Spontaneous pneumothorax is an extremely frequent pathology. Despite this, there is still no clear consensus on managing these patients. Therefore, we perform a systematic review and meta-analysis of the effectiveness of percutaneous aspiration compared with tube drainage for treating idiopathic spontaneous pneumothorax. Controlled, randomised clinical trials were selected, in which the effectiveness of percutaneous aspiration is compared with that of chest tube drainage, in terms of resolution of the pneumothorax and rates of relapse and hospital admission. Relevant articles were identified by searching electronic databases (e.g., Medline, EMBASE, CENTRAL, mRCT and Pascal), as well as the references of the papers found in this manner, with a cut-off date of April 2009. Quality was assessed by two independent evaluators, using the CASPe appraisal tool. Effectiveness of percutaneous aspiration is compared with that of chest tube drainage, in terms of resolution of the pneumothorax and rates of relapse and hospital admission.

Background/Objectives: The aim of this study was to analyze whether the implementation of artificial intelligence (AI), specifically the Natural Language Processing (NLP) branch developed by OpenAI, could help a thoracic multidisciplinary tumor board (MTB) make decisions if provided with all of the patient data presented to the committee and supported by accepted clinical practice guidelines. Methods: This is a retrospective comparative study. The inclusion criteria were defined as all patients who presented at the thoracic MTB with a suspicious or first diagnosis of non-small-cell lung cancer between January 2023 and June 2023. Intervention: GPT 3.5 turbo chat was used, providing the clinical case summary presented in committee proceedings and the latest SEPAR lung cancer treatment guidelines. The application was asked to issue one of the following recommendations: follow-up, surgery, chemotherapy, radiotherapy, or chemoradiotherapy. Statistical analysis: A concordance analysis was performed by measuring the Kappa coefficient to evaluate the consistency between the results of the AI and the committee’s decision. Results: Fifty-two patients were included in the study. The AI had an overall concordance of 76%, with a Kappa index of 0.59 and a consistency and replicability of 92.3% for the patients in whom it recommended surgery (after repeating the cases four times). Conclusions: AI is an interesting tool which could help in decision making in MTBs.

Objective: To perform an external validation of a previously reported machine learning (ML) approach for predicting the diagnosis of pleural tuberculosis. Patients and Methods: We defined two cohorts: a Training group, comprising 273 out of 1,220 effusions from our prospective study (2013–2022); and a Testing group, from a retrospective analysis of 360 effusions from 832 consecutive patients in Bajo Deba health district (1996–2012). All the effusions included were exudative and lymphocytic. In Training and Testing groups respectively, 49 and 104 cases were tuberculous, 143 and 92 were malignant, and 81 and 164 were diagnosed with “other diseases”; pre-test probabilities of pleural tuberculosis were 4% and 12.7%. Variables included were: age, pH, adenosine deaminase, glucose, protein, and lactate dehydrogenase levels, and white cell counts (total and differential) in pleural fluid. We used two ML classifiers: binary (tuberculous and non-tuberculous), and three-class (tuberculous, malignant, and others); and compared them with Bayesian analysis. Results: The best binary classifier yielded a sensitivity of 88%, specificity of 98%, and accuracy of 95%. The best three-class classifier achieved the same accuracy and correctly classified 83% (77/92) of malignant cases. The ML models yielded higher positive predictive values than Bayesian analysis based on ADA > 40 U/l and lymphocyte percentage ≥ 50% (92%). Conclusions: This external validation confirms the good performance of the previously reported ML approach for predicting the diagnosis of pleural tuberculosis based on exudative and lymphocytic pleural effusions, and for discriminating the cases most likely to be malignant. Additionally, ML was more accurate than the Bayesian approach in our study.

Objective: To assess the impact of past liver metastases on the survival duration of patients who are undergoing surgery for lungmetastases. Methods: We conducted a review of literature published from 2007 to 2014. The studies were identified by searching PubMed,MEDLINE, and Embase and were supplemented by a manual search of the references listed by the retrieved studies. The followingsearch terms were used: lung metastasectomy, pulmonary metastasectomy, lung metastases, and lung metastasis. We selectedretrospective and prospective studies published from 2007 to 2014 on patients with lung metastases from colorectal cancer andwere undergoing surgery with curative intent. We excluded reviews, studies that focused on surgical techniques, patients who weretreated non-surgically, analyses of specific subgroups of patients, and those that did not report follow-up of the patientsundergoing surgery. Results: We identified 28 papers that assessed survival after lung metastases, 21 of which were mostly retrospective studies thatidentified previous liver metastases to explore their impact on patient survival. In more than half of the papers analyzed (63.2%),patients with a history of resected liver metastases had a lower survival rate than those who did not have such a history, and thedifference was statistically significant in eight of these studies. However, data were presented differently, and authors reportedmean survival time, survival rates, or hazard ratios. Conclusions: A history of liver metastases seems to be a negative prognostic factor, but the individual data need to undergo ameta-analysis.