{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T04:28:21Z","timestamp":1729225701481,"version":"3.27.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685489","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"<jats:p>As a common subtype of lung cancer, the diagnosis of lung adenocarcinoma has significant importance in clinical practice, particularly in distinguishing between pre-invasive adenocarcinoma (Pre-IA) and invasive adenocarcinoma (IAC). This distinction is critical because the two types of lesions correspond to different clinical treatment strategies: Pre-IAs typically require only regular observation, while IACs necessitate immediate surgical removal. In this article, we propose a novel deep learning model, the Radiomic Feature Deep Factorization Machine (RFDFM) network model, for distinguishing IACs from Pre-IAs in CT images, leveraging both radiomic features and deep learning features. Our novelty resides in pioneering the application of recommendation system model structures to computer-aided diagnosis of pulmonary nodules, demonstrating feasibility and effectively addressing the limitations of traditional methods in handling radiomic features. Moreover, the use of low-level feature fusion convolutional neural networks minimizes the information loss, and an element-wise attention mechanism in feature fusion stage to accentuate key features and improve model fitting. For extensive validation, 1,052 nodule samples were gathered from a total of 791 patients that were diagnosed with lung adenocarcinoma across two top-tier hospitals. The proposed RFDFM method can achieve a sota performance of 94.2% in terms of AUC. Results of extensive ablation studies demonstrate its contribution to improved performance. Finally, to promote more efficient academic communication, the analysis code is publicly available at https:\/\/github.com\/Chengcheng-Guo\/RFDFM.<\/jats:p>","DOI":"10.3233\/faia240506","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T12:42:02Z","timestamp":1729168922000},"source":"Crossref","is-referenced-by-count":0,"title":["RFDFM: A Deep Factorization Machine Network Model for Invasive Lung Adenocarcinoma Screening in CT Images"],"prefix":"10.3233","author":[{"given":"Jing","family":"Zhou","sequence":"first","affiliation":[{"name":"Center for Applied Statistics, Renmin University of China, Beijing, China"},{"name":"School of Statistics, Renmin University of China, Beijing, China"}]},{"given":"Chengcheng","family":"Guo","sequence":"additional","affiliation":[{"name":"Center for Applied Statistics, Renmin University of China, Beijing, China"},{"name":"School of Statistics, Renmin University of China, Beijing, China"}]},{"given":"Ying","family":"Ji","sequence":"additional","affiliation":[{"name":"Department of Thoracic Surgery, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240506","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T12:42:02Z","timestamp":1729168922000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240506"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240506","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}