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In contrast, large vision models exhibit effective feature extraction capabilities, enabling pathological image analysis for gastrointestinal cancer with relatively small sample sizes. In this study, we developed a screening framework leveraging a large vision model for coarse-grained classification of gastric and colorectal tissues. The model was evaluated on multicenter cohorts and under limited-data conditions. Using labeled tiles from only 76 whole-slide images, the model achieved class-averaged sensitivity and precision of 0.9816 and 0.9808 on the internal test set, and 0.9161 and 0.9179 on the external test set. When trained with only 200 tiles per class from 20 wholeslide images, the model maintained comparable performance, achieving sensitivity and precision of 0.9548 and 0.9518. These findings suggest that the model has reliable performance across multicenter cohorts and potential applicability in clinical pathology workflows.<\/jats:p>","DOI":"10.2298\/csis251130024l","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T19:10:40Z","timestamp":1779217840000},"page":"801-826","source":"Crossref","is-referenced-by-count":0,"title":["Development and validation of a few-shot rapid screening model for gastrointestinal cancers using AGI large vision models"],"prefix":"10.2298","volume":"23","author":[{"given":"Lijue","family":"Liu","sequence":"first","affiliation":[{"name":"School of Automation, Central South University, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fangjie","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Automation, Central South University, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Genjian","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Automation, Beijing Information Technology Science and University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Pathology, Beijing Integrated Traditional Chinese and Western Medicine Hospital, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siya","family":"Li","sequence":"additional","affiliation":[{"name":"CAS Blue Bay Cloud Technology (Guangdong) Co., Ltd., Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Teng","family":"Pan","sequence":"additional","affiliation":[{"name":"Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Longgang Maternity and Child Institute of Shantou University Medical College, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ting","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Pathology, Beijing Ditan Hospital, Capital Medical University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Automation, Central South University Changsha, China + Xiangjiang Laboratory, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruijie","family":"Ming","sequence":"additional","affiliation":[{"name":"Department of Oncology, Chongqing University Three Gorges Hospital, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology, Head & Neck Surgery, Peking University First Hospital, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xue","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Respiratory and Critical Care Medicine, Tianjin Chest Hospital, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Wang","sequence":"additional","affiliation":[{"name":"Richard Dimbleby Laboratory of Cancer Research, Randall Division and Division of Cancer and Pharmaceutical Sciences, King\u2019s College London, London, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Pathology, Beijing Ditan Hospital, Capital Medical University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenbai","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation, Beijing Information Technology Science and University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinhai","family":"Deng","sequence":"additional","affiliation":[{"name":"Richard Dimbleby Laboratory of Cancer Research, Randall Division and Division of Cancer and Pharmaceutical Sciences, King\u2019s College London, London, UK + Guangzhou Baiyunshan Pharmaceutical Holding Co., Ltd. 