Machine learning model predicts airway stenosis requiring clinical intervention in patients after lung transplantation: a retrospective case-controlled study.

Background: Patients with airway stenosis (AS) are associated with considerable morbidity and mortality after lung transplantation (LTx). This study aims to develop and validate machine learning (ML) models to predict AS requiring clinical intervention in patients after LTx.

Methods: Patients who underwent LTx between January 2017 and December 2019 were reviewed.

Salvage surgery and conversion surgery for patients with non-small cell lung cancer: A narrative review.

Non-small cell lung cancer (NSCLC) remains the leading cause of cancer-related deaths. With the development of screening, patient selection and treatment strategies, patients' survival outcomes and living quality significantly improved. However, some patients still have local recurrence or residual tumors after receiving definitive therapies.

Dual-Region Computed Tomography Radiomics-Based Machine Learning Predicts Subcarinal Lymph Node Metastasis in Patients with Non-small Cell Lung Cancer.

Background: Noninvasively and accurately predicting subcarinal lymph node metastasis (SLNM) for patients with non-small cell lung cancer (NSCLC) remains challenging. This study was designed to develop and validate a tumor and subcarinal lymph nodes (tumor-SLNs) dual-region computed tomography (CT) radiomics model for predicting SLNM in NSCLC.

Methods: This retrospective study included NSCLC patients who underwent lung resection and SLNs dissection between January 2017 and December 2020.

Pararespiratory and paradigestive lymph node metastases in esophageal squamous cell carcinoma: predicting survival and refining the N staging system.

Background: The site of lymph node metastasis (LNM) may affect the prognosis of patients with esophageal squamous cell carcinoma (ESCC). To investigate the prognoses of pararespiratory and paradigestive LNM and to propose a novel N (nN) staging system that integrates both the LNM site and count.

Methods: This study was a multicenter, large-sample, retrospective cohort study that included ESCC patients with LNM between January 2014 and December 2019 from three Chinese institutes.

Machine Learning-Based Prognostic Model for Patients After Lung Transplantation.

Importance: Although numerous prognostic factors have been found for patients after lung transplantation (LTx) over the years, an accurate prognostic tool for LTx recipients remains unavailable.

Objective: To develop and validate a prognostic model for predicting overall survival in patients after LTx using random survival forests (RSF), a machine learning algorithm.

Design, Setting, And Participants: This retrospective prognostic study included patients who underwent LTx between January 2017 and December 2020.

Machine learning models predict lymph node metastasis in patients with stage T1-T2 esophageal squamous cell carcinoma.

Background: For patients with stage T1-T2 esophageal squamous cell carcinoma (ESCC), accurately predicting lymph node metastasis (LNM) remains challenging. We aimed to investigate the performance of machine learning (ML) models for predicting LNM in patients with stage T1-T2 ESCC.

Methods: Patients with T1-T2 ESCC at three centers between January 2014 and December 2019 were included in this retrospective study and divided into training and external test sets.

A novel web-based dynamic nomogram for recurrent laryngeal nerve lymph node metastasis in esophageal squamous cell carcinoma.

Background: Patients with esophageal squamous cell carcinoma (ESCC) are liable to develop recurrent laryngeal nerve (RLN) lymph node metastasis (LNM). We aimed to assess the predictive value of the long diameter (LD) and short diameter (SD) of RLN lymph node (LN) and construct a web-based dynamic nomogram for RLN LNM prediction.

Methods: We reviewed 186 ESCC patients who underwent RLN LN dissection from January 2016 to December 2018 in the Affiliated Hospital of North Sichuan Medical College.

Double-lung versus heart-lung transplantation for end-stage cardiopulmonary disease: a systematic review and meta-analysis.

We compared posttransplant outcomes following double-lung transplantation (DLTx) and heart-lung transplantation (HLTx), based on a search of PubMed, Cochrane Library, and Embase, from inception to March 8, 2022, for studies that report outcomes of these procedures. We then performed a meta-analysis of baseline characteristics and posttransplant outcomes. Subgroup analyses were implemented according to indication, publication year, and center.

Machine learning-based radiomic computed tomography phenotyping of thymic epithelial tumors: Predicting pathological and survival outcomes.

Objective: For patients with thymic epithelial tumors, accurately predicting clinicopathological outcomes remains challenging. We aimed to investigate the performance of machine learning-based radiomic computed tomography phenotyping for predicting pathological (World Health Organization [WHO] type and TNM stage) and survival outcomes (overall and progression-free survival) in patients with thymic epithelial tumors.

