{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T05:48:31Z","timestamp":1777355311567,"version":"3.51.4"},"reference-count":24,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T00:00:00Z","timestamp":1776384000000},"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>Objective: Existing deep learning (DL) studies on brain metastasis have largely focused on algorithm or reader performance in controlled settings, whereas its role in routine teleradiology quality support remains unestablished. We evaluated the report-level impact of DL assistance on brain metastasis interpretation in a real-world teleradiology workflow using dual-sequence MRI. Materials and Methods: In this retrospective study, 600 patients who underwent contrast-enhanced dual-sequence brain MRI during two consecutive 3-month periods before (pre-DL, n = 286) and after (post-DL, n = 314) DL integration into teleradiology workflow were analyzed. Ten board-certified teleradiologists interpreted all the cases with or without DL-generated overlays. Report-level diagnostic metrics were assessed against a consensus reference standard established by faculty neuroradiologists. Subsequently, exploratory case-level stratified sensitivity analyses were performed for metastasis-positive examinations based on lesion multiplicity and the largest lesion size. Teleradiologists\u2019 perceptions were assessed using a post-interpretation survey. Results: Compared with the pre-DL group, the post-DL group showed higher sensitivity (77.7% vs. 90.8%, p &lt; 0.001), specificity (82.3% vs. 90.8%, p = 0.002), accuracy (80.8% vs. 90.8%, p &lt; 0.001), positive predictive value (68.2% vs. 85.7%, p &lt; 0.001), and negative predictive value (88.3% vs. 94.2%, p = 0.011). False-positive and false-negative rates were lower after DL implementation (11.9% vs. 5.7%, p = 0.009; 7.3% vs. 3.5%, p = 0.045). Sensitivity gains were most pronounced for cases with single metastasis (74.6% vs. 91.2%, p = 0.007) and with the largest lesion \u2264 5 mm (74.3% vs. 92.0%, p = 0.004), whereas sensitivity was similar for multiple metastases and for cases with a largest lesion &gt; 5 mm. Survey responses suggested favorable usability and diagnostic support. Conclusions: In this real-world teleradiology workflow, DL implementation was associated with higher report-level diagnostic metrics and fewer false interpretations. DL assistance may help support quality control for brain metastasis interpretation, particularly in more subtle and diagnostically challenging cases, although radiologist judgment remains essential for subtle or borderline lesions.<\/jats:p>","DOI":"10.3390\/diagnostics16081211","type":"journal-article","created":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:30:36Z","timestamp":1776443436000},"page":"1211","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Report-Level Impact of DL Assistance on Teleradiology Quality Support for Brain Metastases: Real-World Clinical Practice at a Single Tertiary Center"],"prefix":"10.3390","volume":"16","author":[{"given":"Jieun","family":"Roh","sequence":"first","affiliation":[{"name":"Department of Radiology, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, 20 Geumo-ro, Mulgeum-eup, Yangsan-si 50612, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7349-2841","authenticated-orcid":false,"given":"Hye Jin","family":"Baek","sequence":"additional","affiliation":[{"name":"Department of Radiology, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, 20 Geumo-ro, Mulgeum-eup, Yangsan-si 50612, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seung Kug","family":"Baik","sequence":"additional","affiliation":[{"name":"Department of Radiology, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, 20 Geumo-ro, Mulgeum-eup, Yangsan-si 50612, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bora","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Radiology, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, 20 Geumo-ro, Mulgeum-eup, Yangsan-si 50612, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kwang Ho","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Thoracic and Cardiovascular Surgery, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, 20 Geumo-ro, Mulgeum-eup, Yangsan-si 50612, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3143-3733","authenticated-orcid":false,"given":"Hwaseong","family":"Ryu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, 20 Geumo-ro, Mulgeum-eup, Yangsan-si 50612, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bong Kyeong","family":"Son","sequence":"additional","affiliation":[{"name":"Department of Radiology, Pusan National University Yangsan Hospital, 20 Geumo-ro, Mulgeum-eup, Yangsan-si 50612, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1053\/j.seminoncol.2004.07.012","article-title":"Management of brain metastases","volume":"31","author":"Bradley","year":"2004","journal-title":"Semin. Oncol."},{"key":"ref_2","unstructured":"Devita, V.T., Hellman, S., and Rosenberg, S.A. (1997). Metastatic brain cancer. Cancer: Principles and Practice of Oncology, Lippincott Williams & Wilkins. [6th ed.]."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1097\/RLI.0b013e3182319704","article-title":"Detection of small metastatic brain tumors: Comparison of 3D contrast-enhanced whole-brain black-blood imaging and MP-RAGE imaging","volume":"47","author":"Park","year":"2012","journal-title":"Investig. Radiol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1002\/mrm.22261","article-title":"Contrast-enhanced, three-dimensional, whole brain, black-blood imaging: Application to small brain metastases","volume":"63","author":"Park","year":"2010","journal-title":"Magn. Reson. