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高维数据，然后对数据进行挖掘以揭示潜在的病理生理过程，以增强临床决策支持的过程。放射组学分析显示，在捕捉明显的肿瘤特征和预测肿瘤预后方面有希望。我们提出了一种计算微波消融区域的创新方法，将微波组织消融的生物传热学模型补充到微波敏感的放射组学特征中，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。生物传热学建模方法的输入包括目标肿瘤的几何形状、组织的物理性质和微波消融器的尺寸。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。所提出的放射组学分析分三个阶段进行：(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)分析一段时间内的消融肿瘤。

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.