Methods: This retrospective study included patients with thymic epithelial tumors between January 2001 and January 2022.

Right lung transplantation with a left-to-right inverted anastomosis in a rat model.

Objective: Right lung transplantation in rats has been attempted occasionally, but the technical complexity makes it challenging to apply routinely. Additionally, basic research on inverted lobar lung transplantation is scarce because of the lack of a cost-effective experimental model. We first reported right lung transplantation in a rat model using left-to-right inverted anastomosis to imitate the principle of clinically inverted lung transplantation.

Pathological tumor long-to-short axis ratio as a prognostic factor in patients with thymic epithelial tumors.

Background: Thymic epithelial tumors (TETs) exhibit irregular shapes reflective of the heterogeneity in tumor growth and invasive properties. We aimed to identify the prognostic value of the pathological tumor long-to-short axis (L/S) ratio in TETs.

Methods: A retrospective study was performed on patients with TETs who underwent extended thymectomy between January 1999 and December 2019 in our institute.

The minimum number of examined lymph nodes for accurate nodal staging and optimal survival of stage T1-2 esophageal squamous cell carcinoma: A retrospective multicenter cohort with SEER database validation.

Background: The extent of lymphadenectomy during esophagectomy remains controversial for patients with T1-2 ESCC. The aim of this study was to identify the minimum number of examined lymph node (ELN) for accurate nodal staging and overall survival (OS) of patients with T1-2 esophageal squamous cell carcinoma (ESCC).

Materials And Methods: Patients with T1-2 ESCC from three institutes between January 2011 and December 2020 were retrospectively reviewed.

Preoperative Clinical Characteristics Predict Recurrent Laryngeal Nerve Lymph Node Metastasis and Overall Survival in Esophageal Squamous Cell Carcinoma: A Retrospective Study With External Validation.

Background: Recurrent laryngeal nerve (RLN) lymph node metastasis (LNM) is not rare in patients with esophageal squamous cell carcinoma (ESCC). We aimed to develop and externally validate a preoperative nomogram using clinical characteristics to predict RLN LNM in patients with ESCC and evaluate its prognostic value.

Methods: A total of 430 patients with ESCC who underwent esophagectomy with lymphadenectomy of RLN LNs at two centers between May 2015 and June 2019 were reviewed and divided into training (center 1, n = 283) and external validation cohorts (center 2, n = 147).

Noninvasive monitoring of allograft rejection in a rat lung transplant model: Application of machine learning-based F-fluorodeoxyglucose positron emission tomography radiomics.

Background: Standardized uptake values (SUVs) derived from F-fluorodeoxyglucose (F-FDG) positron emission tomography (PET) are valuable but insufficient for detecting lung allograft rejection (AR). Using a rat lung transplantation (LTx) model, we investigated correlations of AR with the SUV and PET-derived radiomics and further evaluated the performance of machine learning (ML)-based radiomics for monitoring AR.

Methods: LTx was performed on 4 groups of rats: isograft, allograft-cyclosporine (CsA), allograft-CsA, and allograft-CsA.

Genetic effect of ischemia-reperfusion injury upon primary graft dysfunction and chronic lung allograft dysfunction in lung transplantation: evidence based on transcriptome data.

The unclear mechanism that ischemia-reperfusion injury (IRI) contributes to the development of primary graft dysfunction (PGD) and chronic lung allograft dysfunction (CLAD) remains a major issue in lung transplantation. Differentially expressed PGD-related genes and CLAD-related genes during IRI (IRI-PGD common genes and IRI-CLAD common genes) were identified using GEO datasets (GSE127003, GSE8021, GSE9102) and GeneCards datasets. Enrichment analysis and four network analyses, namely, protein-protein interaction, microRNA (miRNA)-gene, transcription factor (TF)-gene, and drug-gene networks, were then performed.

Development and validation of an early predictive nomogram for delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage.

Background: Previous studies have shown that platelet is involved in the occurrence and progression of delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH), but the relationship between platelet and DCI is not completely clear. Here, we aimed to screen the early platelet parameters associated with DCI after aSAH and develop an early predictive nomogram for DCI after aSAH.

Methods: The study was carried out in the neurosurgery department of Affiliated Hospital of North Sichuan Medical College.

Risk factors for lymph node metastasis in T1 esophageal squamous cell carcinoma: A systematic review and meta-analysis.

Background: Lymph node metastasis (LNM) affects the application and outcomes of endoscopic resection in T1 esophageal squamous cell carcinoma (ESCC). However, reports of the risk factors for LNM have been controversial.

Aim: To evaluate risk factors for LNM in T1 ESCC.