ArticleJournal of translational medicine2024
Association of the pathomics-collagen signature with lymph node metastasis in colorectal cancer: a retrospective multicenter study.
Article in Journal of translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 13 citations in OpenAlex.
- Dual modal pathomics model for colorectal cancer early recurrence prediction and mutation landscape analysis.iScience · 2026Article
- Development and Validation of a Pathomics-Based Prognostic Model for Patients with Lung Adenocarcinoma Undergoing First-Line EGFR-TKI Therapy.Annals of surgical oncology · 2026Article
- Deep learning-enabled multiphoton microscopy predicts colorectal cancer recurrence from routine FFPE specimens.NPJ digital medicine · 2025Article
- Development and validation of radiopathomics models for predicting molecular subtypes and WHO grades in adult-type diffuse gliomas: a multicenter study.Journal of translational medicine · 2025Article
- Development of a Machine Learning Model Integrating Pathomics and Clinical Data to Predict Axillary Lymph Node Metastasis in Breast Cancer: A Two-Center Study.Cancer reports (Hoboken, N.J.) · 2025Article
- Construction and validation of a prognostic nomogram model integrating machine learning-pathomics and clinical features in IDH-wildtype glioblastoma.Journal of translational medicine · 2025Article
- Article
- MALDI imaging combined with two-photon microscopy reveals local differences in the heterogeneity of colorectal cancer.Npj imaging · 2024Article
- P-MES: Explainable pathology-based distant metastasis risk stratification in locally advanced nasopharyngeal carcinoma.Digital healthArticle
- Machine Learning-Based Pathomics Model to Predict the Prognosis in Clear Cell Renal Cell Carcinoma.Technology in cancer research & treatmentArticle
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Authors and funding
12 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundLymph node metastasis (LNM) is a prognostic biomarker and affects therapeutic selection in colorectal cancer (CRC). Current evaluation methods are not adequate for estimating LNM in CRC. H&E images contain much pathological information, and collagen also affects the biological behavior of tumor cells. Hence, the objective of the study is to investigate whether a fully quantitative pathomics-collagen signature (PCS) in the tumor microenvironment can be used to predict LNM.
methodsPatients with histologically confirmed stage I-III CRC who underwent radical surgery were included in the training cohort (n = 329), the internal validation cohort (n = 329), and the external validation cohort (n = 315). Fully quantitative pathomics features and collagen features were extracted from digital H&E images and multiphoton images of specimens, respectively. LASSO regression was utilized to develop the PCS. Then, a PCS-nomogram was constructed incorporating the PCS and clinicopathological predictors for estimating LNM in the training cohort. The performance of the PCS-nomogram was evaluated via calibration, discrimination, and clinical usefulness. Furthermore, the PCS-nomogram was tested in internal and external validation cohorts.
resultsBy LASSO regression, the PCS was developed based on 11 pathomics and 9 collagen features. A significant association was found between the PCS and LNM in the three cohorts (P < 0.001). Then, the PCS-nomogram based on PCS, preoperative CEA level, lymphadenectasis on CT, venous emboli and/or lymphatic invasion and/or perineural invasion (VELIPI), and pT stage achieved AUROCs of 0.939, 0.895, and 0.893 in the three cohorts. The calibration curves identified good agreement between the nomogram-predicted and actual outcomes. Decision curve analysis indicated that the PCS-nomogram was clinically useful. Moreover, the PCS was still an independent predictor of LNM at station Nos. 1, 2, and 3. The PCS nomogram displayed AUROCs of 0.849-0.939 for the training cohort, 0.837-0.902 for the internal validation cohort, and 0.851-0.895 for the external validation cohorts in the three nodal stations.
conclusionsThis study proposed that PCS integrating pathomics and collagen features was significantly associated with LNM, and the PCS-nomogram has the potential to be a useful tool for predicting individual LNM in CRC patients.
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