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

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

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

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

Proof-of-concept Study to Estimate Individual Post-Therapy Dosimetry in Men with Advanced Prostate Cancer Treated with 177Lu-PSMA I&T Therapy

It is still debating if individualized dose should be applied for the emerging PSMA-targeted radionuclide therapy (RLT). A critical consideration in this debate is the necessity and feasibility of individual estimation of post-therapy dosimetry before the treatment. In this study, we aimed to prove the concept of individual dosimetry prediction based on pre-therapy imaging and laboratory measurements. Methods: 23 patients with metastatic castration-resistant prostate cancer (mCRPC) treated with 177Lu-PSMA-I&T RLT were included retrospectively. Included patients had available pre-therapeutic 68Ga-PSMA-HEBD-CC PET/CT and at least 3 planar and 1 SPECT/CT dosimetry imaging. Overall, 43 cycles of 177Lu-PSMA I&T RLT were applied. Organ-based standard uptake value (SUV) uptake was obtained from pretherapy PET/CT scans. Patient individual dosimetry was calculated for kidney, liver, spleen, and salivary glands using Hermes Hybrid Dosimetry 4.0 from the post-treatment 177Lu-PSMA I&T imaging studies. Machine learning methods were explored for individual dose prediction from PET images. The accuracy of these dose predictions was compared with the accuracy of population-based dosimetry estimates. Mean absolute percentage error was used to assess the prediction error of estimated dosimetry. Results: An optimal machine learning method achieved a dosimetry prediction error of 15.8 ± 13.2% for kidney, 29.6%±13.7% for liver, 23.8%±13.1% for salivary glands and 32.1 ± 31.4% for spleen. In contrast, the prediction based on literature population mean has significantly larger error (p < 0.01), 25.5 ± 17.3% for kidney, 139.1%±111.5% for liver, 67.0 ± 58.3% for salivary glands, and 54.1 ± 215.3% for spleen. Conclusion: The preliminary results confirmed the feasibility of individual estimation of post-therapy dosimetry before the RLT and its added value to empirical population-based estimation. The exploration of individual dose prediction may support the identification of the role of treatment planning for RLT

18F-Choline PET/MR Can Detect and Delineate Local Recurrence After High-Intensity Focused Ultrasound Therapy of Prostate Cancer

Restaging local recurrence after high-intensity focused ultra-sound (HIFU) is based on multiparametric MRI (mpMRI). However, postinterventional changes of the tissue, such as edema or hemorrhage, are limiting tumor detection on mpMRI. We present a case of a rising prostate-specific antigen values, negative mpMRI, and a Gleason score of 4+4 on template biopsy after HIFU. On F-choline PET/MR, high focal uptake was detected at the location of positive biopsy. Re-HIFU based on the fused F-choline PET/MR images was performed, followed by a recurrence-free period of 11 months. Thus, F-choline PET/MR could improve guiding retreatment in patients with recurrence after HIFU

Almost 10 years of PET/MR attenuation correction: the effect on lesion quantification with PSMA: clinical evaluation on 200 prostate cancer patients

Purpose!#!After a decade of PET/MR, the case of attenuation correction (AC) remains open. The initial four-compartment (air, water, fat, soft tissue) Dixon-based AC scheme has since been expanded with several features, the latest being MR field-of-view extension and a bone atlas. As this potentially changes quantification, we evaluated the impact of these features in PET AC in prostate cancer patients.!##!Methods!#!Two hundred prostate cancer patients were examined with either !##!Results!#!High correlation and no visually perceivable differences between all evaluated methods (r &amp;gt; 0.996) were found. The mean relative difference in lesion uptake of !##!Conclusions!#!Based on these results and the encountered bone atlas registration inaccuracy, we deduce that including bones and extending the MR field-of-view did not introduce clinically significant differences in PSMA diagnostic accuracy and tracer uptake quantification in prostate cancer pelvic lesions, facilitating the analysis of serial studies respectively. However, in the absence of ground truth data, we advise against atlas-based methods when comparing serial scans for bone lesions

Pre-therapy PET-based voxel-wise dosimetry prediction by characterizing intra-organ heterogeneity in PSMA-directed radiopharmaceutical theranostics.

