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The P.I.N.K study enrolled 66 women and formed an ABVS+DBT dataset by annotating paired Automated Breast Volume Scanner and Digital Breast Tomosynthesis images. A leave-one-out nested cross-validation pipeline with threshold selection was used for malignant vs. benign classification and for comparing modalities, including adaptive feature selection with model reduction. Results show statistically significant performance and importance distributions, with strong AUC-ROC outcomes using reduced feature sets, and partial complementarity at the patient level, supporting integration toward virtual biopsy workflows.",{"@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":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/adaptive-machine-learning-approach-for-importance-evaluation-of-multimodal-breast-cancer-radiomic-features-abstract/331085/",{"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/adaptive-machine-learning-approach-for-importance-evaluation-of-multimodal-breast-cancer-radiomic-features-abstract/331085.png","ImageObject",300,407,{"name":92,"@type":93},"Oliver","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-01","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What data and imaging modalities were used in the P.I.N.K study?","Question",{"text":112,"@type":113},"The study enrolled 66 women and used paired Automated Breast Volume Scanner (ABVS) and Digital Breast Tomosynthesis (DBT) images, annotated with cancerous lesions to build the first ABVS+DBT dataset.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were the models trained and evaluated for classification?",{"text":117,"@type":113},"Models were trained using a leave-one-out nested cross-validation strategy combined with a proper threshold selection approach.",{"name":119,"@type":110,"acceptedAnswer":120},"What do the results indicate about radiomic features and modality complementarity?",{"text":121,"@type":113},"The approach yields statistically significant results even with medium-sized datasets and identifies informative radiomic features via importance distributions. DBT shows stronger predictive power than ABVS, while patient-level comparison suggests partial complementarity, with only 8.7% of lesions misclassified by both methods.","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},331085,1790232395,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":52,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144,"preview_page_count":4},8796095461610,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Adaptive Machine Learning Approach for Importance Evaluation of Multimodal Breast Cancer Radiomic Features  \nGiulio Del Corso1 · Danila Germanese1 · Claudia Caudai1 · Giada Anastasi2,3 · Paolo Belli4,5 · Alessia Formica2 · Alberto Nicolucci6 · Simone Palma4 · Maria Antonietta Pascali1 · Stefania Pieroni2 · Charlotte Trombadori4 · Sara Colantonio1 · Michela Franchini2 · Sabrina Molinaro2  \nReceived: 8 November 2023 / Revised: 26 January 2024 / Accepted: 14 February 2024 / Published online: 13 March 2024 © The Author(s) 2024  \nAbstract  \nBreast cancer holds the highest diagnosis rate among female tumors and is the leading cause of death among women. Quantitative analysis of radiological images shows the potential to address several medical challenges, including the early detection and classification of breast tumors. In the P.I.N.K study, 66 women were enrolled. Their paired Automated Breast Volume Scanner (ABVS) and Digital Breast Tomosynthesis (DBT) images, annotated with cancerous lesions, populated the first ABVS+DBT dataset. This enabled not only a radiomic analysis for the malignant vs. benign breast cancer classification, but also the comparison of the two modalities. For this purpose, the models were trained using a leave-one-out nested cross-validation strategy combined with a proper threshold selection approach. This approach provides statistically significant results even with medium-sized data sets. Additionally it provides distributional variables of importance, thus identifying the most informative radiomic features. The analysis proved the predictive capacity of radiomic modelseven using a reduced number of features. Indeed, from tomography we achieved AUC-ROC 􀀞􀀝􀀞 using 19 features and 􀀞􀀝􀀜 using 7 of them; while from ABVS we attained an AUC-ROC of 􀀞􀀝􀀜 using 22 features and 􀀞􀀝 using only 3 features. Although the predictive power of DBT outperforms ABVS, when comparing the predictions at the patient level, only 8.7% of lesions are misclassified by both methods, suggesting a partial complementarity. Notably, promising results (AUC-ROC ABVS-DBT 􀀞􀀝􀀜 - 􀀞􀀝􀀜 ) were achieved using non-geometric features, thus opening the way to the integration of virtual biopsy in medical routine.  \nKeywords Breast cancer · Radiomic · Adaptive feature selection · Model reduction  \nIntroduction  \n\n| Giulio Del Corso, Danila Germanese and Claudia Caudai share the first authorship in ascending order of age. |\n| --- |\n| Sara Colantonio, Michela Franchini and Sabrina Molinaro share last authorship. |\n\n* Giulio Del Corso [giulio.delcorso@isti.cnr.it](giulio.delcorso@isti.cnr.it)  \n* Michela Franchini[michela.franchini@cnr.it](michela.franchini@cnr.it)  \n1 Institute of Information Science and Technologies “A. Faedo”(ISTI), National Research Council of Italy (CNR), Pisa, Italy  \n2 Institute of Clinical Physiology (IFC), National Research Council of Italy (CNR), Pisa, Italy  \nBreast cancer (BC) is the leading cause of death among women, and according to the Global Burden of Disease 2019, one in every eight new cancer cases was diagnosed as BC, making it the world’s top most prevalent type of cancer [1] . There are 5 stages of BC, ranging from the  \n3 Department of Computer Science, University of Pisa, Pisa, Italy  \n4 Policlinico Gemelli IRCCS, Rome, Italy  \n5 Università Cattolica del Sacro Cuore, Rome, Italy  \n6 Studi Michelangelo srl, Firenze, Italy  \nnon-invasive ductal carcinoma in situ (stage 0) to the more invasive ones (stages I–IV) . To date, stages 0 and I exhibit an almost 100% 5-year survival rate, in contrast to stages II and III which show survival rates of 93% and 72% respectively [1] . Hence, the detection and classification of early-stage BC has a crucial impact on patients’ prognosis, as it may allow for less invasive surgical procedures.  \nThe screening procedure relies on the assessment of radiological images, essentially based on mammography (MX) [2 , 3], breast ultrasound (US) [4 , 5], or contrastenhanced magnetic resonance imag","cbCaic1vOWUQffDy","https://ap.wps.com/l/cbCaic1vOWUQffDy","pdf",3085906,"English","# Abstract\n# Introduction\n## Breast cancer burden and staging\n## Imaging-based screening methods\n## Role and limitations of biopsy\n## Radiomics and machine learning pipeline","[{\"question\":\"What data and imaging modalities were used in the P.I.N.K study?\",\"answer\":\"The study enrolled 66 women and used paired Automated Breast Volume Scanner (ABVS) and Digital Breast Tomosynthesis (DBT) images, annotated with cancerous lesions to build the first ABVS+DBT dataset.\"},{\"question\":\"How were the models trained and evaluated for classification?\",\"answer\":\"Models were trained using a leave-one-out nested cross-validation strategy combined with a proper threshold selection approach.\"},{\"question\":\"What do the results indicate about radiomic features and modality complementarity?\",\"answer\":\"The approach yields statistically significant results even with medium-sized datasets and identifies informative radiomic features via importance distributions. DBT shows stronger predictive power than ABVS, while patient-level comparison suggests partial complementarity, with only 8.7% of lesions misclassified by both methods.\"}]","Adaptive Machine Learning Approach for Importance Evaluation of Multimodal Breast Cancer Radiomic Features - Abstract | PDF",1789991694,25]