School of Information and Communication Engineering, Xi'an Jiaotong University, Xi'an, China.
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing, China.
Comput Biol Med. 2021 Nov;138:104916. doi: 10.1016/j.compbiomed.2021.104916. Epub 2021 Oct 9.
Based on recent studies, immunotherapy led by immune checkpoint inhibitors has significantly improved the patient survival rate and effectively reduced the recurrence risk. However, immunotherapy has different therapeutic effects for different patients, leading to difficulties in predicting the treatment response. Conversely, delta-radiomic features, which measure the difference between pre- and post-treatment through quantitative image features, have proven to be promising descriptors for treatment outcome prediction. Consequently, we developed an effective model termed as the automated multi-objective delta-radiomics (Auto-MODR) model for the prediction of immunotherapy response in metastatic melanoma. In Auto-MODR, delta-radiomic features and traditional radiomic features were used as inputs. Furthermore, a novel automated multi-objective model was developed to obtain more reliable and balanced results between sensitivity and specificity. We conducted extensive comparisons with existing studies on treatment outcome prediction. Our method achieved an area under the curve (AUC) of 0.86 in a cross-validation study and an AUC of 0.73 in an independent study. Compared with the model using conventional radiomic features (pre- and post-treatment) only, better performance can be obtained when conventional radiomic and delta-radiomic features are combined. Furthermore, Auto-MODR outperformed the currently available radiomic strategies.
基于最近的研究，免疫检查点抑制剂主导的免疫疗法显著提高了患者的生存率，并有效地降低了复发风险。然而，免疫疗法对不同的患者有不同的治疗效果，导致治疗反应的预测存在困难。相反，通过定量图像特征测量治疗前后差异的 delta 放射组学特征已被证明是治疗效果预测的有前途的描述符。因此，我们开发了一种名为自动多目标 delta 放射组学（Auto-MODR）的有效模型，用于预测转移性黑色素瘤的免疫治疗反应。在 Auto-MODR 中，delta 放射组学特征和传统放射组学特征被用作输入。此外，还开发了一种新颖的自动化多目标模型，以在敏感性和特异性之间获得更可靠和平衡的结果。我们与现有的治疗效果预测研究进行了广泛的比较。我们的方法在交叉验证研究中获得了 0.86 的曲线下面积（AUC），在独立研究中获得了 0.73 的 AUC。与仅使用传统放射组学特征（治疗前后）的模型相比，当结合使用传统放射组学和 delta 放射组学特征时，可以获得更好的性能。此外，Auto-MODR 优于目前可用的放射组学策略。
