A clinical tool for automated detection and delineation of intracranial metastases from MRI

随着多发性颅内转移瘤的管理迅速发展， 检测和描绘大量潜在的非常小的转移性病变， 在3D磁共振成像（MRI）上的大脑正在急剧增加。人工智能 (AI)系统可以帮助放射科医师以及放射肿瘤科医师在以下方面发挥作用： 治疗多发性脑转移瘤患者。针对PAR-20- 155，我们已经组建了一个学术-工业伙伴关系，包括来自三个国家的调查人员。 学术机构和工业AI团队开发，翻译和验证AI系统， 解决这个未解决的临床问题。在该建议中，神经网络系统基于 包含分割的多尺度3D全卷积一级客观检测器 将优化和调查头部。培训和测试数据将由临床 接受放射外科治疗的患者的图像，以获得来自三个 学术中心，由专家策划，并通过添加真实的合成病变来增强 注入到图像中。将研究该网络的临床效用，以确定其协助 a）放射科医师准确有效地检测多发性转移瘤，和B）放射 肿瘤学家描述多个转移病灶，以支持治疗策略的选择 和治疗计划。

As the management of multiple intracranial metastases is rapidly evolving, the demands for detection and delineation of a large number of potentially very small metastatic lesions in the brain on 3D magnetic resonance images (MRI) are increasing dramatically. Artificial intelligence (AI) systems can assist both the radiologist as well as radiation oncologist in their roles in management of patients with multiple tumors metastatic to the brain. In response to PAR-20- 155, we have assembled an academic-industrial partnership including investigators from three academic institutes and an industrial AI team to develop, translate and validate AI systems to address this unmet clinical question. In this proposal, a neural network system based upon multiple scale 3D fully convolutional one-stage objective detectors containing segmentation heads will be optimized and investigated. Training and testing data will be provided from clinical images of patients treated with radiosurgery to multiple small metastases acquired from three academic centers, curated by experts, and augmented by addition of realistic synthetic lesions injected into images. The clinical utility of the network will be investigated for its ability to assist a) radiologists in detecting multiple metastases accurately and efficiently, and b) radiation oncologists in delineating multiple metastatic lesions to support selection of therapeutic strategies and planning of treatments.