(1) Background: In advanced non-small cell lung cancer (aNSCLC), programmed death ligand 1 (PD-L1) remains the only biomarker for candidate patients to immunotherapy (IO). This study aimed at using artificial intelligence (AI) and machine learning (ML) tools to improve response and efficacy predictions in aNSCLC patients treated with IO. (2) Methods: Real world data and the blood microRNA signature classifier (MSC) were used. Patients were divided into responders (R) and non-responders (NR) to determine if the overall survival of the patients was likely to be shorter or longer than 24 months from baseline IO. (3) Results: One-hundred sixty-four out of 200 patients (i.e., only those ones with PD-L1 data available) were considered in the model, 73 (44.5%) were R and 91 (55.5%) NR. Overall, the best model was the linear regression (RL) and included 5 features. The model predicting R/NR of patients achieved accuracy ACC = 0.756, F1 score F1 = 0.722, and area under the ROC curve AUC = 0.82. LR was also the best-performing model in predicting patients with long survival (24 months OS), achieving ACC = 0.839, F1 = 0.908, and AUC = 0.87. (4) Conclusions: The results suggest that the integration of multifactorial data provided by ML techniques is a useful tool to select NSCLC patients as candidates for IO.

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First-line immune-checkpoint inhibitor (ICI)-based therapy has deeply changed the treatment landscape and prognosis in advanced non-small cell lung cancer (aNSCLC) patients with no targetable alterations. Nonetheless, a percentage of patients progressed on ICI as monotherapy or combinations. Open questions remain on patients’ selection, the identification of biomarkers of primary resistance to immunotherapy and the treatment strategies to overcome secondary resistance to first-line immunotherapy. Local ablative approaches are the main therapeutic strategies in oligoprogressive disease, and their role is emerging in patients treated with immunotherapy. Many therapeutic strategies can be adapted in aNSCLC patients with systemic progression to personalize the treatment approach according to re-characterization of the tumors, previous ICI response, and type of progression. This review’s aim is to highlight and discuss the current and potential therapeutic approaches beyond first-line ICI-based therapy in aNSCLC patients based on the pattern of disease progression (oligoprogression versus systemic progression).

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Aim: To understand how patients with cancer reacted to the coronavirus disease 2019 (COVID-19) pandemic and whether their quality of life (QoL) was affected. Methods: In June 2020, 111 patients with cancer treated in the supportive care unit of a Comprehensive Cancer Center in Milan and 201 healthy controls from the general population were enrolled and assessed both quantitatively and qualitatively for fears and COVID-19–related beliefs as well as for QoL. Results: Fear of COVID-19 was significantly lower among patients (41% vs 57.6%; p = 0.007), as was fear of cancer (61.5% vs 85.6%; p < 0.001) and other diseases. The perceived risk of getting COVID-19 was lower among patients (25.2% vs 52.7%; p < 0.001), as was the belief of having been exposed to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (18.1% vs 40.8%; p < 0.001). The physical component of QoL was better among the population (54.5 vs 43.8; p < 0.001); the reverse was true for patients’ psychological well-being (44.6 vs 39.6; p < 0.001). The qualitative data supported such results, showing a reduced psychological effect on the patients with cancer compared to the controls. Various reasons explain this result, including the awareness of being treated for cancer and nevertheless protected against getting infected in a cancer center of public health reorganized to continue treating patients by protecting them and personnel from the risk of infection. Conclusions: The experience of a cancer diagnosis, together with proper hospital reorganization, may act as protective factors from fears and psychological consequences of the COVID-19 outbreak.