Histogram analysis combined with morphological characteristics to discriminate adenocarcinoma in situ or minimally invasive adenocarcinoma from invasive adenocarcinoma appearing as pure ground-glass nodule
Author:
Publisher
Elsevier BV
Subject
Radiology Nuclear Medicine and imaging,General Medicine
Reference26 articles.
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1. Lung-PNet: An Automated Deep Learning Model for the Diagnosis of Invasive Adenocarcinoma in Pure Ground-Glass Nodules on Chest CT;American Journal of Roentgenology;2023-10-25
2. Artificial intelligence system-based histogram analysis of computed tomography features to predict tumor invasiveness of ground-glass nodules;Quantitative Imaging in Medicine and Surgery;2023-09
3. A nomogram for predicting invasiveness of lung adenocarcinoma manifesting as pure ground-glass nodules: incorporating subjective CT signs and histogram parameters based on artificial intelligence;Journal of Cancer Research and Clinical Oncology;2023-08-25
4. Reticulation Sign on Thin-Section CT: Utility for Predicting Invasiveness of Pure Ground-Glass Nodules;American Journal of Roentgenology;2023-07
5. Computed tomography-based radiomics machine learning models for prediction of histological invasiveness with sub-centimeter subsolid pulmonary nodules: a retrospective study;PeerJ;2023-01-10
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