{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T02:12:59Z","timestamp":1788228779160,"version":"build-2803163510"},"reference-count":34,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004242","name":"Princess Nourah bint Abdulrahman University","doi-asserted-by":"publisher","award":["PNURSP2026R508"],"award-info":[{"award-number":["PNURSP2026R508"]}],"id":[{"id":"10.13039\/501100004242","id-type":"DOI","asserted-by":"publisher"}]},{"award":["PNURSP2026R508"],"award-info":[{"award-number":["PNURSP2026R508"]}],"id":[{"id":"https:\/\/ror.org\/05b0cyh02","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Lung cancer is still one of the top cancer mortality causes around the world, and there is a need for an accurate and clinically reliable diagnostic tool. While Computed Tomography (CT) imaging is very useful for evaluation of pulmonary nodules and tumor morphology, its interpretation is complicated by inter-patient variability, imaging artifacts, low tissue contrast, and tumor heterogeneity. Although Computer-Aided Diagnosis (CAD) systems have enhanced the diagnostic process, handcrafted feature-based approaches often fail to capture complex tumor characteristics, and numerous deep learning systems lack clinical interpretability. To tackle these challenges, this study suggests a unified diagnostic approach to characterize the tumor comprehensively. Methods: Lung window intensity clipping and the MedSAM foundation model are used to segment the tumor regions. After segmentation, handcrafted texture, shape, morphology and keypoint features are extracted in addition to deep features extracted by ResNet50. Particle Swarm Optimization (PSO) is used to select and refine the features, followed by an LSTM network that learns the sequential relationships among features for histological subtype classification. Results: It was observed that the proposed approach outperformed the benchmark approaches by attaining a higher accuracy of 93.70% and 94.70% on the Lung-PET-CT-Dx and LIDC-IDRI datasets, respectively. The ablation analysis supports the contribution of each module, clearly showing the progressive improvement of the overall classification performance obtained by integrating the complementary modules. Conclusions: The proposed framework effectively incorporated MedSAM-based tumor segmentation, radiomic feature analysis, and deep feature representation and sequential dependency modeling all in a single diagnostic workflow for lung cancer evaluation and diagnosis. These results prove its feasibility for explainable computer-aided diagnosis and decision support for lung cancer evaluation.<\/jats:p>","DOI":"10.3390\/diagnostics16172805","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T01:05:26Z","timestamp":1788224726000},"page":"2805","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-Driven Tumor Characterization and Histological Subtype Classification in Lung Cancer Using CT Imaging"],"prefix":"10.3390","volume":"16","author":[{"given":"Mohammad","family":"Shorfuzzaman","sequence":"first","affiliation":[{"name":"Department of Software Engineering, College of Engineering and Advanced Computing, Alfaisal University, Riyadh 11533, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-8440-8781","authenticated-orcid":false,"given":"Abdullah","family":"Iftikhar","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Riphah International University, Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2186-2342","authenticated-orcid":false,"given":"Shaheryar","family":"Najam","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Bahria University, H-11, Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"visualization","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7460-7257","authenticated-orcid":false,"given":"Jasem","family":"Almotiri","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21974, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2262-2340","authenticated-orcid":false,"given":"Abdullah Fawaz","family":"Aljulayfi","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 16273, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2680-8206","authenticated-orcid":false,"given":"Dina Abdulaziz","family":"AlHammadi","sequence":"additional","affiliation":[{"name":"Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"resources","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"given":"Ahmad","family":"Jalal","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Air University, Islamabad 44000, Pakistan"},{"name":"Department of Computer Science and Engineering, College of Informatics, Korea University, Seoul 02841, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"supervision","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2026, June 09). Global Cancer Burden Growing, Amidst Mounting Need for Services. Available online: https:\/\/www.who.int."