{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T05:07:19Z","timestamp":1790140039095,"version":"4.0.1"},"reference-count":38,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T00:00:00Z","timestamp":1789948800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Automated CT lung nodule classification suffers from domain shift across centers. We propose LoRA-GRL, combining Low-Rank Adaptation (LoRA) with adversarial domain harmonization via a Gradient Reversal Layer. Rank-8 LoRA modules were inserted into all attention and feed-forward linear projections (48 sites) of a frozen ViT-S backbone; a domain discriminator encouraged center-invariant features across three centers. Normal vs. Benign and Normal vs. Malignant tasks used 264 patients with patient-level five-fold stratified cross-validation. LoRA-GRL achieved AUCs of 0.9735 and 0.9869, close to full fine-tuning (0.9747 and 0.9863). Differences from strongest baselines fell within overlapping 95% CIs under paired bootstrap, indicating comparable discrimination, not superiority. Efficiency is the main advantage: only 0.789 M trainable parameters, a 96.4% reduction from 21.865 M, and inference latency matched full fine-tuning after adapter merging. Grad-CAM showed nodule-localized predictions but also slice-selection failures. For benign classification, higher AUC than plain LoRA came with higher specificity but lower sensitivity. For malignant classification, sensitivity was 0.9412 (5.9 pp above full fine-tuning) and specificity 0.9697. The small cross-center AUC range (0.0011) reflects internal consistency across participating centers, not unseen-site generalization. LoRA-GRL is a promising parameter-efficient candidate for multi-center lung nodule classification, potentially reducing scanner-specific bias and storage\/memory needs, but clinical utility requires external validation and prospective evaluation.<\/jats:p>","DOI":"10.3390\/jimaging12090461","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T09:04:17Z","timestamp":1790067857000},"page":"461","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Parameter-Efficient LoRA-GRL Adaptation for Cross-Center Classification of Benign and Malignant Lung Nodules on Heterogeneous Standard-Dose Chest CT: A Multi-Institutional Study from Palestine"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8671-7026","authenticated-orcid":false,"given":"Radwan","family":"Qasrawi","sequence":"first","affiliation":[{"name":"Department of Computer Science, Al-Quds University, Jerusalem P144, Palestine"},{"name":"Department of Computer Engineering, Istinye University, 34010 Istanbul, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5128-2642","authenticated-orcid":false,"given":"Razan","family":"AbuGhoush","sequence":"additional","affiliation":[{"name":"The Center of Technology and Innovation, Al-Quds University, Jerusalem P144, Palestine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4473-1405","authenticated-orcid":false,"given":"Ghada","family":"Issa","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Al-Quds University, Jerusalem P144, Palestine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-5085-3443","authenticated-orcid":false,"given":"Suliman","family":"Thwib","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Al-Quds University, Jerusalem P144, Palestine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Malak","family":"Amro","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Al-Quds University, Jerusalem P144, Palestine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rand","family":"Al Taweel","sequence":"additional","affiliation":[{"name":"The Center of Technology and Innovation, Al-Quds University, Jerusalem P144, Palestine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7953-172X","authenticated-orcid":false,"given":"Sara","family":"Asfour","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, Al-Quds University, Jerusalem P144, Palestine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2928-1861","authenticated-orcid":false,"given":"Yazan","family":"Dibas","sequence":"additional","affiliation":[{"name":"Department of Radiology, Al-Makassed Charity Hospital, Jerusalem P.O. Box 19482, Palestine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tawfiq","family":"Abukeshek","sequence":"additional","affiliation":[{"name":"Department of Radiology, Al-Makassed Charity Hospital, Jerusalem P.O. Box 19482, Palestine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2573-0371","authenticated-orcid":false,"given":"Marwan","family":"Qubja","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, Al-Quds University, Jerusalem P144, Palestine"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,21]]},"reference":[{"key":"ref_1","first-page":"209","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","first-page":"229","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 Cancer J. Clin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"739","DOI":"10.12688\/f1000research.7313.1","article-title":"Early detection of lung cancer","volume":"5","author":"Midthun","year":"2016","journal-title":"F1000Research"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"The National Lung Screening Trial Research Team (2011). Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening. N. Engl. J. Med., 365, 395\u2013409.","DOI":"10.1056\/NEJMoa1102873"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1007\/s00330-009-1596-y","article-title":"Computer-aided detection (CAD) of lung nodules in CT scans: Radiologist performance and reading time with incremental CAD assistance","volume":"20","author":"Roos","year":"2010","journal-title":"Eur. Radiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1842","DOI":"10.1007\/s11604-025-01828-z","article-title":"Performance of a deep-learning-based lung nodule detection system using 0.25-mm thick ultra-high-resolution CT images","volume":"43","author":"Higashibori","year":"2025","journal-title":"Jpn. J. Radiol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liang, H., Hu, M., Ma, Y., Yang, L., Chen, J., Lou, L., Chen, C., and Xiao, Y. (2023). Performance of Deep-Learning Solutions on Lung Nodule Malignancy Classification: A Systematic Review. Life, 13.","DOI":"10.3390\/life13091911"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Al-Areqi, F., \u00c7elebi, A.T., and Konyar, M.Z. (2023). Lung Cancer Classification Using Image Enhancement and CNN-Based Pre-Trained Models. 2023 Innovations in Intelligent Systems and Applications Conference (ASYU), IEEE.","DOI":"10.1109\/ASYU58738.2023.10296677"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3922","DOI":"10.1016\/j.procs.2025.04.644","article-title":"High-Precision Lung Cancer Classification with Custom CNN: Evaluation and Transfer Learning for Broader Cancer Types","volume":"258","author":"Sinha","year":"2025","journal-title":"Procedia Comput. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"107371","DOI":"10.1016\/j.bspc.2024.107371","article-title":"GSC-DVIT: A vision transformer based deep learning model for lung cancer classification in CT images","volume":"103","author":"Mannepalli","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1007\/s42979-024-03120-9","article-title":"Vision transformer based effective model for early detection and classification of lung cancer","volume":"5","author":"Kumar","year":"2024","journal-title":"SN Comput. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Agarwal, R., Gayathri, B., Rao, C.M., Alsalami, Z., and Deepthi, K.J. (2024). Automated Lung Cancer Diagnosis Using Vision Transformers (ViT) on CT Scan Data. 2024 International Conference on IoT, Communication and Automation Technology (ICICAT), IEEE.","DOI":"10.1109\/ICICAT62666.2024.10923455"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, T.Z., Xu, K., Gao, R., Tang, Y., Lasko, T.A., Maldonado, F., Sandler, K.L., and Landman, B.A. (2023). Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomography. Medical Imaging 2023: Image Processing, SPIE.","DOI":"10.1117\/12.2653911"},{"key":"ref_14","unstructured":"Dou, Q., de Castro, D.C., Kamnitsas, K., and Glocker, B. (2019). Domain Generalization via Model-Agnostic Learning of Semantic Features. Adv. Neural Inf. Process. Syst., 32, Available online: https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2019\/file\/2974788b53f73e7950e8aa49f3a306db-Paper.pdf."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.1109\/TBME.2021.3117407","article-title":"Domain Adaptation for Medical Image Analysis: A Survey","volume":"69","author":"Guan","year":"2022","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_16","unstructured":"Ganin, Y., and Lempitsky, V. (2015). Unsupervised domain adaptation by backpropagation. International Conference on Machine Learning, PMLR."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., March, M., and Lempitsky, V. (2016). Domain-adversarial training of neural networks. J. Mach. Learn. Res., 17.","DOI":"10.1007\/978-3-319-58347-1_10"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1109\/TNSE.2023.3304986","article-title":"Domain adaptation for localization using combined autoencoder and gradient reversal layer in dynamic IoT environment","volume":"11","author":"Prasad","year":"2023","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4237","DOI":"10.1109\/TMI.2020.3015224","article-title":"DoFE: Domain-Oriented Feature Embedding for Generalizable Fundus Image Segmentation on Unseen Datasets","volume":"39","author":"Wang","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Nuertey Coleman, E., Quarantiello, L., Liu, Z., Yang, Q., Mukherjee, S., Hurtado, J., and Lomonaco, V. (2025). Parameter-efficient continual fine-tuning: A survey. arXiv.","DOI":"10.2139\/ssrn.5510257"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, L., Chen, S., Jiang, L., Pan, S., Cai, R., Yang, S., and Yang, F. (2024). Parameter-efficient fine-tuning in large models: A survey of methodologies. arXiv.","DOI":"10.21203\/rs.3.rs-5393239\/v1"},{"key":"ref_22","unstructured":"Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W. (2022, January 25\u201329). LoRA: Low-Rank Adaptation of Large Language Models. Proceedings of the International Conference on Learning Representations, Online. Available online: https:\/\/openreview.net\/forum?id=nZeVKeeFYf9."