Artificial Intelligence (AI) has revolutionized the management of non-small-cell lung cancer (NSCLC) by enhancing different aspects, including staging, prognosis assessment, treatment prediction, response evaluation, recurrence/prognosis prediction, and personalized prognostic assessment. AI algorithms may accurately classify NSCLC stages using machine learning techniques and deep imaging data analysis. This could potentially improve precision and efficiency in staging, facilitating personalized treatment decisions. Furthermore, there are data suggesting the potential application of AI-based models in predicting prognosis in terms of survival rates and disease progression by integrating clinical, imaging and molecular data. In the present narrative review, we will analyze the preliminary studies reporting on how AI algorithms could predict responses to various treatment modalities, such as surgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy. There is robust evidence suggesting that AI also plays a crucial role in predicting the likelihood of tumor recurrence after surgery and the pattern of failure, which has significant implications for tailoring adjuvant treatments. The successful implementation of AI in personalized prognostic assessment requires the integration of different data sources, including clinical, molecular, and imaging data. Machine learning (ML) and deep learning (DL) techniques enable AI models to analyze these data and generate personalized prognostic predictions, allowing for a precise and individualized approach to patient care. However, challenges relating to data quality, interpretability, and the ability of AI models to generalize need to be addressed. Collaboration among clinicians, data scientists, and regulators is critical for the responsible implementation of AI and for maximizing its benefits in providing a more personalized prognostic assessment. Continued research, validation, and collaboration are essential to fully exploit the potential of AI in NSCLC management and improve patient outcomes. Herein, we have summarized the state of the art of applications of AI in lung cancer for predicting staging, prognosis, and pattern of recurrence after treatment in order to provide to the readers a large comprehensive overview of this challenging issue.

Background: Video-Assisted Thoracic Surgery (VATS) is conventionally performed through multiple small incisions (C-VATS). Recent studies have reported encouraging results with the single-incision VATS (S-VATS) over the conventional technique. However, these studies were either small in size, unfocused, nonuniform, retrospective, lacking follow-up information, or focused on pain. We aim to validate previously reported results in a single large meta-analysis, including only the best evidence studies available. Methods: Systematic review of the PubMed archive was conducted to include only full English articles with Newcastle Ottawa Scale score ≥7. The primary outcome was the complications rate while secondary outcomes were operative time, resected lymph nodes (LNs), chest tube duration, estimated blood loss, length of postoperative stay (LOS), and postoperative pain on day 1 after surgery. Odds ratio and standard mean difference were used as effect estimates. Random model and leave-one-out analysis were used. Results: A total of 39 studies were included with 4635 patients (1686 S-VATS versus 2949 C-VATS). S-VATS has resulted in significantly less postoperative pain (P < .001), blood loss (P = .006), LOS (P < .001), and chest tube duration (P < .001). In lung cancer patients, the number of retrieved LNs was similar to that of C-VATS (P > .05). Subgroup comparison of the rate of complications between lung resections versus other intrathoracic procedures, lung cancer versus pneumothorax, and lung cancer versus other lung-only lesions did not show any significant differences between the groups. Conclusion: Performing S-VATS technique has shown superior postoperative outcomes over the C-VATS technique in the treatment of thoracic disorders. Substantial benefit was confirmed in terms of less postoperative pain, blood loss, drainage time, and postoperative hospital stay.