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

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Abstract

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 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).

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.

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Data Availability

The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.

Notes

  1. Prof. Richard Baum.

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Funding

This work was supported by grants R01CA240706 and P30CA046592 from the National Cancer Institute; and the Euratom research and training programme 2019–2020 Sinfonia project under grant agreement No 945196.

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Authors and Affiliations

  1. Department of Radiology, University of Michigan, 1301 Catherine, 2276 Medical Science I/5610, Ann Arbor, MI, 48109, USA

    Azadeh Akhavanallaf, Avery B. Peterson, Kellen Fitzpatrick, Molly Roseland, Ka Kit Wong & Yuni K. Dewaraja

  2. Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland

    Azadeh Akhavanallaf & Habib Zaidi

  3. Department of Machine Learning, Moffitt Cancer Center, Tampa, FL, USA

    Issam El-Naqa

  4. Geneva University Neurocenter, Geneva University, CH-1205, Geneva, Switzerland

    Habib Zaidi

  5. Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, 9700 RB, Groningen, Netherlands

    Habib Zaidi

  6. Department of Nuclear Medicine, University of Southern Denmark, DK-500, Odense, Denmark

    Habib Zaidi

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  1. Azadeh Akhavanallaf
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Correspondence to Azadeh Akhavanallaf.

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Akhavanallaf, A., Peterson, A.B., Fitzpatrick, K. et al. The predictive value of pretherapy [68Ga]Ga-DOTA-TATE PET and biomarkers in [177Lu]Lu-PRRT tumor dosimetry. Eur J Nucl Med Mol Imaging 50, 2984–2996 (2023). https://doi.org/10.1007/s00259-023-06252-x

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