Efficacy of stereotactic body radiotherapy and response prediction using artificial intelligence in oligometastatic gynaecologic cancer.

PURPOSE: We present a large real-world multicentric dataset of ovarian, uterine and cervical oligometastatic lesions treated with SBRT exploring efficacy and clinical outcomes. In addition, an exploratory machine learning analysis was performed. METHODS: A pooled analysis of gynecological oligometastases in terms of efficacy and clinical outcomes as well an exploratory machine learning model to predict the CR to SBRT were carried out. The CR rate following radiotherapy (RT) was the study main endpoint. The secondary endpoints included the 2-year actuarial LC, DMFS, PFS, and OS. RESULTS: 501 patients from 21 radiation oncology institutions with 846 gynecological metastases were analyzed, mainly ovarian (53.1%) and uterine metastases(32.1%).Multiple fraction radiotherapy was used in 762 metastases(90.1%).The most frequent schedule was 24 Gy in 3 fractions(13.4%). CR was observed in 538(63.7%) lesions. The Machine learning analysis showed a poor ability to find covariates strong enough to predict CR in the whole series. Analyzing them separately, in uterine cancer, if RT dose≥78.3Gy, the CR probability was 75.4%; if volume was <13.7 cc, the CR probability became 85.1%. In ovarian cancer, if the lesion was a lymph node, the CR probability was 71.4%; if volume was <17 cc, the CR probability rose to 78.4%. No covariate predicted the CR for cervical lesions. The overall 2-year actuarial LC was 79.2%, however it was 91.5% for CR and 52.5% for not CR lesions(p < 0.001). The overall 2-year DMFS, PFS and OS rate were 27.3%, 24.8% and 71.0%, with significant differences between CR and not CR. CONCLUSIONS: CR was substantially associated to patient outcomes in our series of gynecological cancer oligometastatic lesions. The ability to predict a CR through artificial intelligence could also drive treatment choices in the context of personalized oncology.

Patterns of failure after adjuvant "sandwich" chemo-radio-chemotherapy in locally advanced (stage III-IVA) endometrial cancer.

PURPOSE: To investigate oncological outcomes and patterns of recurrence of patients undergoing adjuvant "sandwich" chemo-radio-chemotherapy for locally advanced endometrial cancer. METHODS: This is a multi-institutional retrospective study evaluating chart of consecutive patients undergoing chemo-radio-chemotherapy for FIGO stage III-IVA endometrial caner. RESULTS: The study population included 45 patients who had adjuvant sandwich regimen. Median age of the study population was 66 years. The majority of patients were diagnosed with endometrioid histology and with stage III disease. After a median follow-up of 35 months, 15 patients developed recurrent disease. Three-year disease-free and overall survivals was 45% and 81%, respectively. Three-years site-specific disease-free survival was 85%, 92% and 48% for local, loco-regional, and distant recurrence, respectively. All patients included in the study had nodal dissection. Nodal assessment included: sentinel node mapping, sentinel node mapping plus backup lymphadenectomy and lymphadenectomy in 15, 6 and 24 patients, respectively. The latter group included four patients detected by suspected enlarged nodes, intraoperatively. Even after the exclusion of patients with enlarged nodes, the type of nodal assessment did not impact on survival outcomes (p > 0.2). Positive peritoneal cytology was the only factor associated with an increased risk of developing (any site) recurrence and distant-specific recurrence, independently. No factor predicted for overall survival. CONCLUSION: Adjuvant "sandwich" chemo-radio-chemotherapy for locally advanced endometrial cancer guarantee promising local and loco-regional controls, but distant failure rate is high, thus suggesting the need for applying other systemic treatment strategies for these patients.