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A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans

SIMPLE SUMMARY: The great majority of pulmonary nodules on screening CT scans are benign (95%). Due to inaccurate diagnoses of granulomas from adenocarcinomas on CT scans, many patients with benign nodules are subjected to unnecessary surgical procedures. The aim of this retrospective study is to ev...

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Autores principales: Alilou, Mehdi, Prasanna, Prateek, Bera, Kaustav, Gupta, Amit, Rajiah, Prabhakar, Yang, Michael, Jacono, Frank, Velcheti, Vamsidhar, Gilkeson, Robert, Linden, Philip, Madabhushi, Anant
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8199879/
https://www.ncbi.nlm.nih.gov/pubmed/34205005
http://dx.doi.org/10.3390/cancers13112781
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author Alilou, Mehdi
Prasanna, Prateek
Bera, Kaustav
Gupta, Amit
Rajiah, Prabhakar
Yang, Michael
Jacono, Frank
Velcheti, Vamsidhar
Gilkeson, Robert
Linden, Philip
Madabhushi, Anant
author_facet Alilou, Mehdi
Prasanna, Prateek
Bera, Kaustav
Gupta, Amit
Rajiah, Prabhakar
Yang, Michael
Jacono, Frank
Velcheti, Vamsidhar
Gilkeson, Robert
Linden, Philip
Madabhushi, Anant
author_sort Alilou, Mehdi
collection PubMed
description SIMPLE SUMMARY: The great majority of pulmonary nodules on screening CT scans are benign (95%). Due to inaccurate diagnoses of granulomas from adenocarcinomas on CT scans, many patients with benign nodules are subjected to unnecessary surgical procedures. The aim of this retrospective study is to evaluate the discriminability of a new radiomic feature, nodule edge/interface sharpness (NIS), for distinguishing lung adenocarcinomas from benign granulomas on non-contrast CT scans. Moreover, we aim to evaluate whether NIS can improve the performance of Lung-RADS, by reclassifying benign nodules that were initially assessed as suspicious. In a cohort of 352 patients with diagnostic non-contrast CT scans, NIS radiomics was able to classify nodules with an area under the receiver operating characteristic curve (ROC AUC) of 0.77, and when combined with intra-tumoral textural and shape features, classification performance increased to AUC of 0.84. Additionally, the NIS classifier correctly reclassified 46% of those lesions that were actually benign but deemed suspicious by Lung-RADS. Combining NIS with Lung-RADS has the potential to alter patient management by significantly decreasing unnecessary biopsies/follow up imaging. ABSTRACT: The aim of this study is to evaluate whether NIS radiomics can distinguish lung adenocarcinomas from granulomas on non-contrast CT scans, and also to improve the performance of Lung-RADS by reclassifying benign nodules that were initially assessed as suspicious. The screening or standard diagnostic non-contrast CT scans of 362 patients was divided into training (S(t), N = 145), validation (S(v), N = 145), and independent validation (S(iv), N = 62) sets from different institutions. Nodules were identified and manually segmented on CT images by a radiologist. A series of 264 features relating to the edge sharpness transition from the inside to the outside of the nodule were extracted. The top 10 features were used to train a linear discriminant analysis (LDA) machine learning classifier on St. In conjunction with the LDA classifier, NIS radiomics classified nodules with an AUC of 0.82 ± 0.04, 0.77, and 0.71 respectively on S(t), S(v), and S(iv). We evaluated the ability of the NIS classifier to determine the proportion of the patients in S(v) that were identified initially as suspicious by Lung-RADS but were reclassified as benign by applying the NIS scores. The NIS classifier was able to correctly reclassify 46% of those lesions that were actually benign but deemed suspicious by Lung-RADS alone on S(v).
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spelling pubmed-81998792021-06-14 A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans Alilou, Mehdi Prasanna, Prateek Bera, Kaustav Gupta, Amit Rajiah, Prabhakar Yang, Michael Jacono, Frank Velcheti, Vamsidhar Gilkeson, Robert Linden, Philip Madabhushi, Anant Cancers (Basel) Article SIMPLE SUMMARY: The great majority of pulmonary nodules on screening CT scans are benign (95%). Due to inaccurate diagnoses of granulomas from adenocarcinomas on CT scans, many patients with benign nodules are subjected to unnecessary surgical procedures. The aim of this retrospective study is to evaluate the discriminability of a new radiomic feature, nodule edge/interface sharpness (NIS), for distinguishing lung adenocarcinomas from benign granulomas on non-contrast CT scans. Moreover, we aim to evaluate whether NIS can improve the performance of Lung-RADS, by reclassifying benign nodules that were initially assessed as suspicious. In a cohort of 352 patients with diagnostic non-contrast CT scans, NIS radiomics was able to classify nodules with an area under the receiver operating characteristic curve (ROC AUC) of 0.77, and when combined with intra-tumoral textural and shape features, classification performance increased to AUC of 0.84. Additionally, the NIS classifier correctly reclassified 46% of those lesions that were actually benign but deemed suspicious by Lung-RADS. Combining NIS with Lung-RADS has the potential to alter patient management by significantly decreasing unnecessary biopsies/follow up imaging. ABSTRACT: The aim of this study is to evaluate whether NIS radiomics can distinguish lung adenocarcinomas from granulomas on non-contrast CT scans, and also to improve the performance of Lung-RADS by reclassifying benign nodules that were initially assessed as suspicious. The screening or standard diagnostic non-contrast CT scans of 362 patients was divided into training (S(t), N = 145), validation (S(v), N = 145), and independent validation (S(iv), N = 62) sets from different institutions. Nodules were identified and manually segmented on CT images by a radiologist. A series of 264 features relating to the edge sharpness transition from the inside to the outside of the nodule were extracted. The top 10 features were used to train a linear discriminant analysis (LDA) machine learning classifier on St. In conjunction with the LDA classifier, NIS radiomics classified nodules with an AUC of 0.82 ± 0.04, 0.77, and 0.71 respectively on S(t), S(v), and S(iv). We evaluated the ability of the NIS classifier to determine the proportion of the patients in S(v) that were identified initially as suspicious by Lung-RADS but were reclassified as benign by applying the NIS scores. The NIS classifier was able to correctly reclassify 46% of those lesions that were actually benign but deemed suspicious by Lung-RADS alone on S(v). MDPI 2021-06-03 /pmc/articles/PMC8199879/ /pubmed/34205005 http://dx.doi.org/10.3390/cancers13112781 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Alilou, Mehdi
Prasanna, Prateek
Bera, Kaustav
Gupta, Amit
Rajiah, Prabhakar
Yang, Michael
Jacono, Frank
Velcheti, Vamsidhar
Gilkeson, Robert
Linden, Philip
Madabhushi, Anant
A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans
title A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans
title_full A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans
title_fullStr A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans
title_full_unstemmed A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans
title_short A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans
title_sort novel nodule edge sharpness radiomic biomarker improves performance of lung-rads for distinguishing adenocarcinomas from granulomas on non-contrast ct scans
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8199879/
https://www.ncbi.nlm.nih.gov/pubmed/34205005
http://dx.doi.org/10.3390/cancers13112781
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