Additional file 1 of Association of the pathomics-collagen signature with lymph node metastasis in colorectal cancer: a retrospective multicenter study
Creators: name: Jiang, Wei
name: Wang, Huaiming
name: Dong, Xiaoyu
name: Zhao, Yandong
name: Long, Chenyan
name: Chen, Dexin
name: Yan, Botao
name: Cheng, Jiaxin
name: Lin, Zexi
name: Zhuo, Shuangmu
name: Wang, Hui
name: Yan, Jun
Publisher: name: figshare
Year: 2024
Subjects: subject: Biochemistry
subject: Space Science
subject: Medicine
subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: Biotechnology
subject: Chemical Sciences not elsewhere classified
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Mathematical Sciences not elsewhere classified
subject: Cancer

description: Additional file 1: Figure S1. Recruitment pathway for the patient in this study. Figure S2. Feature selection and pathomics-collagen signature construction. Figure S3. Feature selection and single-modality signature construction. Figure S4. ROC curves of the pathomics-collagen signature and other predictors. Figure S5. Multicollinearity of the predictors of the PCS-nomogram. Figure S6. Kaplan−Meier survival analysis in all patients. Figure S7. PCS-nomogram to predict LNM at station No. 1 in patients with CRC. Figure S8. PCS-nomogram to predict LNM at station No. 2 in patients with CRC. Figure S9. PCS-nomogram to predict LNM at station No. 3 in patients with CRC. Figure S10. Performance of the PCS-nomogram to predict LNM at station No. 1. Figure S11. Performance of the PCS-nomogram to predict LNM at station No. 2. Figure S12. Performance of the PCS-nomogram to predict LNM at station No. 3. Table S1. Extracted 114 pathomics features. Table S2. Extracted 142 collagen features. Table S3. Stratified analysis of the association between the PCS and LNM in the training, internal validation, and external validation cohorts. Table S4. Univariate and multivariable analyses of the predictors of LNM without the pathomics-collagen signature in the training cohort. Table S5. NRI and IDI test for the prediction of LNM improvements of the PCS-nomogram compared with the traditional model. Table S6. Cox regression analysis of the predictors of survival in all patients. Table S7. Univariate and multivariable analyses of the predictors of LNM at station No. 1 in the training cohort. Table S8. Univariate and multivariable analyses of the predictors of LNM at station No. 2 in the training cohort. Table S9. Univariate and multivariable analyses of the predictors of LNM at station No. 3 in the training cohort. Table S10. Predictive power of LNM at station No. 1 between the PCS-nomogram and traditional model. Table S11. Predictive power of LNM at station No. 2 between the PCS-nomogram and traditional model. Table S12. Predictive power of LNM at station No. 3 between the PCS-nomogram and traditional model. Table S13. Univariate and multivariable analyses of the predictors of LNM at station No. 1 without pathomics-collagen signature in the training cohort. Table S14. Univariate and multivariable analyses of the predictors of LNM at station No. 2 without pathomics-collagen signature in the training cohort. Table S15. Univariate and multivariable analyses of the predictors of LNM at station No. 3 without pathomics-collagen signature in the training cohort. Table S16. NRI and IDI test for prediction of LNM at station No 1. improvements of PCS-nomogram compared with the traditional model. Table S17. NRI and IDI test for prediction of LNM at station No. 2 improvements of PCS-nomogram compared with the traditional model. Table S18. NRI and IDI test for prediction of LNM at station No. 3 improvements of PCS-nomogram compared with the traditional model.