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Predictive Efficacy of a Radiomics Random Forest Model for Identifying Pathological Subtypes of Lung Adenocarcinoma Presenting as Ground-Glass Nodules

PURPOSE: To establish and verify the ability of a radiomics prediction model to distinguish invasive adenocarcinoma (IAC) and minimal invasive adenocarcinoma (MIA) presenting as ground-glass nodules (GGNs). METHODS: We retrospectively analyzed 118 lung GGN images and clinical data from 106 patients...

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Autores principales: Zhao, Fen-hua, Fan, Hong-jie, Shan, Kang-fei, Zhou, Long, Pang, Zhen-zhu, Fu, Chun-long, Yang, Ze-bin, Wu, Mei-kang, Sun, Ji-hong, Yang, Xiao-ming, Huang, Zhao-hui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9133455/
https://www.ncbi.nlm.nih.gov/pubmed/35646675
http://dx.doi.org/10.3389/fonc.2022.872503
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author Zhao, Fen-hua
Fan, Hong-jie
Shan, Kang-fei
Zhou, Long
Pang, Zhen-zhu
Fu, Chun-long
Yang, Ze-bin
Wu, Mei-kang
Sun, Ji-hong
Yang, Xiao-ming
Huang, Zhao-hui
author_facet Zhao, Fen-hua
Fan, Hong-jie
Shan, Kang-fei
Zhou, Long
Pang, Zhen-zhu
Fu, Chun-long
Yang, Ze-bin
Wu, Mei-kang
Sun, Ji-hong
Yang, Xiao-ming
Huang, Zhao-hui
author_sort Zhao, Fen-hua
collection PubMed
description PURPOSE: To establish and verify the ability of a radiomics prediction model to distinguish invasive adenocarcinoma (IAC) and minimal invasive adenocarcinoma (MIA) presenting as ground-glass nodules (GGNs). METHODS: We retrospectively analyzed 118 lung GGN images and clinical data from 106 patients in our hospital from March 2016 to April 2019. All pathological classifications of lung GGN were confirmed as IAC or MIA by two pathologists. R language software (version 3.5.1) was used for the statistical analysis of the general clinical data. ITK-SNAP (version 3.6) and A.K. software (Analysis Kit, American GE Company) were used to manually outline the regions of interest of lung GGNs and collect three-dimensional radiomics features. Patients were randomly divided into training and verification groups (ratio, 7:3). Random forest combined with hyperparameter tuning was used for feature selection and prediction modeling. The receiver operating characteristic curve and the area under the curve (AUC) were used to evaluate model prediction efficacy. The calibration curve was used to evaluate the calibration effect. RESULTS: There was no significant difference between IAC and MIA in terms of age, gender, smoking history, tumor history, and lung GGN location in both the training and verification groups (P>0.05). For each lung GGN, the collected data included 396 three-dimensional radiomics features in six categories. Based on the training cohort, nine optimal radiomics features in three categories were finally screened out, and a prediction model was established. We found that the training group had a high diagnostic efficacy [accuracy, sensitivity, specificity, and AUC of the training group were 0.89 (95%CI, 0.73 - 0.99), 0.98 (95%CI, 0.78 - 1.00), 0.81 (95%CI, 0.59 - 1.00), and 0.97 (95%CI, 0.92-1.00), respectively; those of the validation group were 0.80 (95%CI, 0.58 - 0.93), 0.82 (95%CI, 0.55 - 1.00), 0.78 (95%CI, 0.57 - 1.00), and 0.92 (95%CI, 0.83 - 1.00), respectively]. The model calibration curve showed good consistency between the predicted and actual probabilities. CONCLUSIONS: The radiomics prediction model established by combining random forest with hyperparameter tuning effectively distinguished IAC from MIA presenting as GGNs and represents a noninvasive, low-cost, rapid, and reproducible preoperative prediction method for clinical application.
