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Retrospective analysis used CT images from 117 cervical cancer patients, with manual segmentation to delineate gross tumor volume and normal tissue as distinct ROIs. Radiomic features were extracted, and Least Absolute Shrinkage and Selection Operator (LASSO) regression performed feature selection. Models using SVM, RF, KNN, XGBoost, and Decision Tree were evaluated on stratified train and validation sets. Nineteen key features were identified, and all models achieved strong test performance with AUCs mostly above 0.8.",{"@graph":69,"@context":127},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/constructing-a-classification-model-for-cervical-cancer-tumor-tissue-and-normal-tissue-based-on-ct-radiomics/329395/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/constructing-a-classification-model-for-cervical-cancer-tumor-tissue-and-normal-tissue-based-on-ct-radiomics/329395.png","ImageObject",300,407,{"name":92,"@type":93},"Quinn","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119,123],{"name":110,"@type":111,"acceptedAnswer":112},"What is the primary goal of this study?","Question",{"text":113,"@type":114},"To develop an automated classification framework that distinguishes cervical cancer tumor tissue from normal uterine tissue using CT radiomics.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How were regions of interest (ROIs) defined for modeling?",{"text":118,"@type":114},"Gross tumor volume and normal uterine tissue were segmented as separate ROIs using manual segmentation techniques.",{"name":120,"@type":111,"acceptedAnswer":121},"Which machine learning algorithms were evaluated?",{"text":122,"@type":114},"Five algorithms were tested: SVM, Random Forest (RF), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), and Decision Tree (DT).",{"name":124,"@type":111,"acceptedAnswer":125},"What result indicates strong diagnostic performance?",{"text":126,"@type":114},"The models achieved high AUC values on test data (generally above 0.8), with XGBoost showing the best diagnostic accuracy based on reported AUC.","https://schema.org",{"og:url":83,"og:type":129,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":131,"canonical":83},"index,follow",{"doc_id":133,"site_id":62},329395,1790113215,{"code":4,"msg":5,"data":136},{"doc_id":133,"user_id":137,"nickname":92,"user_avatar":138,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":139,"file_id":140,"file_url":141,"file_type":142,"file_size":143,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":105,"language":144,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":145,"faqs":146,"seo_title":147,"seo_description":67,"update_tm":148,"read_time":149,"preview_page_count":4},962075114765,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Constructing a Classification Model for Cervical Cancer Tumor Tissue and Normal Tissue Based on CT Radiomics  \nJinghong Pei, BD 1,\\#, Jing Yu, BD2,\\#, Ping Ge, BD3,\\#, Liman Bao, BD4, Haowen Pang, MS5, and Huaiwen Zhang, MS6   \nTechnology in Cancer Research & Treatment  \nVolume 23: 1-11  \n© The Author(s) 2024  \nArticle reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/15330338241298554](DOI: 10.1177/15330338241298554)[ ](DOI: 10.1177/15330338241298554)[journals.sagepub.com/home/tct](journals.sagepub.com/home/tct)  \nAbstract  \nThis study aimed to develop an automated classification framework for distinguishing between cervical cancer tumor and normal uterine tissue, leveraging CT images for radiomics feature extraction. We retrospectively analyzed CT images from 117 cervical cancer patients. To distinguish between cancerous and healthy tissue, we segmented gross tumor volume and normal uterine tissue as distinct regions of interest (ROIs) using manual segmentation techniques. Key radiomic parameters were extracted from these ROIs. To bolster model’s predictive capability, the data was stratified into train data (70%) and validation data (30%) . During feature selection phase, we applied Least Absolute Shrinkage and Selection Operator regression algorithm to identify most relevant features. Subsequently, we built classification models using five state-of-theart machine learning algorithms: Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), and Decision Tree (DT) . Ultimately, the performance of each model was evaluated. Through stringent feature selection process, we identified 18 pivotal radiomic features for classification of cervical cancer and normal uterine tissue. When applied to test data, all five models achieved excellent performance, with area under the curve (AUC) values ranging from 0.8866 to 0.9190 (SVM: 0.9144, RF: 0.9078, KNN: 0.9051, DT: 0.8866, XGBoost: 0.9190), all surpassing threshold of 0.8 . In terms of test data, all five models had high sensitivity; accuracy of SVM, RF, and XGBoost models was comparable; and specificity of five models was similar. XGBoost model outperformed the others in terms of diagnostic accuracy, achieving an AUC of 0.8737 (95% CI: 0.8198-0.9277) for train data and 0.9190 (95% CI: 0.8525-0.9854) for test data. Our findings underscore the potential of CT radiomics combined with machine learning algorithms for accurately classifying cervical cancer tumors and normal uterine tissue with high recognition capabilities. This approach holds significant promise for clinical diagnostics.  \n1 Nursing Department, The Second People’s Hospital of Jingdezhen, Jingdezhen, China  \n2 Department of Oncology, The Second People’s Hospital of Jingdezhen, Jingdezhen, China  \n3 Department of General Practice Medicine, The Second People’s Hospital of Jingdezhen, Jingdezhen, China  \n4 Department of Public Health, The Second People’s Hospital of Jingdezhen, Jingdezhen, China  \n5 Department of Oncology, The Affiliated Hospital of Southwest Medical University, Sichuan, China  \n6 Department of Radiotherapy, Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College, Jiangxi Clinical Research Center for Cancer, Nanchang, China  \n\\#  \nThese authors have contributed equally to this work.  \nCorresponding Authors:  \nJinghong Pei, Nursing Department, The second people’s hospital of Jingdezhen, Jingdezhen 333000, China.  \nEmail: [jdzpeijinghong@163.com](jdzpeijinghong@163.com)  \nHao-wen Pang, Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, China.  \nEmail: [haowenpang@foxmail.com](haowenpang@foxmail.com)  \nHuaiwen Zhang, Department of Radiation Oncology; Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College, Jiangxi Cancer Institute; Nanchang, 330029, China.  \nEmail: [1761580890@qq.com](176","cbCaiokM2epDUcUs","https://ap.wps.com/l/cbCaiokM2epDUcUs","pdf",2439922,"English","# Abstract\n## Introduction\n## Methods and Data Processing\n## Feature Selection and Model Building\n## Model Evaluation and Results\n## Clinical Implications","[{\"question\":\"What is the primary goal of this study?\",\"answer\":\"To develop an automated classification framework that distinguishes cervical cancer tumor tissue from normal uterine tissue using CT radiomics.\"},{\"question\":\"How were regions of interest (ROIs) defined for modeling?\",\"answer\":\"Gross tumor volume and normal uterine tissue were segmented as separate ROIs using manual segmentation techniques.\"},{\"question\":\"Which machine learning algorithms were evaluated?\",\"answer\":\"Five algorithms were tested: SVM, Random Forest (RF), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), and Decision Tree (DT).\"},{\"question\":\"What result indicates strong diagnostic performance?\",\"answer\":\"The models achieved high AUC values on test data (generally above 0.8), with XGBoost showing the best diagnostic accuracy based on reported AUC.\"}]","Constructing a Classification Model for Cervical Cancer Tumor Tissue and Normal Tissue Based on CT Radiomics | PDF",1789984018,28]