Outcomes in ovarian Sertoli-Leydig cell tumor: A report from the International Pleuropulmonary Blastoma/DICER1 and Ovarian and Testicular Stromal Tumor Registries.

OBJECTIVE: Sertoli-Leydig cell tumors (SLCTs) are rare sex cord-stromal tumors, representing <0.5% of all ovarian tumors. We sought to describe prognostic factors, treatment and outcomes for individuals with ovarian SLCT. METHODS: Individuals with SLCT were enrolled in the International Pleuropulmonary Blastoma/DICER1 Registry and/or the International Ovarian and Testicular Stromal Tumor Registry. Medical records were systematically abstracted, and pathology was centrally reviewed when available. RESULTS: In total, 191 participants with ovarian SLCT enrolled, with most (92%, 175/191) presenting with FIGO stage I disease. Germline DICER1 results were available for 156 patients; of these 58% had a pathogenic or likely pathogenic germline variant. Somatic (tumor) DICER1 testing showed RNase IIIb hotspot variants in 97% (88/91) of intermediately and poorly differentiated tumors. Adjuvant chemotherapy was administered in 40% (77/191) of cases, and among these, nearly all patients received platinum-based regimens (95%, 73/77), and 30% (23/77) received regimens that included an alkylating agent. Three-year recurrence-free survival for patients with stage IA tumors was 93.6% (95% CI: 88.2-99.3%) compared to 67.1% (95% CI: 55.2-81.6%) for all stage IC and 60.6% (95% CI: 40.3-91.0%) for stage II-IV (p < .001) tumors. Among patients with FIGO stage I tumors, those with mesenchymal heterologous elements treated with surgery alone were at higher risk for recurrence (HR: 74.18, 95% CI: 17.99-305.85). CONCLUSION: Most individuals with SLCT fare well, though specific risk factors such as mesenchymal heterologous elements are associated with poor prognosis. We also highlight the role of DICER1 surveillance in early detection of SLCT, facilitating stage IA resection.

Towards Consistency in Pediatric Brain Tumor Measurements: Challenges, Solutions, and the Role of AI-Based Segmentation.

MR imaging is central to the assessment of tumor burden and changes over time in neuro-oncology. Several response assessment guidelines have been set forth by the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working groups in different tumor histologies; however, the visual delineation of tumor components using MRIs is not always straightforward, and complexities not currently addressed by these criteria can introduce inter- and intra-observer variability in manual assessments. Differentiation of non-enhancing tumor from peritumoral edema, mild enhancement from absence of enhancement, and various cystic components can be challenging; particularly given a lack of sufficient and uniform imaging protocols in clinical practice. Automated tumor segmentation with artificial intelligence (AI) may be able to provide more objective delineations, but rely on accurate and consistent training data created manually (ground truth). Herein, this paper reviews existing challenges and potential solutions to identifying and defining subregions of pediatric brain tumors (PBTs) that are not explicitly addressed by current guidelines. The goal is to assert the importance of defining and adopting criteria for addressing these challenges, as it will be critical to achieving standardized tumor measurements and reproducible response assessment in PBTs, ultimately leading to more precise outcome metrics and accurate comparisons among clinical studies.