Amadeus Schraag1, Bernhard Klumpp2, Saif Afat1, Sergios Gatidis1, Konstantin Nikolaou1, Thomas K Eigentler2, Ahmed E Othman3. 1. Department for Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, University Hospital Tuebingen, 72076, Tübingen, Germany. 2. Division of Dermatooncology, Department of Dermatology, Eberhard Karls University Tuebingen, University Hospital Tuebingen, Liebermeisterstr. 25, 72074, Tuebingen, Germany. 3. Department for Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, University Hospital Tuebingen, 72076, Tübingen, Germany. Electronic address: ahmed.e.othman@googlemail.com.
Abstract
PURPOSE: We aimed to identify predictive clinical and CT imaging biomarkers and assess their predictive capacity regarding overall survival (OS) and treatment response in patients with metastatic melanoma undergoing immunotherapy. METHODS: The local institutional ethics committee approved this retrospective study and waived informed patient consent. 103 patients with immunotherapy for metastatic melanoma were randomly divided into training (n = 69) and validation cohort (n = 34). Baseline tumor markers (LDH, S100B), baseline CT imaging biomarkers (tumor burden, Choi density) and CT texture parameters (Entropy, Kurtosis, Skewness, uniformity, MPP, UPP) of the largest target lesion were extracted. To identify treatment response predictors, binary logistic regression analysis was performed in the training cohort and tested in the validation cohort. For OS, Cox regression and Kaplan Maier analyses were performed in the training cohort. Bivariate and multivariate models were established. Goodness of fit was assessed with Harrell's C-index. Potential predictors were tested in the validation cohort also using Cox-regression and Kaplan-Meier analyses. RESULTS: Baseline S100B (Hazard ratio(HR) = 2.543, p0.018), tumor burden (HR = 1.657, p = 0.002) and Kurtosis (HR = 2.484, p < 0.001) were independent predictors of OS and were confirmed in the validation cohort (p < 0.048). Tumor burden and Kurtosis showed incremental predictive capacity allowing a good predictive model when combined with baseline S100B levels (C-index = 0.720). Only S100B was predictive of treatment response (OR ≤ 0.630, p ≤ 0.022). Imaging biomarkers did not predict treatment response. CONCLUSION: We identified easily obtainable baseline clinical (S100B) and CT predictors (tumor burden and Kurtosis) of OS in patients with metastatic melanoma undergoing immunotherapy. However, imaging predictors did not predict treatment response.
PURPOSE: We aimed to identify predictive clinical and CT imaging biomarkers and assess their predictive capacity regarding overall survival (OS) and treatment response in patients with metastatic melanoma undergoing immunotherapy. METHODS: The local institutional ethics committee approved this retrospective study and waived informed patient consent. 103 patients with immunotherapy for metastatic melanoma were randomly divided into training (n = 69) and validation cohort (n = 34). Baseline tumor markers (LDH, S100B), baseline CT imaging biomarkers (tumor burden, Choi density) and CT texture parameters (Entropy, Kurtosis, Skewness, uniformity, MPP, UPP) of the largest target lesion were extracted. To identify treatment response predictors, binary logistic regression analysis was performed in the training cohort and tested in the validation cohort. For OS, Cox regression and Kaplan Maier analyses were performed in the training cohort. Bivariate and multivariate models were established. Goodness of fit was assessed with Harrell's C-index. Potential predictors were tested in the validation cohort also using Cox-regression and Kaplan-Meier analyses. RESULTS: Baseline S100B (Hazard ratio(HR) = 2.543, p0.018), tumor burden (HR = 1.657, p = 0.002) and Kurtosis (HR = 2.484, p < 0.001) were independent predictors of OS and were confirmed in the validation cohort (p < 0.048). Tumor burden and Kurtosis showed incremental predictive capacity allowing a good predictive model when combined with baseline S100B levels (C-index = 0.720). Only S100B was predictive of treatment response (OR ≤ 0.630, p ≤ 0.022). Imaging biomarkers did not predict treatment response. CONCLUSION: We identified easily obtainable baseline clinical (S100B) and CT predictors (tumor burden and Kurtosis) of OS in patients with metastatic melanoma undergoing immunotherapy. However, imaging predictors did not predict treatment response.
Authors: Andreas Stefan Brendlin; Felix Peisen; Haidara Almansour; Saif Afat; Thomas Eigentler; Teresa Amaral; Sebastian Faby; Adria Font Calvarons; Konstantin Nikolaou; Ahmed E Othman Journal: J Immunother Cancer Date: 2021-11 Impact factor: 13.751
Authors: Laurent Dercle; Jeremy McGale; Shawn Sun; Aurelien Marabelle; Randy Yeh; Eric Deutsch; Fatima-Zohra Mokrane; Michael Farwell; Samy Ammari; Heiko Schoder; Binsheng Zhao; Lawrence H Schwartz Journal: J Immunother Cancer Date: 2022-09 Impact factor: 12.469