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

热消融系统通常伴随着消融治疗计划系统，以优化术前CT扫描的治疗效果。放射组学是一个将医学图像转换为高维数据并随后挖掘数据以揭示潜在病理生理以增强临床决策支持的过程。放射组学分析在捕捉肿瘤特征和预测肿瘤预后方面显示出前景。我们提出了一种创新的方法，通过将微波组织消融的生物传热模型与微波敏感放射组学特征相结合来计算微波消融区域，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效果。生物传热建模方法的输入包括目标肿瘤的几何形状、组织的物理特性和微波消融应用器的尺寸。放射组学算法从CT图像中提取目标肿瘤的大小和形状以及纹理的属性。因此，通过三维小波计算的形状、大小和纹理数据被用作放射组学特征，以便更准确地预测剂量。提出的放射组学分析分三个阶段进行：(1)候选肿瘤的自动检测，(2)选定肿瘤的自动分割，(3)从分割的肿瘤中提取特征，(4)在一段时间内分析消融肿瘤。

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.