Development and Validation of a Radiomics Nomogram for Differentiating Pulmonary Cryptococcosis and Lung Adenocarcinoma in Solitary Pulmonary Solid Nodule

ObjectiveTo establish a CT-based radiomics nomogram model for classifying pulmonary cryptococcosis (PC) and lung adenocarcinoma (LAC) in patients with a solitary pulmonary solid nodule (SPSN) and assess its differentiation ability.Materials and MethodsA total of 213 patients with PC and 213 cases of...

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Autores principales:	Jiabi Zhao, Lin Sun, Ke Sun, Tingting Wang, Bin Wang, Yang Yang, Chunyan Wu, Xiwen Sun
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	radiomics solitary pulmonary solid nodule differentiate lung adenocarcinoma pulmonary cryptococcosis Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/f8183c181e3d4e92928b7249ca959135
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spelling	oai:doaj.org-article:f8183c181e3d4e92928b7249ca9591352021-11-09T05:56:17ZDevelopment and Validation of a Radiomics Nomogram for Differentiating Pulmonary Cryptococcosis and Lung Adenocarcinoma in Solitary Pulmonary Solid Nodule2234-943X10.3389/fonc.2021.759840https://doaj.org/article/f8183c181e3d4e92928b7249ca9591352021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fonc.2021.759840/fullhttps://doaj.org/toc/2234-943XObjectiveTo establish a CT-based radiomics nomogram model for classifying pulmonary cryptococcosis (PC) and lung adenocarcinoma (LAC) in patients with a solitary pulmonary solid nodule (SPSN) and assess its differentiation ability.Materials and MethodsA total of 213 patients with PC and 213 cases of LAC (matched based on age and gender) were recruited into this retrospective research with their clinical characteristics and radiological features. High-dimensional radiomics features were acquired from each mask delineated by radiologists manually. We adopted the max-relevance and min-redundancy (mRMR) approach to filter the redundant features and retained the relevant features at first. Then, we used the least absolute shrinkage and operator (LASSO) algorithms as an analysis tool to calculate the coefficients of features and remove the low-weight features. After multivariable logistic regression analysis, a radiomics nomogram model was constructed with clinical characteristics, radiological signs, and radiomics score. We calculated the performance assessment parameters, such as sensitivity, specificity, accuracy, negative predictive value (NPV), and positive predictive value (PPV), in various models. The receiver operating characteristic (ROC) curve analysis and the decision curve analysis (DCA) were drawn to visualize the diagnostic ability and the clinical benefit.ResultsWe extracted 1,130 radiomics features from each CT image. The 24 most significant radiomics features in distinguishing PC and LAC were retained, and the radiomics signature was constructed through a three-step feature selection process. Three factors—maximum diameter, lobulation, and pleural retraction—were still statistically significant in multivariate analysis and incorporated into a combined model with radiomics signature to develop the predictive nomogram, which showed excellent classification ability. The area under curve (AUC) yielded 0.91 (sensitivity, 80%; specificity, 83%; accuracy, 82%; NPV, 80%; PPV, 83%) and 0.89 (sensitivity, 81%; specificity, 83%; accuracy, 82%; NPV, 81%; PPV, 82%) in training and test cohorts, respectively. The net reclassification indexes (NRIs) were greater than zero (p < 0.05). The Delong test showed a significant difference (p < 0.0001) between the AUCs from the clinical model and the nomogram.ConclusionsThe radiomics technology can preoperatively differentiate PC and lung adenocarcinoma. The nomogram-integrated CT findings and radiomics feature can provide more clinical benefits in solitary pulmonary solid nodule diagnosis.Jiabi ZhaoLin SunKe SunTingting WangBin WangYang YangChunyan WuXiwen SunFrontiers Media S.A.articleradiomicssolitary pulmonary solid noduledifferentiatelung adenocarcinomapulmonary cryptococcosisNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENFrontiers in Oncology, Vol 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	radiomics solitary pulmonary solid nodule differentiate lung adenocarcinoma pulmonary cryptococcosis Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	radiomics solitary pulmonary solid nodule differentiate lung adenocarcinoma pulmonary cryptococcosis Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Jiabi Zhao Lin Sun Ke Sun Tingting Wang Bin Wang Yang Yang Chunyan Wu Xiwen Sun Development and Validation of a Radiomics Nomogram for Differentiating Pulmonary Cryptococcosis and Lung Adenocarcinoma in Solitary Pulmonary Solid Nodule
