Quality Assurance Considerations in Radiopharmaceutical Therapy Dosimetry Using PLANETDose: An International Atomic Energy Agency Study.

Implementation of radiopharmaceutical therapy dosimetry varies depending on the clinical application, dosimetry protocol, software, and ultimately the operator. Assessing clinical dosimetry accuracy and precision is therefore a challenging task. This work emphasizes some pitfalls encountered during a structured analysis, performed on a single-patient dataset consisting of SPECT/CT images by various participants using a standard protocol and clinically approved commercial software. Methods: The clinical dataset consisted of the dosimetric study of a patient administered with [177Lu]Lu-DOTATATE at Tygerberg Hospital, South Africa, as a part of International Atomic Energy Agency-coordinated research project E23005. SPECT/CT images were acquired at 5 time points postinjection. Patient and calibration images were reconstructed on a workstation, and a calibration factor of 122.6 Bq/count was derived independently and provided to the participants. A standard dosimetric protocol was defined, and PLANETDose (version 3.1.1) software was installed at 9 centers to perform the dosimetry of 3 treatment cycles. The protocol included rigid image registration, segmentation (semimanual for organs, activity threshold for tumors), and dose voxel kernel convolution of activity followed by absorbed dose (AD) rate integration to obtain the ADs. Iterations of the protocol were performed by participants individually and within collective training, the results of which were analyzed for dosimetric variability, as well as for quality assurance and error analysis. Intermediary checkpoints were developed to understand possible sources of variation and to differentiate user error from legitimate user variability. Results: Initial dosimetric results for organs (liver and kidneys) and lesions showed considerable interoperator variability. Not only was the generation of intermediate checkpoints such as total counts, volumes, and activity required, but also activity-to-count ratio, activity concentration, and AD rate-to-activity concentration ratio to determine the source of variability. Conclusion: When the same patient dataset was analyzed using the same dosimetry procedure and software, significant disparities were observed in the results despite multiple sessions of training and feedback. Variations due to human error could be minimized or avoided by performing intensive training sessions, establishing intermediate checkpoints, conducting sanity checks, and cross-validating results across physicists or with standardized datasets. This finding promotes the development of quality assurance in clinical dosimetry.

Voxel-wise supervised analysis of tumors with multimodal engineered features to highlight interpretable biological patterns.

BACKGROUND: Translation of predictive and prognostic image-based learning models to clinical applications is challenging due in part to their lack of interpretability. Some deep-learning-based methods provide information about the regions driving the model output. Yet, due to the high-level abstraction of deep features, these methods do not completely solve the interpretation challenge. In addition, low sample size cohorts can lead to instabilities and suboptimal convergence for models involving a large number of parameters such as convolutional neural networks. PURPOSE: Here, we propose a method for designing radiomic models that combines the interpretability of handcrafted radiomics with a sub-regional analysis. MATERIALS AND METHODS: Our approach relies on voxel-wise engineered radiomic features with average global aggregation and logistic regression. The method is illustrated using a small dataset of 51 soft tissue sarcoma (STS) patients where the task is to predict the risk of lung metastasis occurrence during the follow-up period. RESULTS: Using positron emission tomography/computed tomography and two magnetic resonance imaging sequences separately to build two radiomic models, we show that our approach produces quantitative maps that highlight the signal that contributes to the decision within the tumor region of interest. In our STS example, the analysis of these maps identified two biological patterns that are consistent with STS grading systems and knowledge: necrosis development and glucose metabolism of the tumor. CONCLUSIONS: We demonstrate how that method makes it possible to spatially and quantitatively interpret radiomic models amenable to sub-regions identification and biological interpretation for patient stratification.

Radiation Dosimetry and Biodistribution of 68Ga-FAPI-46 PET Imaging in Cancer Patients.

Targeting cancer-associated fibroblasts (CAFs) has become an attractive goal for diagnostic imaging and therapy because they can constitute as much as 90% of a tumor mass. The serine protease fibroblast activation protein (FAP) is overexpressed selectively in CAFs, drawing interest in FAP as a stromal target. The quinoline-based FAP inhibitor (FAPI) PET tracer 68Ga-FAPI-04 has been previously shown to yield high tumor-to-background ratios (TBRs) in patients with various cancers. Recent developments toward an improved compound for therapeutic application have identified FAPI-46 as a promising agent because of an increased tumor retention time in comparison with FAPI-04. Here, we present a PET biodistribution and radiation dosimetry study of 68Ga-FAPI-46 in cancer patients. Methods: Six patients with different cancers underwent serial 68Ga-FAPI-46 PET/CT scans at 3 time points after radiotracer injection: 10 min, 1 h, and 3 h. The source organs consisted of the kidneys, bladder, liver, heart, spleen, bone marrow, uterus, and remainder of body. OLINDA/EXM software, version 1.1, was used to fit and integrate the kinetic organ activity data to yield total-body and organ time-integrated activity coefficients and residence times and, finally, organ-absorbed doses. SUVs and TBR were generated from the contoured tumor and source-organ volumes. Spheric volumes in muscle and blood pool were also obtained for TBR (tumor SUVmax/organ SUVmean). Results: At all time points, average SUVmax was highest in the liver. Tumor and organ SUVmean decreased over time, whereas TBRs in all organs but the uterus increased. The organs with the highest effective doses were bladder wall (2.41E-03 mSv/MBq), followed by ovaries (1.15E-03 mSv/MBq) and red marrow (8.49E-04 mSv/MBq). The average effective total-body dose was 7.80E-03 mSv/MBq. Conclusion:68Ga-FAPI-46 PET/CT has a favorable dosimetry profile, with an estimated whole-body dose of 5.3 mSv for an administration of 200 MBq (5.4 mCi) of 68Ga-FAPI-46 (1.56 ± 0.26 mSv from the PET tracer and 3.7 mSv from 1 low-dose CT scan). The biodistribution study showed high TBRs increasing over time, suggesting high diagnostic performance and favorable tracer kinetics for potential therapeutic applications.

