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Simple Summary Immune checkpoint inhibitors have improved the prognosis for patients with advanced melanoma. Despite the recent success of immunotherapy, many patients still do not benefit from these treatments, and their real-life application may yield different outcomes compared to the advantage presented in clinical trials. There is therefore a need to select patients who can really benefit from these treatments. We have focused our study on a real-life retrospective analysis of metastatic melanoma patients treated with immunotherapy at a single institution—the Istituto Nazionale Tumori IRCCS Fondazione “G. Pascale” of Napoli, Italy. With the help of AI and machine learning we validated an algorithm based on clinical variables of patients—namely, the Clinical Categorization Algorithm (CLICAL)—that defines five predictable cohorts of benefit to immunotherapy with 95% accuracy. It can be a useful tool for the stratification of metastatic melanoma patients who may or may not improve from immunotherapy treatment. Abstract The real-life application of immune checkpoint inhibitors (ICIs) may yield different outcomes compared to the benefit presented in clinical trials. For this reason, there is a need to define the group of patients that may benefit from treatment. We retrospectively investigated 578 metastatic melanoma patients treated with ICIs at the Istituto Nazionale Tumori IRCCS Fondazione “G. Pascale” of Napoli, Italy (INT-NA). To compare patients’ clinical variables (i.e., age, lactate dehydrogenase (LDH), neutrophil–lymphocyte ratio (NLR), eosinophil, BRAF status, previous treatment) and their predictive and prognostic power in a comprehensive, non-hierarchical manner, a clinical categorization algorithm (CLICAL) was defined and validated by the application of a machine learning algorithm—survival random forest (SRF-CLICAL). The comprehensive analysis of the clinical parameters by log risk-based algorithms resulted in predictive signatures that could identify groups of patients with great benefit or not, regardless of the ICI received. From a real-life retrospective analysis of metastatic melanoma patients, we generated and validated an algorithm based on machine learning that could assist with the clinical decision of whether or not to apply ICI therapy by defining five signatures of predictability with 95% accuracy.

The median overall survival (OS) and local control (LC) of patients with melanoma brain metastases (MBMs) are poor even with immune checkpoint inhibitors and/or radiotherapy (RT). The aims of the study were to evaluate the association and timing of stereotactic radiotherapy (SRT)/radiosurgery (SRS) performed with the CyberKnife® System and ipilimumab (IPI). A total of 63 MBMs patients were analyzed: 53 received RT+IPI and 10 RT alone. Therefore, the patients were divided into four groups: RT PRE-PI (&gt, 4 weeks before IPI) (18), RT CONC-IPI (4 weeks before/between first and last cycle/within 3 months of last cycle of IPI) (20), RT POST-IPI (&gt, 3 months after IPI) (15), and NO-IPI (10). A total of 127 lesions were treated: 75 with SRS (one fraction) and 24 with SRT (three to five fractions). The median follow-up was 10.6 months. The median OS was 10.6 months for all patients, 10.7 months for RT+IPI, and 3.3 months for NO-IPI (p = 0.96). One-year LC was 50% for all patients, 56% for RT+IPI, and 18% for NO-IPI (p = 0.08). The 1-year intracranial control was 45% for all patients, 44% for RT+IPI, and 51% for NO-IPI (p = 0.73). IPI with SRS/SRT in MBMs treatment could improve LC. However, the impact and timing of the two modalities on patients’ outcomes are still unclear.