SBIR TOPIC 417 Multiple Brain Metastases Stereotactic Radiosurgery Management Platform - Detection, Planning and Follow-up

SBIR TOPIC 417 多发性脑转移瘤立体定向放射外科管理平台 - 检测、规划和随访

• 批准号: 10579810
• 负责人: HAO JIANG
• 金额: $ 5.5万
• 依托单位: NEURALRAD LLC
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2022-06-14
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10579810
• 关键词: Artificial Intelligence; Brain; Clinical; Community Practice; Diagnosis; Environment; Institutes; Institution; Learning; Manuals; Metastatic malignant neoplasm to brain; Patient Care; Patients; Performance; Population; Privatization; Radiation therapy; Radiosurgery; Site; Small Business Innovation Research Grant; Toxic effect; Translating; United States; base; clinical practice; design; detection platform; follow-up; performance site; success

It was estimated that ~170,000 patients diagnosed with Brain metastases (BMs) in United States each year and most patients have clinical manifestation of multiple BMs (mBMs). Stereotactic radiosurgery (SRS) has been used frequently for treating mBMs and demonstrated much less toxicity than conventional whole brain radiotherapy. Current SRS platform was designed for single-fraction treatment on limited number of targets, thus clinical workflow of mBMs SRS using extant platform is cumbersome: 1) labor-intensive manual mBMs identification and segmentation; 2) cumbersome manual planning mBMs into different treatment sessions; and 3) tedious tracking mBMs changes in treatment follow-up. Our team have been actively developing (R01-supported) an Artificial intelligence (AI)-based mBMs management platform, namely AimBMs, to optimize SRS mBMs patient care. This SBIR proposal targets to translate, validate and commercialize the AimBMs platform. The proposal consists of three aims: 1) develop a private cloud to integrate AimBMs platform into clinical practice environment; 2) refine and optimize AimBMs platform performance for site-specific practice using on-site incremental learning; and 3) validate AimBMs platform performance in 5 collaborating institutes. The success of this project will lead to bringing the AimBMs platform from a single academic institution use to a broad SRS community practice, benefitting a large mBMs population.

据估计，每年在美国诊断出患有脑转移（BMS）的患者约有17万名患者，大多数患者的临床表现为多个BMS（MBMS）。 立体定向放射外科手术（SRS）经常用于治疗MBM，并且与常规的全脑放射疗法相比，毒性少得多。当前的SRS平台是针对目标有限的单分数处理设计的，因此使用现有平台的MBMS SR的临床工作流程很麻烦：1）劳动密集型手动MBMS识别和分段； 2）繁琐的手动计划MBM进行不同的治疗课程； 3）乏味的跟踪治疗随访中的MBMS变化。我们的团队一直在积极开发基于人工智能（AI）的MBMS管理平台，即AIMBMS，以优化SRS MBMS患者护理。该SBIR提案的目标是翻译，验证和商业化AIMBMS平台。该提案包括三个目标：1）开发私有云，将AIMBMS平台集成到临床实践环境中； 2）使用现场增量学习来完善和优化AIMBMS平台性能，以供特定于网站的实践； 3）在5个合作机构中验证AIMBMS平台性能。该项目的成功将导致将AIMBMS平台从单个学术机构使用到广泛的SRS社区实践，从而使大量MBMS受益。