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

We present a large real-word 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 pooed 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 radiotehrapy (RT) was the study main endpoint. the secondary endpoints included the 2-year actuarial LC, DMFS, PFS, and OS. Results. 501 patients fron 21 radiation oncology institutions with 846 gynecological metastases were analyzed, mainly ovarian (53.1%) and uterine metastases ( 32.1%). Multiple faction radiotherapy was used in 762 metastases ( 90.1%). The most frequent schedule was 24 Gy in 3 fraction (13.4%). CR was observed in 538 (63.7%) lesions. The Machine Learning analysis showed a poor ability to find covariates strong enough to èredict CR in the wghole series. Analyzing them separately, in uterine cancer, if RT dose > 78.3 Gy, the CR probability was 75.4%; if volume was <13.7 cc the CR probability bcame 85.1%: In ovarian cancer, if the lesion was a lymph node, the CR probability was 71.4%; if the volume was < 17 , the CR probability rose to 78.4%. No covariate predicted the CR for cervical lesions: The overall 2-year acturial LC wa 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 patents 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.