3 results match your criteria: "Q.L.). Electronic address: 202770@hospital.cqmu.edu.cn.[Affiliation]"
Early Lung Adenocarcinoma Manifesting as Irregular Subsolid Nodules: Clinical and CT Characteristics.
Acad Radiol
December 2024
Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China (P.-l.Z., T.-y.L., F.-j.L., Q.L.). Electronic address:
Rationale And Objectives: To explore the clinical and computed tomography (CT) characteristics of early-stage lung adenocarcinoma (LADC) that presents with an irregular shape.
Materials And Methods: The CT data of 575 patients with stage IA LADC and 295 with persistent inflammatory lesion (PIL) manifesting as subsolid nodules (SSNs) were analyzed retrospectively. Among these patients, we selected 233 patients with LADC and 140 patients with PIL, who showed irregular SSNs, hereinafter referred to as irregular LADC (I-LADC) and irregular PIL (I-PIL), respectively.
Clinical, Imaging, and Pathological-Molecular Characteristics Associated with Stage IA Invasive Lung Adenocarcinoma Recurrence After Sub-lobar Resection.
Acad Radiol
July 2024
Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China (X.F., Q.L.). Electronic address:
Rationale And Objectives: This study aimed to investigate the association of clinical, imaging, and pathological-molecular characteristics with the prediction of patient prognosis with stage IA invasive lung adenocarcinoma (ILADC) after sub-lobar resection.
Materials And Methods: This study assessed 360 patients, including 91 and 269 with and without recurrence 3 years postoperatively, respectively, with stage IA ILADC undergoing preoperative chest computed tomography (CT) scans and subsequent sub-lobar resection at our institution. Their clinical and CT features and histological subtypes and gene mutation status were compared.
Value of CT-Based Deep Learning Model in Differentiating Benign and Malignant Solid Pulmonary Nodules ≤ 8 mm.
Acad Radiol
December 2024
Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, No.1 Youyi Road, Yuzhong District, Chongqing, China (F.J.L., X.Q.H., Q.L.). Electronic address:
Article Synopsis
- - The study focused on the ability of deep learning models using CT scans to tell apart benign and malignant small pulmonary nodules (SPNs) that are 8 mm or smaller.
- - Researchers developed and tested multiple models, finding that Model 4 was the most effective in distinguishing between benign and malignant SPNs, outperforming traditional algorithms.
- - Ultimately, the study suggests that these CT-based deep learning models are reliable tools for identifying the nature of small pulmonary nodules, which can aid in better diagnosis and treatment.