{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T14:25:48Z","timestamp":1791296748152,"version":"4.1.0"},"reference-count":40,"publisher":"IOP Publishing","issue":"1","license":[{"start":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T00:00:00Z","timestamp":1766016000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T00:00:00Z","timestamp":1766016000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Biomed. Phys. Eng. Express"],"published-print":{"date-parts":[[2026,2,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Early and accurate detection of pulmonary nodules in computed tomography (CT) scans is critical for reducing lung cancer mortality. While convolutional neural networks (CNNs) and Transformer-based architectures have been widely used for this task, they often suffer from insufficient global context awareness, quadratic complexity, and dependence on post-processing steps such as non-maximum suppression (NMS). This study aims to develop a novel 3D lung nodule detection framework that balances local and global contextual awareness with low computational complexity, while minimizing reliance on manual threshold tuning and redundant post-processing. We propose FCMamba, a flexible connected visual state-space model adapted from the recently introduced Mamba architecture. To enhance spatial modelling, we introduce a flexible path encoding strategy that reorders 3D feature sequences adaptively based on input relevance. In addition, a Top Query Matcher, guided by the Hungarian matching algorithm, is integrated into the training process to replace traditional NMS and enable end-to-end one-to-one nodule matching. The model is trained and evaluated using 10-fold cross-validation on the LIDC-IDRI dataset, which contains 888 CT scans. FCMamba outperforms several state-of-the-art methods, including CNN, Transformer, and hybrid models, across seven predefined false positives per scan (FPs\/scan) levels. It achieves a sensitivity improvement of 2.6% to 20.3% at low FPs\/scan (0.125) and delivers the highest CPM and FROC-AUC scores. The proposed method demonstrates balanced performance across nodule sizes, reduced false positives, and improved robustness, particularly in high-confidence predictions. FCMamba provides an efficient, scalable and accurate solution for 3D lung nodule detection. Its flexible spatial modeling and elimination of post-processing make it well-suited for clinical usage and adaptable to other medical imaging tasks.<\/jats:p>","DOI":"10.1088\/2057-1976\/ae2a37","type":"journal-article","created":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T22:47:23Z","timestamp":1765320443000},"page":"015023","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Flexible state space modelling for accurate and efficient 3D lung nodule detection"],"prefix":"10.1088","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3548-1815","authenticated-orcid":true,"given":"Wenjia","family":"Song","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangfang","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Henry","family":"Marshall","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kwun M","family":"Fong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1074-2601","authenticated-orcid":true,"given":"Feng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,12,18]]},"reference":[{"key":"bpexae2a37bib1","doi-asserted-by":"publisher","first-page":"229","DOI":"10.3322\/caac.21834","type":"journal-article","article-title":"Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA: A Cancer Journal for Clinicians"},{"key":"bpexae2a37bib2","doi-asserted-by":"publisher","first-page":"12","DOI":"10.3322\/caac.21820","type":"journal-article","article-title":"Cancer statistics, 2024","volume":"74","author":"Siegel","year":"2024","journal-title":"CA: A Cancer Journal for Clinicians"},{"key":"bpexae2a37bib3","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1056\/NEJMoa1102873","type":"journal-article","article-title":"Reduced lung-cancer mortality with low-dose computed tomographic screening","volume":"365","author":"The National Lung Screening Trial Research Team","year":"2011","journal-title":"New Engl. 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Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2025-09-05","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-12-09","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-12-18","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}