Combined radiomic and visual assessment for improved detection of lung adenocarcinoma invasiveness on computed tomography scans: A multi-institutional study Journal Article

   

Authors:	Vaidya, P.; Bera, K.; Linden, P. A.; Gupta, A.; Rajiah, P. S.; Jones, D. R.; Bott, M.; Pass, H.; Gilkeson, R.; Jacono, F.; Hsieh, K. L. C.; Lan, G. Y.; Velcheti, V.; Madabhushi, A.
Article Title:	Combined radiomic and visual assessment for improved detection of lung adenocarcinoma invasiveness on computed tomography scans: A multi-institutional study
Abstract:	Objective: The timing and nature of surgical intervention for semisolid abnormalities are dependent upon distinguishing between adenocarcinoma-in-situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (INV). We sought to develop and evaluate a quantitative imaging method to determine invasiveness of small, ground-glass lesions on computed tomography (CT) chest scans. Methods: The study comprised 268 patients from 4 institutions with resected (<=3 cm) semisolid lesions with confirmed histopathological diagnosis of MIA/AIS or INV. A total of 248 radiomic texture features from within the tumor nodule (intratumoral) and adjacent to the nodule (peritumoral) were extracted from manually annotated lung nodules of chest CT scans. The datasets were randomly divided, with 40% of patients used for training and 60% used for testing the machine classifier (Training DTrain, N=106; Testing, DTest, N=162). Results: The top five radiomic stable features included four intratumoral (Laws and Haralick feature families) and one peritumoral feature within 3 to 6 mm of the nodule (CoLlAGe feature family), which successfully differentiated INV from MIA/AIS nodules with an AUC of 0.917 [0.867-0.967] on DTrain and 0.863 [0.79-0.931] on DTest. The radiomics model successfully differentiated INV from MIA cases (<1 cm AUC: 0.76 [0.53-0.98], 1-2 cm AUC: 0.92 [0.85-0.98], 2-3 cm AUC: 0.95 [0.88-1]). The final integrated model combining the classifier with the radiologists’ score gave the best AUC on DTest (AUC=0.909, p<0.001). Conclusions: Addition of advanced image analysis via radiomics to the routine visual assessment of CT scans help better differentiate adenocarcinoma subtypes and can aid in clinical decision making. Further prospective validation in this direction is warranted. Copyright © 2022 Vaidya, Bera, Linden, Gupta, Rajiah, Jones, Bott, Pass, Gilkeson, Jacono, Hsieh, Lan, Velcheti and Madabhushi.
Keywords:	major clinical study; cancer patient; cancer diagnosis; computer assisted tomography; image analysis; tumor volume; clinical assessment; cohort analysis; retrospective study; cancer model; lung adenocarcinoma; training; algorithm; multicenter study; cancer size; clinical decision making; image segmentation; principal component analysis; clinical outcome; minimally invasive adenocarcinoma; machine learning; human; article; radiomics; visual assessment; ct scan (ct); integrated model analysis; invasive adenocarcinoma (ia); minimally invasive adenocarcinoma (mia); radiologists interpretation; classifier construction; combined tumor area radiomics based model; radiomics model
Journal Title:	Frontiers in Oncology
Volume:	12
ISSN:	2234-943X
Publisher:	Frontiers Media S.A.
Date Published:	2022-05-01
Start Page:	902056
Language:	English
DOI:	10.3389/fonc.2022.902056
PROVIDER:	scopus
PMCID:	PMC9190758
PUBMED:	35707362
DOI/URL:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85132242778&doi=10.3389%2ffonc.2022.902056&partnerID=40&md5=42b06271c8f25172c05ce83b5c35214a
Notes:	Article -- Export Date: 1 August 2022 -- Source: Scopus

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