{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T16:59:45Z","timestamp":1788454785042,"version":"build-2803163510"},"reference-count":52,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:00:00Z","timestamp":1781568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background: Heterogeneous data integration remains a major challenge in intelligent information systems, particularly under missing-modality and cross-domain conditions. Existing multimodal fusion approaches often rely on complete datasets and weak alignment mechanisms, limiting their robustness and practical applicability. Objectives: This study aims to develop and evaluate a genomics-guided multimodal representation learning framework that enables robust heterogeneous data fusion, reliable cross-modal correspondence, and accurate prediction under incomplete-data conditions. Methods: We propose a multimodal learning architecture that models genomics as the primary biological anchor and learns conditional projections to imaging modalities, including multiparametric MRI and whole-slide histopathology (WSI). The framework formulates multimodal fusion as a genomics-guided contrastive learning problem, incorporates domain-specific optimization constraints, and learns a latent shared-state representation to support inference without requiring fully paired datasets. Evaluation was conducted using public datasets, including TCGA-PRAD and TCIA, across low-risk versus higher-risk\/clinically significant prostate cancer (csPCa) discrimination, Gleason-based risk stratification, and clinically significant outcome prediction tasks under realistic multimodal and missing-modality scenarios. Results: In the adequately powered Genomics+WSI cohort (n = 486), the framework achieved an AUROC of 0.985 \u00b1 0.005 for low-risk versus higher-risk\/csPCa discrimination (p &lt; 0.001). Exploratory analysis in a small, matched Genomics+MRI cohort (n = 28) yielded an AUROC of 0.980 \u00b1 0.006 for the same endpoint; these findings are reported descriptively with bootstrap confidence intervals due to limited sample size. Because the negative reference group consisted of low-risk prostate cancer cases rather than cancer-free controls, results are interpreted as within-cancer risk discrimination rather than de novo cancer detection. The framework achieved weighted accuracy up to 92.1%, Cohen\u2019s \u03ba up to 0.86, and reduced critical decision errors by 58%. Calibration remained strong (ECE 0.021\u20130.024), and decision-curve analysis indicated improved utility with reduced unnecessary invasive workups in retrospective modeling. Robustness analysis demonstrated AUROC degradation below 0.04 under domain shifts. Single-modality inference using genomics alone maintained AUROC &gt; 0.90. Interpretability analysis revealed feature attributions aligned with domain-relevant genomic markers. Conclusions: The proposed framework provides a scalable and generalizable solution for heterogeneous multimodal data fusion, supporting reliable prediction, robustness to missing modalities, and applicability to complex information systems beyond the studied domain.<\/jats:p>","DOI":"10.3390\/cancers18121952","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T01:58:38Z","timestamp":1781661518000},"page":"1952","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Genomics-Guided Multimodal Contrastive Learning Framework for Clinically Significant Prostate Cancer Risk Stratification with Missing Clinical Data"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7983-2189","authenticated-orcid":false,"family":"Abdullah","sequence":"first","affiliation":[{"name":"Centro de Investigaci\u00f3n en Computaci\u00f3n (CIC), Instituto Polit\u00e9cnico Nacional (IPN), Mexico City 07738, Mexico"},{"name":"Department of Computer Sciences, Bahria University, Lahore 54600, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3284-0533","authenticated-orcid":false,"given":"Muhammad","family":"Shahid","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, Bahria University, Lahore 54600, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-5397-6768","authenticated-orcid":false,"given":"Muhammad Ateeb","family":"Ather","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, Bahria University, Lahore 54600, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zulaikha","family":"Fatima","sequence":"additional","affiliation":[{"name":"Faculty of Allied Health Sciences, Superior University, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6935-2870","authenticated-orcid":false,"given":"Carlos Guzm\u00e1n S\u00e1nchez","family":"Mejorada","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n en Computaci\u00f3n (CIC), Instituto Polit\u00e9cnico Nacional (IPN), Mexico City 07738, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8289-6979","authenticated-orcid":false,"given":"Miguel Jes\u00fas Torres","family":"Ruiz","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n en Computaci\u00f3n (CIC), Instituto Polit\u00e9cnico Nacional (IPN), Mexico City 07738, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4454-8791","authenticated-orcid":false,"given":"Rolando Quintero","family":"T\u00e9llez","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n