Lesion-Based Radiomics Signature in Pretherapy 18F-FDG PET Predicts Treatment Response to Ibrutinib in Lymphoma.
Lesion-Based Radiomics Signature in Pretherapy 18F-FDG PET Predicts Treatment Response to Ibrutinib in Lymphoma.
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DOI：
10.1097/rlu.0000000000004060
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发表时间：
2022-03-01
影响因子：
10.6
通讯作者：
Lu Y
中科院分区：
文献类型：
--
作者：
Jimenez JE;Dai D;Xu G;Zhao R;Li T;Pan T;Wang L;Lin Y;Wang Z;Jaffray D;Hazle JD;Macapinlac HA;Wu J;Lu Y
开发一种基于治疗前PET/CT的预测模型，用于预测淋巴瘤患者对伊曲替尼的治疗反应。回顾性研究了169例淋巴瘤患者的2441个病灶。对治疗前18F-FDG PET图像上所有符合条件的淋巴瘤进行轮廓勾画和分割，以进行放射组学分析。使用Lugano分类法回顾性确定了病变和患者对伊鲁替尼的反应性。提取PET放射组学特征。建立放射组学模型以预测伊鲁替尼反应。放射组学模型的预后意义在测试队列中独立评估，并与常规PET指标进行比较：最大标准摄取值（SUVmax），代谢肿瘤体积（MTV）和总病变糖酵解（TLG）。放射组学模型的受试者工作特征曲线下面积（ROC AUC）为0.860（敏感性，92.9%，特异性，81.4%; P < 0.001）预测对伊鲁替尼的反应，优于SUVmax（ROC AUC，0.519; P = 0.823），MTV（ROC AUC，0.579; P = 0.412），TLG（ROC AUC，0.576; P = 0.199），以及使用所有三种（ROC AUC，0.562; P = 0.046）建立的复合模型。放射组学模型将准确预测伊匹替尼反应性病变的概率从84.8%（前测）增加至96.5%（后测）。在患者水平，该模型的性能（ROC AUC = 0.811; P = 0.007）上级传统的PET指标。此外，当在治疗亚组中验证时，放射组学模型显示出稳健性：第一（ROC AUC，0.916; P < 0.001）与第二或更高（ROC AUC，0.842; P < 0.001）防线和单次治疗（ROC AUC，0.931; P < 0.001）与多次治疗（ROC AUC，0.824; P < 0.001）。我们开发并验证了一种基于PET的治疗前放射组学模型，用于预测不同淋巴瘤患者队列中对伊曲替尼治疗的反应。
To develop a pretherapy PET/CT-based prediction model for treatment response to ibrutinib in lymphoma patients. One hundred sixty-nine lymphoma patients with 2441 lesions were studied retrospectively. All eligible lymphomas on pretherapy 18F-FDG PET images were contoured and segmented for radiomic analysis. Lesion- and patient-based responsiveness to ibrutinib were determined retrospectively using the Lugano classification. PET radiomic features were extracted. A radiomic model was built to predict ibrutinib response. The prognostic significance of the radiomic model was evaluated independently in a test cohort and compared with conventional PET metrics: maximum standard uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG). The radiomic model had an area under the receiver operating characteristic curve (ROC AUC) of 0.860 (sensitivity, 92.9%, specificity, 81.4%; P < 0.001) for predicting response to ibrutinib, outperforming the SUVmax (ROC AUC, 0.519; P = 0.823), MTV (ROC AUC, 0.579; P = 0.412), TLG (ROC AUC, 0.576; P = 0.199), and a composite model built using all three (ROC AUC, 0.562; P = 0.046). The radiomic model increased the probability of accurately predicting ibrutinib-responsive lesions from 84.8% (pretest) to 96.5% (posttest). At the patient level, the model’s performance (ROC AUC = 0.811; P = 0.007) was superior to that of conventional PET metrics. Furthermore, the radiomics model showed robustness when validated in treatment subgroups: first (ROC AUC, 0.916; P < 0.001) versus second or greater (ROC AUC, 0.842; P < 0.001) line of defense and single treatment (ROC AUC, 0.931; P < 0.001) versus multiple treatments (ROC AUC, 0.824; P < 0.001). We developed and validated a pretherapy PET-based radiomic model to predict response to treatment with ibrutinib in a diverse cohort of lymphoma patients.