{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T06:31:16Z","timestamp":1788849076470,"version":"build-2803163510"},"reference-count":43,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T00:00:00Z","timestamp":1747180800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Survival prediction in patients with brain metastases remains a major clinical challenge, where timely and individualized prognostic estimates are critical for guiding treatment strategies and patient counseling. Methods: We propose a novel hybrid deep learning framework that integrates volumetric MRI-derived imaging biomarkers with structured clinical and demographic data to predict overall survival time. Our dataset includes 148 patients from three institutions, featuring expert-annotated segmentations of enhancing tumors, necrosis, and peritumoral edema. Two convolutional neural network backbones\u2014ResNet-50 and EfficientNet-B0\u2014were fused with fully connected layers processing tabular data. Models were trained using mean squared error loss and evaluated through stratified cross-validation and an independent held-out test set. Results: The hybrid model based on EfficientNet-B0 achieved state-of-the-art performance, attaining an R2 score of 0.970 and a mean absolute error of 3.05 days on the test set. Permutation feature importance highlighted edema-to-tumor ratio and enhancing tumor volume as the most informative predictors. Grad-CAM visualizations confirmed the model\u2019s attention to anatomically and clinically relevant regions. Performance consistency across validation folds confirmed the framework\u2019s robustness and generalizability. Conclusions: This study demonstrates that multimodal deep learning can deliver accurate, explainable, and clinically actionable survival predictions in brain metastases. The proposed framework offers a promising foundation for integration into real-world oncology workflows to support personalized prognosis and informed therapeutic decision-making.<\/jats:p>","DOI":"10.3390\/diagnostics15101242","type":"journal-article","created":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T08:44:58Z","timestamp":1747212298000},"page":"1242","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Hybrid Deep Learning for Survival Prediction in Brain Metastases Using Multimodal MRI and Clinical Data"],"prefix":"10.3390","volume":"15","author":[{"given":"Cristian Constantin","family":"Volov\u0103\u021b","sequence":"first","affiliation":[{"name":"Radiology Department, \u201cGrigore T. Popa\u201d University of Medicine and Pharmacy Ia\u0219i, 700115 Ia\u0219i, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2791-3400","authenticated-orcid":false,"given":"C\u0103lin Gheorghe","family":"Buzea","sequence":"additional","affiliation":[{"name":"\u201cProf. Dr. Nicolae Oblu\u201d Clinical Emergency Hospital Ia\u0219i, 700309 Ia\u0219i, Romania"},{"name":"National Institute of Research and Development for Technical Physics, IFT Ia\u0219i, 700050 Ia\u0219i, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7650-7798","authenticated-orcid":false,"given":"Diana-Ioana","family":"Boboc","sequence":"additional","affiliation":[{"name":"Medical Oncology-Radiotherapy Department, \u201cGrigore T. Popa\u201d University of Medicine and Pharmacy Ia\u0219i, 700115 Ia\u0219i, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M\u0103d\u0103lina-Raluca","family":"Ostafe","sequence":"additional","affiliation":[{"name":"Medical Oncology-Radiotherapy Department, \u201cGrigore T. Popa\u201d University of Medicine and Pharmacy Ia\u0219i, 700115 Ia\u0219i, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maricel","family":"Agop","sequence":"additional","affiliation":[{"name":"Physics Department, \u201cGheorghe Asachi\u201d Technical University Ia\u0219i, 700050 Ia\u0219i, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6447-0958","authenticated-orcid":false,"given":"L\u0103cr\u0103mioara","family":"Ochiuz","sequence":"additional","affiliation":[{"name":"Faculty of Pharmacy, \u201cGrigore T. Popa\u201d University of Medicine and Pharmacy Ia\u0219i, 700115 Ia\u0219i, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u0218tefan Lucian","family":"Burlea","sequence":"additional","affiliation":[{"name":"Faculty of Pharmacy, \u201cGrigore T. Popa\u201d University of Medicine and Pharmacy Ia\u0219i, 700115 Ia\u0219i, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5485-0620","authenticated-orcid":false,"given":"Drago\u0219 Ioan","family":"Rusu","sequence":"additional","affiliation":[{"name":"Department of Environmental Engineering, Mechanical Engineering, Faculty of Engineering, \u201cV. Alecsandri\u201d University of Bac\u0103u, 600115 Bac\u0103u, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lauren\u021biu","family":"Bujor","sequence":"additional","affiliation":[{"name":"Amethyst Radiotherapy, Drumul Od\u0103ii nr. 42, 719241 Otopeni, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Drago\u0219 Teodor","family":"Iancu","sequence":"additional","affiliation":[{"name":"Medical Oncology-Radiotherapy Department, \u201cGrigore T. Popa\u201d University of Medicine and Pharmacy Ia\u0219i, 700115 Ia\u0219i, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simona Ruxandra","family":"Volov\u0103\u021b","sequence":"additional","affiliation":[{"name":"Medical Oncology-Radiotherapy Department, \u201cGrigore T. Popa\u201d University of Medicine and Pharmacy Ia\u0219i, 700115 Ia\u0219i, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,14]]},"reference":[{"key":"ref_1","unstructured":"Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R.T., Berger, C., Ha, S.M., and Rozycki, M. (2018). Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge. arXiv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"127","DOI":"10.3322\/caac.21552","article-title":"Artificial intelligence in cancer imaging: Clinical challenges and applications","volume":"69","author":"Bi","year":"2019","journal-title":"CA A Cancer J. 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