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spelling pubmed-91334552022-05-27 Predictive Efficacy of a Radiomics Random Forest Model for Identifying Pathological Subtypes of Lung Adenocarcinoma Presenting as Ground-Glass Nodules Zhao, Fen-hua Fan, Hong-jie Shan, Kang-fei Zhou, Long Pang, Zhen-zhu Fu, Chun-long Yang, Ze-bin Wu, Mei-kang Sun, Ji-hong Yang, Xiao-ming Huang, Zhao-hui Front Oncol Oncology PURPOSE: To establish and verify the ability of a radiomics prediction model to distinguish invasive adenocarcinoma (IAC) and minimal invasive adenocarcinoma (MIA) presenting as ground-glass nodules (GGNs). METHODS: We retrospectively analyzed 118 lung GGN images and clinical data from 106 patients in our hospital from March 2016 to April 2019. All pathological classifications of lung GGN were confirmed as IAC or MIA by two pathologists. R language software (version 3.5.1) was used for the statistical analysis of the general clinical data. ITK-SNAP (version 3.6) and A.K. software (Analysis Kit, American GE Company) were used to manually outline the regions of interest of lung GGNs and collect three-dimensional radiomics features. Patients were randomly divided into training and verification groups (ratio, 7:3). Random forest combined with hyperparameter tuning was used for feature selection and prediction modeling. The receiver operating characteristic curve and the area under the curve (AUC) were used to evaluate model prediction efficacy. The calibration curve was used to evaluate the calibration effect. RESULTS: There was no significant difference between IAC and MIA in terms of age, gender, smoking history, tumor history, and lung GGN location in both the training and verification groups (P>0.05). For each lung GGN, the collected data included 396 three-dimensional radiomics features in six categories. Based on the training cohort, nine optimal radiomics features in three categories were finally screened out, and a prediction model was established. We found that the training group had a high diagnostic efficacy [accuracy, sensitivity, specificity, and AUC of the training group were 0.89 (95%CI, 0.73 - 0.99), 0.98 (95%CI, 0.78 - 1.00), 0.81 (95%CI, 0.59 - 1.00), and 0.97 (95%CI, 0.92-1.00), respectively; those of the validation group were 0.80 (95%CI, 0.58 - 0.93), 0.82 (95%CI, 0.55 - 1.00), 0.78 (95%CI, 0.57 - 1.00), and 0.92 (95%CI, 0.83 - 1.00), respectively]. The model calibration curve showed good consistency between the predicted and actual probabilities. CONCLUSIONS: The radiomics prediction model established by combining random forest with hyperparameter tuning effectively distinguished IAC from MIA presenting as GGNs and represents a noninvasive, low-cost, rapid, and reproducible preoperative prediction method for clinical application. Frontiers Media S.A. 2022-05-12 /pmc/articles/PMC9133455/ /pubmed/35646675 http://dx.doi.org/10.3389/fonc.2022.872503 Text en Copyright © 2022 Zhao, Fan, Shan, Zhou, Pang, Fu, Yang, Wu, Sun, Yang and Huang 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
Zhao, Fen-hua
Fan, Hong-jie
Shan, Kang-fei
Zhou, Long
Pang, Zhen-zhu
Fu, Chun-long
Yang, Ze-bin
Wu, Mei-kang
Sun, Ji-hong
Yang, Xiao-ming
Huang, Zhao-hui
Predictive Efficacy of a Radiomics Random Forest Model for Identifying Pathological Subtypes of Lung Adenocarcinoma Presenting as Ground-Glass Nodules
title Predictive Efficacy of a Radiomics Random Forest Model for Identifying Pathological Subtypes of Lung Adenocarcinoma Presenting as Ground-Glass Nodules
title_full Predictive Efficacy of a Radiomics Random Forest Model for Identifying Pathological Subtypes of Lung Adenocarcinoma Presenting as Ground-Glass Nodules
title_fullStr Predictive Efficacy of a Radiomics Random Forest Model for Identifying Pathological Subtypes of Lung Adenocarcinoma Presenting as Ground-Glass Nodules
title_full_unstemmed Predictive Efficacy of a Radiomics Random Forest Model for Identifying Pathological Subtypes of Lung Adenocarcinoma Presenting as Ground-Glass Nodules
title_short Predictive Efficacy of a Radiomics Random Forest Model for Identifying Pathological Subtypes of Lung Adenocarcinoma Presenting as Ground-Glass Nodules
title_sort predictive efficacy of a radiomics random forest model for identifying pathological subtypes of lung adenocarcinoma presenting as ground-glass nodules
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9133455/
https://www.ncbi.nlm.nih.gov/pubmed/35646675
http://dx.doi.org/10.3389/fonc.2022.872503
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