BACKGROUND AND OBJECTIVE Treatment planning through the diagnostic dimension of theranostics provides insights into predicting the absorbed dose of RPT, with the potential to individualize radiation doses for enhancing treatment efficacy. However, existing studies focusing on dose prediction from diagnostic data often rely on organ-level estimations, overlooking intra-organ variations. This study aims to characterize the intra-organ theranostic heterogeneity and utilize artificial intelligence techniques to localize them, i.e. to predict voxel-wise absorbed dose map based on pre-therapy PET. METHODS 23 patients with metastatic castration-resistant prostate cancer treated with [177Lu]Lu-PSMA I&T RPT were retrospectively included. 48 treatment cycles with pre-treatment PET imaging and at least 3 post-therapeutic SPECT/CT imaging were selected. The distribution of PET tracer and RPT dose was compared for kidney, liver and spleen, characterizing intra-organ heterogeneity differences. Pharmacokinetic simulations were performed to enhance the understanding of the correlation. Two strategies were explored for pre-therapy voxel-wise dosimetry prediction: (1) organ-dose guided direct projection; (2) deep learning (DL)-based distribution prediction. Physical metrics, dose volume histogram (DVH) analysis, and identity plots were applied to investigate the predicted absorbed dose map. RESULTS Inconsistent intra-organ patterns emerged between PET imaging and dose map, with moderate correlations existing in the kidney (r = 0.77), liver (r = 0.5), and spleen (r = 0.58) (P < 0.025). Simulation results indicated the intra-organ pharmacokinetic heterogeneity might explain this inconsistency. The DL-based method achieved a lower average voxel-wise normalized root mean squared error of 0.79 ± 0.27%, regarding to ground-truth dose map, outperforming the organ-dose guided projection (1.11 ± 0.57%) (P < 0.05). DVH analysis demonstrated good prediction accuracy (R2 = 0.92 for kidney). The DL model improved the mean slope of fitting lines in identity plots (199% for liver), when compared to the theoretical optimal results of the organ-dose approach. CONCLUSION Our results demonstrated the intra-organ heterogeneity of pharmacokinetics may complicate pre-therapy dosimetry prediction. DL has the potential to bridge this gap for pre-therapy prediction of voxel-wise heterogeneous dose map

Voxel based comparison and texture analysis of 18F-FDG and 18F-FMISO PET of patients with head-and-neck cancer.

BACKGROUND:Hypoxia can induce radiation resistance and is an independent prognostic marker for outcome in head and neck cancer. As 18F-FMISO (FMISO), a hypoxia tracer for PET, is far less common than 18F-FDG (FDG) and two separate PET scans result in doubled cost and radiation exposure to the patient, we aimed to predict hypoxia from FDG PET with new techniques of voxel based analysis and texture analysis. METHODS:Thirty-eight patients with head-and-neck cancer underwent consecutive FDG and FMISO PET scans before any treatment. ROIs enclosing the primary cancer were compared in a voxel-by-voxel manner between FDG and FMISO PET. Tumour hypoxia was defined as the volume with a tumour-to-muscle ratio (TMR) > 1.25 in the FMISO PET and hypermetabolic volume was defined as >50% SUVmax in the FDG PET. The concordance rate was defined as percentage of voxels within the tumour which were both hypermetabolic and hypoxic. 38 different texture analysis (TA) parameters were computed based on the ROIs and correlated with presence of hypoxia. RESULTS:Within the hypoxic tumour regions, the FDG uptake was twice as high as in the non-hypoxic tumour regions (SUVmean 10.9 vs. 5.4; p<0.001). A moderate correlation between FDG and FMISO uptake was found by a voxel-by-voxel comparison (r = 0.664 p<0.001). The average concordance rate was 25% (± 22%). Entropy was the TA parameter showing the highest correlation with hypoxia (r = 0.524 p<0.001). CONCLUSION:FDG uptake was higher in hypoxic tumour regions than in non-hypoxic regions as expected by tumour biology. A moderate correlation between FDG and FMISO PET was found by voxel-based analysis. TA yielded similar results in FDG and FMISO PET. However, it may not be possible to predict tumour hypoxia even with the help of texture analysis

Deep Neural Network for Automatic Characterization of Lesions on 68Ga-PSMA PET/CT Images.

The emerging PSMA-targeted radionuclide therapy provides an effective method for the treatment of advanced metastatic prostate cancer. To optimize the therapeutic effect and maximize the theranostic benefit, there is a need to identify and quantify target lesions prior to treatment. However, this is extremely challenging considering that a high number of lesions of heterogeneous size and uptake may distribute in a variety of anatomical context with different backgrounds. This study proposes an end-to-end deep neural network to characterize the prostate cancer lesions on PSMA imaging automatically. A 68Ga-PSMA-11 PET/CT image dataset including 71 patients with metastatic prostate cancer was collected from three medical centres for training and evaluating the proposed network. For proof-of-concept, we focus on the detection of bone and lymph node lesions in the pelvic area suggestive for metastases of prostate cancer. The preliminary test on pelvic area confirms the potential of deep learning methods. Increasing the amount of training data may further enhance the performance of the proposed deep learning method