Non-contrast and contrast enhanced computed tomography radiomics in preoperative discrimination of lung invasive and non-invasive adenocarcinoma
ObjectiveThis study aimed to assess the value of radiomics based on non-contrast computed tomography (NCCT) and contrast-enhanced computed tomography (CECT) images in the preoperative discrimination between lung invasive adenocarcinomas (IAC) and non-invasive adenocarcinomas (non-IAC).MethodsWe enro...
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| Format: | Article |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2022.939434/full |
| _version_ | 1811334390996795392 |
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| author | Yingli Sun Wei Zhao Kaiming Kuang Liang Jin Pan Gao Shaofeng Duan Yi Xiao Jun Liu Ming Li |
| author_facet | Yingli Sun Wei Zhao Kaiming Kuang Liang Jin Pan Gao Shaofeng Duan Yi Xiao Jun Liu Ming Li |
| author_sort | Yingli Sun |
| collection | DOAJ |
| description | ObjectiveThis study aimed to assess the value of radiomics based on non-contrast computed tomography (NCCT) and contrast-enhanced computed tomography (CECT) images in the preoperative discrimination between lung invasive adenocarcinomas (IAC) and non-invasive adenocarcinomas (non-IAC).MethodsWe enrolled 1,185 pulmonary nodules (478 non-IACs and 707 IACs) to build and validate radiomics models. An external testing set comprising 63 pulmonary nodules was collected to verify the generalization of the models. Radiomic features were extracted from both NCCT and CECT images. The predictive performance of radiomics models in the validation and external testing sets were evaluated and compared with radiologists’ evaluations. The predictive performances of the radiomics models were also compared between three subgroups in the validation set (Group 1: solid nodules, Group 2: part-solid nodules, and Group 3: pure ground-glass nodules).ResultsThe NCCT, CECT, and combined models showed good ability to discriminate between IAC and non-IAC [respective areas under the curve (AUCs): validation set = 0.91, 0.90, and 0.91; Group 1 = 0.82, 0.79, and 0.81; Group 2 = 0.93, 0.92, and 0.93; and Group 3 = 0.90, 0.90, and 0.89]. In the external testing set, the AUC of the three models were 0.89, 0.91, and 0.89, respectively. The accuracies of these three models were comparable to those of the senior radiologist and better those that of the junior radiologist.ConclusionRadiomic models based on CT images showed good predictive performance in discriminating between lung IAC and non-IAC, especially in part solid nodule group. However, radiomics based on CECT images provided no additional value compared to NCCT images. |
| first_indexed | 2024-04-13T17:08:03Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-04-13T17:08:03Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj.art-846bf04ecda04a409650caada6132f4f2022-12-22T02:38:24ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2022-11-01910.3389/fmed.2022.939434939434Non-contrast and contrast enhanced computed tomography radiomics in preoperative discrimination of lung invasive and non-invasive adenocarcinomaYingli Sun0Wei Zhao1Kaiming Kuang2Liang Jin3Pan Gao4Shaofeng Duan5Yi Xiao6Jun Liu7Ming Li8Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, ChinaDepartment of Radiology, Second Xiangya Hospital, Central South University, Changsha, ChinaDianei Technology, Shanghai, ChinaDepartment of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, ChinaDepartment of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, ChinaGE Healthcare, Shanghai, ChinaDepartment of Radiology, Changzheng Hospital, Second Military Medical University, Shanghai, ChinaDepartment of Radiology, Second Xiangya Hospital, Central South University, Changsha, ChinaDepartment of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, ChinaObjectiveThis study aimed to assess the value of radiomics based on non-contrast computed tomography (NCCT) and contrast-enhanced computed tomography (CECT) images in the preoperative discrimination between lung invasive adenocarcinomas (IAC) and non-invasive adenocarcinomas (non-IAC).MethodsWe enrolled 1,185 pulmonary nodules (478 non-IACs and 707 IACs) to build and validate radiomics models. An external testing set comprising 63 pulmonary nodules was collected to verify the generalization of the models. Radiomic features were extracted from both NCCT and CECT images. The predictive performance of radiomics models in the validation and external testing sets were evaluated and compared with radiologists’ evaluations. The predictive performances of the radiomics models were also compared between three subgroups in the validation set (Group 1: solid nodules, Group 2: part-solid nodules, and Group 3: pure ground-glass nodules).ResultsThe NCCT, CECT, and combined models showed good ability to discriminate between IAC and non-IAC [respective areas under the curve (AUCs): validation set = 0.91, 0.90, and 0.91; Group 1 = 0.82, 0.79, and 0.81; Group 2 = 0.93, 0.92, and 0.93; and Group 3 = 0.90, 0.90, and 0.89]. In the external testing set, the AUC of the three models were 0.89, 0.91, and 0.89, respectively. The accuracies of these three models were comparable to those of the senior radiologist and better those that of the junior radiologist.ConclusionRadiomic models based on CT images showed good predictive performance in discriminating between lung IAC and non-IAC, especially in part solid nodule group. However, radiomics based on CECT images provided no additional value compared to NCCT images.https://www.frontiersin.org/articles/10.3389/fmed.2022.939434/fulladenocarcinomalungradiomicssolitary pulmonary noduleX-ray computed tomography |
| spellingShingle | Yingli Sun Wei Zhao Kaiming Kuang Liang Jin Pan Gao Shaofeng Duan Yi Xiao Jun Liu Ming Li Non-contrast and contrast enhanced computed tomography radiomics in preoperative discrimination of lung invasive and non-invasive adenocarcinoma Frontiers in Medicine adenocarcinoma lung radiomics solitary pulmonary nodule X-ray computed tomography |
| title | Non-contrast and contrast enhanced computed tomography radiomics in preoperative discrimination of lung invasive and non-invasive adenocarcinoma |
| title_full | Non-contrast and contrast enhanced computed tomography radiomics in preoperative discrimination of lung invasive and non-invasive adenocarcinoma |
| title_fullStr | Non-contrast and contrast enhanced computed tomography radiomics in preoperative discrimination of lung invasive and non-invasive adenocarcinoma |
| title_full_unstemmed | Non-contrast and contrast enhanced computed tomography radiomics in preoperative discrimination of lung invasive and non-invasive adenocarcinoma |
| title_short | Non-contrast and contrast enhanced computed tomography radiomics in preoperative discrimination of lung invasive and non-invasive adenocarcinoma |
| title_sort | non contrast and contrast enhanced computed tomography radiomics in preoperative discrimination of lung invasive and non invasive adenocarcinoma |
| topic | adenocarcinoma lung radiomics solitary pulmonary nodule X-ray computed tomography |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2022.939434/full |
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