ArticlePloS one2024
CLIP-based multimodal endorectal ultrasound enhances prediction of neoadjuvant chemoradiotherapy response in locally advanced rectal cancer.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Differential diagnosis of solitary rectal ulcer syndrome and early rectal cancer via endorectal ultrasound: a retrospective matched case-control study.BMC gastroenterology · 2026Article
- Radiomics model integrating MRI and ECV enhances prediction accuracy for progression in high-grade glioma.NPJ precision oncology · 2026Article
- Case Report: Ultrasound features of pathological complete response in middle and low rectal cancer with high microsatellite instability after neoadjuvant immunotherapy: a case series.Frontiers in immunology · 2026Article
- Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation.Frontiers in digital health · 2025Review
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5 authors.
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Abstract
backgroundForecasting the patient's response to neoadjuvant chemoradiotherapy (nCRT) is crucial for managing locally advanced rectal cancer (LARC). This study investigates whether a predictive model using image-text features extracted from endorectal ultrasound (ERUS) via Contrastive Language-Image Pretraining (CLIP) can predict tumor regression grade (TRG) before nCRT.
methodsA retrospective analysis of 577 LARC patients who received nCRT followed by surgery was conducted from January 2018 to December 2023. ERUS scans and TRG were used to assess nCRT response, categorizing patients into good (TRG 0) and poor (TRG 1-3) responders. Image and text features were extracted using the ResNet50+RBT3 (RN50) and ViT-B/16+RoBERTa-wwm (VB16) components of the Chinese-CLIP model. LightGBM was used for model construction and comparison. A subset of 100 patients from each responder group was used to compare the CLIP method with manual radiomics methods (logistic regression, support vector machines, and random forest). SHapley Additive exPlanations (SHAP) technique was used to analyze feature contributions.
resultsThe RN50 and VB16 models achieved AUROC scores of 0.928 (95% CI: 0.90-0.96) and 0.900 (95% CI: 0.86-0.93), respectively, outperforming manual radiomics methods. SHAP analysis indicated that image features dominated the RN50 model, while both image and text features were significant in the VB16 model.
conclusionsThe CLIP-based predictive model using ERUS image-text features and LightGBM showed potential for improving personalized treatment strategies. However, this study is limited by its retrospective design and single-center data.
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