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The Lancet Digital Health 5(11), e786-e797 (2023)","DOI":"10.1016\/S2589-7500(23)00148-6"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"Black-Schaffer,W.S., Morrow, J.S., Prystowsky, M.B., Steinberg, J.J.: Training pathology residents to practice 21st century medicine: a proposal. Academic pathology 3, 2374289516665393 (2016)","DOI":"10.1177\/2374289516665393"},{"key":"ref4","doi-asserted-by":"crossref","unstructured":"Cai, C., Shi, Q., Li, J., Jiao, Y., Xu, A., Zhou, Y.,Wang, X., Peng, C., Zhang, X., Cui, X., et al.: Pathologist-level diagnosis of ulcerative colitis inflammatory activity level using an automated histological grading method. International Journal of Medical Informatics 192, 105648 (2024)","DOI":"10.1016\/j.ijmedinf.2024.105648"},{"key":"ref5","doi-asserted-by":"crossref","unstructured":"Campanella, G., Hanna, M.G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K.J., Brogi, E., Reuter, V.E., Klimstra, D.S., Fuchs, T.J.: Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nature medicine 25(8), 1301-1309 (2019)","DOI":"10.1038\/s41591-019-0508-1"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"Chen, R.J., Ding, T., Lu, M.Y., Williamson, D.F., Jaume, G., Song, A.H., Chen, B., Zhang, A., Shao, D., Shaban, M., et al.: Towards a general-purpose foundation model for computational pathology. 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Journal of Gastric Cancer 23(3), 410 (2023)","DOI":"10.5230\/jgc.2023.23.e25"},{"key":"ref10","doi-asserted-by":"crossref","unstructured":"Coudray, N., Ocampo, P.S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyo, D., Moreira, A.L., Razavian, N., Tsirigos, A.: Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nature medicine 24(10), 1559-1567 (2018)","DOI":"10.1038\/s41591-018-0177-5"},{"key":"ref11","unstructured":"Da, Q.,Wang, S.,Wang,W., Yang, C.,Wang, B., Ruan, M., Fu, Z., Xu, Y., Zhou, Y.,Wang, C., et al.: Progress and challenges of pathological artificial intelligence in the era of large models. Zhonghua bing li xue za zhi= Chinese journal of pathology 54(3), 305-309 (2025)"},{"key":"ref12","doi-asserted-by":"crossref","unstructured":"Ding, T., Wagner, S.J., Song, A.H., Chen, R.J., Lu, M.Y., Zhang, A., Vaidya, A.J., Jaume, G., Shaban, M., Kim, A.,Williamson, D.F.K., Robertson, H., Chen, B., Almagro-Perez, C., Doucet, P., Sahai, S., Chen, C., Chen, C.S., Komura, D., Kawabe, A., Ochi, M., Sato, S., Yokose, T., Miyagi, Y., Ishikawa, S., Gerber, G., Peng, T., Le, L.P., Mahmood, F.: A multimodal wholeslide foundation model for pathology. Nature Medicine 31(11), 3749-3761 (Nov 2025)","DOI":"10.1038\/s41591-025-03982-3"},{"key":"ref13","doi-asserted-by":"crossref","unstructured":"Du, Y., Liu, X., Yue, L., Feng, L., Tao, P., Jing, Q.: Minidigpath: A new standard for pathology images few-shot learning classification. In: 2023 2nd International Conference on Cloud Computing, Big Data Application and Software Engineering (CBASE). pp. 136-140 (2023)","DOI":"10.1109\/CBASE60015.2023.10439094"},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"Fu, B., Zhang, M., He, J., Cao, Y., Guo, Y., Wang, R.: Stohisnet: A hybrid multi-classification model with cnn and transformer for gastric pathology images. Computer Methods and Programs in Biomedicine 221, 106924 (2022)","DOI":"10.1016\/j.cmpb.2022.106924"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"Griem, J., Eich, M.L., Schallenberg, S., Pryalukhin, A., Bychkov, A., Fukuoka, J., Zayats, V., Hulla, W., Munkhdelger, J., Seper, A., et al.: Artificial intelligence-based tool for tumor detection and quantitative tissue analysis in colorectal specimens. Modern Pathology 36(12), 100327 (2023)","DOI":"10.1016\/j.modpat.2023.100327"},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"Han, K.,Wang, Y., Chen, H., Chen, X., Guo, J., Liu, Z., Tang, Y., Xiao, A., Xu, C., Xu, Y., et al.: A survey on vision transformer. IEEE transactions on pattern analysis and machine intelligence 45(1), 87-110 (2022)","DOI":"10.1109\/TPAMI.2022.3152247"},{"key":"ref17","doi-asserted-by":"crossref","unstructured":"Hasan, K.R., Kim, S., Cho, J., Han, H.S.: Prototypical few-shot learning for histopathology classification: Leveraging foundation models with adapter architectures. IEEE ACCESS 13, 86356-86379 (2025)","DOI":"10.1109\/ACCESS.2025.3570673"},{"key":"ref18","doi-asserted-by":"crossref","unstructured":"Hinata, M., Ushiku, T.: Detecting immunotherapy-sensitive subtype in gastric cancer using histologic image-based deep learning. Scientific reports 11(1), 22636 (2021)","DOI":"10.1038\/s41598-021-02168-4"},{"key":"ref19","doi-asserted-by":"crossref","unstructured":"Huang, B., Tian, S., Zhan, N., Ma, J., Huang, Z., Zhang, C., Zhang, H., Ming, F., Liao, F., Ji, M., et al.: Accurate diagnosis and prognosis prediction of gastric cancer using deep learning on digital pathological images: A retrospective multicentre study. EBioMedicine 73 (2021)","DOI":"10.1016\/j.ebiom.2021.103631"},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"Iizuka, O., Kanavati, F., Kato, K., Rambeau, M., Arihiro, K., Tsuneki, M.: Deep learning models for histopathological classification of gastric and colonic epithelial tumours. Scientific reports 10(1), 1504 (2020)","DOI":"10.1038\/s41598-020-58467-9"},{"key":"ref21","doi-asserted-by":"crossref","unstructured":"Komura, D., Ishikawa, S.: Machine learning methods for histopathological image analysis. Computational and structural biotechnology journal 16, 34-42 (2018)","DOI":"10.1016\/j.csbj.2018.01.001"},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"Korbar, B., Olofson, A.M., Miraflor, A.P., Nicka, C.M., Suriawinata, M.A., Torresani, L., Suriawinata, A.A., Hassanpour, S.: Deep learning for classification of colorectal polyps on wholeslide images. Journal of pathology informatics 8, 30 (2017)","DOI":"10.4103\/jpi.jpi_34_17"},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"Lan, J., Chen, M., Wang, J., Du, M., Wu, Z., Zhang, H., Xue, Y., Wang, T., Chen, L., Xu, C., et al.: Using less annotation workload to establish a pathological auxiliary diagnosis system for gastric cancer. Cell Reports Medicine 4(4) (2023)","DOI":"10.1016\/j.xcrm.2023.101004"},{"key":"ref24","doi-asserted-by":"crossref","unstructured":"Le Page, A.L., Ballot, E., Truntzer, C., Derangere, V., Ilie, A., Rageot, D., Bibeau, F., Ghiringhelli, F.: Using a convolutional neural network for classification of squamous and nonsquamous non-small cell lung cancer based on diagnostic histopathology hes images. Scientific Reports 11(1), 23912 (2021)","DOI":"10.1038\/s41598-021-03206-x"},{"key":"ref25","doi-asserted-by":"crossref","unstructured":"Lu, M.Y., Chen, B., Williamson, D.F.K., Chen, R.J., Liang, I., Ding, T., Jaume, G., Odintsov, I., Le, L.P., Gerber, G., Parwani, A.V., Zhang, A., Mahmood, F.: A visual-language foundation model for computational pathology. 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Diagnostic Histopathology (2024)","DOI":"10.1016\/j.mpdhp.2024.08.001"},{"key":"ref29","doi-asserted-by":"crossref","unstructured":"Mudeng, V., Farid, M.N., Ayana, G., Choe, S.w.: Domain and histopathology adaptations- based classification for malignancy grading system. The American Journal of Pathology 193(12), 2080-2098 (2023)","DOI":"10.1016\/j.ajpath.2023.07.007"},{"key":"ref30","doi-asserted-by":"crossref","unstructured":"Nagtegaal, I.D., Odze, R.D., Klimstra, D., Paradis, V., Rugge, M., Schirmacher, P.,Washington, K.M., Carneiro, F., Cree, I.A., et al.: The 2019 who classification of tumours of the digestive system. Histopathology 76(2), 182 (2019)","DOI":"10.1111\/his.13975"},{"key":"ref31","doi-asserted-by":"crossref","unstructured":"Neidlinger, P., El Nahhas, O.S.M., Muti, H.S., Lenz, T., Hoffmeister, M., Brenner, H., van Treeck, M., Langer, R., Dislich, B., Behrens, H.M., Rocken, C., Foersch, S., Truhn, D., Marra, A., Saldanha, O.L., Kather, J.N.: Benchmarking foundation models as feature extractors for weakly supervised computational pathology. Nature Biomedical Engineering (Oct 