en Computaci\u00f3n (CIC), Instituto Polit\u00e9cnico Nacional (IPN), Mexico City 07738, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9714-7137","authenticated-orcid":false,"given":"Miguel F\u00e9lix","family":"Mata-Rivera","sequence":"additional","affiliation":[{"name":"Interdisciplinary Professional Unit in Engineering and Advanced Technologies (UPIITA), Instituto Polit\u00e9cnico Nacional (IPN), Mexico City 07340, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5649-6189","authenticated-orcid":false,"given":"Roberto","family":"Zagal-Flores","sequence":"additional","affiliation":[{"name":"Higher School of Computing (ESCOM), Instituto Polit\u00e9cnico Nacional (IPN), Mexico City 07738, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1016\/j.euo.2021.09.006","article-title":"Epidemiology and prevention of prostate cancer","volume":"4","author":"Gandaglia","year":"2021","journal-title":"Eur. Urol. Oncol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e70878","DOI":"10.1002\/hsr2.70878","article-title":"Advancements in Biomarkers for Early Detection and Risk Stratification of Cardiovascular Diseases\u2014A Literature Review","volume":"8","author":"Nazir","year":"2025","journal-title":"Health Sci. Rep."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Pesapane, F., Acquasanta, M., Meo, R.D., Agazzi, G.M., Tantrige, P., Codari, M., Schiaffino, S., Patella, F., Esseridou, A., and Sardanelli, F. (2021). Comparison of sensitivity and specificity of biparametric versus multiparametric prostate MRI in the detection of prostate cancer in 431 men with elevated prostate-specific antigen levels. Diagnostics, 11.","DOI":"10.3390\/diagnostics11071223"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Unger, M., and Kather, J.N. (2024). A systematic analysis of deep learning in genomics and histopathology for precision oncology. BMC Med. Genom., 17.","DOI":"10.1186\/s12920-024-01796-9"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ather, M.A., Abdullah Fatima, Z., Rodr\u00edguez, J.L.O., and Sidorov, G. (2026). An Interpretable Multi-Dataset Learning Framework for Breast Cancer Prediction Using Clinical and Biomedical Tabular Data. Computers, 15.","DOI":"10.3390\/computers15020097"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"14721","DOI":"10.1038\/s41598-026-50418-0","article-title":"A novel intelligent hybrid reinforcement learning framework for autonomous decision making in complex health cognitive systems","volume":"16","author":"Abdullah","year":"2026","journal-title":"Sci. Rep."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1016\/j.pathol.2021.08.003","article-title":"Precision diagnostics: Integration of tissue pathology and genomics in cancer","volume":"53","author":"Sharma","year":"2021","journal-title":"Pathology"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Murillo-Rinc\u00f3n, A.P., and Kaucka, M. (2020). Insights into the complexity of craniofacial development from a cellular perspective. Front. Cell Dev. Biol., 8.","DOI":"10.3389\/fcell.2020.620735"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1038\/s41746-022-00689-4","article-title":"Integrated multimodal artificial intelligence framework for healthcare applications","volume":"5","author":"Soenksen","year":"2022","journal-title":"npj Digit. Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1038\/s41416-025-03291-z","article-title":"Correlation between imaging-detected and pathological extranodal extension in a randomised trial in Human Papillomavirus-positive oropharyngeal cancer","volume":"134","author":"Evans","year":"2025","journal-title":"Br. J. Cancer"},{"key":"ref_11","first-page":"183","article-title":"Deep learning for multi-modal medical imaging fusion: Enhancing diagnostic accuracy in complex disease detection","volume":"6","author":"Kumar","year":"2022","journal-title":"Int. J. Eng. Technol. Res. Manag."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1093\/jnci\/dji010","article-title":"Epigenetic changes in prostate cancer: Implication for diagnosis and treatment","volume":"97","author":"Li","year":"2005","journal-title":"J. Natl. Cancer Inst."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Chaddad, A., Tan, G., Liang, X., Hassan, L., Rathore, S., Desrosiers, C., Katib, Y., and Niazi, T. (2023). Advancements in mri-based radiomics and artificial intelligence for prostate cancer: A comprehensive review and future prospects. Cancers, 15.","DOI":"10.3390\/cancers15153839"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1007\/s00424-024-03002-2","article-title":"Decoding pathology: The role of computational pathology in research and diagnostics","volume":"477","year":"2025","journal-title":"Pfl\u00fcgers Arch.