The predictive value of pretherapy [⁶⁸Ga]Ga-DOTA-TATE PET and biomarkers in [¹⁷⁷Lu]Lu-PRRT tumor dosimetry.

Abstract

Metastatic neuroendocrine tumors (NETs) overexpressing type 2 somatostatin receptors are the target for peptide receptor radionuclide therapy (PRRT) through the theragnostic pair of ⁶⁸Ga/¹⁷⁷Lu-DOTATATE. The main purpose of this study was to develop machine learning models to predict therapeutic tumor dose using pre therapy ⁶⁸Ga -PET and clinicopathological biomarkers. We retrospectively analyzed 90 segmented metastatic NETs from 25 patients (M14/F11, age 63.7 ± 9.5, range 38-76) treated by ¹⁷⁷Lu-DOTATATE at our institute. Patients underwent both pretherapy [⁶⁸Ga]Ga-DOTA-TATE PET/CT and four timepoints SPECT/CT at  ~ 4, 24, 96, and 168 h post-¹⁷⁷Lu-DOTATATE infusion. Tumors were segmented by a radiologist on baseline CT or MRI and transferred to co-registered PET/CT and SPECT/CT, and normal organs were segmented by deep learning-based method on CT of the PET and SPECT. The SUV metrics and tumor-to-normal tissue SUV ratios (SUV_TNRs) were calculated from ⁶⁸Ga -PET at the contour-level. Posttherapy dosimetry was performed based on the co-registration of SPECT/CTs to generate time-integrated-activity, followed by an in-house Monte Carlo-based absorbed dose estimation. The correlation between delivered ¹⁷⁷Lu Tumor absorbed dose and PET-derived metrics along with baseline clinicopathological biomarkers (such as Creatinine, Chromogranin A and prior therapies) were evaluated. Multiple interpretable machine-learning algorithms were developed to predict tumor dose using these pretherapy information. Model performance on a nested tenfold cross-validation was evaluated in terms of coefficient of determination (R²), mean-absolute-error (MAE), and mean-relative-absolute-error (MRAE). SUV_mean showed a significant correlation (q-value < 0.05) with absorbed dose (Spearman ρ = 0.64), followed by TLSUV_mean (SUV_mean of total-lesion-burden) and SUV_peak (ρ = 0.45 and 0.41, respectively). The predictive value of PET-SUV_mean in estimation of posttherapy absorbed dose was stronger compared to PET-SUV_peak, and SUV_TNRs in terms of univariate analysis (R² = 0.28 vs. R² ≤ 0.12). An optimal trivariate random forest model composed of SUV_mean, TLSUV_mean, and total liver SUV_mean (normal and tumoral liver) provided the best performance in tumor dose prediction with R² = 0.64, MAE = 0.73 Gy/GBq, and MRAE = 0.2. Our preliminary results demonstrate the feasibility of using baseline PET images for prediction of absorbed dose prior to ¹⁷⁷Lu-PRRT. Machine learning models combining multiple PET-based metrics performed better than using a single SUV value and using other investigated clinicopathological biomarkers. Developing such quantitative models forms the groundwork for the role of ⁶⁸Ga -PET not only for the implementation of personalized treatment planning but also for patient stratification in the era of precision medicine.