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Background: Standardized patient‐specific pretreatment dosimetry planning is mandatory in the modern era of nuclear molecular radiotherapy, which may eventually lead to improvements in the final therapeutic outcome. Only a comprehensive definition of a dosage therapeutic window encompassing the range of absorbed doses, that is, helpful without being detrimental can lead to therapy individualization and improved outcomes. As a result, setting absorbed dose safety limits for organs at risk (OARs) requires knowledge of the absorbed dose–effect relationship. Data sets of consistent and reliable inter‐center dosimetry findings are required to characterize this relationship. Purpose: We developed and standardized a new pretreatment planning model consisting of a predictive dosimetry procedure for OARs in patients with neuroendocrine tumors (NETs) treated with 177Lu‐DOTATATE (Lutathera). In the retrospective study described herein, we used machine learning (ML) regression algorithms to predict absorbed doses in OARs by exploiting a combination of radiomic and dosiomic features extracted from patients' imaging data. Methods: Pretreatment and posttreatment data for 20 patients with NETs treated with 177Lu‐DOTATATE were collected from two clinical centers. A total of 3412 radiomic and dosiomic features were extracted from the patients' computed tomography (CT) scans and dose maps, respectively. All dose maps were generated using Monte Carlo simulations. An ML regression model was designed based on ML algorithms for predicting the absorbed dose in every OAR (liver, left kidney, right kidney, and spleen) before and after the therapy and between each therapy session, thus predicting any possible radiotoxic effects. Results: We evaluated nine ML regression algorithms. Our predictive model achieved a mean absolute dose error (MAE, in Gy) of 0.61 for the liver, 1.58 for the spleen, 1.30 for the left kidney, and 1.35 for the right kidney between pretherapy 68Ga‐DOTATOC positron emission tomography (PET)/CT and posttherapy 177Lu‐DOTATATE single photon emission (SPECT)/CT scans. Τhe best predictive performance observed was based on the gradient boost for the liver, the left kidney and the right kidney, and on the extra tree regressor for the spleen. Evaluation of the model's performance according to its ability to predict the absorbed dose in each OAR in every possible combination of pretherapy 68Ga‐DOTATOC PET/CT and any posttherapy 177Lu‐DOTATATE treatment cycle SPECT/CT scans as well as any 177Lu‐DOTATATE SPECT/CT treatment cycle and the consequent 177Lu‐DOTATATE SPECT/CT treatment cycle revealed mean absorbed dose differences ranges from −0.55 to 0.68 Gy. Incorporating radiodosiomics features from the 68Ga‐DOTATOC PET/CT and first 177Lu‐DOTATATE SPECT/CT treatment cycle scans further improved the precision and minimized the standard deviation of the predictions in nine out of 12 instances. An average improvement of 57.34% was observed (range: 17.53%–96.12%). However, it's important to note that in three instances (i.e., Ga,C.1 → C3 in spleen and left kidney, and Ga,C.1 → C2 in right kidney) we did not observe an improvement (absolute differences of 0.17, 0.08, and 0.05 Gy, respectively). Wavelet‐based features proved to have high correlated predictive value, whereas non‐linear‐based ML regression algorithms proved to be more capable than the linear‐based of producing precise prediction in our case. Conclusions: The combination of radiomics and dosiomics has potential utility for personalized molecular radiotherapy (PMR) response evaluation and OAR dose prediction. These radiodosiomic features can potentially provide information on any possible disease recurrence and may be highly useful in clinical decision‐making, especially regarding dose escalation issues. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to investigate the quantification performance of a 360° CZT camera for 177Lu-based treatment monitoring. Methods: Three phantoms with known 177Lu activity concentrations were acquired: (1) a uniform cylindrical phantom for calibration, (2) a NEMA IEC body phantom for analysis of different-sized spheres to optimise quantification parameters and (3) a phantom containing two large vials simulating organs at risk for tests. Four sets of reconstruction parameters were tested: (1) Scatter, (2) Scatter and Point Spread Function Recovery (PSFR), (3) PSFR only and (4) Penalised likelihood option and Scatter, varying the number of updates (iterations × subsets) with CT-based attenuation correction only. For each, activity concentration (ARC) and contrast recovery coefficients (CRC) were estimated as well as root mean square. Visualisation and quantification parameters were applied to reconstructed patient image data. Results: Optimised quantification parameters were determined to be: CT-based attenuation correction, scatter correction, 12 iterations, 8 subsets and no filter. ARC, CRC and RMS results were dependant on the methodology used for calculations. Two different reconstruction parameters were recommended for visualisation and for quantification. 3D whole-body SPECT images were acquired and reconstructed for 177Lu-PSMA patients in 2–3 times faster than the time taken for a conventional gamma camera. Conclusion: Quantification of whole-body 3D images of patients treated with 177Lu-PSMA is feasible and an optimised set of parameters has been determined. This camera greatly reduces procedure time for whole-body SPECT. [ABSTRACT FROM AUTHOR]

Abstract: A 69-year-old man diagnosed with progressive bone metastatic castration-resistant prostate adenocarcinoma and concurrent alcoholic cirrhosis with multiple hepatocellular carcinoma (HCC) nodules was referred to our nuclear medicine service for 177 Lu-PSMA-617 therapy. The patient's pretreatment screening using 68 Ga-PSMA-11 PET/CT revealed high prostate-specific membrane antigen expression in both prostatic and HCC lesions. The patient underwent 2 doses of 177 Lu-PSMA-617. Subsequent imaging assessments with 68 Ga-PSMA-11 PET/CT and hepatic MRI indicated progressive HCC nodules, while showing a partial response in prostatic bone metastases. Positive clinical and biological responses were observed only in prostatic disease, but not in HCC nodules., Competing Interests: Conflicts of interest and sources of funding: none declared., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)