Exploration of optimal thresholds for predicting the invasive nature of stage T1 lung adenocarcinoma using artificial intelligence-based 3D solid component volume segmentation.

Background: The accuracy of intraoperative rapid frozen pathology is suboptimal, and the assessment of invasiveness in malignant pulmonary nodules significantly influences surgical resection strategies. Predicting the invasiveness of lung adenocarcinoma based on preoperative imaging is a clinical challenge, and there are no established standards for the optimal threshold value using the threshold segmentation method to predict the invasiveness of stage T1 lung adenocarcinoma. This study aimed to explore the efficacy of three-dimensional solid component volume (3D SCV) [calculated by artificial intelligence (AI) threshold segmentation method] in predicting the aggressiveness of T1 lung adenocarcinoma and to determine its optimal threshold and cut-off point.

Development of a clinical prediction model for benign and malignant pulmonary nodules with a CTR ≥ 50% utilizing artificial intelligence-driven radiomics analysis.

Objective: In clinical practice, diagnosing the benignity and malignancy of solid-component-predominant pulmonary nodules is challenging, especially when 3D consolidation-to-tumor ratio (CTR) ≥ 50%, as malignant ones are more invasive. This study aims to develop and validate an AI-driven radiomics prediction model for such nodules to enhance diagnostic accuracy.

Methods: Data of 2,591 pulmonary nodules from five medical centers (Zhengzhou People's Hospital, etc.

Exploring the optimal threshold of 3D consolidation tumor ratio value segmentation based on artificial intelligence for predicting the invasive degree of T1 lung adenocarcinoma.

Background: The assessment of lung adenocarcinoma significantly depends on the proportion of solid components in lung nodules. Traditional one-dimensional consolidation tumor ratio (1D CTR) based on ideal, uniformly dense solid components lacks precision. There is no consensus on the CT threshold for evaluating invasiveness using the threshold segmentation method.

Quantitative analysis of imaging characteristics in lung adenocarcinoma in situ using artificial intelligence.

Background: With the rising incidence of pulmonary nodules (PNs), lung adenocarcinoma in situ (AIS) is a critical early stage of lung cancer, necessitating accurate diagnosis for early intervention. This study applies artificial intelligence (AI) for quantitative imaging analysis to differentiate AIS from atypical adenomatous hyperplasia (AAH) and minimally invasive adenocarcinoma (MIA), aiming to enhance clinical diagnosis and prevent misdiagnosis.

Methods: The study analyzed 1215 PNs with confirmed AAH, AIS, and MIA from six centers using the Shukun AI diagnostic module.

Experience of a modified chest tube suture-fixation technique in uniportal thoracoscopic pulmonary resection.

Objective: This study aimed to explore the feasibility and advantages of a modified chest tube suture-fixation technique in uniportal video-assisted thoracic surgery for pulmonary resection.

Methods: A retrospective analysis was conducted on 116 patients who underwent uniportal video-assisted thoracic surgery (U-VATS) for lung diseases in Zhengzhou People's Hospital between October 2019 and October 2021. Patients were stratified into two groups based on the applied suture-fixation methods, i.

Paraganglioma of the anterior superior mediastinum: Presentation of a case of mistaken diagnosis so long and review of the literature.

Introduction And Importance: Mediastinal paraganglioma is a rare tumor with neuroendocrine activity, highly susceptible to misdiagnosis. Herein, we present a case misdiagnosed as Castleman disease for nearly a decade, significantly enlarged, lost the opportunity of thoracoscopic surgery, and was removed by median thoracotomy.

Case Presentation: A 59-year-old woman complained of having a mediastinal mass, was diagnosed with Castleman disease for nearly a decade and was admitted to the hospital due to neck tightness.

Application of bilateral simultaneous sequential single-incision video-assisted thoracic surgery in multiple nodules both lungs: a single-center experience of 10 cases.

Objective: To discuss the application of bilateral simultaneous sequential single-incision video-assisted thoracic surgery in multiple nodules in both lungs.

Methods: A retrospective analysis of 10 patients in Zhengzhou People's Hospital who underwent single-incision thoracoscopic surgery to treat multiple nodules in both lungs at the same time from September 2019 to January 2021, and analyze the perioperative indicators (general condition, smoking history, family history, follow-up time of pulmonary nodules, size, location, height and weight, pulmonary function, intraoperative blood loss, operation time, color and volume of drainage fluid, catheterization time, perioperative complications, length of stay, pathology, patient satisfaction, etc.).

Peripheral blood oxidative stress markers for obstructive sleep apnea-a meta-analysis.

Objectives: The purpose of this study was to clarify the relationship between obstructive sleep apnea (OSA) and oxidative stress markers in blood.

Methods: We conducted a systematic literature search on databases including Pubmed and Embase for studies reporting circulating oxidative stress markers in patients with OSA and controls that were published between 1988 and June 2019. Standardized mean differences (SMDs) and 95% confidence intervals (95%CI) were calculated.

A meta-analysis on the relationship of exosomes and the prognosis of lung cancer.

Background: A lot of research evidence shows that exosomes play an indelible role in the prognosis of lung cancer, but there are many disputes. Therefore, we conduct a meta-analysis to further demonstrate.

Methods: A literature retrieval was performed through a search of PubMed, Embase, Web of Science, Cochrane, CKNI, Wanfang, and other databases to locate documents from the literature that satisfied the inclusion criteria.