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Combined Radiomic and Visual Assessment for Improved Detection of Lung Adenocarcinoma Invasiveness on Computed Tomography Scans: A Multi-Institutional Study

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...

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Autores principales: Vaidya, Pranjal, Bera, Kaustav, Linden, Philip A., Gupta, Amit, Rajiah, Prabhakar Shantha, Jones, David R., Bott, Matthew, Pass, Harvey, Gilkeson, Robert, Jacono, Frank, Hsieh, Kevin Li-Chun, Lan, Gong-Yau, Velcheti, Vamsidhar, Madabhushi, Anant
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9190758/
https://www.ncbi.nlm.nih.gov/pubmed/35707362
http://dx.doi.org/10.3389/fonc.2022.902056
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author Vaidya, Pranjal
Bera, Kaustav
Linden, Philip A.
Gupta, Amit
Rajiah, Prabhakar Shantha
Jones, David R.
Bott, Matthew
Pass, Harvey
Gilkeson, Robert
Jacono, Frank
Hsieh, Kevin Li-Chun
Lan, Gong-Yau
Velcheti, Vamsidhar
Madabhushi, Anant
author_facet Vaidya, Pranjal
Bera, Kaustav
Linden, Philip A.
Gupta, Amit
Rajiah, Prabhakar Shantha
Jones, David R.
Bott, Matthew
Pass, Harvey
Gilkeson, Robert
Jacono, Frank
Hsieh, Kevin Li-Chun
Lan, Gong-Yau
Velcheti, Vamsidhar
Madabhushi, Anant
author_sort Vaidya, Pranjal
collection PubMed
description 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 D(Train), N=106; Testing, D(Test,) 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 D(Train) and 0.863 [0.79-0.931] on D(Test). 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 D(Test) (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.
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spelling pubmed-91907582022-06-14 Combined Radiomic and Visual Assessment for Improved Detection of Lung Adenocarcinoma Invasiveness on Computed Tomography Scans: A Multi-Institutional Study Vaidya, Pranjal Bera, Kaustav Linden, Philip A. Gupta, Amit Rajiah, Prabhakar Shantha Jones, David R. Bott, Matthew Pass, Harvey Gilkeson, Robert Jacono, Frank Hsieh, Kevin Li-Chun Lan, Gong-Yau Velcheti, Vamsidhar Madabhushi, Anant Front Oncol Oncology 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 D(Train), N=106; Testing, D(Test,) 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 D(Train) and 0.863 [0.79-0.931] on D(Test). 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 D(Test) (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. Frontiers Media S.A. 2022-05-30 /pmc/articles/PMC9190758/ /pubmed/35707362 http://dx.doi.org/10.3389/fonc.2022.902056 Text en Copyright © 2022 Vaidya, Bera, Linden, Gupta, Rajiah, Jones, Bott, Pass, Gilkeson, Jacono, Hsieh, Lan, Velcheti and Madabhushi https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Vaidya, Pranjal
Bera, Kaustav
Linden, Philip A.
Gupta, Amit
Rajiah, Prabhakar Shantha
Jones, David R.
Bott, Matthew
Pass, Harvey
Gilkeson, Robert
Jacono, Frank
Hsieh, Kevin Li-Chun
Lan, Gong-Yau
Velcheti, Vamsidhar
Madabhushi, Anant
Combined Radiomic and Visual Assessment for Improved Detection of Lung Adenocarcinoma Invasiveness on Computed Tomography Scans: A Multi-Institutional Study
title Combined Radiomic and Visual Assessment for Improved Detection of Lung Adenocarcinoma Invasiveness on Computed Tomography Scans: A Multi-Institutional Study
title_full Combined Radiomic and Visual Assessment for Improved Detection of Lung Adenocarcinoma Invasiveness on Computed Tomography Scans: A Multi-Institutional Study
title_fullStr Combined Radiomic and Visual Assessment for Improved Detection of Lung Adenocarcinoma Invasiveness on Computed Tomography Scans: A Multi-Institutional Study
title_full_unstemmed Combined Radiomic and Visual Assessment for Improved Detection of Lung Adenocarcinoma Invasiveness on Computed Tomography Scans: A Multi-Institutional Study
title_short Combined Radiomic and Visual Assessment for Improved Detection of Lung Adenocarcinoma Invasiveness on Computed Tomography Scans: A Multi-Institutional Study
title_sort combined radiomic and visual assessment for improved detection of lung adenocarcinoma invasiveness on computed tomography scans: a multi-institutional study
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9190758/
https://www.ncbi.nlm.nih.gov/pubmed/35707362
http://dx.doi.org/10.3389/fonc.2022.902056
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