A comprehensive nomogram combining CT-based radiomics with clinical features for differentiation of benign and malignant lung subcentimeter solid nodules
ObjectiveTo establish a nomogram based on non-enhanced computed tomography(CT) imaging radiomics and clinical features for use in predicting the malignancy of sub-centimeter solid nodules (SCSNs).Materials and methodsRetrospective analysis was performed of records for 198 patients with SCSNs that we...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-03-01
|
| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2023.1066360/full |
| _version_ | 1797868026304921600 |
|---|---|
| author | Chengyu Chen Chengyu Chen Qun Geng Gesheng Song Qian Zhang Youruo Wang Dongfeng Sun Qingshi Zeng Zhengjun Dai Gongchao Wang |
| author_facet | Chengyu Chen Chengyu Chen Qun Geng Gesheng Song Qian Zhang Youruo Wang Dongfeng Sun Qingshi Zeng Zhengjun Dai Gongchao Wang |
| author_sort | Chengyu Chen |
| collection | DOAJ |
| description | ObjectiveTo establish a nomogram based on non-enhanced computed tomography(CT) imaging radiomics and clinical features for use in predicting the malignancy of sub-centimeter solid nodules (SCSNs).Materials and methodsRetrospective analysis was performed of records for 198 patients with SCSNs that were surgically resected and examined pathologically at two medical institutions between January 2020 and June 2021. Patients from Center 1 were included in the training cohort (n = 147), and patients from Center 2 were included in the external validation cohort (n = 52). Radiomic features were extracted from chest CT images. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomic feature extraction and computation of radiomic scores. Clinical features, subjective CT findings, and radiomic scores were used to build multiple predictive models. Model performance was examined by evaluating the area under the receiver operating characteristic curve (AUC). The best model was selected for efficacy evaluation in a validation cohort, and column line plots were created.ResultsPulmonary malignant nodules were significantly associated with vascular alterations in both the training (p < 0.001) and external validation (p < 0.001) cohorts. Eleven radiomic features were selected after a dimensionality reduction to calculate the radiomic scores. Based on these findings, three prediction models were constructed: subjective model (Model 1), radiomic score model (Model 2), and comprehensive model (Model 3), with AUCs of 0.672, 0.888, and 0.930, respectively. The optimal model with an AUC of 0.905 was applied to the validation cohort, and decision curve analysis indicated that the comprehensive model column line plot was clinically useful.ConclusionPredictive models constructed based on CT-based radiomics with clinical features can help clinicians diagnose pulmonary nodules and guide clinical decision making. |
| first_indexed | 2024-04-09T23:50:42Z |
| format | Article |
| id | doaj.art-c4a15e3fd5c5481abd457e7d36251b5d |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-09T23:50:42Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-c4a15e3fd5c5481abd457e7d36251b5d2023-03-17T12:19:52ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-03-011310.3389/fonc.2023.10663601066360A comprehensive nomogram combining CT-based radiomics with clinical features for differentiation of benign and malignant lung subcentimeter solid nodulesChengyu Chen0Chengyu Chen1Qun Geng2Gesheng Song3Qian Zhang4Youruo Wang5Dongfeng Sun6Qingshi Zeng7Zhengjun Dai8Gongchao Wang9Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, ChinaDepartment of Thoracic Surgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, ChinaDepartment of Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, ChinaDepartment of Radiology, The First Affiliated Hospital of Shandong First Medical Unversity, Jinan, ChinaDepartment of General Surgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, ChinaElite Class of 2017, Shandong First Medical University, Jinan, ChinaDepartment of Thoracic Surgery, The First Affiliated Hospital of Shandong First Medical University, Jinan, ChinaDepartment of Radiology, The First Affiliated Hospital of Shandong First Medical Unversity, Jinan, ChinaScientific Research Department, Huiying Medical Technology Co., Ltd, Beijing, ChinaDepartment of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, ChinaObjectiveTo establish a nomogram based on non-enhanced computed tomography(CT) imaging radiomics and clinical features for use in predicting the malignancy of sub-centimeter solid nodules (SCSNs).Materials and methodsRetrospective analysis was performed of records for 198 patients with SCSNs that were surgically resected and examined pathologically at two medical institutions between January 2020 and June 2021. Patients from Center 1 were included in the training cohort (n = 147), and patients from Center 2 were included in the external validation cohort (n = 52). Radiomic features were extracted from chest CT images. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomic feature extraction and computation of radiomic scores. Clinical features, subjective CT findings, and radiomic scores were used to build multiple predictive models. Model performance was examined by evaluating the area under the receiver operating characteristic curve (AUC). The best model was selected for efficacy evaluation in a validation cohort, and column line plots were created.ResultsPulmonary malignant nodules were significantly associated with vascular alterations in both the training (p < 0.001) and external validation (p < 0.001) cohorts. Eleven radiomic features were selected after a dimensionality reduction to calculate the radiomic scores. Based on these findings, three prediction models were constructed: subjective model (Model 1), radiomic score model (Model 2), and comprehensive model (Model 3), with AUCs of 0.672, 0.888, and 0.930, respectively. The optimal model with an AUC of 0.905 was applied to the validation cohort, and decision curve analysis indicated that the comprehensive model column line plot was clinically useful.ConclusionPredictive models constructed based on CT-based radiomics with clinical features can help clinicians diagnose pulmonary nodules and guide clinical decision making.https://www.frontiersin.org/articles/10.3389/fonc.2023.1066360/fullradiomicsnomogramCTlung cancersubcentimeter solid nodules |
| spellingShingle | Chengyu Chen Chengyu Chen Qun Geng Gesheng Song Qian Zhang Youruo Wang Dongfeng Sun Qingshi Zeng Zhengjun Dai Gongchao Wang A comprehensive nomogram combining CT-based radiomics with clinical features for differentiation of benign and malignant lung subcentimeter solid nodules Frontiers in Oncology radiomics nomogram CT lung cancer subcentimeter solid nodules |
| title | A comprehensive nomogram combining CT-based radiomics with clinical features for differentiation of benign and malignant lung subcentimeter solid nodules |
| title_full | A comprehensive nomogram combining CT-based radiomics with clinical features for differentiation of benign and malignant lung subcentimeter solid nodules |
| title_fullStr | A comprehensive nomogram combining CT-based radiomics with clinical features for differentiation of benign and malignant lung subcentimeter solid nodules |
| title_full_unstemmed | A comprehensive nomogram combining CT-based radiomics with clinical features for differentiation of benign and malignant lung subcentimeter solid nodules |
| title_short | A comprehensive nomogram combining CT-based radiomics with clinical features for differentiation of benign and malignant lung subcentimeter solid nodules |
| title_sort | comprehensive nomogram combining ct based radiomics with clinical features for differentiation of benign and malignant lung subcentimeter solid nodules |
| topic | radiomics nomogram CT lung cancer subcentimeter solid nodules |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2023.1066360/full |
| work_keys_str_mv | AT chengyuchen acomprehensivenomogramcombiningctbasedradiomicswithclinicalfeaturesfordifferentiationofbenignandmalignantlungsubcentimetersolidnodules AT chengyuchen acomprehensivenomogramcombiningctbasedradiomicswithclinicalfeaturesfordifferentiationofbenignandmalignantlungsubcentimetersolidnodules AT qungeng acomprehensivenomogramcombiningctbasedradiomicswithclinicalfeaturesfordifferentiationofbenignandmalignantlungsubcentimetersolidnodules AT geshengsong acomprehensivenomogramcombiningctbasedradiomicswithclinicalfeaturesfordifferentiationofbenignandmalignantlungsubcentimetersolidnodules AT qianzhang acomprehensivenomogramcombiningctbasedradiomicswithclinicalfeaturesfordifferentiationofbenignandmalignantlungsubcentimetersolidnodules AT youruowang acomprehensivenomogramcombiningctbasedradiomicswithclinicalfeaturesfordifferentiationofbenignandmalignantlungsubcentimetersolidnodules AT dongfengsun acomprehensivenomogramcombiningctbasedradiomicswithclinicalfeaturesfordifferentiationofbenignandmalignantlungsubcentimetersolidnodules AT qingshizeng acomprehensivenomogramcombiningctbasedradiomicswithclinicalfeaturesfordifferentiationofbenignandmalignantlungsubcentimetersolidnodules AT zhengjundai acomprehensivenomogramcombiningctbasedradiomicswithclinicalfeaturesfordifferentiationofbenignandmalignantlungsubcentimetersolidnodules AT gongchaowang acomprehensivenomogramcombiningctbasedradiomicswithclinicalfeaturesfordifferentiationofbenignandmalignantlungsubcentimetersolidnodules AT chengyuchen comprehensivenomogramcombiningctbasedradiomicswithclinicalfeaturesfordifferentiationofbenignandmalignantlungsubcentimetersolidnodules AT chengyuchen comprehensivenomogramcombiningctbasedradiomicswithclinicalfeaturesfordifferentiationofbenignandmalignantlungsubcentimetersolidnodules AT qungeng comprehensivenomogramcombiningctbasedradiomicswithclinicalfeaturesfordifferentiationofbenignandmalignantlungsubcentimetersolidnodules AT geshengsong comprehensivenomogramcombiningctbasedradiomicswithclinicalfeaturesfordifferentiationofbenignandmalignantlungsubcentimetersolidnodules AT qianzhang comprehensivenomogramcombiningctbasedradiomicswithclinicalfeaturesfordifferentiationofbenignandmalignantlungsubcentimetersolidnodules AT youruowang comprehensivenomogramcombiningctbasedradiomicswithclinicalfeaturesfordifferentiationofbenignandmalignantlungsubcentimetersolidnodules AT dongfengsun comprehensivenomogramcombiningctbasedradiomicswithclinicalfeaturesfordifferentiationofbenignandmalignantlungsubcentimetersolidnodules AT qingshizeng comprehensivenomogramcombiningctbasedradiomicswithclinicalfeaturesfordifferentiationofbenignandmalignantlungsubcentimetersolidnodules AT zhengjundai comprehensivenomogramcombiningctbasedradiomicswithclinicalfeaturesfordifferentiationofbenignandmalignantlungsubcentimetersolidnodules AT gongchaowang comprehensivenomogramcombiningctbasedradiomicswithclinicalfeaturesfordifferentiationofbenignandmalignantlungsubcentimetersolidnodules |