AIQ Solutions, 8000 Excelsior Dr Suite 400, Madison, WI, 53717, United States of America.
Department of Nuclear Medicine, The First Hospital of China Medical University, Shenyang, Liaoning, China.
Eur J Nucl Med Mol Imaging. 2024 Oct;51(12):3505-3517. doi: 10.1007/s00259-024-06764-0. Epub 2024 May 31.
Standardized reporting of treatment response in oncology patients has traditionally relied on methods like RECIST, PERCIST and Deauville score. These endpoints assess only a few lesions, potentially overlooking the response heterogeneity of all disease. This study hypothesizes that comprehensive spatial-temporal evaluation of all individual lesions is necessary for superior prognostication of clinical outcome.
[F]FDG PET/CT scans from 241 patients (127 diffuse large B-cell lymphoma (DLBCL) and 114 non-small cell lung cancer (NSCLC)) were retrospectively obtained at baseline and either during chemotherapy or post-chemoradiotherapy. An automated TRAQinform IQ software (AIQ Solutions) analyzed the images, performing quantification of change in regions of interest suspicious of cancer (lesion-ROI). Multivariable Cox proportional hazards (CoxPH) models were trained to predict overall survival (OS) with varied sets of quantitative features and lesion-ROI, compared by bootstrapping with C-index and t-tests. The best-fit model was compared to automated versions of previously established methods like RECIST, PERCIST and Deauville score.
Multivariable CoxPH models demonstrated superior prognostic power when trained with features quantifying response heterogeneity in all individual lesion-ROI in DLBCL (C-index = 0.84, p < 0.001) and NSCLC (C-index = 0.71, p < 0.001). Prognostic power significantly deteriorated (p < 0.001) when using subsets of lesion-ROI (C-index = 0.78 and 0.67 for DLBCL and NSCLC, respectively) or excluding response heterogeneity (C-index = 0.67 and 0.70). RECIST, PERCIST, and Deauville score could not significantly associate with OS (C-index < 0.65 and p > 0.1), performing significantly worse than the multivariable models (p < 0.001).
Quantitative evaluation of response heterogeneity of all individual lesions is necessary for the superior prognostication of clinical outcome.
传统上，肿瘤患者的治疗反应的标准化报告依赖于 RECIST、PERCIST 和 Deauville 评分等方法。这些终点仅评估少数病变，可能会忽略所有疾病的反应异质性。本研究假设对所有个体病变进行全面的时空评估对于更好地预测临床结局是必要的。
回顾性获取了 241 名患者（127 名弥漫性大 B 细胞淋巴瘤（DLBCL）和 114 名非小细胞肺癌（NSCLC））的 [F]FDG PET/CT 扫描，这些患者在基线时以及化疗或放化疗后获得了扫描。自动 TRAQinform IQ 软件（AIQ Solutions）分析了图像，对可疑癌症的感兴趣区域（病变-ROI）的变化进行了定量分析。使用不同的定量特征和病变-ROI 训练多变量 Cox 比例风险（CoxPH）模型，通过 bootstrap 与 C 指数和 t 检验进行比较。通过与 RECIST、PERCIST 和 Deauville 评分等已建立的自动方法进行比较，比较最佳拟合模型。
多变量 CoxPH 模型在训练中使用定量特征来量化 DLBCL（C 指数=0.84，p<0.001）和 NSCLC（C 指数=0.71，p<0.001）中所有个体病变-ROI 的反应异质性时，显示出更好的预后能力。当使用病变-ROI 的子集（DLBCL 和 NSCLC 的 C 指数分别为 0.78 和 0.67）或排除反应异质性（C 指数分别为 0.67 和 0.70）时，预后能力显著恶化（p<0.001）。RECIST、PERCIST 和 Deauville 评分与 OS 无显著相关性（C 指数<0.65，p>0.1），性能明显逊于多变量模型（p<0.001）。
对所有个体病变的反应异质性进行定量评估对于更好地预测临床结局是必要的。
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