IEEE Trans Med Imaging. 2024 Mar;43(3):916-927. doi: 10.1109/TMI.2023.3325295. Epub 2024 Mar 5.
Directionally sensitive radiomic features including the histogram of oriented gradient (HOG) have been shown to provide objective and quantitative measures for predicting disease outcomes in multiple cancers. However, radiomic features are sensitive to imaging variabilities including acquisition differences, imaging artifacts and noise, making them impractical for using in the clinic to inform patient care. We treat the problem of extracting robust local directionality features by mapping via optimal transport a given local image patch to an iso-intense patch of its mean. We decompose the transport map into sub-work costs each transporting in different directions. To test our approach, we evaluated the ability of the proposed approach to quantify tumor heterogeneity from magnetic resonance imaging (MRI) scans of brain glioblastoma multiforme, computed tomography (CT) scans of head and neck squamous cell carcinoma as well as longitudinal CT scans in lung cancer patients treated with immunotherapy. By considering the entropy difference of the extracted local directionality within tumor regions, we found that patients with higher entropy in their images, had significantly worse overall survival for all three datasets, which indicates that tumors that have images exhibiting flows in many directions may be more malignant. This may seem to reflect high tumor histologic grade or disorganization. Furthermore, by comparing the changes in entropy longitudinally using two imaging time points, we found patients with reduction in entropy from baseline CT are associated with longer overall survival (hazard ratio = 1.95, 95% confidence interval of 1.4-2.8, p = 1.65e-5). The proposed method provides a robust, training free approach to quantify the local directionality contained in images.
方向敏感的放射组学特征，包括方向梯度直方图（HOG），已被证明可以为多种癌症的疾病结果预测提供客观和定量的测量。然而，放射组学特征对成像变异性很敏感，包括采集差异、成像伪影和噪声，这使得它们在临床上不切实际，无法用于为患者护理提供信息。我们通过最优传输将给定的局部图像补丁映射到其平均的等强度补丁上来处理提取稳健局部方向性特征的问题。我们将传输图分解为子工作成本，每个成本都在不同的方向上进行传输。为了测试我们的方法，我们评估了所提出的方法从脑胶质母细胞瘤多形性的磁共振成像（MRI）扫描、头颈部鳞状细胞癌的计算机断层扫描（CT）以及接受免疫治疗的肺癌患者的纵向 CT 扫描中量化肿瘤异质性的能力。通过考虑提取的肿瘤区域内局部方向性的熵差，我们发现图像熵较高的患者在所有三个数据集的总体生存率都显著较差，这表明图像中具有多个方向流动的肿瘤可能更恶性。这似乎反映了高肿瘤组织学分级或组织混乱。此外，通过使用两个成像时间点比较熵的纵向变化，我们发现基线 CT 上的熵减少的患者与更长的总体生存率相关（风险比=1.95，95%置信区间为 1.4-2.8，p=1.65e-5）。所提出的方法提供了一种稳健的、无需训练的方法来量化图像中包含的局部方向性。
