Thymectomy plays an important role in patients with myasthenia gravis (MG). This study aimed to explore predictors of postoperative myasthenic crisis (POMC) after thymectomy and to define a predictive score of respiratory failure. The clinical data of 177 patients with MG undergoing thymectomy from January 1995 to December 2011 were retrospectively reviewed. The following factors were analysed in relation to the occurrence of myasthenic crisis: gender, age, body mass index (BMI), anti-acetylcholine receptor-antibody level, bulbar symptoms, comorbidities, duration of symptoms, Osserman-stage, Myasthenia Gravis Foundation of America (MGFA) stage, history of myasthenic crisis, use of immoglobulins or plasmapheresis, kind of therapy, spirometric and blood gas parameters, histology, kind of surgery, non-myasthenic complications and duration of intubation. Twenty-two patients experienced postoperative respiratory failure after thymectomy. Univariate analysis revealed a correlation with age >60 years (odds ratio (OR) = 1.79, 95% confidence interval (CI) = 1.04–6.78; P = 0.040); Osserman-stage (IIB- OR = 5.16, 95% CI = 1.10–24.18; P = 0.037, III–IV- OR = 8.75, 95% CI = 1.53–50.05; P = 0.015); bulbar symptoms (OR = 7.42, 95% CI = 1.67–32.84; P = 0.008); BMI >28 (OR = 3.99, 95% CI = 1.58–10.03; P = 0.003); preoperative plasmapheresis (OR = 2.97, 95% CI = 1.18–14.04; P = 0.021); duration of symptoms >2 years (OR = 4.00, 95% CI = 1.09–14.762; P = 0.036); extended surgery (OR = 2.52, 95% CI = 1.02–6.22; P = 0.045); lung (OR = 4.05, 95% CI = 1.44–11.42; P = 0.008), pericardial (OR = 3.78, 95% CI = 1.45–9.82; P = 0.006) or pleural resection (OR = 3.23, 95% CI = 1.30–8.03; P = 0.012); Vital Capacity % 40 mmHg (OR = 3.76, 95% CI = 1.12–12.68; P = 0.032). Multivariate logistic regression analysis showed that Osserman-stage (IIB- OR = 5.69, 95% CI = 1.09–29.69; P = 0.039 (III–IV- OR = 11.33, 95% CI = 1.67–76.72; P = 0.013), BMI >28 (OR = 3.65, 95% CI = 1.10–12.15; P = 0.035), history of myasthenic crisis (OR = 24.10, 95% CI = 2.34–248.04; P = 0.007), duration of symptoms >2 years (OR = 5.94, 95% CI = 1.12–31.48; P = 0.036) and lung resection (OR = 8.48, 95% CI = 2.18–32.97; P = 0.002) independently predict POMC. Excluding history of preoperative myasthenic crisis (statistically associated with Osserman-stage), we built a scoring system according to the OR of Osserman-stage (I–IIA, IIB, III–IV), BMI (<28, ≥28), duration of symptoms (2 years) and association with a pulmonary resection. This model helped in creating four classes with increasing risk of respiratory failure (Group I, 6%; Group II, 10%; Group III, 25%; Group IV, 50%). Our model facilitates the stratification of patient risk and prediction of the occurrence of POMC. Moreover, it could help to guide the anaesthesiologist's decision on the duration of intubation. Further studies based on larger series are needed to confirm these preliminary data.

Large-cell neuroendocrine carcinomas of the lung (LCNECs) are rare tumors representing 1–3% of all primary lung cancers. Patients with LCNEC are predominantly male, older, and heavy smokers. Histologically, these tumors are characterized by large cells with abundant cytoplasm, high mitotic rate, and neuroendocrine immunohistochemistry-detected markers (chromogranin-A, synaptophysin, and CD56). In 2015 the World Health Organization classified LCNEC as a distinct subtype of pulmonary large-cell carcinoma and, therefore, as a subtype of non-small cell lung carcinoma (NSCLC). Because of the small-sized tissue samples and the likeness to other neuroendocrine tumors, the histological diagnosis of LCNEC remains difficult. Clinically, the prognosis of metastatic LCNECs is poor, with high rates of recurrence after surgery alone and overall survival of approximately 35% at 5 years, even for patients with early stage disease that is dramatically shorter compared with other NSCLC subtypes. First-line treatment options have been largely discussed but with limited data based on phase II studies with small sample sizes, and there are no second-line well defined treatments. To date, no standard treatment regimen has been developed, and how to treat LCNEC is still on debate. In the immunotherapy and targeted therapy era, in which NSCLC treatment strategies have been radically reshaped, a few data are available regarding these opportunities in LCNEC. Due to lack of knowledge in this field, many efforts have been done for a deeper understanding of the biological and molecular characteristics of LCNEC. Next generation sequencing analyses have identified subtypes of LCNEC that may be relevant for prognosis and response to therapy, but further studies are needed to better define the clinical impact of these results. Moreover, scarce data exist about PD-L1 expression in LCNEC and its predictive value in this histotype with regard to immunotherapy efficacy. In the literature some cases are reported concerning LCNEC metastatic patients carrying driver mutations, especially EGFR alterations, showing targeted therapy efficacy in this setting of disease. Due to the rarity and the challenging understanding of LCNEC, in this review we aim to summarize the management options currently available for treatment of LCNEC.

