Saskia Litière

Purpose:Currently, guidelines for PET with 18F-fluorodeoxyglucose (FDG-PET) interpretation for assessment of therapy response in oncology primarily involve visual evaluation of FDG-PET/CT scans. However, quantitative measurements of the metabolic activity in tumors may be even more useful in evaluating response to treatment. Guidelines based on such measurements, including the European Organization for Research and Treatment of Cancer Criteria and PET Response Criteria in Solid Tumors, have been proposed. However, more rigorous analysis of response criteria based on FDG-PET measurements is needed to adopt regular use in practice.Experimental Design:Well-defined boundaries of repeatability and reproducibility of quantitative measurements to discriminate noise from true signal changes are a needed initial step. An extension of the meta-analysis from de Langen and colleagues (2012) of the test–retest repeatability of quantitative FDG-PET measurements, including mean, maximum, and peak standardized uptake values (SUVmax, SUVmean, and SUVpeak, respectively), was performed. Data from 11 studies in the literature were used to estimate the relationship between the variance in test–retest measurements with uptake level and various study-level, patient-level, and lesion-level characteristics.Results:Test–retest repeatability of percentage fluctuations for all three types of SUV measurement (max, mean, and peak) improved with higher FDG uptake levels. Repeatability in all three SUV measurements varied for different lesion locations. Worse repeatability in SUVmean was also associated with higher tumor volumes.Conclusions:On the basis of these results, recommendations regarding SUV measurements for assessing minimal detectable changes based on repeatability and reproducibility are proposed. These should be applied to differentiate between response categories for a future set of FDG-PET–based criteria that assess clinically significant changes in tumor response.

ROC curves: Time-dependent ROC curves at 5 and 10 years for OS (A and B) and DFS (C and D) in the pooled cohort, comparing different prognostic models. The Sarculator model (black), Sarculator + CINSARC (yellow), and CINSARC alone (blue) are shown. The diagonal reference line represents a nondiscriminatory model. AUC values quantify the discriminative performance of each model.

Purpose:The Complexity INdex in SARComas (CINSARC) predicts the metastatic risk in patients with soft-tissue sarcoma. The aims of this study were to provide the first independent validation of CINSARC in patients with retroperitoneal sarcoma (RPS) and evaluate whether CINSARC could enhance the performance of Sarculator.Experimental Design:A retrospective cohort included patients with primary localized RPS resected with curative intent (2011–2015) at a single institution. The STRASS cohort comprised patients from the surgery-only arm of the EORTC-STBSG-62092 (STRASS) trial who had undergone CINSARC categorization. Patients were classified as CINSARC low-risk (C1) versus high-risk (C2). Primary study endpoints were overall survival (OS) and disease-free survival (DFS). Sarculator performance was assessed in terms of discrimination (the Harrell C-index) and calibration (calibration plots and the Brier score) before and after adding CINSARC.Results:The study cohorts included 104 and 69 patients, respectively, with similar OS. In a pooled cohort, in multivariable analysis for OS considering Sarculator and CINSARC, only Sarculator was significantly associated with OS [HR, 1.93; 95% confidence interval (CI), 1.35–2.74; P < 0.001]. In multivariable analysis for DFS, both Sarculator (HR, 1.51; 95% CI, 1.09–2.09; P = 0.013) and CINSARC (HR, 2.01; 95% CI, 1.26–3.23; P = 0.004) were significantly associated with DFS. However, the addition of CINSARC did not improve Sarculator’s discrimination or calibration for either OS or DFS.Conclusions:This study validates CINSARC as a prognostic predictor for OS and DFS in patients with primary RPS. CINSARC did not improve the performance of Sarculator, suggesting that its addition to the Sarculator may not provide added clinical benefit.

Supplementary Table 1: distribution of histology and grading by CINSARC for DDLPS and LMS

Supplemental Figure 3: boxplot showing the distribution of Sarculator predicted overall survival (A, C) and Sarculator predicted disease free survival (B, D) at 5 years (A, B) and at 10 years (C, D) stratified by CINSARC risk category (blue, C1; yellow, C2). The median predicted OS/DFS is marked by the central horizontal line in each box with the box edges representing the interquartile range.

Supplementary Figure 1: Overall Survival curves. A, INT cohort; B, STRASS cohort; C, INT cohort according to CINSARC; D, STRASS according to CINSARC

Supplementary Table 3: univariate and multivariable analyses for DFS in specific subgroups