-Eur. J. Physiol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, B., Wan, Z., Luo, Y., Zhao, X., Samayoa, J., Zhao, W., and Wu, S. (2025). Multimodal integration strategies for clinical application in oncology. Front. Pharmacol., 16.","DOI":"10.3389\/fphar.2025.1609079"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Alasker, A., Alsalamah, S., Alshathri, N., Almansour, N., Alsalamah, F., Alghafees, M., AlKhamees, M., and Alsaikhan, B. (2024). Performance of large language models (LLMs) in providing prostate cancer information. BMC Urol., 24.","DOI":"10.1186\/s12894-024-01570-0"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ramos, B., Garrido, C., Narv\u00e1ez, P., Gelerstein Claro, S., Li, H., Salvador, R., V\u00e1squez-Venegas, C., Gallegos, I., Casta\u00f1eda, V., and Acevedo, C. (2026). Non-Invasive Detection of Prostate Cancer with Novel Time-Dependent Diffusion MRI and AI-Enhanced Quantitative Radiological Interpretation: PROS-TD-AI. J. Imaging, 12.","DOI":"10.3390\/jimaging12010053"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Tapper, W., Carneiro, G., Mikropoulos, C., Thomas, S.A., Evans, P.M., and Boussios, S. (2024). The application of radiomics and AI to molecular imaging for prostate cancer. J. Pers. Med., 14.","DOI":"10.3390\/jpm14030287"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1186\/s12938-024-01212-4","article-title":"Advantages of transformer and its application for medical image segmentation: A survey","volume":"23","author":"Pu","year":"2024","journal-title":"Biomed. Eng. Online"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Talyshinskii, A., Hameed, B.Z., Ravinder, P.P., Naik, N., Randhawa, P., Shah, M., Rai, B.P., Tokas, T., and Somani, B.K. (2024). Catalyzing precision medicine: Artificial intelligence advancements in prostate cancer diagnosis and management. Cancers, 16.","DOI":"10.3390\/cancers16101809"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"108739","DOI":"10.1016\/j.dib.2022.108739","article-title":"Dataset of prostate MRI annotated for anatomical zones and cancer","volume":"45","author":"Adams","year":"2022","journal-title":"Data Brief."},{"key":"ref_22","first-page":"485","article-title":"Prostate cancer statistics, 2025","volume":"75","author":"Kratzer","year":"2025","journal-title":"CA A Cancer J. Clin."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1038\/s41585-024-00900-z","article-title":"Biological determinants of PSMA expression, regulation and heterogeneity in prostate cancer","volume":"22","author":"Bakht","year":"2025","journal-title":"Nat. Rev. Urol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1038\/s41746-022-00613-w","article-title":"Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials","volume":"5","author":"Esteva","year":"2022","journal-title":"npj Digit. Med."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, T., Lin, M., Guo, H., Zhang, X., Chiu, K.F.P., Feragen, A., and Dou, Q. (2024). Incorporating clinical guidelines through adapting multi-modal large language model for prostate cancer PI-RADS scoring. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer Nature.","DOI":"10.1007\/978-3-031-72086-4_34"},{"key":"ref_26","first-page":"404","article-title":"FastMRI Prostate: A public, biparametric MRI dataset to advance machine learning for prostate cancer imaging","volume":"11","author":"Tibrewala","year":"2024","journal-title":"ScientificData"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1016\/j.procs.2025.09.163","article-title":"Medformer: A Multitask Multimodal Foundational Model for Medical Imaging","volume":"270","author":"Simionescu","year":"2025","journal-title":"Procedia Comput. Sci."},{"key":"ref_28","unstructured":"Jahanandish, H., Sang, S., Li, C.X., Vesal, S., Bhattacharya, I., Lee, J.H., Fan, R., Sonna, G.A., and Rusu, M. (2025). Multimodal MRI-Ultrasound AI for Prostate Cancer Detection Outperforms Radiologist MRI Interpretation: A Multi-Center Study. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"708","DOI":"10.3390\/analytics2030039","article-title":"Application of machine learning and deep learning models in prostate cancer diagnosis using medical images: A systematic review","volume":"2","author":"Olabanjo","year":"2023","journal-title":"Analytics"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.eururo.2024.07.019","article-title":"Prostate Cancer early detection in the European union and UK","volume":"87","author":"Leenen","year":"2025","journal-title":"Eur. Urol."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Fatima, Z., Hafeez, N., Ather, M.A., Tellez, R.Q., Sidorov, G., S\u00e1nchez-Mejorada, C.G., and Ruiz, M.J.T. (2026). Enhancing context-aware SARS disorder management: A proposed multi-agent simulation framework with machine learning and bio-sensor data integration. Front. Med. Technol., 8.","DOI":"10.3389\/fmedt.2026.1780837"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2096","DOI":"10.1002\/mp.16703","article-title":"SwinCross: Cross-modal Swin transformer for head-and-neck tumor segmentation in PET\/CT images","volume":"51","author":"Li","year":"2024","journal-title":"Med. Phys."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1016\/j.cell.2015.10.025","article-title":"The molecular taxonomy of primary prostate cancer","volume":"163","author":"Abeshouse","year":"2015","journal-title":"Cell"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"295","DOI":"10.36548\/jtcsst.2025.3.001","article-title":"Automating Histologic Assessment of Prostate Cancer with a ResNet50-Based Hybrid Vision Model","volume":"7","author":"Degadwala","year":"2025","journal-title":"J. Trends Comput. Sci. Smart Technol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"103984","DOI":"10.1016\/j.clon.2025.103984","article-title":"Prostate Cancer: Artificial Intelligence Advancements in Diagnosis and Early Detection","volume":"49","author":"Shahzad","year":"2026","journal-title":"Clin. Oncol. (R. Coll. Radiol.)"