Trial reportAnnals of surgical oncology2021
A Nomogram Based on a Collagen Feature Support Vector Machine for Predicting the Treatment Response to Neoadjuvant Chemoradiotherapy in Rectal Cancer Patients.
Trial report in Annals of surgical oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
- Clinical Prediction Models for Contact X-Ray Brachytherapy in Managing Rectal Cancers: A Scoping Review.Cancer medicine · 2025Pooled it
- Combining tertiary lymphoid structures and tumor stroma percentage for predicting prognosis and chemotherapy benefits in stage II-III colorectal cancer.BMC cancer · 2026Article
- Development and validation of a collagen score-based prediction model for neoadjuvant chemoimmunotherapy efficacy in locally advanced esophageal squamous cell carcinoma.Journal of thoracic disease · 2026Article
- Convergence of evolving artificial intelligence and machine learning techniques in precision oncology.NPJ digital medicine · 2025Article
- Nomograms integrating the collagen signature and systemic immune-inflammation index for predicting prognosis in rectal cancer patients.BJS open · 2024Article
- Association of the pathomics-collagen signature with lymph node metastasis in colorectal cancer: a retrospective multicenter study.Journal of translational medicine · 2024Article
- Artificial Intelligence Applications in the Treatment of Colorectal Cancer: A Narrative Review.Clinical Medicine Insights. Oncology · 2024Review
- Diagnostic and prognostic significance of keloid-like collagen remodeling patterns in the extracellular matrix of colorectal cancer.Pathology oncology research : POR · 2024Article
- Review
- Association of collagen deep learning classifier with prognosis and chemotherapy benefits in stage II-III colon cancer.Bioengineering & translational medicine · 2023Article
- Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect.Frontiers in medicine · 2023Review
- ASO Author Reflections: Panoramic Histologic Patterns of Caudate Lobe Invasion in Hilar Cholangiocarcinoma.Annals of surgical oncology · 2022Article
- Association of the collagen signature with pathological complete response in rectal cancer patients.Cancer science · 2022Article
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Authors and funding
14 authors at 6 institutions in 1 country.
Funding
Abstract
backgroundThe relationship between collagen features (CFs) in the tumor microenvironment and the treatment response to neoadjuvant chemoradiotherapy (nCRT) is still unknown. This study aimed to develop and validate a perdition model based on the CFs and clinicopathological characteristics to predict the treatment response to nCRT among locally advanced rectal cancer (LARC) patients.
methodsIn this multicenter, retrospective analysis, 428 patients were included and randomly divided into a training cohort (299 patients) and validation cohort (129 patients) [7:3 ratio]. A total of 11 CFs were extracted from a multiphoton image of pretreatment biopsy, and a support vector machine (SVM) was then used to construct a CFs-SVM classifier. A prediction model was developed and presented with a nomogram using multivariable analysis. Further validation of the nomogram was performed in the validation cohort.
resultsThe CFs-SVM classifier, which integrated collagen area, straightness, and crosslink density, was significantly associated with treatment response. Predictors contained in the nomogram included the CFs-SVM classifier and clinicopathological characteristics by multivariable analysis. The CFs nomogram demonstrated good discrimination, with area under the receiver operating characteristic curves (AUROCs) of 0.834 in the training cohort and 0.854 in the validation cohort. Decision curve analysis indicated that the CFs nomogram was clinically useful. Moreover, compared with the traditional clinicopathological model, the CFs nomogram showed more powerful discrimination in determining the response to nCRT.
conclusionsThe CFs-SVM classifier based on CFs in the tumor microenvironment is associated with treatment response, and the CFs nomogram integrating the CFs-SVM classifier and clinicopathological characteristics is useful for individualized prediction of the treatment response to nCRT among LARC patients.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.