SBIR Phase I Topic 402 - Artificial Intelligence-Aided Imaging for Cancer Prevention, Diagnosis, and Monitoring

SBIR 第一阶段主题 402 - 用于癌症预防、诊断和监测的人工智能辅助成像

• 批准号: 10269839
• 负责人: HENKY WIBOWO
• 金额: $ 39.9万
• 依托单位: PHENOMAPPER, LLC
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-16 至 2021-06-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10269839
• 关键词: 3-Dimensional; Ablation; Aftercare; Algorithmic Analysis; Algorithms; Artificial Intelligence; Characteristics; Computer software; Data; Data Set; Detection; Development; Devices; Diagnosis; Dimensions; Dose; Functional disorder; Geometry; Image; Medical Imaging; Methods; Modeling; Monitor; Phase; Process; Property; Radiofrequency Interstitial Ablation; Shapes; Small Business Innovation Research Grant; System; Texture; Thermal Ablation Therapy; Time; Tissues; Treatment outcome; X-Ray Computed Tomography; automated segmentation; base; cancer imaging; cancer prevention; clinical decision support; cloud based; data mining; feature extraction; innovation; microwave ablation; microwave electromagnetic radiation; multidimensional data; outcome forecast; physical property; preclinical study; radiomics; treatment optimization; treatment planning; tumor

Thermal ablation systems are typically accompanied by ablation treatment planning system to optimize the treatment outcome using pre-operative CT scan. Radiomics is a process of converting medical images into higher-dimensional data and subsequent mining of data to reveal underlying pathophysiology for enhancing clinical decision support making. Radiomics analysis have shown promises in capturing distinct tumor characteristics and predicting prognosis of the tumor. We propose innovative method to calculate microwave ablation zones by supplementing a bioheat transfer model of microwave tissue ablation with microwave sensitive radiomics features, which will generate more accurate and personalized ablation prediction leading to better treatment outcome. Inputs to the bioheat transfer modeling approach include the geometry of the target tumor, physical properties of the tissue, and dimensions of the microwave ablation applicator. The radiomics algorithm extracts properties of the targeted tumor’s size and shape, as well as texture from CT images. Therefore, shape, size, and texture data computed through 3D wavelets are employed as radiomics features for more accurate dose prediction. The proposed radiomics analysis is conducted in three stages: (1)automatic detection of candidate tumors, (2)automatic segmentation of a selected tumor, (3)extraction of features from the segmented tumor, (4)analysis of ablated tumor over period of time.

热消融系统通常伴随消融治疗计划系统，以使用术前 CT 扫描来优化治疗结果。放射组学是将医学图像转换为更高维数据并随后挖掘数据以揭示潜在病理生理学以增强临床决策支持的过程。放射组学分析显示出在捕获不同肿瘤特征和预测肿瘤预后方面的前景，我们提出了通过用微波敏感放射组学补充微波组织消融的生物传热模型来计算微波消融区域的创新方法。特征，这将产生更准确和个性化的消融预测，从而获得更好的治疗结果。生物热传递建模方法的输入包括放射组学算法提取的目标肿瘤的几何形状、组织的物理特性和微波消融施加器的尺寸。因此，通过 3D 小波计算的形状、大小和纹理数据被用作放射组学特征，以实现更准确的剂量预测。分三个阶段进行：（1）自动检测候选肿瘤，（2）自动分割选定的肿瘤，（3）从分割的肿瘤中提取特征，（4）分析一段时间内消融的肿瘤。