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http://dx.doi.org/10.21037/jtd.2019.04.95DOI Listing

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Deep learning-based solid component measuring enabled interpretable prediction of tumor invasiveness for lung adenocarcinoma.

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Taizhou Hospital, Zhejiang University School of Medicine, Taizhou, China; Department of Thoracic Surgery, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, China. Electronic address:

Background: The nature of the solid component of subsolid nodules (SSNs) can indicate tumor pathological invasiveness. However, preoperative solid component assessment still lacks a reference standard.

Methods: In this retrospective study, an AI algorithm was proposed for measuring the solid components ratio in SSNs, which was used to assess the diameter ratio (1D), area ratio (2D), and volume ratio (3D).

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Purpose: We compared three-dimensional (3D) and two-dimensional (2D) measurements of the solid component to determine radiological criteria for sublobar resection of lung adenocarcinoma ≤ 2 cm in size.

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