Development and validation of a radiomic nomogram based on pretherapy dual-energy CT for distinguishing adenocarcinoma from squamous cell carcinoma of the lung
ObjectiveBased on pretherapy dual-energy computed tomography (DECT) images, we developed and validated a nomogram combined with clinical parameters and radiomic features to predict the pathologic subtypes of non-small cell lung cancer (NSCLC) — adenocarcinoma (ADC) and squamous cell carcinoma (SCC)....
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.949111/full |
| _version_ | 1811216305575952384 |
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| author | Zhiyong Chen Li Yi Zhiwei Peng Jianzhong Zhou Zhaotao Zhang Yahong Tao Ze Lin Anjing He Mengni Jin Minjing Zuo |
| author_facet | Zhiyong Chen Li Yi Zhiwei Peng Jianzhong Zhou Zhaotao Zhang Yahong Tao Ze Lin Anjing He Mengni Jin Minjing Zuo |
| author_sort | Zhiyong Chen |
| collection | DOAJ |
| description | ObjectiveBased on pretherapy dual-energy computed tomography (DECT) images, we developed and validated a nomogram combined with clinical parameters and radiomic features to predict the pathologic subtypes of non-small cell lung cancer (NSCLC) — adenocarcinoma (ADC) and squamous cell carcinoma (SCC).MethodsA total of 129 pathologically confirmed NSCLC patients treated at the Second Affiliated Hospital of Nanchang University from October 2017 to October 2021 were retrospectively analyzed. Patients were randomly divided in a ratio of 7:3 (n=90) into training and validation cohorts (n=39). Patients’ pretherapy clinical parameters were recorded. Radiomics features of the primary lesion were extracted from two sets of monoenergetic images (40 keV and 100 keV) in arterial phases (AP) and venous phases (VP). Features were selected successively through the intra-class correlation coefficient (ICC) and the least absolute shrinkage and selection operator (LASSO). Multivariate logistic regression analysis was then performed to establish predictive models. The prediction performance between models was evaluated and compared using the receiver operating characteristic (ROC) curve, DeLong test, and Akaike information criterion (AIC). A nomogram was developed based on the model with the best predictive performance to evaluate its calibration and clinical utility.ResultsA total of 87 ADC and 42 SCC patients were enrolled in this study. Among the five constructed models, the integrative model (AUC: Model 4 = 0.92, Model 5 = 0.93) combining clinical parameters and radiomic features had a higher AUC than the individual clinical models or radiomic models (AUC: Model 1 = 0.84, Model 2 = 0.79, Model 3 = 0.84). The combined clinical-venous phase radiomics model had the best predictive performance, goodness of fit, and parsimony; the area under the ROC curve (AUC) of the training and validation cohorts was 0.93 and 0.90, respectively, and the AIC value was 60.16. Then, this model was visualized as a nomogram. The calibration curves demonstrated it’s good calibration, and decision curve analysis (DCA) proved its clinical utility.ConclusionThe combined clinical-radiomics model based on pretherapy DECT showed good performance in distinguishing ADC and SCC of the lung. The nomogram constructed based on the best-performing combined clinical-venous phase radiomics model provides a relatively accurate, convenient and noninvasive method for predicting the pathological subtypes of ADC and SCC in NSCLC. |
| first_indexed | 2024-04-12T06:36:55Z |
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| id | doaj.art-63e050d80b6f42958c7170dfce6d7ebd |
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| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-12T06:36:55Z |
| publishDate | 2022-11-01 |
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| series | Frontiers in Oncology |
| spelling | doaj.art-63e050d80b6f42958c7170dfce6d7ebd2022-12-22T03:43:50ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-11-011210.3389/fonc.2022.949111949111Development and validation of a radiomic nomogram based on pretherapy dual-energy CT for distinguishing adenocarcinoma from squamous cell carcinoma of the lungZhiyong Chen0Li Yi1Zhiwei Peng2Jianzhong Zhou3Zhaotao Zhang4Yahong Tao5Ze Lin6Anjing He7Mengni Jin8Minjing Zuo9Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, ChinaDepartment of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, ChinaDepartment of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, ChinaDepartment of Radiology, The Quzhou City People’s Hospital, Quzhou, Zhejiang, ChinaDepartment of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, ChinaDepartment of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, ChinaDepartment