description	ObjectiveTo establish a CT-based radiomics nomogram model for classifying pulmonary cryptococcosis (PC) and lung adenocarcinoma (LAC) in patients with a solitary pulmonary solid nodule (SPSN) and assess its differentiation ability.Materials and MethodsA total of 213 patients with PC and 213 cases of LAC (matched based on age and gender) were recruited into this retrospective research with their clinical characteristics and radiological features. High-dimensional radiomics features were acquired from each mask delineated by radiologists manually. We adopted the max-relevance and min-redundancy (mRMR) approach to filter the redundant features and retained the relevant features at first. Then, we used the least absolute shrinkage and operator (LASSO) algorithms as an analysis tool to calculate the coefficients of features and remove the low-weight features. After multivariable logistic regression analysis, a radiomics nomogram model was constructed with clinical characteristics, radiological signs, and radiomics score. We calculated the performance assessment parameters, such as sensitivity, specificity, accuracy, negative predictive value (NPV), and positive predictive value (PPV), in various models. The receiver operating characteristic (ROC) curve analysis and the decision curve analysis (DCA) were drawn to visualize the diagnostic ability and the clinical benefit.ResultsWe extracted 1,130 radiomics features from each CT image. The 24 most significant radiomics features in distinguishing PC and LAC were retained, and the radiomics signature was constructed through a three-step feature selection process. Three factors—maximum diameter, lobulation, and pleural retraction—were still statistically significant in multivariate analysis and incorporated into a combined model with radiomics signature to develop the predictive nomogram, which showed excellent classification ability. The area under curve (AUC) yielded 0.91 (sensitivity, 80%; specificity, 83%; accuracy, 82%; NPV, 80%; PPV, 83%) and 0.89 (sensitivity, 81%; specificity, 83%; accuracy, 82%; NPV, 81%; PPV, 82%) in training and test cohorts, respectively. The net reclassification indexes (NRIs) were greater than zero (p < 0.05). The Delong test showed a significant difference (p < 0.0001) between the AUCs from the clinical model and the nomogram.ConclusionsThe radiomics technology can preoperatively differentiate PC and lung adenocarcinoma. The nomogram-integrated CT findings and radiomics feature can provide more clinical benefits in solitary pulmonary solid nodule diagnosis.
format	article
author	Jiabi Zhao Lin Sun Ke Sun Tingting Wang Bin Wang Yang Yang Chunyan Wu Xiwen Sun
author_facet	Jiabi Zhao Lin Sun Ke Sun Tingting Wang Bin Wang Yang Yang Chunyan Wu Xiwen Sun
author_sort	Jiabi Zhao
title	Development and Validation of a Radiomics Nomogram for Differentiating Pulmonary Cryptococcosis and Lung Adenocarcinoma in Solitary Pulmonary Solid Nodule
title_short	Development and Validation of a Radiomics Nomogram for Differentiating Pulmonary Cryptococcosis and Lung Adenocarcinoma in Solitary Pulmonary Solid Nodule
title_full	Development and Validation of a Radiomics Nomogram for Differentiating Pulmonary Cryptococcosis and Lung Adenocarcinoma in Solitary Pulmonary Solid Nodule
title_fullStr	Development and Validation of a Radiomics Nomogram for Differentiating Pulmonary Cryptococcosis and Lung Adenocarcinoma in Solitary Pulmonary Solid Nodule
title_full_unstemmed	Development and Validation of a Radiomics Nomogram for Differentiating Pulmonary Cryptococcosis and Lung Adenocarcinoma in Solitary Pulmonary Solid Nodule
title_sort	development and validation of a radiomics nomogram for differentiating pulmonary cryptococcosis and lung adenocarcinoma in solitary pulmonary solid nodule
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/f8183c181e3d4e92928b7249ca959135
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Development and Validation of a Radiomics Nomogram for Differentiating Pulmonary Cryptococcosis and Lung Adenocarcinoma in Solitary Pulmonary Solid Nodule

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