| Autor: |
Fred Saad, Philip Kantoff, Jonathan I. Epstein, Mathieu Latour, Cristina Magi-Galluzzi, Eric A. Klein, Massimo Loda, James Dunyak, Julie Nardone, Eldar Giladi, Hua Chang, Alex Kaprelyants, Aeron Hurley, Christina Ernst, Louis Coupal, Teresa Capela, Sibgat Choudhury, Clayton Small, Thomas P. Nifong, Mathew Putzi, Michail Shipitsin, David L. Rimm, David M. Berman, Peter Blume-Jensen |
| Rok vydání: |
2023 |
| Popis: |
Supplementary Methods, Table S1, Figures S1-7. Methods for quantitative multiplex proteomics imaging (QMPI) Clinical studies: Statistical plan Table S1. Clinical Validation Study. Comparison of Predictive Value of the 8-Biomarker Assay for Favorable Pathology with D'Amico Risk Categories. Figure S1. Outline of all four quantitative multiplex immunofluorescence triplex assay formats Figure S2. Clinical validation study, full cohort (N=276): performance for "GS 6" pathology (surgical Gleason =3+3 and localized {less than or equal to}T3a). A) Sensitivity (P[risk score> threshold| "non-GS 6" pathology]) of the assay, as a function of medical decision level. Figure S3. Clinical validation study, full cohort (N=274): performance for prediction of favorable pathology (surgical Gleason {less than or equal to}3+4 and organ-confined {less than or equal to}T2). Figure S4. Clinical validation study, Subset of validation cohort that contained sufficient annotation for National Comprehensive Cancer Network (NCCN) and D'Amico categorization (N=256) Figure S5. Clinical validation study: performance for prediction of favorable pathology. Figure S6. Net Reclassification Index analysis illustrates how biomarker assay categories of favorable (risk score {less than or equal to}0·33) and non-favorable (risk score >0·8) may supplement NCCN Figure S7. Decision Curve Analysis provides another method for characterizing performance of different risk systems and at different cut points. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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