Personalized dosimetry of177Lu-DOTATATE: a comparison of organ- and voxel-level approaches using open-access images

Impact of different models based on blood samples and images for bone marrow dosimetry after 177Lu-labeled somatostatin-receptor therapy

BACKGROUND

Peptide receptor radionuclide therapy with 177Lu-DOTATATE is a recognized option for treating neuroendocrine tumors and has few toxicities, except for the kidneys and bone marrow. The bone marrow dose is generally derived from a SPECT/CT image-based method with four timepoints or from a blood-based method with up to 9 timepoints, but there is still no reference method. This retrospective single-center study on the same cohort of patients compared the calculated bone marrow dose administered with both methods using mono, bi- or tri-exponential models. For the image-based method, the dose was estimated using Planetdose© software. Pearson correlation coefficients were calculated. We also studied the impact of late timepoints for both methods.

RESULTS

The bone marrow dose was calculated for 131 treatments with the blood-based method and for 17 with the image-based method. In the former, the median absorbed dose was 15.3, 20.5 and 28.3 mGy/GBq with the mono-, bi- and tri-exponential model, respectively. With the image-based method, the median absorbed dose was 63.9, 41.9 and 60.8 with the mono-, bi- and tri-exponential model, respectively. Blood samples after 24h post-injection did not evidence any change in the absorbed bone marrow dose with the bi-exponential model. On the contrary, the 6-day post-injection timepoint was more informative with the image-based model.

CONCLUSION

This study confirms that the estimated bone marrow dose is significantly lower with the blood-based method than with the image-based method. The blood-based method with a bi-exponential model proved particularly useful, without the need for blood samples after 24h post-injection. Nevertheless, this blood-based method is based on an assumption that needs to be more validated. The important difference between the two methods does not allow to determine the optimal one to estimate the true absorbed dose and further studies are necessary to compare with biological effects.

Impact of deep learning-based multiorgan segmentation methods on patient-specific internal dosimetry in PET/CT imaging: A comparative study

PURPOSE

Accurate and fast multiorgan segmentation is essential in image-based internal dosimetry in nuclear medicine. While conventional manual PET image segmentation is widely used, it suffers from both being time-consuming as well as subject to human error. This study exploited 2D and 3D deep learning (DL) models. Key organs in the trunk of the body were segmented and then used as a reference for networks.

METHODS

The pre-trained p2p-U-Net-GAN and HighRes3D architectures were fine-tuned with PET-only images as inputs. Additionally, the HighRes3D model was alternatively trained with PET/CT images. Evaluation metrics such as sensitivity (SEN), specificity (SPC), intersection over union (IoU), and Dice scores were considered to assess the performance of the networks. The impact of DL-assisted PET image segmentation methods was further assessed using the Monte Carlo (MC)-derived S-values to be used for internal dosimetry.

RESULTS

A fair comparison with manual low-dose CT-aided segmentation of the PET images was also conducted. Although both 2D and 3D models performed well, the HighRes3D offers superior performance with Dice scores higher than 0.90. Key evaluation metrics such as SEN, SPC, and IoU vary between 0.89-0.93, 0.98-0.99, and 0.87-0.89 intervals, respectively, indicating the encouraging performance of the models. The percentage differences between the manual and DL segmentation methods in the calculated S-values varied between 0.1% and 6% with a maximum attributed to the stomach.

CONCLUSION

The findings prove while the incorporation of anatomical information provided by the CT data offers superior performance in terms of Dice score, the performance of HighRes3D remains comparable without the extra CT channel. It is concluded that both proposed DL-based methods provide automated and fast segmentation of whole-body PET/CT images with promising evaluation metrics. Between them, the HighRes3D is more pronounced by providing better performance and can therefore be the method of choice for 18F-FDG-PET image segmentation.

The Challenge of Single-Photon Emission Computed Tomography Image Segmentation in the Internal Dosimetry of 177Lu Molecular Therapies

The aim of this article is to review the single photon emission computed tomography (SPECT) segmentation methods used in patient-specific dosimetry of 177Lu molecular therapy. Notably, 177Lu-labelled radiopharmaceuticals are currently used in molecular therapy of metastatic neuroendocrine tumours (ligands for somatostatin receptors) and metastatic prostate adenocarcinomas (PSMA ligands). The proper segmentation of the organs at risk and tumours in targeted radionuclide therapy is an important part of the optimisation process of internal patient dosimetry in this kind of therapy. Because this is the first step in dosimetry assessments, on which further dose calculations are based, it is important to know the level of uncertainty that is associated with this part of the analysis. However, the robust quantification of SPECT images, which would ensure accurate dosimetry assessments, is very hard to achieve due to the intrinsic features of this device. In this article, papers on this topic were collected and reviewed to weigh up the advantages and disadvantages of the segmentation methods used in clinical practice. Degrading factors of SPECT images were also studied to assess their impact on the quantification of 177Lu therapy images. Our review of the recent literature gives an insight into this important topic. However, based on the PubMed and IEEE databases, only a few papers investigating segmentation methods in 177Lumolecular therapy were found. Although segmentation is an important step in internal dose calculations, this subject has been relatively lightly investigated for SPECT systems. This is mostly due to the inner features of SPECT. What is more, even when studies are conducted, they usually utilise the diagnostic radionuclide 99mTc and not a therapeutic one like 177Lu, which could be of concern regarding SPECT camera performance and its overall outcome on dosimetry.

Implementation of xSPECT, xSPECT bone and Broadquant from literature, clinical survey and innovative phantom study with task-based image quality assessment

OBJECTIVE

From patient and phantom studies, we aimed to highlight an original implementation process and share a two-years experience clinical feedback on xSPECT (xS), xSPECT Bone (xB) and Broadquant quantification (Siemens) for 99mTc-bone and 177Lu-NET (neuroendocrine tumors) imaging.

METHODS

Firstly, we checked the relevance of implemented protocols and Broadquant module on the basis of literature and with a homogeneous phantom study respectively. Then, we described xS and xB behaviours with reconstruction parameters (10i-0mm to 40i-20mm) and optimized the protocols through a blinded survey (7 physicians). Finally, the preferred 99mTc-bone reconstruction was assessed through an IEC NEMA phantom including liquid bone spheres. Conventional SNR, CNR, spatial resolution, Q.%error, and recovery curves; and innovative NPS, TTF and detectability score d' were performed (ImQuest software). We also sought to review the adoption of these tools in clinical routine and showed the potential of quantitative xB in the context of theranostics (Xofigo®).

RESULTS

We showed the need of optimization of implemented reconstruction algorithms and pointed out a decay correction particularity with Broadquant. Preferred parameters were 1s-25i-8mm and 1s-25i-5mm for xS/xB-bone and xS-NET imaging respectively. The phantom study highlighted the different image quality especially for the enhanced spatial resolution xB algorithm (1/TTF10%=2.1 mm) and showed F3D and xB shared the best performances in terms of image quality and quantification. xS was generally less efficient.

CONCLUSIONS

Qualitative F3D still remains the clinical standard, xB and Broadquant offer challenging perspectives in theranostics. We introduced the potential of innovative metrics for image quality analysis and showed how CT tools should be adapted to fit nuclear medicine imaging.