The prognostic role of the number of resected and metastatic lymph nodes in non-small-cell lung cancer (NSCLC) is still being debated. The aim of this study was to evaluate the impact of lymphadenectomy in addition to the already validated variables in NSCLC survival. From January 2002 to December 2012, data on 4858 patients with NSCLC undergoing anatomical lung resection and hilomediastinal lymphadenectomy in 6 institutions were analysed retrospectively. Established prognostic factors in addition to the number of resected lymph nodes and the ratio between the number of metastatic lymph nodes and the number of resected lymph nodes (NR) were correlated to overall survival (OS) and disease-free survival (DFS) using the multivariable Cox regression model. Harrell's C-statistic with the 95% confidence interval (CI) was determined. Analysis by means of maximally selected log-rank statistics was performed to find optimal cut-off points in order to split patients into groups with different outcome probabilities. The median numbers of resected lymph nodes and of metastatic lymph nodes were 17 (range 6-85) and 2 (1-36), respectively. Hilar (N1) and mediastinal (N2) metastases were identified in 21.3% and 20.0% of cases, respectively. Overall, the 5-year OS and DFS rates were 54.6% and 44.8%, respectively. At multivariable analysis, age, gender, pathological stage, R0 resection, type of surgery and NR correlated with longer OS rates; the same variables plus tumour grading were further related to DFS. C-statistics were 66.0 (95% CI 62.7-69.4) for DFS and 60.5 (95% CI 58.3-62.6) for OS. An NR <40% significantly correlated with a higher 5-year survival rate in the total sample (OS 57.6% vs 23.8%, P < 0.001; DFS 48.2% vs 11.4, P < 0.001) and in patients with N1 (OS 47.9% vs 36.1%, P = 0.03; DFS 39% vs 24.2%, P = 0.02) and N2 (OS 36.9% vs 21.8%, P < 0.001 DFS 23.9% vs 9.1%, P < 0.001). Our study confirms that the number of resected lymph nodes is a strong prognostic indicator in NSCLC. In particular, an NR cut-off value of 40% may predict both OS and DFS.

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: The aim of this study was to evaluate the Masaoka–Koga and the tumor node metastases (TNM) staging system in thymic epithelial tumors (TET) considering possible improvements. Methods: We reviewed the data of 379 patients who underwent surgical resection for TET from 1 January 1985 to 1 January 2018, collecting and classifying the pathological report according to the Masaoka–Koga and the TMN system. The number of involved organs was also considered as a possible prognostic factor and integrated in the two staging systems to verify its impact. Results: Considering the Masaoka–Koga system, 5- and 10-year overall survival (5–10YOS) was 96.4% and 88.9% in stage I, 95% and 89.5% in stage II and 85.4% and 72.8% in stage III (p = 0.01), with overlapping in stage I and stage II curves. Considering the TNM system, 5–10YOS was 95.5% and 88.8% in T1, 84.8% and 70.7% in T2 and 88% and 76.3% in T3 (p = 0.02), with overlapping T2–T3 curves. Including the number of involved structures, in Masaoka–Koga stage III, patients with singular involved organs had a 100% and 76.6% vs. 87.7% 5–10YOS, which was 76.6% in patients with multiple organ infiltration. Considering the TNM, T3 patients with singular involved structures presented a 5–10YOS of 100% vs. 62.5% and 37.5% in patients with multiple organ involvement (p = 0.07). Conclusion: The two staging systems present limitations due to overlapping curves in early Masaoka–Koga stages and in advanced T stages for TNM. The addition of the number of involved organs seems to be a promising factor for the prognosis stratification in these patients.