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着消融治疗计划系统，以使用术前CT扫描来优化治疗结果。放射素学是将医学图像转换为更高维度数据的过程，并随后挖掘数据以揭示基本的病理生理学，以增强临床决策支持。放射学分析表明，在捕获明显的肿瘤特征和预测肿瘤预后方面有希望。我们提出了创新的方法来通过补充微波组织消融的生物热转移模型来计算微波炉消融区，该模型具有微波敏感的放射线特征，这将产生更准确和个性化的消融预测，从而导致更好的治疗结果。生物热转移建模方法的输入包括靶肿瘤的几何形状，组织的物理特性以及微波消融涂抹器的尺寸。放射学算法提取了靶向肿瘤的大小和形状的特性，以及CT图像的纹理。因此，通过3D小波计算的形状，大小和纹理数据被用作放射线特征，以进行更准确的剂量预测。提出的放射分析分析分为三个阶段：（1）自动检测候选肿瘤，（2）自动分割选定的肿瘤，（3）从分段的肿瘤中提取特征，（4）分析在一段时间内对消融肿瘤进行分析。