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

Purpose 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.
Methods We retrospectively analyzed 90 segmented metastaticNETs 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).
Results SUVmean showed a significant correlation (q-value < 0.05) with absorbed dose (Spearman ρ = 0.64), followed by TLSUVmean
(
SUVmean of total-lesion-burden) and SUVpeak
(ρ = 0.45 and 0.41, respectively). The predictive value of PET-SUVmean in estimation
of posttherapy absorbed dose was stronger compared to PET-SUVpeak, and SUV_TNRs in terms of univariate analysis (R2 = 0.28
vs. R2 ≤ 0.12). An optimal trivariate random forest model composed of SUVmean,
TLSUVmean,
and total liver SUVmean
(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.
Conclusion 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.