Deep Learning in Barrett's Esophagus Diagnosis: Current Status and Future Directions.

Barrett's esophagus (BE) represents a pre-malignant condition characterized by abnormal cellular proliferation in the distal esophagus. A timely and accurate diagnosis of BE is imperative to prevent its progression to esophageal adenocarcinoma, a malignancy associated with a significantly reduced survival rate. In this digital age, deep learning (DL) has emerged as a powerful tool for medical image analysis and diagnostic applications, showcasing vast potential across various medical disciplines. In this comprehensive review, we meticulously assess 33 primary studies employing varied DL techniques, predominantly featuring convolutional neural networks (CNNs), for the diagnosis and understanding of BE. Our primary focus revolves around evaluating the current applications of DL in BE diagnosis, encompassing tasks such as image segmentation and classification, as well as their potential impact and implications in real-world clinical settings. While the applications of DL in BE diagnosis exhibit promising results, they are not without challenges, such as dataset issues and the "black box" nature of models. We discuss these challenges in the concluding section. Essentially, while DL holds tremendous potential to revolutionize BE diagnosis, addressing these challenges is paramount to harnessing its full capacity and ensuring its widespread application in clinical practice.

Feasibility and safety of secondary video-assisted thoracoscopic surgery for ipsilateral lung cancer after prior pulmonary resection.

BACKGROUND: Video-assisted thoracoscopic surgery (VATS) is the preferred treatment for resectable non-small cell lung cancer. The increased survival of patients after a first operation has caused increases in the incidence of locoregional recurrence or second primary lung cancer and a concomitant increase in the number of patients who require secondary surgery. Ipsilateral secondary operation is also commonly practiced, albeit with enhanced difficulty. Therefore, it is necessary to evaluate the feasibility and safety of VATS for ipsilateral lung cancer after pulmonary resection. METHODS: Patients who underwent ipsilateral secondary VATS in the West China Hospital, Sichuan University from 2012 to 2021 were assessed retrospectively. All included patients had a pulmonary resection. Clinical characteristics, perioperative outcomes, and survival data were collected, with an emphasis on conversion to thoracotomy, postoperative complications, 30-day mortality, and survival. Logistic regression analysis was used to identify predictors of postoperative complications. RESULTS: Seventy patients were enrolled, of which 10 (14.3%) had converted thoracotomy, 17 (24.3%) had postoperative complications, and two (2.9%) had grade III complications. No patient died within 30 days after surgery. High Charlson comorbidity index (CCI) and severe pleural adhesion were independent predictors for complications. The median follow-up was 50 months (range: 3-120), and the 5-year overall survival was 78.2%. CONCLUSION: Secondary VATS for ipsilateral lung cancer for patients who had pulmonary resection was feasible and safe. Strict preoperative evaluation and careful management of pleural adhesion are crucial for the success of the surgery.

What does radiomics do in PD-L1 blockade therapy of NSCLC patients?

With the in-depth understanding of programmed cell death 1 ligand 1 (PD-L1) in non-small cell lung cancer (NSCLC), PD-L1 has become a vital immunotherapy target and a significant biomarker. The clinical utility of detecting PD-L1 by immunohistochemistry or next-generation sequencing has been written into guidelines. However, the application of these methods is limited in some circumstances where the biopsy size is small or not accessible, or a dynamic monitor is needed. Radiomics can noninvasively, in real-time, and quantitatively analyze medical images to reflect deeper information about diseases. Since radiomics was proposed in 2012, it has been widely used in disease diagnosis and differential diagnosis, tumor staging and grading, gene and protein phenotype prediction, treatment plan decision-making, efficacy evaluation, and prognosis prediction. To explore the feasibility of the clinical application of radiomics in predicting PD-L1 expression, immunotherapy response, and long-term prognosis, we comprehensively reviewed and summarized recently published works in NSCLC. In conclusion, radiomics is expected to be a companion to the whole immunotherapy process.