This study evaluates the relevance of ¹⁸F-DOPA PET static and dynamic radiomics for differentiation of high-grade glioma (HGG) progression from treatment-related changes (TRC) by comparing diagnostic performances to the current PET imaging standard of care. Eighty-five patients with histologically confirmed HGG and investigated by dynamic ¹⁸F-FDOPA PET in two institutions were retrospectively selected. ElasticNet logistic regression, Random Forest and XGBoost machine models were trained with different sets of features—radiomics extracted from static tumor-to-background-ratio (TBR) parametric images, radiomics extracted from time-to-peak (TTP) parametric images, as well as combination of both—in order to discriminate glioma progression from TRC at 6 months from the PET scan. Diagnostic performances of the models were compared to a logistic regression model with TBR_mean ± clinical features used as reference. Training was performed on data from the first center, while external validation was performed on data from the second center. Best radiomics models showed only slightly better performances than the reference model (respective AUCs of 0.834 vs. 0.792, p < 0.001). Our current results show similar findings at the multicentric level using different machine learning models and report a marginal additional value for TBR static and TTP dynamic radiomics over the classical analysis based on TBR values.

Purpose: This study aims to investigate the effects of applying the point spread function deconvolution (PSFd) to the radiomics analysis of dynamic L-3,4-dihydroxy-6-[¹⁸F]-fluoro-phenyl-alanine (¹⁸F-FDOPA) positron emission tomography (PET) images, to non-invasively identify isocitrate dehydrogenase (IDH) mutated and/or 1p/19q codeleted gliomas. Methods: Fifty-seven newly diagnosed glioma patients underwent dynamic ¹⁸F-FDOPA imaging on the same digital PET system. All images were reconstructed with and without PSFd. An L1-penalized (Lasso) logistic regression model, with 5-fold cross-validation and 20 repetitions, was trained with radiomics features extracted from the static tumor-to-background-ratio (TBR) and dynamic time-to-peak (TTP) parametric images, as well as a combination of both. Feature importance was assessed using Shapley additive explanation values. Results: The PSFd significantly modified 95% of TBR, but only 79% of TTP radiomics features. Applying the PSFd significantly improved the ability to identify IDH-mutated and/or 1p/19q codeleted gliomas, compared to PET images not processed with PSFd, with respective areas under the curve of 0.83 versus 0.79 and 0.75 versus 0.68 for a combination of static and dynamic radiomics features (p < 0.001). Without the PSFd, four and eight radiomics features contributed to 50% of the model for detecting IDH-mutated and/or 1p/19q codeleted gliomas, respectively. Application of the PSFd reduced this to three and seven contributive radiomics features. Conclusion: Application of the PSFd to dynamic ¹⁸F-FDOPA PET imaging significantly improves the detection of molecular parameters in newly diagnosed gliomas, most notably by modifying TBR radiomics features.

This study aimed to assess the prognostic value of medullary total metabolic tumor volume (mTMTV) derived from fluorodeoxyglucose–positron emission tomography/computed tomography ([18F]FDG-PET/CT) compared with conventional PET-derived features and biological/chromosomal abnormalities in patients with newly diagnosed multiple myeloma (NDMM) treated with daratumumab for induction/consolidation and/or maintenance and enrolled in CASSIOPET, a companion study of CASSIOPEIA, with long-term follow-up. Automated bone/liver CT–based segmentation were applied to the baseline [18F]FDG-PET images, with mTMTV being defined using the median liver background as the cut-off, including focal lesions and diffuse bone marrow (BM) involvement. Both univariate/multivariate Cox and machine learning (ML)–based survival models were performed. A total of 195 patients were included, 81% of them PET-positive. Multivariate analysis demonstrated independent prognostic value of mTMTV for progression-free survival (PFS) (P< .001) and overall survival (OS) (P< .001), complementary to Revised International Staging System (R-ISS) (P = .008 and P< .001, respectively). The ML model confirmed these findings, achieving concordance index of 0.609 and 0.659 and identifying mTMTV as the most informative feature for PFS and OS. Adding R-ISS, BM maximum standardized uptake value (SUVmax) and anemia to mTMTV accounted for >60% of the ML model explanation for PFS and adding R-ISS, the number of focal lesions and BM SUVmax for >60% of the model for OS. Combining R-ISS and mTMTV enabled the creation of 2 new-risk subgroups. In conclusion, this prospective study demonstrated the prognostic relevance of [18F]FDG-PET/CT–based parameters in the initial workup of patients with NDMM in the era of anti-CD38–based therapy. mTMTV was found to have strong independent prognostic value, complementary to R-ISS and refining risk stratification. This trial was registered at www.clinicaltrials.gov as #NCT02541383.