Prediction of Ki-67 Expression in HIV-Associated Lung Adenocarcinoma Patients Using Multiple Machine Learning Models Based on CT Imaging Radiomics
Chang Song,1,* Jingsong Chen,2,* Chunyan Zhao,1,* Shulin Song,3,* Tong Yang,4 Aichun Huang,1 Renhao Liu,1 Yanxi Pan,3 Chaoyan Xu,1 Canling Chen,1 Qingdong Zhu1 1Tuberculosis Department, Nanning Fourth People’s Hospital, Nanning, Guangxi, 530023, People’s Republic of C...
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Dove Medical Press
2025-04-01
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| Series: | Cancer Management and Research |
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| author | Song C Chen J Zhao C Song S Yang T Huang A Liu R Pan Y Xu C Chen C Zhu Q |
| author_facet | Song C Chen J Zhao C Song S Yang T Huang A Liu R Pan Y Xu C Chen C Zhu Q |
| author_sort | Song C |
| collection | DOAJ |
| description | Chang Song,1,* Jingsong Chen,2,* Chunyan Zhao,1,* Shulin Song,3,* Tong Yang,4 Aichun Huang,1 Renhao Liu,1 Yanxi Pan,3 Chaoyan Xu,1 Canling Chen,1 Qingdong Zhu1 1Tuberculosis Department, Nanning Fourth People’s Hospital, Nanning, Guangxi, 530023, People’s Republic of China; 2Gastroenterology Department, Hepu County People’s Hospital, Beihai, Guangxi, 536100, People’s Republic of China; 3Radiology Department, Nanning Fourth People’s Hospital, Nanning, Guangxi, 530023, People’s Republic of China; 4Rehabilitation Department, Hepu County People’s Hospital, Beihai, Guangxi, 536100, People’s Republic of China*These authors contributed equally to this workCorrespondence: Qingdong Zhu, Tuberculosis Department, Nanning Fourth People’s Hospital, Nanning, Guangxi, 530023, People’s Republic of China, Email zhuqingdong2003@163.comPurpose: The incidence of lung adenocarcinoma (LUAD) in HIV-infected individuals is significantly increased. However, invasive procedures for Ki-67 assessment may increase the risk of complications. Therefore, developing a non-invasive and accurate method for Ki-67 prediction holds significant clinical importance. This study aims to explore the feasibility and value of a radiomics model based on preoperative CT images in predicting Ki-67 expression levels in HIV-associated LUAD.Patients and Methods: A total of 237 patients with HIV-associated LUAD were included. Of these, 102 were classified into the high Ki-67 expression group, and 135 into the low Ki-67 expression group. The patients were randomly divided into a training group (n=189) and a validation group (n=48) in a 4:1 ratio. Feature selection was based on intra-class correlation coefficient (ICC), Spearman correlation coefficient, and Least Absolute Shrinkage and Selection Operator (LASSO) regression, yielding 16 optimal radiomic features for building a logistic regression model. Model performance was evaluated by sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score, and the area under the receiver operating characteristic curve (AUC).Results: 1834 CT image features were extracted, with 16 retained for further analysis. The Support Vector Machine (SVM) model demonstrated the most balanced and optimal performance among the seven developed models. It achieved robust sensitivity (training set: 0.89; testing set: 0.86), specificity (training set: 0.92; testing set: 0.89), PPV (training set: 0.89; testing set: 0.86), NPV (training set: 0.92; testing set: 0.89), F1 score (training set: 0.89; testing set: 0.86), and AUC (training set: 0.975; testing set: 0.905), indicating excellent predictive accuracy.Conclusion: This study first demonstrates that a preoperative CT-based radiomics model can non-invasively predict Ki-67 expression levels in HIV-associated LUAD patients. This finding not only provides a precise assessment tool for the HIV-infected population to avoid the risks of invasive examinations but also paves new interdisciplinary research avenues for exploring tumor heterogeneity under immunodeficiency conditions.Keywords: HIV, lung adenocarcinoma, Ki-67, radiomics, machine learning, SVM |
| format | Article |
| id | doaj-art-e58751151bee4b02a022814fb8da6708 |
| institution | OA Journals |
| issn | 1179-1322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Dove Medical Press |
| record_format | Article |
| series | Cancer Management and Research |