of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, ChinaDepartment of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, ChinaDepartment of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, ChinaDepartment of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, ChinaObjectiveBased on pretherapy dual-energy computed tomography (DECT) images, we developed and validated a nomogram combined with clinical parameters and radiomic features to predict the pathologic subtypes of non-small cell lung cancer (NSCLC) — adenocarcinoma (ADC) and squamous cell carcinoma (SCC).MethodsA total of 129 pathologically confirmed NSCLC patients treated at the Second Affiliated Hospital of Nanchang University from October 2017 to October 2021 were retrospectively analyzed. Patients were randomly divided in a ratio of 7:3 (n=90) into training and validation cohorts (n=39). Patients’ pretherapy clinical parameters were recorded. Radiomics features of the primary lesion were extracted from two sets of monoenergetic images (40 keV and 100 keV) in arterial phases (AP) and venous phases (VP). Features were selected successively through the intra-class correlation coefficient (ICC) and the least absolute shrinkage and selection operator (LASSO). Multivariate logistic regression analysis was then performed to establish predictive models. The prediction performance between models was evaluated and compared using the receiver operating characteristic (ROC) curve, DeLong test, and Akaike information criterion (AIC). A nomogram was developed based on the model with the best predictive performance to evaluate its calibration and clinical utility.ResultsA total of 87 ADC and 42 SCC patients were enrolled in this study. Among the five constructed models, the integrative model (AUC: Model 4 = 0.92, Model 5 = 0.93) combining clinical parameters and radiomic features had a higher AUC than the individual clinical models or radiomic models (AUC: Model 1 = 0.84, Model 2 = 0.79, Model 3 = 0.84). The combined clinical-venous phase radiomics model had the best predictive performance, goodness of fit, and parsimony; the area under the ROC curve (AUC) of the training and validation cohorts was 0.93 and 0.90, respectively, and the AIC value was 60.16. Then, this model was visualized as a nomogram. The calibration curves demonstrated it’s good calibration, and decision curve analysis (DCA) proved its clinical utility.ConclusionThe combined clinical-radiomics model based on pretherapy DECT showed good performance in distinguishing ADC and SCC of the lung. The nomogram constructed based on the best-performing combined clinical-venous phase radiomics model provides a relatively accurate, convenient and noninvasive method for predicting the pathological subtypes of ADC and SCC in NSCLC.https://www.frontiersin.org/articles/10.3389/fonc.2022.949111/fulldual-energy CTdual-energy CT quantitative parametersradiomicslung adenocarcinomalung squamous cell carcinoma |
| spellingShingle | Zhiyong Chen Li Yi Zhiwei Peng Jianzhong Zhou Zhaotao Zhang Yahong Tao Ze Lin Anjing He Mengni Jin Minjing Zuo Development and validation of a radiomic nomogram based on pretherapy dual-energy CT for distinguishing adenocarcinoma from squamous cell carcinoma of the lung Frontiers in Oncology dual-energy CT dual-energy CT quantitative parameters radiomics lung adenocarcinoma lung squamous cell carcinoma |
| title | Development and validation of a radiomic nomogram based on pretherapy dual-energy CT for distinguishing adenocarcinoma from squamous cell carcinoma of the lung |
| title_full | Development and validation of a radiomic nomogram based on pretherapy dual-energy CT for distinguishing adenocarcinoma from squamous cell carcinoma of the lung |
| title_fullStr | Development and validation of a radiomic nomogram based on pretherapy dual-energy CT for distinguishing adenocarcinoma from squamous cell carcinoma of the lung |
| title_full_unstemmed | Development and validation of a radiomic nomogram based on pretherapy dual-energy CT for distinguishing adenocarcinoma from squamous cell carcinoma of the lung |
| title_short | Development and validation of a radiomic nomogram based on pretherapy dual-energy CT for distinguishing adenocarcinoma from squamous cell carcinoma of the lung |
| title_sort | development and validation of a radiomic nomogram based on pretherapy dual energy ct for distinguishing adenocarcinoma from squamous cell carcinoma of the lung |
| topic | dual-energy CT dual-energy CT quantitative parameters radiomics lung adenocarcinoma lung squamous cell carcinoma |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.949111/full |
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