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1186\/s40644-024-00669-9","article-title":"Deep learning-based metastasis detection in patients with lung cancer to enhance reproducibility and reduce workload in brain metastasis screening with MRI: A multi-center study","volume":"24","author":"Park","year":"2024","journal-title":"Cancer Imaging"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2873","DOI":"10.1007\/s00330-023-10318-7","article-title":"Reducing false positives in deep learning-based brain metastasis detection by using both gradient-echo and spin-echo contrast-enhanced MRI: Validation in a multi-center diagnostic cohort","volume":"34","author":"Yun","year":"2024","journal-title":"Eur. Radiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e242416","DOI":"10.1148\/radiol.242416","article-title":"A data-centric approach to deep learning for brain metastasis analysis at MRI","volume":"315","author":"Topff","year":"2025","journal-title":"Radiology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2140","DOI":"10.1093\/neuonc\/noae113","article-title":"Automated segmentation of brain metastases with deep learning: A multi-center, randomized crossover, multi-reader evaluation study","volume":"26","author":"Luo","year":"2024","journal-title":"Neuro-Oncology"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1002\/jmri.26766","article-title":"Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI","volume":"51","author":"Yi","year":"2020","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Son, S., Joo, B., Park, M., Suh, S.H., Oh, H.S., Kim, J.W., Lee, S., Ahn, S.J., and Lee, J.-M. (2023). Development of RLK-Unet: A clinically favorable deep learning algorithm for brain metastasis detection and treatment response assessment. Front. Oncol., 13.","DOI":"10.3389\/fonc.2023.1273013"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/j.jacr.2023.08.046","article-title":"The staffing crisis and burnout in academic radiology: Insights from a survey study in Korea","volume":"21","author":"Koo","year":"2024","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e232625","DOI":"10.1148\/radiol.232625","article-title":"The growing nationwide radiologist shortage: Current opportunities and ongoing challenges for international medical graduate radiologists","volume":"314","author":"Tirumani","year":"2025","journal-title":"Radiology"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Nigatu, A.M., Yilma, T.M., Gezie, L.D., Gebrewold, Y., Gullslett, M.K., Mengiste, S.A., and Tilahun, B. (2025). Effect of teleradiology on patient waiting time and service satisfaction in public hospitals, Northwest Ethiopia: A quasi-experimental study. BMC Health Serv. Res., 25.","DOI":"10.1186\/s12913-025-12545-8"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"86","DOI":"10.2471\/BLT.25.020225","article-title":"Leveraging teleradiology with artificial intelligence","volume":"103","author":"Humphreys","year":"2025","journal-title":"Bull. World Health Organ."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Alruwaili, A.R., Alshammari, A.A., Alsalhi, F.M., Aldamen, S.A., and Alamri, H.S. (2024). Teleradiology in Saudi Arabia: A national survey and retrospective review of associated MRI reports. BMC Health Serv. Res., 24.","DOI":"10.1186\/s12913-024-11706-5"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1038\/s44401-025-00023-6","article-title":"AI solutions to the radiology workforce shortage","volume":"2","author":"Jing","year":"2025","journal-title":"npj Health Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.jacr.2010.08.017","article-title":"Outsourcing to teleradiology companies: Bad for radiology, bad for radiologists","volume":"8","author":"Levin","year":"2011","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1016\/j.jacr.2005.07.003","article-title":"Outsourced teleradiology imaging services: An analysis of discordant interpretation in 124,870 cases","volume":"2","author":"FitzGerald","year":"2005","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1649","DOI":"10.1007\/s00330-018-5873-5","article-title":"Clinicians\u2019 perceptions of the quality of outsourced radiology and actions taken around perceived imaging errors in practice","volume":"29","author":"Graham","year":"2019","journal-title":"Eur. Radiol."},{"key":"ref_20","first-page":"e73152","article-title":"Accuracy of outsourced radiology reports in emergency surgical care: Do they provide a high-quality, cost-effective service?","volume":"16","author":"Obayi","year":"2024","journal-title":"Cureus"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1292","DOI":"10.1016\/j.jacr.2023.12.005","article-title":"Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement From the ACR, CAR, ESR, RANZCR & RSNA","volume":"21","author":"Brady","year":"2024","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1093\/neuonc\/noaa030","article-title":"Consensus recommendations for a standardized brain tumor imaging protocol for clinical trials in brain metastases","volume":"22","author":"Kaufmann","year":"2020","journal-title":"Neuro Oncol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2062","DOI":"10.1007\/s00330-023-10120-5","article-title":"Deep learning-based detection and quantification of brain metastases on black-blood imaging can provide treatment suggestions: A clinical cohort study","volume":"34","author":"Jeong","year":"2024","journal-title":"Eur. Radiol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.jacr.2025.10.018","article-title":"Radiology Workflow Assistance With Artificial Intelligence: Establishing the Link to Outcomes","volume":"23","author":"Gu","year":"2026","journal-title":"J. Am. Coll. Radiol."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/8\/1211\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T05:10:09Z","timestamp":1777353009000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/8\/1211"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,17]]},"references-count":24,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["diagnostics16081211"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16081211","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,17]]}}}