本研究旨在评估基于深度学习的脑转移（BM）检测和定量是否可以为患有BM的患者提供治疗选择。我们在193名患者中开发了用于检测和定量BM的深度学习系统（DLS），并将其应用于新发现的112名患者的黑血造影T1加权成像。根据DLS检测到的BM数量和体积，将患者分为三组治疗建议组，分别是根据欧洲神经肿瘤协会（EANO）-欧洲医学肿瘤学协会（ESMO）建议进行短期影像随访无需治疗（A组）、手术或立体定向放射外科（有限BM，B组）或全脑放射治疗或全身化疗（广泛BM，C组）。分析DLS基于组别与临床决策之间的一致性，并考虑靶向药物。计算高风险（B+C）的辨别性能。112名患者中（平均年龄64.3岁，男性63人），C组的BM数量和体积最大，其次是B组（4.4个和851.6 mm3）和A组（1.5个和15.5 mm3）。基于DLS的组别与实际临床决策一致，准确率为76.8%（112中的86）。考虑靶向药物的修正准确率为81.3%（112中的91）。对于辨别高风险，DLS显示了95%（86/82）的敏感性和81%（26/21）的特异性。基于DLS的BM检测和定量在有限BM和广泛BM的低风险和高风险群体的治疗选择中具有潜在帮助作用。对于新诊断的脑转移患者，基于深度学习的检测和定量可以在需要快速和准确的治疗决策的临床环境中使用，从而改善患者预后。·基于深度学习的脑转移检测和定量与实际分类结果具备极高一致性。·通过设置算法根据深度学习系统检测到的脑转移数量和体积来建议治疗，一致性达到81.3％。·将患者分为低风险和高风险组时，对于检测后者的敏感性为95％。© 2023年。作者（们）。

We aimed to evaluate whether deep learning-based detection and quantification of brain metastasis (BM) may suggest treatment options for patients with BMs.The deep learning system (DLS) for detection and quantification of BM was developed in 193 patients and applied to 112 patients that were newly detected on black-blood contrast-enhanced T1-weighted imaging. Patients were assigned to one of 3 treatment suggestion groups according to the European Association of Neuro-Oncology (EANO)-European Society for Medical Oncology (ESMO) recommendations using number and volume of the BMs detected by the DLS: short-term imaging follow-up without treatment (group A), surgery or stereotactic radiosurgery (limited BM, group B), or whole-brain radiotherapy or systemic chemotherapy (extensive BM, group C). The concordance between the DLS-based groups and clinical decisions was analyzed with or without consideration of targeted agents. The performance of distinguishing high-risk (B + C) was calculated.Among 112 patients (mean age 64.3 years, 63 men), group C had the largest number and volume of BM, followed by group B (4.4 and 851.6 mm3) and A (1.5 and 15.5 mm3). The DLS-based groups were concordant with the actual clinical decisions, with an accuracy of 76.8% (86 of 112). Modified accuracy considering targeted agents was 81.3% (91 of 112). The DLS showed 95% (82/86) sensitivity and 81% (21/26) specificity for distinguishing the high risk.DLS-based detection and quantification of BM have the potential to be helpful in the determination of treatment options for both low- and high-risk groups of limited and extensive BMs.For patients with newly diagnosed brain metastasis, deep learning-based detection and quantification may be used in clinical settings where prompt and accurate treatment decisions are required, which can lead to better patient outcomes.• Deep learning-based brain metastasis detection and quantification showed excellent agreement with ground-truth classifications. • By setting an algorithm to suggest treatment based on the number and volume of brain metastases detected by the deep learning system, the concordance was 81.3%. • When dividing patients into low- and high-risk groups, the sensitivity for detecting the latter was 95%.© 2023. The Author(s).