In the absence of randomised head-to-head trials, we conducted a population-adjusted indirect treatment comparison (PA-ITC) of phase III trial data to evaluate the relative efficacy and safety of maintenance olaparib and bevacizumab alone and in combination in patients with newly diagnosed, advanced ovarian cancer and a BRCA mutation (BRCAm). An unanchored PA-ITC was performed on investigator-assessed progression-free survival (PFS) data. Individual patient data from SOLO1 (olaparib versus placebo) and from BRCA-mutated patients in PAOLA-1/ENGOT-ov25 (olaparib plus bevacizumab versus placebo plus bevacizumab) were pooled. Each arm of PAOLA-1 was weighted so that key baseline patient characteristics were similar to the SOLO1 cohort. Analyses were performed in patients with complete baseline data. Weighted Cox regression analysis was used to estimate the comparative efficacy of different maintenance therapy strategies, supplemented by weighted Kaplan-Meier analyses. Data from SOLO1 patients (olaparib, n = 254; placebo, n = 126) were compared with data from BRCA-mutated PAOLA-1 patients (olaparib plus bevacizumab, n = 151; placebo plus bevacizumab, n = 71). Adding bevacizumab to olaparib was associated with a numerical improvement in PFS compared with olaparib alone (hazard ratio [HR] 0.71; 95% confidence interval [CI] 0.45-1.09). Statistically significant improvements in PFS were seen with olaparib alone versus placebo plus bevacizumab (HR 0.48; 95% CI 0.30-0.75), olaparib plus bevacizumab versus placebo (0.23; 0.14-0.34), and placebo plus bevacizumab versus placebo (0.65; 0.43-0.95). Results of this hypothesis-generating PA-ITC analysis support the use of maintenance olaparib alone or with bevacizumab in patients with newly diagnosed, advanced ovarian cancer and a BRCAm.

We conducted a retrospective, multi-institutional, collaborative study to accumulate cases of neuroendocrine carcinoma of the endometrium, to clarify its clinicopathologic features, treatment, prognosis and prognostic factors to collate findings to establish future individualized treatment regimens. To our knowledge, this is the largest case study and the first study to statistically analyze the prognosis of this disease. At medical institutions participating in the Kansai Clinical Oncology Group/Intergroup, cases diagnosed at a central pathologic review as neuroendocrine carcinoma of the endometrium between 1995 and 2014 were enrolled. We retrospectively analyzed the clinicopathologic features, treatment, prognosis and prognostic factors of this disease. A total of 65 cases were registered from 18 medical institutions in Japan. Of these, 42 (64.6%) cases were diagnosed as neuroendocrine carcinoma of the endometrium based on the central pathological review and thus included in the study. Advanced International Federation of Gynecology and Obstetrics stages (stage III and IV) and pure type small cell neuroendocrine carcinoma cases had a significantly worse prognosis. Upon multivariate analysis, only histologic subtypes and surgery were significant prognostic factors. Pure type cases had a significantly worse prognosis compared to mixed type cases and complete surgery cases had a significantly better prognosis compared to cases with no or incomplete surgery. Our findings suggest that complete surgery improves the prognosis of neuroendocrine carcinoma of the endometrium. Even among cases with advanced disease stages, if complete surgery is expected to be achieved, clinicians should consider curative surgery to improve the prognosis of neuroendocrine carcinoma of the endometrium.

Konno Y, et al. J Gynecol Oncol. 2021 Jan;32(2):e25. https://doi.org/10.3802/jgo.2021.32.e25

Yonemori K, et al. J Gynecol Oncol. 2024 Jan;35:e40. https://doi.org/10.3802/jgo.2024.35.e40

This review explores the significant progress and applications of artificial intelligence (AI) in obstetrics and gynecological MRI, charting its development from foundational algorithmic techniques to deep learning strategies and advanced radiomics. This review features research published over the last few years that has used AI with MRI to identify specific conditions such as uterine leiomyosarcoma, endometrial cancer, cervical cancer, ovarian tumors, and placenta accreta. In addition, it covers studies on the application of AI for segmentation and quality improvement in obstetrics and gynecology MRI. The review also outlines the existing challenges and envisions future directions for AI research in this domain. The growing accessibility of extensive datasets across various institutions and the application of multiparametric MRI are significantly enhancing the accuracy and adaptability of AI. This progress has the potential to enable more accurate and efficient diagnosis, offering opportunities for personalized medicine in the field of obstetrics and gynecology.