| spelling | doaj-art-e58751151bee4b02a022814fb8da67082025-08-20T02:20:25ZengDove Medical PressCancer Management and Research1179-13222025-04-01Volume 17881892102420Prediction of Ki-67 Expression in HIV-Associated Lung Adenocarcinoma Patients Using Multiple Machine Learning Models Based on CT Imaging RadiomicsSong CChen JZhao CSong SYang THuang ALiu RPan YXu CChen CZhu QChang Song,1,* Jingsong Chen,2,* Chunyan Zhao,1,* Shulin Song,3,* Tong Yang,4 Aichun Huang,1 Renhao Liu,1 Yanxi Pan,3 Chaoyan Xu,1 Canling Chen,1 Qingdong Zhu1 1Tuberculosis Department, Nanning Fourth People’s Hospital, Nanning, Guangxi, 530023, People’s Republic of China; 2Gastroenterology Department, Hepu County People’s Hospital, Beihai, Guangxi, 536100, People’s Republic of China; 3Radiology Department, Nanning Fourth People’s Hospital, Nanning, Guangxi, 530023, People’s Republic of China; 4Rehabilitation Department, Hepu County People’s Hospital, Beihai, Guangxi, 536100, People’s Republic of China*These authors contributed equally to this workCorrespondence: Qingdong Zhu, Tuberculosis Department, Nanning Fourth People’s Hospital, Nanning, Guangxi, 530023, People’s Republic of China, Email zhuqingdong2003@163.comPurpose: The incidence of lung adenocarcinoma (LUAD) in HIV-infected individuals is significantly increased. However, invasive procedures for Ki-67 assessment may increase the risk of complications. Therefore, developing a non-invasive and accurate method for Ki-67 prediction holds significant clinical importance. This study aims to explore the feasibility and value of a radiomics model based on preoperative CT images in predicting Ki-67 expression levels in HIV-associated LUAD.Patients and Methods: A total of 237 patients with HIV-associated LUAD were included. Of these, 102 were classified into the high Ki-67 expression group, and 135 into the low Ki-67 expression group. The patients were randomly divided into a training group (n=189) and a validation group (n=48) in a 4:1 ratio. Feature selection was based on intra-class correlation coefficient (ICC), Spearman correlation coefficient, and Least Absolute Shrinkage and Selection Operator (LASSO) regression, yielding 16 optimal radiomic features for building a logistic regression model. Model performance was evaluated by sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score, and the area under the receiver operating characteristic curve (AUC).Results: 1834 CT image features were extracted, with 16 retained for further analysis. The Support Vector Machine (SVM) model demonstrated the most balanced and optimal performance among the seven developed models. It achieved robust sensitivity (training set: 0.89; testing set: 0.86), specificity (training set: 0.92; testing set: 0.89), PPV (training set: 0.89; testing set: 0.86), NPV (training set: 0.92; testing set: 0.89), F1 score (training set: 0.89; testing set: 0.86), and AUC (training set: 0.975; testing set: 0.905), indicating excellent predictive accuracy.Conclusion: This study first demonstrates that a preoperative CT-based radiomics model can non-invasively predict Ki-67 expression levels in HIV-associated LUAD patients. This finding not only provides a precise assessment tool for the HIV-infected population to avoid the risks of invasive examinations but also paves new interdisciplinary research avenues for exploring tumor heterogeneity under immunodeficiency conditions.Keywords: HIV, lung adenocarcinoma, Ki-67, radiomics, machine learning, SVMhttps://www.dovepress.com/prediction-of-ki-67-expression-in-hiv-associated-lung-adenocarcinoma-p-peer-reviewed-fulltext-article-CMARhivlung adenocarcinomaki-67radiomicsmachine learningsvm |
| spellingShingle | Song C Chen J Zhao C Song S Yang T Huang A Liu R Pan Y Xu C Chen C Zhu Q Prediction of Ki-67 Expression in HIV-Associated Lung Adenocarcinoma Patients Using Multiple Machine Learning Models Based on CT Imaging Radiomics Cancer Management and Research hiv lung adenocarcinoma ki-67 radiomics machine learning svm |
| title | Prediction of Ki-67 Expression in HIV-Associated Lung Adenocarcinoma Patients Using Multiple Machine Learning Models Based on CT Imaging Radiomics |
| title_full | Prediction of Ki-67 Expression in HIV-Associated Lung Adenocarcinoma Patients Using Multiple Machine Learning Models Based on CT Imaging Radiomics |
| title_fullStr | Prediction of Ki-67 Expression in HIV-Associated Lung Adenocarcinoma Patients Using Multiple Machine Learning Models Based on CT Imaging Radiomics |
| title_full_unstemmed | Prediction of Ki-67 Expression in HIV-Associated Lung Adenocarcinoma Patients Using Multiple Machine Learning Models Based on CT Imaging Radiomics |
| title_short | Prediction of Ki-67 Expression in HIV-Associated Lung Adenocarcinoma Patients Using Multiple Machine Learning Models Based on CT Imaging Radiomics |
| title_sort | prediction of ki 67 expression in hiv associated lung adenocarcinoma patients using multiple machine learning models based on ct imaging radiomics |
| topic | hiv lung adenocarcinoma ki-67 radiomics machine learning svm |
| url | https://www.dovepress.com/prediction-of-ki-67-expression-in-hiv-associated-lung-adenocarcinoma-p-peer-reviewed-fulltext-article-CMAR |
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