{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T18:08:46Z","timestamp":1758478126662,"version":"3.41.2"},"reference-count":50,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T00:00:00Z","timestamp":1718582400000},"content-version":"vor","delay-in-days":168,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004829","name":"Department of Science and Technology of Sichuan Province","doi-asserted-by":"publisher","award":["2022ZDZX0023"],"award-info":[{"award-number":["2022ZDZX0023"]}],"id":[{"id":"10.13039\/501100004829","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:p>In colorectal cancer (CRC), accurately predicting lymph node metastasis (LNM) contributes to developing appropriate treatment plans and serves as the key to long\u2010term survival of patients. In the clinical settings, preoperative LNM diagnosis in CRC predominantly depends on computed tomography (CT). Nevertheless, lymph nodes are small in size and difficult to identify on 3D CT scans, and CT\u2010based diagnosis of metastatic lymph nodes is prone to a significant misdiagnosis rate and lacks consistency across clinicians. Currently, there is no automatic system available for LNM prediction in CRC via 3D CT scans. In addition, existing deep learning\u2010 (DL\u2010) based lymph node detection models present low detection accuracy and high false\u2010positive rates, and most existing DL\u2010based lymph node metastasis prediction models mainly use tumor area characteristics but fail to adequately utilize lymph node information, thus not yielding satisfactory results. To tackle these issues, we propose an intelligent diagnosis system for this challenging task, mainly including a lymph node detection (LND) model and a lymph node metastasis prediction (LNMP) model. In detail, the LND model utilizes an encoder\u2010decoder network to detect lymph nodes, and the LNMP model employs an innovative attention\u2010based multiple instance learning (MIL) network. An instance\u2010level self\u2010attention feature enhancement module is designed to extract and augment lymph node features as a bag of instances. Furthermore, a bag\u2010level MIL prediction module is employed to extract instance features and create a bag representation for the ultimate LNM prediction. As far as we know, the proposed intelligent system represents the pioneering method for addressing this complex clinical challenge. In experiments, our proposed intelligent system achieves the AUC of 75.4% and the accuracy of 73.9%, showcasing a significant enhancement compared to physicians specialising in CRC and highlighting its strong clinical applicability. The accessible code can be found at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/SCU-MI\/IS-LNM\">https:\/\/github.com\/SCU-MI\/IS-LNM<\/jats:ext-link>.<\/jats:p>","DOI":"10.1155\/2024\/7629441","type":"journal-article","created":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T15:34:21Z","timestamp":1718638461000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Intelligent System of Predicting Lymph Node Metastasis in Colorectal Cancer Using 3D CT Scans"],"prefix":"10.1155","volume":"2024","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3075-9141","authenticated-orcid":false,"given":"Min","family":"Xie","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6218-5923","authenticated-orcid":false,"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7815-3994","authenticated-orcid":false,"given":"Xinyang","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4440-7365","authenticated-orcid":false,"given":"Jiayue","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7299-3152","authenticated-orcid":false,"given":"Xingyu","family":"Zou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9577-8925","authenticated-orcid":false,"given":"Yiji","family":"Mao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9821-508X","authenticated-orcid":false,"given":"Haixian","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2024,6,17]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21660"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.23736\/s0026-4733.18.07708-8"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.4143\/crt.2014.312"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1093\/jnci\/djk092"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.18632\/oncotarget.8919"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