Radiomics Analysis for the Identification of Invasive Pulmonary Subsolid Nodules From Longitudinal Presurgical CT Scans.

Autor:	Singh A; Departments of Radiology.; Bioengineering., Roshkovan L; Departments of Radiology., Horng H; Bioengineering., Chen A; Departments of Radiology.; Department of Radiology, Columbia University, New York, NY., Katz SI; Departments of Radiology., Thompson JC; Department of Medicine, Pulmonary, Allergy and Critical Care Medicine, Thoracic Oncology Group, University of Pennsylvania, Philadelphia, PA., Kontos D; Departments of Radiology.; Department of Radiology, Columbia University, New York, NY.
Jazyk:	angličtina
Zdroj:	Journal of thoracic imaging [J Thorac Imaging] 2025 Jan 01; Vol. 40 (1). Date of Electronic Publication: 2025 Jan 01.
DOI:	10.1097/RTI.0000000000000800
Abstrakt:	Purpose: Effective identification of malignant part-solid lung nodules is crucial to eliminate risks due to therapeutic intervention or lack thereof. We aimed to develop delta radiomics and volumetric signatures, characterize changes in nodule properties over three presurgical time points, and assess the accuracy of nodule invasiveness identification when combined with immediate presurgical time point radiomics signature and clinical biomarkers. Materials and Methods: Cohort included 156 part-solid lung nodules with immediate presurgical CT scans and a subset of 122 nodules with scans at 3 presurgical time points. Region of interest segmentation was performed using ITK-SNAP, and feature extraction using CaPTk. Image parameter heterogeneity was mitigated at each time point using nested ComBat harmonization. For 122 nodules, delta radiomics features (ΔR AB = (R B -R A )/R A ) and delta volumes (ΔV AB = (V B -V A )/V A ) were computed between the time points. Principal Component Analysis was performed to construct immediate presurgical radiomics (Rs 1 ) and delta radiomics signatures (ΔRs 31 + ΔRs 21 + ΔRs 32 ). Identification of nodule pathology was performed using logistic regression on delta radiomics and immediate presurgical time point signatures, delta volumes (ΔV 31 + ΔV 21 + ΔV 32 ), and clinical variable (smoking status, BMI) models (train test split (2:1)). Results: In delta radiomics analysis (n= 122 nodules), the best-performing model combined immediate pre-surgical time point and delta radiomics signatures, delta volumes, and clinical factors (classification accuracy [AUC]): (77.5% [0.73]) (train); (71.6% [0.69]) (test). Conclusions: Delta radiomics and volumes can detect changes in nodule properties over time, which are predictive of nodule invasiveness. These tools could improve conventional radiologic assessment, allow for earlier intervention for aggressive nodules, and decrease unnecessary intervention-related morbidity. Competing Interests: The authors declare no conflicts of interest. (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)
Databáze:	MEDLINE
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