},{"key":"ref_23","unstructured":"Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., Laroussilhe, Q.D., Gesmundo, A., Attariyan, M., and Gelly, S. (2019, January 24). Parameter-Efficient Transfer Learning for NLP. Proceedings of the International Conference on Machine Learning Research, Long Beach, CA, USA. Available online: https:\/\/proceedings.mlr.press\/v97\/houlsby19a.html."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lester, B., Al-Rfou, R., and Constant, N. (2021, January 7\u201311). The Power of Scale for Parameter-Efficient Prompt Tuning. Proceedings of the EMNLP 2021\u20142021 Conference on Empirical Methods in Natural Language Processing, Punta Cana, Dominican Republic.","DOI":"10.18653\/v1\/2021.emnlp-main.243"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ben-Zaken, E., Ravfogel, S., and Goldberg, Y. (2022, January 22\u201327). BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Dublin, Ireland.","DOI":"10.18653\/v1\/2022.acl-short.1"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1109\/OJEMB.2025.3530841","article-title":"Low-Rank Adaptation of Pre-Trained Large Vision Models for Improved Lung Nodule Malignancy Classification","volume":"6","author":"Veasey","year":"2025","journal-title":"IEEE Open J. Eng. Med. Biol."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hrizi, D., Tbarki, K., and Elasmi, S. (2025). Optimized Lung Nodule Classification Using CLAHE-Enhanced CT Imaging and Swin Transformer-Based Deep Feature Extraction. J. Imaging, 11.","DOI":"10.3390\/jimaging11100346"},{"key":"ref_28","unstructured":"Steiner, A., Kolesnikov, A., Zhai, X., Wightman, R., Uszkoreit, J., and Beyer, L. (2021). How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers. arXiv."},{"key":"ref_29","unstructured":"Loshchilov, I., and Hutter, F. (2017). Decoupled weight decay regularization. arXiv."},{"key":"ref_30","unstructured":"Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y. (2016). Deep Learning, MIT Press."},{"key":"ref_31","unstructured":"Stevens, E., Antiga, L., and Viehmann, T. (2020). Deep Learning with PyTorch: Build, Train, and Tune Neural Networks Using Python Tools, Manning."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Faizi, M.K., Qiang, Y., Wei, Y., Qiao, Y., Zhao, J., Aftab, R., and Urrehman, Z. (2025). Deep learning-based lung cancer classification of CT images. BMC Cancer, 25.","DOI":"10.1186\/s12885-025-14320-8"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"15420","DOI":"10.1038\/s41598-026-41161-7","article-title":"A hybrid deep learning approach integrating CNN and transformer for lung cancer classification using CT scans","volume":"16","author":"Yousafzai","year":"2026","journal-title":"Sci. Rep."},{"key":"ref_34","unstructured":"Korkmaz, C., Mehta, N., and Timofte, R. (2025). AdaptSR: Low-Rank Adaptation for Efficient and Scalable Real-World Super-Resolution. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1736","DOI":"10.1038\/s41598-024-84193-7","article-title":"A benchmark of deep learning approaches to predict lung cancer risk using national lung screening trial cohort","volume":"15","author":"Jiang","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_36","unstructured":"van Ginneken, B., Armato, S.G., de Hoop, B., van de Vorst, S., Duindam, T., Niemeijer, M., Murphy, K., Schilham, A.M.R., Retico, A., and Fantacci, M.E. (2026, July 07). Automatic Nodule Detection 2009 (ANODE09): A Benchmark for Pulmonary Nodule Detection. Available online: https:\/\/anode09.grand-challenge.org\/."},{"key":"ref_37","unstructured":"Zhong, Z., Tang, Z., He, T., Fang, H., and Yuan, C. (2024). Convolution meets lora: Parameter efficient finetuning for segment anything model. arXiv."},{"key":"ref_38","first-page":"101672","article-title":"A novel approach to integrating Vision Transformers and machine learning for robust lung nodule classification using CT imaging","volume":"18","author":"Huang","year":"2025","journal-title":"J. Radiat. Res. Appl. Sci."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/9\/461\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T04:45:55Z","timestamp":1790138755000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/9\/461"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,21]]},"references-count":38,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["jimaging12090461"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12090461","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,21]]}}}