2025)","DOI":"10.1038\/s41551-025-01516-3"},{"key":"ref32","doi-asserted-by":"crossref","unstructured":"Oh, Y., Bae, G.E., Kim, K.H., Yeo, M.K., Ye, J.C.: Multi-scale hybrid vision transformer for learning gastric histology: Ai-based decision support system for gastric cancer treatment. IEEE journal of biomedical and health informatics 27(8), 4143-4153 (2023)","DOI":"10.1109\/JBHI.2023.3276778"},{"key":"ref33","unstructured":"Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., Assran, M., Ballas, N., Galuba, W., Howes, R., Huang, P.Y., Li, S.W., Misra, I., Rabbat, M., Sharma, V., Synnaeve, G., Xu, H., Jegou, H., Mairal, J., Labatut, P., Joulin, A., Bojanowski, P.: Dinov2: Learning robust visual features without supervision (2024)"},{"key":"ref34","doi-asserted-by":"crossref","unstructured":"Park, S.y., Ayana, G., Wako, B.D., Jeong, K.C., Yoon, S.D., Choe, S.w.: Vision transformers for low-quality histopathological images: A case study on squamous cell carcinoma margin classification. Diagnostics 15(3), 260 (2025)","DOI":"10.3390\/diagnostics15030260"},{"key":"ref35","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: Machine learning in python. the Journal of machine Learning research 12, 2825-2830 (2011)"},{"key":"ref36","doi-asserted-by":"crossref","unstructured":"Rahman, T., Baras, A.S., Chellappa, R.: Evaluation of a task-specific self-supervised learning framework in digital pathology relative to transfer learning approaches and existing foundation models. Modern Pathology 38(1), 100636 (2025)","DOI":"10.1016\/j.modpat.2024.100636"},{"key":"ref37","unstructured":"Saillard, C., Jenatton, R., Llinares-Lopez, F., Mariet, Z., Cahane, D., Durand, E., Vert, J.P.: H-optimus-0 (2024), https:\/\/github.com\/bioptimus\/releases\/tree\/main\/models\/h-optimus\/v0"},{"key":"ref38","doi-asserted-by":"crossref","unstructured":"Song, Y., Wang, T., Cai, P., Mondal, S.K., Sahoo, J.P.: A comprehensive survey of few-shot learning: Evolution, applications, challenges, and opportunities. ACM Comput. Surv. 55(13s) (Jul 2023)","DOI":"10.1145\/3582688"},{"key":"ref39","doi-asserted-by":"crossref","unstructured":"Song, Z., Zou, S., Zhou, W., Huang, Y., Shao, L., Yuan, J., Gou, X., Jin, W., Wang, Z., Chen, X., et al.: Clinically applicable histopathological diagnosis system for gastric cancer detection using deep learning. Nature communications 11(1), 4294 (2020)","DOI":"10.1038\/s41467-020-18147-8"},{"key":"ref40","doi-asserted-by":"crossref","unstructured":"Srinidhi, C.L., Ciga, O., Martel, A.L.: Deep neural network models for computational histopathology: A survey. Medical Image Analysis 67, 101813 (2021)","DOI":"10.1016\/j.media.2020.101813"},{"key":"ref41","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2818-2826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref42","doi-asserted-by":"crossref","unstructured":"Tsuneki, M., Kanavati, F.: Weakly supervised learning for poorly differentiated adenocarcinoma classification in gastricendoscopic submucosal dissection whole slide images. Technology in Cancer Research & Treatment 21, 15330338221142674 (2022)","DOI":"10.1177\/15330338221142674"},{"key":"ref43","doi-asserted-by":"crossref","unstructured":"Tung, C.L., Chang, H.C., Yang, B.Z., Hou, K.J., Tsai, H.H., Tsai, C.Y., Yu, P.T.: Identifying pathological slices of gastric cancer via deep learning. Journal of the Formosan Medical Association 121(12), 2457-2464 (2022)","DOI":"10.1016\/j.jfma.2022.05.004"},{"key":"ref44","doi-asserted-by":"crossref","unstructured":"Veldhuizen, G.P., Rocken, C., Behrens, H.M., Cifci, D., Muti, H.S., Yoshikawa, T., Arai, T., Oshima, T., Tan, P., Ebert, M.P., et al.: Deep learning-based subtyping of gastric cancer histology predicts clinical outcome: a multi-institutional retrospective study. Gastric Cancer 26(5), 708-720 (2023)","DOI":"10.1007\/s10120-023-01398-x"},{"key":"ref45","unstructured":"Vinay Kumar, Abul K\u02d9 Abbas, D.J., Aster, J.C.: Robbins & Cotran Pathologic Basis of Disease. Elsevier, Illinois, USA (2020)"},{"key":"ref46","doi-asserted-by":"crossref","unstructured":"Vorontsov, E., Bozkurt, A., Casson, A., Shaikovski, G., Zelechowski, M., Severson, K., Zimmermann, E., Hall, J., Tenenholtz, N., Fusi, N., et al.: A foundation model for clinical-grade computational pathology and rare cancers detection. Nature medicine 30(10), 2924-2935 (2024)","DOI":"10.1038\/s41591-024-03141-0"},{"key":"ref47","doi-asserted-by":"crossref","unstructured":"Wang, J., Liu, X.: Medical image recognition and segmentation of pathological slices of gastric cancer based on deeplab v3+ neural network. Computer methods and programs in biomedicine 207, 106210 (2021)","DOI":"10.1016\/j.cmpb.2021.106210"},{"key":"ref48","doi-asserted-by":"crossref","unstructured":"Wang, K.S., Yu, G., Xu, C., Meng, X.H., Zhou, J., Zheng, C., Deng, Z., Shang, L., Liu, R., Su, S., et al.: Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence. BMC medicine 19, 1-12 (2021)","DOI":"10.1186\/s12916-021-01942-5"},{"key":"ref49","doi-asserted-by":"crossref","unstructured":"Wang, S., Zhu, Y., Yu, L., Chen, H., Lin, H., Wan, X., Fan, X., Heng, P.A.: Rmdl: Recalibrated multi-instance deep learning for whole slide gastric image classification. Medical image analysis 58, 101549 (2019)","DOI":"10.1016\/j.media.2019.101549"},{"key":"ref50","doi-asserted-by":"crossref","unstructured":"Wang, X., Zhao, J., Marostica, E., Yuan, W., Jin, J., Zhang, J., Li, R., Tang, H., Wang, K., Li, Y., et al.: A pathology foundation model for cancer diagnosis and prognosis prediction. Nature 634(8035), 970-978 (2024)","DOI":"10.1038\/s41586-024-07894-z"},{"key":"ref51","doi-asserted-by":"crossref","unstructured":"Wang, Z., Peng, H., Wan, J., Song, A.: Identification of histopathological classification and establishment of prognostic indicators of gastric adenocarcinoma based on deep learning algorithm. Medical molecular morphology pp. 1-13 (2024)","DOI":"10.1007\/s00795-024-00399-8"},{"key":"ref52","doi-asserted-by":"crossref","unstructured":"Wei, J.W., Suriawinata, A.A., Vaickus, L.J., Ren, B., Liu, X., Lisovsky, M., Tomita, N., Abdollahi, B., Kim, A.S., Snover, D.C., et al.: Evaluation of a deep neural network for automated classification of colorectal polyps on histopathologic slides. JAMA network open 3(4), e203398-e203398 (2020)","DOI":"10.1001\/jamanetworkopen.2020.3398"},{"key":"ref53","doi-asserted-by":"crossref","unstructured":"Xie, Y., Shi, L., He, X., Luo, Y.: Gastrointestinal cancers in china, the usa, and europe. Gastroenterology report 9(2), 91-104 (2021)","DOI":"10.1093\/gastro\/goab010"},{"key":"ref54","doi-asserted-by":"crossref","unstructured":"Xu, H., Usuyama, N., Bagga, J., Zhang, S., Rao, R., Naumann, T., Wong, C., Gero, Z., Gonzalez, J., Gu, Y., et al.: A whole-slide foundation model for digital pathology from realworld data. Nature 630(8015), 181-188 (2024)","DOI":"10.1038\/s41586-024-07441-w"},{"key":"ref55","doi-asserted-by":"crossref","unstructured":"Yang, Z., Wei, T., Liang, Y., Yuan, X., Gao, R., Xia, Y., Zhou, J., Zhang, Y., Yu, Z.: A foundation model for generalizable cancer diagnosis and survival prediction from histopathological images. Nature Communications 16(1), 2366 (2025)","DOI":"10.1038\/s41467-025-57587-y"},{"key":"ref56","unstructured":"Zimmermann, E., Vorontsov, E., Viret, J., Casson, A., Zelechowski, M., Shaikovski, G., Tenenholtz, N., Hall, J., Klimstra, D., Yousfi, R., et al.: Virchow2: Scaling self-supervised mixed magnification models in pathology. arXiv preprint arXiv:2408.00738 (2024)"}],"container-title":["Computer Science and Information Systems"],"original-title":[],"language":"en","deposited":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T19:12:21Z","timestamp":1779217941000},"score":1,"resource":{"primary":{"URL":"https:\/\/doiserbia.nb.rs\/Article.aspx?ID=1820-02142600024L"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":56,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026]]}},"URL":"https:\/\/doi.org\/10.2298\/csis251130024l","relation":{},"ISSN":["1820-0214","2406-1018"],"issn-type":[{"value":"1820-0214","type":"print"},{"value":"2406-1018","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}