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Using ADC, DCE, and PET data from 22 patients across pre-, mid-, and post-treatment stages, the study assessed zero-, first-, second-, and higher-order texture descriptors. Gray Level Co-occurrence Matrix texture measures in 2D planes outperformed other features (AUC 0.73±0.12), and adding shape information increased AUC to 0.75. Contrast GLCM features were the most predictive, while modality combinations improved stratification, suggesting reduced time-point designs may retain strong performance.",{"@graph":69,"@context":122},[70,84,105],{"@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":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/multimodality-and-temporal-analysis-of-cervical-cancer-treatment-response/457321/",{"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/multimodality-and-temporal-analysis-of-cervical-cancer-treatment-response/457321.png","ImageObject",300,407,{"name":92,"@type":93},"Cart","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What imaging modalities and time stages were used to predict cervical cancer treatment response?","Question",{"text":112,"@type":113},"The study used ADC, DCE, and PET imaging across pre-, mid-, and post-treatment stages in 22 cervical cancer patients.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which feature type performed best in predicting treatment response?",{"text":117,"@type":113},"Texture features derived from the Gray Level Co-occurrence Matrix (GLCM) in 2D planes were most effective, achieving an AUC of 0.73±0.12 and reaching 0.75 when combined with shape features.",{"name":119,"@type":110,"acceptedAnswer":120},"How did using a reduced set of imaging time points affect performance?",{"text":121,"@type":113},"Using only post-stage GLCM2D features produced an AUC only about 4% lower than using all time points, indicating reduced sampling may still retain strong predictive power.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},457321,1791752178,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":19,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},18829141979164,"https://eur-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Original Article  \nMultimodality and temporal analysis of cervical cancer treatment response  \nHaotian Feng^, Emi Yoshida, Ke Sheng  \nDepartment of Radiation Oncology, University of California-San Francisco, San Francisco, CA, USA  \nContributions: (I) Conception and design: H Feng, K Sheng; (II) Administrative support: K Sheng; (III) Provision of study materials or patients: E Yoshida, K Sheng; (IV) Collection and assembly of data: H Feng; (V) Data analysis and interpretation: H Feng; (VI) Manuscript writing: All authors;  \n(VII) Final approval of manuscript: All authors.  \nCorrespondence to: Ke Sheng, PhD. Department of Radiation Oncology, University of California-San Francisco, No. 1600 Divisadero St, San  \nFrancisco, CA 94115, [USA. Email: Ke.Sheng@ucsf.edu](USA. Email: Ke.Sheng@ucsf.edu).  \nBackground: Cervical cancer remains a significant global health challenge, requiring improved diagnostic and prognostic tools to enhance treatment planning and outcomes. Noninvasive medical imaging offers a promising route for precision diagnostics. This study aimed to develop and evaluate a predictive model for cervical cancer treatment response during radiation therapy using features extracted from multimodal  \nmedical imaging.  \nMethods: This study evaluated the use of multimodal medical imaging, including apparent diffusion coefficient (ADC), dynamic contrast-enhanced (DCE), and positron emission tomography (PET), across different treatment stages (pre-, mid-, and post-stage) in 22 patients with cervical cancer. From these images, we extracted and assessed the predictive performance of various feature types, including zero-order, first  \norder, second-order, and higher-order features.  \nResults: Texture features, particularly those derived from the Gray Level Co-occurrence Matrix (GLCM) in two-dimensional (2D) plane, were more effective compared to other image features, achieving an area under the curve (AUC) of 0.73±0.12 . Combining GLCM with shape features further increased the AUC to 0.75. Among the GLCM features,“contrast” was identified as the most predictive for treatment response (AUC of 0.74 for the top five contrast features) . Among single-modality analyses, ADC demonstrated the best prediction compared to PET/computed tomography (CT) (15% AUC increase) and DCE (12% AUC increase). The combination of imaging modalities and texture analysis further enhanced patient stratification, yielding an average 8% AUC increase compared to single-modality models. Using only post-stage GLCM2D features resulted in an AUC only 4% lower than using all time points, suggesting that reduced  \nimaging time points and modalities may still retain strong predictive power.  \nConclusions: Integrating texture features from multimodal imaging can improve cervical cancer prognostication and guide personalized treatment strategies. These findings support the potential of imaging biomarkers in optimizing therapy and reducing the diagnostic burden, contributing to more efficient and  \ntailored cancer care.  \nKeywords: Cervical cancer; multimodality analysis; temporal analysis; feature extraction; feature selection  \nSubmitted Aug 25, 2024. Accepted for publication Aug 25, 2025. Published online Dec 11, 2025.  \ndoi: 10.21037/qims-24-1775  \nView this article at: [https://dx.doi.org/10.21037/qims-24-1775](https://dx.doi.org/10.21037/qims-24-1775)  \n^ ORCID: 0000-0002-0012-4594.  \n© AME Publishing Company. Quant Imaging Med Surg 2026;16(1):78 | [https://dx.doi.org/10.21037/qims-24-1775](https://dx.doi.org/10.21037/qims-24-1775)  \nPage 2 of 11 Feng et al. Longitudinal radiomics for cervical cancer outcome prediction  \nIntroduction  \nCervical cancer is the fourth most common female malignancy worldwide and has been a significant global health burden. Cervical cancer is considered preventable, and early-stage detection is associated with significantly improved survival rates. Nevertheless, the disease remains a major cause of female mortality in low- and middlei","cbCaiudmfRbsSRYK","https://ap.wps.com/l/cbCaiudmfRbsSRYK","pdf",319848,11,"English","# Background\n# Methods\n# Results\n# Conclusions\n# Keywords","[{\"question\":\"What imaging modalities and time stages were used to predict cervical cancer treatment response?\",\"answer\":\"The study used ADC, DCE, and PET imaging across pre-, mid-, and post-treatment stages in 22 cervical cancer patients.\"},{\"question\":\"Which feature type performed best in predicting treatment response?\",\"answer\":\"Texture features derived from the Gray Level Co-occurrence Matrix (GLCM) in 2D planes were most effective, achieving an AUC of 0.73±0.12 and reaching 0.75 when combined with shape features.\"},{\"question\":\"How did using a reduced set of imaging time points affect performance?\",\"answer\":\"Using only post-stage GLCM2D features produced an AUC only about 4% lower than using all time points, indicating reduced sampling may still retain strong predictive power.\"}]","Multimodality and temporal analysis of cervical cancer treatment response | PDF",1790748874,28]