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","article-title":"The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository","volume":"26","author":"Clark","year":"2013","journal-title":"J. Digit. Imaging"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4188","DOI":"10.1158\/0008-5472.CAN-21-0950","article-title":"NCI imaging data commons","volume":"81","author":"Fedorov","year":"2021","journal-title":"Cancer Res."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1109","DOI":"10.1056\/NEJMp1607591","article-title":"Toward a shared vision for cancer genomic data","volume":"375","author":"Grossman","year":"2016","journal-title":"N. Engl. J. Med."},{"key":"ref_39","first-page":"K9","article-title":"The Cancer Genome Atlas Prostate Adenocarcinoma Collection (TCGA-PRAD) (Version 4) [Data set]","volume":"10","author":"Zuley","year":"2016","journal-title":"Cancer Imaging Arch."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1038\/s41591-024-02856-4","article-title":"A visual-language foundation model for computational pathology","volume":"30","author":"Lu","year":"2024","journal-title":"Nat. Med."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1038\/s41591-024-02857-3","article-title":"Towards a general-purpose foundation model for computational pathology","volume":"30","author":"Chen","year":"2024","journal-title":"Nat. Med."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chen, R.J., Lu, M.Y., Weng, W.H., Chen, T.Y., Williamson, D.F., Manz, T., Shady, M., and Mahmood, F. (2021, January 10\u201317). Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00398"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Bachmann, R., Mizrahi, D., Atanov, A., and Zamir, A. (2022). Multimae: Multi-modal multi-task masked autoencoders. European Conference on Computer Vision, Springer Nature.","DOI":"10.1007\/978-3-031-19836-6_20"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, H., Patkar, S., Lis, R., Merino, M.J., Pinto, P.A., Choyke, P.L., Turkbey, B., and Harmon, S. (2024). Masked Image Modeling Meets Self-Distillation: A Transformer-Based Prostate Gland Segmentation Framework for Pathology Slides. Cancers, 16.","DOI":"10.3390\/cancers16233897"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"106617","DOI":"10.1016\/j.compbiomed.2023.106617","article-title":"A hybrid cancer prediction based on multi-omics data and reinforcement learning state action reward state action (SARSA)","volume":"154","author":"Mohammed","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"104932","DOI":"10.1016\/j.chemolab.2023.104932","article-title":"Federated auto-encoder and XGBoost schemes for multi-omics cancer detection in distributed fog computing paradigm","volume":"241","author":"Mohammed","year":"2023","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"102459","DOI":"10.1016\/j.compmedimag.2024.102459","article-title":"Exploring transformer reliability in clinically significant prostate cancer segmentation: A comprehensive in-depth investigation","volume":"118","author":"Vega","year":"2024","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Vizza, P., Aracri, F., Guzzi, P.H., Gaspari, M., Veltri, P., and Tradigo, G. (2024). Machine learning pipeline to analyze clinical and proteomics data: Experiences on a prostate cancer case. BMC Med. Inform. Decis. Mak., 24.","DOI":"10.1186\/s12911-024-02491-6"},{"key":"ref_49","unstructured":"Ghalamkarian, R., Ghalamkarian, M., Ahmadi, M., Ahmadi, S.M., and Diyanat, A. (2025). Leveraging Machine Learning and Deep Learning Techniques for Improved Pathological Staging of Prostate Cancer. arXiv."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"100923","DOI":"10.1016\/j.imu.2022.100923","article-title":"Robust multi-modal prostate cancer classification via feature autoencoder and dual attention","volume":"30","author":"Li","year":"2022","journal-title":"Inform. Med. Unlocked"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3997","DOI":"10.1109\/JBHI.2024.3384970","article-title":"Protecting prostate cancer classification from rectal artifacts via targeted adversarial training","volume":"28","author":"Hu","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.ucl.2023.08.003","article-title":"Comprehensive assessment of MRI-based artificial intelligence frameworks performance in the detection, segmentation, and classification of prostate lesions using open-source databases","volume":"51","author":"Ramacciotti","year":"2024","journal-title":"Urol. Clin."}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/18\/12\/1952\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T02:13:37Z","timestamp":1781662417000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/18\/12\/1952"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,16]]},"references-count":52,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["cancers18121952"],"URL":"https:\/\/doi.org\/10.3390\/cancers18121952","relation":{},"ISSN":["2072-6694"],"issn-type":[{"value":"2072-6694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,16]]}}}