1097\/dcr.0000000000001387"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.3748\/wjg.v20.i45.16964"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10120-021-01158-9"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-07038-1"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/s0720-048x(02)00036-0"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00261-007-9262-9"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-011-2104-8"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1093\/jjco\/hyn032"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1136\/gutjnl-2014-309086"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.1200\/jco.2016.68.9091"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1136\/jclinpath-2013-202146"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jamcollsurg.2004.09.037"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.crad.2010.01.024"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature21056"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101555"},{"key":"e_1_2_9_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2020.04.045"},{"key":"e_1_2_9_22_2","doi-asserted-by":"publisher","DOI":"10.1200\/jco.2015.65.9128"},{"key":"e_1_2_9_23_2","doi-asserted-by":"publisher","DOI":"10.1053\/j.gastro.2020.09.027"},{"key":"e_1_2_9_24_2","doi-asserted-by":"crossref","unstructured":"WangH. WangH. SongL. andGuoQ. Automatic diagnosis of rectal cancer based on ct images by deep learning method Proceedings of the 2019 12th International Congress on Image and Signal Processing BioMedical Engineering and Informatics (CISP-BMEI) October 2019 Suzhou China IEEE 1\u20135.","DOI":"10.1109\/CISP-BMEI48845.2019.8965731"},{"key":"e_1_2_9_25_2","doi-asserted-by":"crossref","unstructured":"GlaserS. MaicasG. BedrikovetskiS. SammourT. andCarneiroG. Semi-supervised multi-domain multi-task training for metastatic colon lymph node diagnosis from abdominal ct Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) April 2020 Iowa City IA USA IEEE 1478\u20131481.","DOI":"10.1109\/ISBI45749.2020.9098372"},{"key":"e_1_2_9_26_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12885-021-08773-w"},{"key":"e_1_2_9_27_2","doi-asserted-by":"crossref","unstructured":"RonnebergerO. FischerP. andBroxT. U-net: convolutional networks for biomedical image segmentation Lecture Notes in Computer Science Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015 October 2015 Munich Germany 234\u2013241 https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28 2-s2.0-84951834022.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_9_28_2","doi-asserted-by":"crossref","unstructured":"HuangG. LiuZ. Van Der MaatenL. andWeinbergerK. Q. Densely connected convolutional networks Proceedings of the IEEE conference on computer vision and pattern recognition July 2017 Honolulu HI USA 4700\u20134708.","DOI":"10.1109\/CVPR.2017.243"},{"key":"e_1_2_9_29_2","doi-asserted-by":"crossref","unstructured":"XieM. ZhangY. LiX. MaoY. ZouX. andZhangH. Predicting lymph node metastasis of colorectal cancer in ct scans using attention-based multiple instance learning Proceedings of the 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) December 2023 Istanbul Turkiye 2695\u20132701.","DOI":"10.1109\/BIBM58861.2023.10385936"},{"key":"e_1_2_9_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2018.02.010"},{"key":"e_1_2_9_31_2","first-page":"166","article-title":"Mediastinal lymph node detection on thoracic ct scans using spatial prior from multi-atlas label fusion","volume":"9035","author":"Liu J.","year":"2014","journal-title":"Medical Imaging 2014: Computer-Aided Diagnosis"},{"key":"e_1_2_9_32_2","doi-asserted-by":"crossref","unstructured":"SeffA. LuL. CherryK. M. RothH. R. LiuJ. WangS. HoffmanJ. TurkbeyE. B. andSummersR. M. 2d view aggregation for lymph node detection using a shallow hierarchy of linear classifiers Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2014 September 2014 Boston MA USA Springer 544\u2013552.","DOI":"10.1007\/978-3-319-10404-1_68"},{"key":"e_1_2_9_33_2","first-page":"381","article-title":"Abdominal lymphadenopathy detection using random forest","volume":"9035","author":"Cherry K. M.","year":"2014","journal-title":"Medical Imaging 2014: Computer-Aided Diagnosis"},{"key":"e_1_2_9_34_2","doi-asserted-by":"crossref","unstructured":"HuangY. XueY. LanJ. DengY. ChenG. ZhangH. DangM. andTongT. Deep