Introduction: Peptide Receptor Radionuclide Therapy (PRRT) with [177Lu]Lu-DOTA-TATE is effective in treating advanced Neuroendocrine Tumors (NETs), yet predicting individual response in this treatment remains a challenge due to inter-lesion heterogeneity. There is a lack of standardized, effective methods for using multi-lesion radiomics to predict progression and Time to Progression (TTP) in PRRT-treated patients. This study evaluated how aggregating radiomic features from multiple PET-identified lesions can be used to predict disease progression (event [progression and death] vs. event-free) and TTP. Methods: Eighty-one NETs patients with multiple lesions underwent pre-treatment PET/CT imaging. Lesions were segmented and ranked by minimum Standard Uptake Value (SUVmin) (both descending and ascending), SUVmean, SUVmax, and volume (descending). From each sorting, the top one, three, and five lesions were selected. For the selected lesions, radiomic features were extracted (using the Pyradiomics library) and lesion aggregation was performed using stacked vs. statistical methods. Eight classification models along with three feature selection methods were used to predict progression, and five survival models and three feature selection methods were used to predict TTP under a nested cross-validation framework. Results: The overall appraisal showed that sorting lesions based on SUVmin(descending) yields better classification performance in progression prediction. This is in addition to the fact that aggregating features extracted from all the lesions, as well as the top five lesions sorted by SUVmean, lead to the highest overall performance in TTP prediction. The individual appraisal in progression prediction models trained on the single top lesion sorted by SUVmin(descending) showed the highest recall and specificity despite data imbalance. The best-performing model was the Logistic Regression (LR) classifier with Recursive Feature Elimination (RFE) (recall: 0.75, specificity: 0.77). In TTP prediction, the highest concordance index was obtained using a Random Survival Forest (RSF) trained on statistically aggregated features from the top five lesions ranked by SUVmean, selected via Univariate C-Index (UCI) (C-index = 0.68). Across both tasks, features from the Gray Level Size Zone Matrix (GLSZM) family were consistently among the most predictive, highlighting the importance of spatial heterogeneity in treatment response. Conclusions: This study demonstrates that informed lesion selection and tailored aggregation strategies significantly impact the predictive performance of radiomics-based models for progression and TTP prediction in PRRT-treated NET patients. These approaches can potentially enhance model accuracy and better capture tumor heterogeneity, supporting more personalized and practical PRRT implementation.
引言：采用[¹⁷⁷Lu]Lu-DOTA-TATE的肽受体放射性核素疗法（PRRT）在治疗晚期神经内分泌肿瘤（NETs）方面具有疗效，但由于病灶间异质性，预测个体对该治疗的反应仍具挑战。目前缺乏标准化、有效的方法利用多病灶影像组学预测PRRT治疗患者的疾病进展及进展时间（TTP）。本研究评估了如何通过聚合PET识别多病灶的影像组学特征来预测疾病进展（事件[进展与死亡] vs. 无事件）及TTP。方法：81例伴有多发病灶的NET患者接受了治疗前PET/CT成像。病灶按最小标准摄取值（SUVmin）（降序与升序）、SUVmean、SUVmax及体积（降序）进行分割与排序。从每种排序中选取前1、3、5个病灶。对所选病灶提取影像组学特征（使用Pyradiomics库），并通过堆叠法与统计法进行病灶特征聚合。在嵌套交叉验证框架下，采用八种分类模型结合三种特征选择方法预测疾病进展，并采用五种生存模型结合三种特征选择方法预测TTP。结果：总体评估显示，基于SUVmin（降序）的病灶排序在进展预测中具有更好的分类性能。此外，聚合所有病灶提取的特征以及按SUVmean排序的前五个病灶的特征，在TTP预测中获得了最高的整体性能。在进展预测模型中，基于SUVmin（降序）排序的单个最高病灶训练的模型，尽管存在数据不平衡，仍显示出最高的召回率与特异性。表现最佳的模型为结合递归特征消除（RFE）的逻辑回归（LR）分类器（召回率：0.75，特异性：0.77）。在TTP预测中，最高的C指数是通过随机生存森林（RSF）模型获得，该模型训练于按SUVmean排序的前五个病灶经统计聚合的特征，并采用单变量C指数（UCI）进行特征选择（C指数 = 0.68）。在所有任务中，灰度区域大小矩阵（GLSZM）家族的特征始终最具预测性，凸显了空间异质性在治疗反应中的重要性。结论：本研究证明，基于先验知识的病灶选择与定制化的聚合策略显著影响基于影像组学的模型对PRRT治疗NET患者疾病进展及TTP的预测性能。这些方法有望提升模型准确性，更好地捕捉肿瘤异质性，从而支持更个性化、更实用的PRRT临床实施。
肿瘤(癌症)患者之家