CT Radiomics Model for Predicting the Ki-67 Index of Lung Cancer： An Exploratory Study
ObjectiveTo establish a radiomics signature and a nomogram model based on enhanced CT images to predict the Ki-67 index of lung cancer.MethodsFrom January 2014 to December 2018, 282 patients with lung cancer who had undergone enhanced CT scans and Ki-67 examination within 2 weeks were retrospectivel...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-10-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2021.743490/full |
| _version_ | 1818916588039962624 |
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| author | Qing Fu Shun li Liu Da peng Hao Ya bin Hu Xue jun Liu Zaixian Zhang Wen hong Wang Xiao yan Tang Chuan yu Zhang Shi he Liu |
| author_facet | Qing Fu Shun li Liu Da peng Hao Ya bin Hu Xue jun Liu Zaixian Zhang Wen hong Wang Xiao yan Tang Chuan yu Zhang Shi he Liu |
| author_sort | Qing Fu |
| collection | DOAJ |
| description | ObjectiveTo establish a radiomics signature and a nomogram model based on enhanced CT images to predict the Ki-67 index of lung cancer.MethodsFrom January 2014 to December 2018, 282 patients with lung cancer who had undergone enhanced CT scans and Ki-67 examination within 2 weeks were retrospectively enrolled and analyzed. The clinical data of the patients were collected, such as age, sex, smoking history, maximum tumor diameter and serum tumor markers. Our primary cohort was randomly divided into a training group (n=197) and a validation group (n=85) at a 7:3 ratio. A Ki-67 index ≤ 40% indicated low expression, and a Ki-67 index > 40% indicated high expression. In total, 396 radiomics features were extracted using AK software. Feature reduction and selection were performed using the lasso regression model. Logistic regression analysis was used to establish a multivariate predictive model to identify high and low Ki-67 expression in lung cancer. A nomogram integrating the radiomics score was established based on multiple logistic regression analysis. Area under the curve (AUC) was used to evaluate the prediction efficiency of the radiomics signature and nomogram.ResultsThe AUC,sensitivity, specificity and accuracy of the radiomics signature in the training and validation groups were 0.88 (95% CI: 0.82~0.93),79.2%,84.3%,81.2% and 0.86 (95% CI: 0.78~0.94),74.6%,88.1%,79.8%, respectively. A nomogram combining radiomics features and clinical risk factors (smoking history and NSE) was developed. The AUC, sensitivity, specificity and accuracy were 0.87 (95% CI: 0.80~0.95), 75.0%, 90.2% and 83.5% in the validation group, respectively.ConclusionThe radiomics signature and nomogram based on enhanced CT images provide a way to predict the Ki-67 expression level in lung cancer. |
| first_indexed | 2024-12-20T00:20:33Z |
| format | Article |
| id | doaj.art-972c729b770447c89fec4a0e8298a246 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-12-20T00:20:33Z |
| publishDate | 2021-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-972c729b770447c89fec4a0e8298a2462022-12-21T20:00:12ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2021-10-011110.3389/fonc.2021.743490743490CT Radiomics Model for Predicting the Ki-67 Index of Lung Cancer： An Exploratory StudyQing FuShun li LiuDa peng HaoYa bin HuXue jun LiuZaixian ZhangWen hong WangXiao yan TangChuan yu ZhangShi he LiuObjectiveTo establish a radiomics signature and a nomogram model based on enhanced CT images to predict the Ki-67 index of lung cancer.MethodsFrom January 2014 to December 2018, 282 patients with lung cancer who had undergone enhanced CT scans and Ki-67 examination within 2 weeks were retrospectively enrolled and analyzed. The clinical data of the patients were collected, such as age, sex, smoking history, maximum tumor diameter and serum tumor markers. Our primary cohort was randomly divided into a training group (n=197) and a validation group (n=85) at a 7:3 ratio. A Ki-67 index ≤ 40% indicated low expression, and a Ki-67 index > 40% indicated high expression. In total, 396 radiomics features were extracted using AK software. Feature reduction and selection were performed using the lasso regression model. Logistic regression analysis was used to establish a multivariate predictive model to identify high and low Ki-67 expression in lung cancer. A nomogram integrating the radiomics score was established based on multiple logistic regression analysis. Area under the curve (AUC) was used to evaluate the prediction efficiency of the radiomics signature and nomogram.ResultsThe AUC,sensitivity, specificity and accuracy of the radiomics signature in the training and validation groups were 0.88 (95% CI: 0.82~0.93),79.2%,84.3%,81.2% and 0.86 (95% CI: 0.78~0.94),74.6%,88.1%,79.8%, respectively. A nomogram combining radiomics features and clinical risk factors (smoking history and NSE) was developed. The AUC, sensitivity, specificity and accuracy were 0.87 (95% CI: 0.80~0.95), 75.0%, 90.2% and 83.5% in the validation group, respectively.ConclusionThe radiomics signature and nomogram based on enhanced CT images provide a way to predict the Ki-67 expression level in lung cancer.https://www.frontiersin.org/articles/10.3389/fonc.2021.743490/fulllung cancerKi-67tomographyX rayradiomics modelclassification |
| spellingShingle | Qing Fu Shun li Liu Da peng Hao Ya bin Hu Xue jun Liu Zaixian Zhang Wen hong Wang Xiao yan Tang Chuan yu Zhang Shi he Liu CT Radiomics Model for Predicting the Ki-67 Index of Lung Cancer： An Exploratory Study Frontiers in Oncology lung cancer Ki-67 tomography X ray radiomics model classification |
| title | CT Radiomics Model for Predicting the Ki-67 Index of Lung Cancer： An Exploratory Study |
| title_full | CT Radiomics Model for Predicting the Ki-67 Index of Lung Cancer： An Exploratory Study |
| title_fullStr | CT Radiomics Model for Predicting the Ki-67 Index of Lung Cancer： An Exploratory Study |
| title_full_unstemmed | CT Radiomics Model for Predicting the Ki-67 Index of Lung Cancer： An Exploratory Study |
| title_short | CT Radiomics Model for Predicting the Ki-67 Index of Lung Cancer： An Exploratory Study |
| title_sort | ct radiomics model for predicting the ki 67 index of lung cancer an exploratory study |
| topic | lung cancer Ki-67 tomography X ray radiomics model classification |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2021.743490/full |
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