learning framework for detecting positive lymph nodes of gastric cancer on histopathological images Proceedings of the 2021 6th International Conference on Biomedical Imaging Signal Processing January 2021 Xiamen China 14\u201323.","DOI":"10.1145\/3502803.3502806"},{"key":"e_1_2_9_35_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12112660"},{"key":"e_1_2_9_36_2","doi-asserted-by":"crossref","unstructured":"RothH. R. LuL. SeffA. CherryK. M. HoffmanJ. WangS. LiuJ. TurkbeyE. andSummersR. M. A new 2.5 d representation for lymph node detection using random sets of deep convolutional neural network observations Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2014 September 2014 Boston MA USA Springer 520\u2013527.","DOI":"10.1007\/978-3-319-10404-1_65"},{"key":"e_1_2_9_37_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-019-01948-8"},{"key":"e_1_2_9_38_2","doi-asserted-by":"publisher","DOI":"10.1002\/int.22452"},{"key":"e_1_2_9_39_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.106953"},{"key":"e_1_2_9_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2019.2892409"},{"key":"e_1_2_9_41_2","unstructured":"RenS. HeK. GirshickR. andSunJ. Faster r-cnn: towards real-time object detection with region proposal networks Advances in Neural Information Processing Systems 28."},{"key":"e_1_2_9_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105128"},{"key":"e_1_2_9_43_2","unstructured":"IslamM. A. JiaS. andBruceN. D. How much position information do convolutional neural networks encode? https:\/\/arxiv.org\/abs\/2001.08248."},{"key":"e_1_2_9_44_2","doi-asserted-by":"crossref","unstructured":"HeK. ZhangX. RenS. andSunJ. Deep residual learning for image recognition Proceedings of the IEEE conference on computer vision and pattern recognition June 2016 Las Vegas NV USA 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_9_45_2","unstructured":"VaswaniA. ShazeerN. ParmarN. UszkoreitJ. JonesL. GomezA. N. Kaiser\u0141. andPolosukhinI. Attention is all you need Advances in Neural Information Processing Systems 30."},{"key":"e_1_2_9_46_2","unstructured":"IlseM. TomczakJ. andWellingM. Attention-based deep multiple instance learning Proceedings of the International conference on machine learning June 2018 Vienna Austria PMLR 2127\u20132136."},{"key":"e_1_2_9_47_2","doi-asserted-by":"crossref","unstructured":"TangH. ZhangC. andXieX. Nodulenet: decoupled false positive reduction for pulmonary nodule detection and segmentation Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference October 2019 Shenzhen China Springer 266\u2013274.","DOI":"10.1007\/978-3-030-32226-7_30"},{"key":"e_1_2_9_48_2","doi-asserted-by":"crossref","unstructured":"LiY.andFanY. Deepseed: 3d squeeze-and-excitation encoder-decoder convolutional neural networks for pulmonary nodule detection Proceedings. IEEE International Symposium on Biomedical Imaging 2020 Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) April 2020 Iowa City IA USA 1866\u20131869 https:\/\/doi.org\/10.1109\/ISBI45749.2020.9098317.","DOI":"10.1109\/ISBI45749.2020.9098317"},{"key":"e_1_2_9_49_2","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2020.2996256"},{"key":"e_1_2_9_50_2","doi-asserted-by":"crossref","unstructured":"LiB. LiY. andEliceiriK. W. Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition June 2021 Nashville TN USA 14318\u201314328.","DOI":"10.1109\/CVPR46437.2021.01409"}],"container-title":["International Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2024\/7629441","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T15:34:32Z","timestamp":1718638472000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2024\/7629441"}},"subtitle":[],"editor":[{"given":"Subrata Kumar","family":"Sarker","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2024,1]]},"references-count":50,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["10.1155\/2024\/7629441"],"URL":"https:\/\/doi.org\/10.1155\/2024\/7629441","archive":["Portico"],"relation":{},"ISSN":["0884-8173","1098-111X"],"issn-type":[{"type":"print","value":"0884-8173"},{"type":"electronic","value":"1098-111X"}],"subject":[],"published":{"date-parts":[[2024,1]]},"assertion":[{"value":"2023-10-21","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-06-04","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-06-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"7629441"}}