Scientific article
Open access

Deep transformer-based personalized dosimetry from SPECT/CT images: a hybrid approach for [177Lu]Lu-DOTATATE radiopharmaceutical therapy

Published inEuropean journal of nuclear medicine and molecular imaging, vol. 51, no. 6, p. 1516-1529
Publication date2024-05
First online date2024-01-25

Purpose: Accurate dosimetry is critical for ensuring the safety and efficacy of radiopharmaceutical therapies. In current clinical dosimetry practice, MIRD formalisms are widely employed. However, with the rapid advancement of deep learning (DL) algorithms, there has been an increasing interest in leveraging the calculation speed and automation capabilities for different tasks. We aimed to develop a hybrid transformer-based deep learning (DL) model that incorporates a multiple voxel S-value (MSV) approach for voxel-level dosimetry in [177Lu]Lu-DOTATATE therapy. The goal was to enhance the performance of the model to achieve accuracy levels closely aligned with Monte Carlo (MC) simulations, considered as the standard of reference. We extended our analysis to include MIRD formalisms (SSV and MSV), thereby conducting a comprehensive dosimetry study.

Methods: We used a dataset consisting of 22 patients undergoing up to 4 cycles of [177Lu]Lu-DOTATATE therapy. MC simulations were used to generate reference absorbed dose maps. In addition, MIRD formalism approaches, namely, single S-value (SSV) and MSV techniques, were performed. A UNEt TRansformer (UNETR) DL architecture was trained using five-fold cross-validation to generate MC-based dose maps. Co-registered CT images were fed into the network as input, whereas the difference between MC and MSV (MC-MSV) was set as output. DL results are then integrated to MSV to revive the MC dose maps. Finally, the dose maps generated by MSV, SSV, and DL were quantitatively compared to the MC reference at both voxel level and organ level (organs at risk and lesions).

Results: The DL approach showed slightly better performance (voxel relative absolute error (RAE) = 5.28 ± 1.32) compared to MSV (voxel RAE = 5.54 ± 1.4) and outperformed SSV (voxel RAE = 7.8 ± 3.02). Gamma analysis pass rates were 99.0 ± 1.2%, 98.8 ± 1.3%, and 98.7 ± 1.52% for DL, MSV, and SSV approaches, respectively. The computational time for MC was the highest (~2 days for a single-bed SPECT study) compared to MSV, SSV, and DL, whereas the DL-based approach outperformed the other approaches in terms of time efficiency (3 s for a single-bed SPECT). Organ-wise analysis showed absolute percent errors of 1.44 ± 3.05%, 1.18 ± 2.65%, and 1.15 ± 2.5% for SSV, MSV, and DL approaches, respectively, in lesion-absorbed doses.

Conclusion: A hybrid transformer-based deep learning model was developed for fast and accurate dose map generation, outperforming the MIRD approaches, specifically in heterogenous regions. The model achieved accuracy close to MC gold standard and has potential for clinical implementation for use on large-scale datasets.

  • Deep learning
  • Monte Carlo simulation
  • Radiation dosimetry
  • Radionuclide therapy
  • [177Lu]Lu-DOTATATE
  • Deep Learning
  • Female
  • Humans
  • Image Processing, Computer-Assisted / methods
  • Male
  • Monte Carlo Method
  • Neuroendocrine Tumors / diagnostic imaging
  • Neuroendocrine Tumors / radiotherapy
  • Octreotide / analogs & derivatives
  • Octreotide / therapeutic use
  • Organometallic Compounds / therapeutic use
  • Precision Medicine / methods
  • Radiometry / methods
  • Radiopharmaceuticals / therapeutic use
  • Single Photon Emission Computed Tomography Computed Tomography / methods
Citation (ISO format)
MANSOURI, Zahra et al. Deep transformer-based personalized dosimetry from SPECT/CT images: a hybrid approach for [177Lu]Lu-DOTATATE radiopharmaceutical therapy. In: European journal of nuclear medicine and molecular imaging, 2024, vol. 51, n° 6, p. 1516–1529. doi: 10.1007/s00259-024-06618-9
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Article (Published version)
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ISSN of the journal1619-7070

Technical informations

Creation01/25/2024 8:18:49 AM
First validation05/03/2024 1:25:45 PM
Update time05/03/2024 1:25:45 PM
Status update05/03/2024 1:25:45 PM
Last indexation05/03/2024 1:26:06 PM
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