography (CT) and magnetic resonance imaging (MRI) before neoadjuvant chemotherapy between October 2016 and June 2019. Histopathological findings were used as the reference standard for pathological response. Patients were randomly divided into a training set (
resultsEighty out of 118 patients (68%) achieved a pathological response. For an individual radiomics model, HR-T2WI performed better (AUC = 0.859, ACC = 0.896) than CT (AUC = 0.766, ACC = 0.792), DCE-T1 (AUC = 0.812, ACC = 0.854), and ADC (AUC = 0.828, ACC = 0.833) in the validation set. The imaging performance for extramural venous invasion detection was relatively low in both the training (AUC = 0.73, ACC = 0.714) and validation (AUC = 0.578, ACC = 0.583) sets. The multi-modal radiomics model reached an AUC of 0.925 and ACC of 0.886 in the training set, and an AUC of 0.93 and ACC of 0.875 in the validation set. For the clinical radiomics nomogram, good agreement was found between the nomogram prediction and actual observation.
conclusionA multi-modal nomogram using traditional imaging features and radiomics of preoperative CT and MRI adds accuracy to the prediction of treatment outcome, and thus contributes to the personalized selection of neoadjuvant chemotherapy for LARC.
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