Purpose: We aim to investigate the localization, visibility, and measurement of lung nodules in digital chest tomosynthesis (DTS).
Approach: Computed tomography (CT), maximum intensity projections (CT-MIP) (transaxial versus coronal orientation), and computer-aided detection (CAD) were used as location reference, and inter- and intra-observer agreement regarding lung nodule size was assessed. Five radiologists analyzed DTS and CT images from 24 participants with lung , focusing on lung nodule localization, visibility, and measurement on DTS. Visual grading was used to compare if coronal or transaxial CT-MIP better facilitated the localization of lung nodules in DTS.
Results: The majority of the lung nodules (79%) were rated as visible in DTS, although less clearly in comparison with CT. Coronal CT-MIP was the preferred orientation in the task of locating nodules on DTS. On DTS, area-based lung nodule size estimates resulted in significantly less measurement variability when compared with nodule size estimated based on mean diameter (mD) ( ). Also, on DTS, area-based lung nodule size estimates were more accurate ( ) than lung nodule size estimates based on mean diameter ( ).
Conclusions: Coronal CT-MIP images are superior to transaxial CT-MIP images in facilitating lung nodule localization in DTS. Most found on CT can be visualized, correctly localized, and measured in DTS, and area-based measurement may be the key to more precise and less variable nodule measurements on DTS.
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http://dx.doi.org/10.1117/1.JMI.12.S1.S13007 | DOI Listing |
J Cardiothorac Surg
December 2024
Department of Radiology, Sakai City Medical Center Hospital, Ebaraji-Cho, Nishi-Ku, Sakai-Shi, Osaka, 593-8304, Japan.
Background: The detection of tumor localization is difficult in robotic surgery because surgeons have no sense of touch and rely on visual information. This study aimed to evaluate the efficacy of preoperative CT-guided dye marking of lung nodules prior to robotic surgery.
Methods: Patients undergoing CT-guided dye marking prior to robotic surgery at our hospital between September 2019 and April 2024 were retrospectively analyzed.
Comput Biol Med
December 2024
Diagnostic Imaging Analysis Group, Medical Imaging Department, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands.
Artificial Intelligence (AI) models may fail or suffer from reduced performance when applied to unseen data that differs from the training data distribution, referred to as dataset shift. Automatic detection of out-of-distribution (OOD) data contributes to safe and reliable clinical implementation of AI models. In this study, we propose a recognized OOD detection method that utilizes the Mahalanobis distance (MD) and compare its performance to widely known classical methods.
View Article and Find Full Text PDFFront Cell Infect Microbiol
December 2024
Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
Objective: We aimed to identify the diagnostic value of next-generation sequencing (NGS) of bronchoalveolar lavage fluid (BALF) from patients with non-small-cell lung cancer (NSCLC).
Methods: Forty patients who were initially diagnosed with pulmonary nodules were enrolled. Frozen section histology was used to identify the NSCLC cell types.
Sci Rep
December 2024
Department of Radiology, Stanford University, Lucile Packard Children's Hospital, 725 Welch Road, Palo Alto, CA, 94304, USA.
The purpose of this study was to evaluate whether the optimal operating points of adult-oriented artificial intelligence (AI) software differ for pediatric chest radiographs and to assess its diagnostic performance. Chest radiographs from patients under 19 years old, collected between March and November 2021, were divided into test and exploring sets. A commercial adult-oriented AI software was utilized to detect lung lesions, including pneumothorax, consolidation, nodule, and pleural effusion, using a standard operating point of 15%.
View Article and Find Full Text PDFAcad 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.
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