Application of machine learning to pretherapeutically estimate dosimetry in men with advanced prostate cancer treated with 177Lu-PSMA I&T therapy.

Abstract

PURPOSE:

Although treatment planning and individualized dose application for emerging prostate-specific membrane antigen (PSMA)-targeted radioligand therapy (RLT) are generally recommended, it is still difficult to implement in practice at the moment. In this study, we aimed to prove the concept of pretherapeutic prediction of dosimetry based on imaging and laboratory measurements before the RLT treatment.

METHODS:

Twenty-three patients with metastatic castration-resistant prostate cancer (mCRPC) treated with 177Lu-PSMA I&T RLT were included retrospectively. They had available pre-therapy 68 Ga-PSMA-HEBD-CC PET/CT and at least 3 planar and 1 SPECT/CT imaging for dosimetry. Overall, 43 cycles of 177Lu-PSMA I&T RLT were applied. Organ-based standard uptake values (SUVs) were obtained from pre-therapy PET/CT scans. Patient dosimetry was calculated for the kidney, liver, spleen, and salivary glands using Hermes Hybrid Dosimetry 4.0 from the planar and SPECT/CT images. Machine learning methods were explored for dose prediction from organ SUVs and laboratory measurements. The uncertainty of these dose predictions was compared with the population-based dosimetry estimates. Mean absolute percentage error (MAPE) was used to assess the prediction uncertainty of estimated dosimetry.

RESULTS:

An optimal machine learning method achieved a dosimetry prediction MAPE of 15.8 ± 13.2% for the kidney, 29.6% ± 13.7% for the liver, 23.8% ± 13.1% for the salivary glands, and 32.1 ± 31.4% for the spleen. In contrast, the prediction based on literature population mean has significantly larger MAPE (p < 0.01), 25.5 ± 17.3% for the kidney, 139.1% ± 111.5% for the liver, 67.0 ± 58.3% for the salivary glands, and 54.1 ± 215.3% for the spleen.

CONCLUSION:

The preliminary results confirmed the feasibility of pretherapeutic estimation of treatment dosimetry and its added value to empirical population-based estimation. The exploration of dose prediction may support the implementation of treatment planning for RLT.

• Authors: Song Xue, Andrei Gafita, Chao Dong, Yu Zhao, Giles Tetteh, Bjoern H Menze, Sibylle Ziegler, Wolfgang Weber, Ali Afshar-Oromieh, Axel Rominger, Matthias Eiber, Kuangyu Shi
• Journal: European journal of nuclear medicine and molecular imaging (Eur J Nucl Med Mol Imaging) Vol. 49 Issue 12 Pg. 4064-4072 (Oct 2022) ISSN: 1619-7089 [Electronic] Germany
• PMID: 35771265 (Publication Type: Journal Article)
• Copyright: © 2022. The Author(s).

Chemical References

• Dipeptides
• Heterocyclic Compounds, 1-Ring
• PSMA I&T
• Lutetium
• Urea
• Prostate-Specific Antigen

Topics

• Dipeptides (therapeutic use)
• Heterocyclic Compounds, 1-Ring (therapeutic use)
• Humans
• Lutetium (therapeutic use)
• Machine Learning
• Male
• Positron Emission Tomography Computed Tomography (methods)
• Prostate-Specific Antigen
• Prostatic Neoplasms, Castration-Resistant (diagnostic imaging, drug therapy, radiotherapy)
• Retrospective Studies
• Urea (analogs & derivatives)