},{"key":"ref_2","unstructured":"Li, P., Wang, S., Li, T., Lu, J., HuangFu, Y., and Wang, D. (2020). A large-scale CT and PET\/CT dataset for lung cancer diagnosis. Cancer Imaging Arch."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1177\/11769351241290608","article-title":"Advancements and Challenges in the Image-Based Diagnosis of Lung and Colon Cancer: A Comprehensive Review","volume":"23","author":"Patharia","year":"2024","journal-title":"Cancer Inform."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1186\/s41747-025-00617-7","article-title":"Advancing deep learning-based segmentation for multiple lung cancer lesions in real-world multicenter CT scans","volume":"9","author":"Laudazi","year":"2025","journal-title":"Eur. Radiol. Exp."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Hiraman, A., Viriri, S., and Gwetu, M. (2024). Lung tumor segmentation: A review of the state of the art. Front. Comput. Sci., 6.","DOI":"10.3389\/fcomp.2024.1423693"},{"key":"ref_6","first-page":"1001724","article-title":"Multi-modal feature fusion: A hybrid framework for lung cancer subtype classification using CT imaging with radiomic and deep features","volume":"18","author":"Leng","year":"2025","journal-title":"J. Radiat. Res. Appl. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3535","DOI":"10.1007\/s10278-025-01442-5","article-title":"Hybrid approach to classifying histological subtypes of non-small cell lung cancer (NSCLC): Combining radiomics and deep learning features from CT images","volume":"38","author":"Oh","year":"2025","journal-title":"J. Imaging Inform. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1777","DOI":"10.1038\/s41598-025-31432-0","article-title":"An efficient hybrid artificial intelligence framework for lung cancer classification using CT images","volume":"16","author":"Kamala","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_9","first-page":"101721","article-title":"Multicenter deep learning study for lung tumor subtype segmentation in CT images using Swin UNETR, nnU-Net, and TransUNet","volume":"18","author":"He","year":"2025","journal-title":"J. Radiat. Res. Appl. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3711","DOI":"10.1007\/s10278-025-01458-x","article-title":"Landscape of 2D deep learning segmentation networks for lung cancer CT segmentation","volume":"38","author":"Mehrnia","year":"2025","journal-title":"J. Imaging Inform. Med."},{"key":"ref_11","first-page":"387","article-title":"Lung cancer screening with low-dose computed tomography","volume":"120","author":"Reck","year":"2023","journal-title":"J. Thorac. Oncol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"110344","DOI":"10.1016\/j.radonc.2024.110344","article-title":"Systematic review and meta-analysis of deep learning applications in computed tomography lung cancer segmentation","volume":"197","author":"Wang","year":"2024","journal-title":"Radiother. Oncol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e233029","DOI":"10.1148\/radiol.233029","article-title":"Automated deep learning\u2013based detection and segmentation of lung tumors at CT imaging","volume":"314","author":"Kashyap","year":"2025","journal-title":"Radiology"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s12672-025-02115-z","article-title":"Enhanced lung cancer subtype classification using attention-integrated DeepCNN and radiomic features from CT images: A focus on feature reproducibility","volume":"16","author":"Alsallal","year":"2025","journal-title":"Discov. Oncol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Dunn, B., Pierobon, M., and Wei, Q. (2023). Automated Classification of Lung Cancer Subtypes Using Deep Learning and CT-Scan Based Radiomic Analysis. Bioengineering, 10.","DOI":"10.3390\/bioengineering10060690"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1515\/med-2020-0028","article-title":"Classification of pathological types of lung cancer from CT images by deep residual neural networks with transfer learning strategy","volume":"15","author":"Wang","year":"2020","journal-title":"Open Med."},{"key":"ref_17","unstructured":"Yang, X.S., Sherratt, S., Dey, N., and Joshi, A. (2025). PET Image Classification for Lung Cancer Diagnosis: Deep Learning with Transfer Learning, Data Augmentation and Region-Based Prediction Explanation by Integrated Gradients. Proceedings of the Tenth International Congress on Information and Communication Technology (ICICT 2025), Springer. Lecture Notes in Networks and Systems."},{"key":"ref_18","unstructured":"Chowdhury, S.K., Rahman, T., Khandakar, A., Mazhar, R., Qiblawey, Y., and Chowdhury, M.E.H. (2025). An anatomy aware hybrid deep learning framework for lung cancer detection and classification on PET\/CT images. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1002\/ima.22858","article-title":"A transformer-based deep neural network for detection and classification of lung cancer via PET\/CT images","volume":"33","author":"Barbouchi","year":"2023","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zeng, Q., Hu, T., Chen, Z., Zheng, J., Li, J., and Pan, Y. (2025). YOLO-ED: An efficient lung cancer detection model based on improved YOLOv8 using the Lung-PET-CT-Dx dataset. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0330732"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","article-title":"Segment anything in medical images","volume":"15","author":"Ma","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"19233","DOI":"10.1038\/s41598-026-50449-7","article-title":"LungDxFormer: A transformer-CNN hybrid model with dynamic spatial attention for accurate lung cancer detection and classification","volume":"16","author":"Rao","year":"2026","journal-title":"Sci. Rep."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Huang, K.Y., Chung, C.L., and Xu, J.L. (2025). Deep learning object detection-based early detection of lung cancer using the Lung-PET-CT-Dx dataset. Front. Med., 12.","DOI":"10.3389\/fmed.2025.1567119"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1507","DOI":"10.1016\/j.procs.2025.09.272","article-title":"Simultaneous Identification and Classification of Lung Nodules in CT Images\u2014A Hierarchical Network Approach","volume":"270","author":"Hahn","year":"2025","journal-title":"Procedia Comput. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e210027","DOI":"10.1148\/ryai.2021210027","article-title":"Deep learning for lung cancer detection on screening CT scans: Results of a Large-Scale Public Competition and an Observer Study with 11 Radiologists","volume":"3","author":"Jacobs","year":"2021","journal-title":"Radiol. Artif. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1868","DOI":"10.19139\/soic-2310-5070-2814","article-title":"Lung cancer segmentation and classification with multi-dataset integration","volume":"15","author":"Abdulqader","year":"2025","journal-title":"Stat. Optim. Inf. Comput."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"108505","DOI":"10.1016\/j.compbiomed.2024.108505","article-title":"NoduleCLIP: Lung nodule classification based on multi-modal contrastive learning","volume":"175","author":"Sun","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"101739","DOI":"10.1016\/j.imu.2026.101739","article-title":"AutoLungDx: A Hybrid Deep Learning Approach for Early Lung Cancer Diagnosis Using 3D Res-U-Net, YOLOv5, and Vision Transformers","volume":"61","author":"Shuvo","year":"2026","journal-title":"Inform. Med. Unlocked"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"8159","DOI":"10.1038\/s41598-026-37187-6","article-title":"Interpretable hybrid ensemble with attention-based fusion and EAOO-GA optimization for lung cancer detection","volume":"16","author":"Aldawsari","year":"2026","journal-title":"Sci. Rep."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3860","DOI":"10.1109\/JBHI.2022.3171851","article-title":"Self-Supervised Transfer Learning Based on Domain Adaptation for Benign-Malignant Lung Nodule Classification on Thoracic CT","volume":"26","author":"Huang","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"119339","DOI":"10.1016\/j.eswa.2022.119339","article-title":"Self-supervised transfer learning framework driven by visual attention for benign\u2013malignant lung nodule classification on chest CT","volume":"215","author":"Wu","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"105543","DOI":"10.1016\/j.bspc.2023.105543","article-title":"Texture and radiomics inspired data-driven cancerous lung nodules severity classification","volume":"88","author":"Gupta","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lyu, J., Bi, X., and Ling, S.H. (2020). Multi-Level Cross Residual Network for Lung Nodule Classification. Sensors, 20.","DOI":"10.3390\/s20102837"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lyu, J., and Ling, S.H. (2018, January 17\u201321). Using Multi-Level Convolutional Neural Network for Classification of Lung Nodules on CT Images. Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, USA.","DOI":"10.1109\/EMBC.2018.8512376"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/17\/2805\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T01:14:08Z","timestamp":1788225248000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/17\/2805"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,31]]},"references-count":34,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["diagnostics16172805"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16172805","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,31]]}}}