Analysis of differences between 99mTc-MAA SPECT- and 90Y-microsphere PET-based dosimetry for hepatocellular carcinoma selective internal radiation therapy.

BACKGROUND: The aim of this study was to compare predictive and post-treatment dosimetry and analyze the differences, investigating factors related to activity preparation and delivery, imaging modality used, and interventional radiology. METHODS: Twenty-three HCC patients treated by selective internal radiation therapy with 90Y glass microspheres were included in this study. Predictive and post-treatment dosimetry were calculated at the voxel level based on 99mTc-MAA SPECT/CT and 90Y-microsphere PET/CT respectively. Dose distribution was analyzed through mean dose, metrics extracted from dose-volume histograms, and Dice similarity coefficients applied on isodoses. Reproducibility of the radiological gesture and its influence on dose deviation was evaluated. RESULTS: 90Y delivered activity was lower than expected in 67% (16/24) of the cases mainly due to the residual activity. A mean deviation of - 6 ± 11% was observed between the delivered activity and the 90Y PET's FOV activity. In addition, a substantial difference of - 20 ± 8% was measured on 90Y PET images between the activity in the liver and in the whole FOV. After normalization, 99mTc-MAA SPECT dosimetry was highly correlated and concordant with 90Y-microsphere PET dosimetry for all dose metrics evaluated (ρ = 0.87, ρc = 0.86, P = 3.10-8 and ρ = 0.91, ρc = 0.90, P = 7.10-10 for tumor and normal liver mean dose respectively for example). Besides, mean tumor dose deviation was lower when the catheter position was identical than when it differed (16 Gy vs. 37 Gy, P = 0.007). Concordance between predictive and post-treatment dosimetry, evaluated with Dice similarity coefficients applied on isodoses, significantly correlated with the distance of the catheter position from artery bifurcation (P = 0.04, 0.0004, and 0.05, for 50 Gy, 100 Gy, and 150 Gy isodoses respectively). CONCLUSIONS: Discrepancies between planned activity and activity measured on 90Y PET images were observed and seemed to be mainly related to clinical hazards and equipment issues. Predictive vs. post-treatment comparison of relative dose distributions between tumor and normal liver showed a good correlation and no significant difference highlighting the predictive value of 99mTc MAA SPECT/CT-based dosimetry. Besides, the reproducibility of catheter tip position appears critical in the agreement between predictive and actual dose distribution.

Retrospective Voxel-Based Dosimetry for Assessing the Ability of the Body-Surface-Area Model to Predict Delivered Dose and Radioembolization Outcome.

The aim of this study was to quantitatively evaluate the ability of the body-surface-area (BSA) model to predict tumor-absorbed dose and treatment outcome through retrospective voxel-based dosimetry. Methods: Data from 35 hepatocellular carcinoma patients with a total of 42 90Y-resin microsphere radioembolization treatments were included. Injected activity was planned with the BSA model. Voxel dosimetry based on 99mTc-labeled macroaggregated albumin SPECT and 90Y-microsphere PET was retrospectively performed using a dedicated treatment planning system. Average dose and dose-volume histograms (DVHs) of the anatomically defined tumors were analyzed. The selected dose metrics extracted from DVHs were minimum dose to 50% and 70% of the tumor volume and percentage of the volume receiving at least 120 Gy. Treatment response was evaluated 6 mo after therapy according to the criteria of the European Association for the Study of the Liver. Results: Six-month response was evaluated in 26 treatments: 14 were considered to produce an objective response and 12 a nonresponse. Retrospective evaluation of 90Y-microsphere PET-based dosimetry showed a large interpatient variability with a median average absorbed dose of 60 Gy to the tumor. In 62% (26/42) of the cases, tumor, nontumoral liver, and lung doses would have complied with the recommended thresholds if the injected activity calculated by the BSA method had been increased. Average doses, minimum dose to 50% and 70% of the tumor volume, and percentage of the volume receiving at least 120 Gy were significantly higher in cases of objective response than in nonresponse. Conclusion: In our population, average tumor-absorbed dose and DVH metrics were associated with tumor response. However, the activity calculated by the BSA method could have been increased to reach the recommended tumor dose threshold. Tumor uptake, target and nontarget volumes, and dose distribution heterogeneity should be considered for activity planning.

Predictive value of initial FDG-PET features for treatment response and survival in esophageal cancer patients treated with chemo-radiation therapy using a random forest classifier.

PURPOSE: In oncology, texture features extracted from positron emission tomography with 18-fluorodeoxyglucose images (FDG-PET) are of increasing interest for predictive and prognostic studies, leading to several tens of features per tumor. To select the best features, the use of a random forest (RF) classifier was investigated. METHODS: Sixty-five patients with an esophageal cancer treated with a combined chemo-radiation therapy were retrospectively included. All patients underwent a pretreatment whole-body FDG-PET. The patients were followed for 3 years after the end of the treatment. The response assessment was performed 1 month after the end of the therapy. Patients were classified as complete responders and non-complete responders. Sixty-one features were extracted from medical records and PET images. First, Spearman's analysis was performed to eliminate correlated features. Then, the best predictive and prognostic subsets of features were selected using a RF algorithm. These results were compared to those obtained by a Mann-Whitney U test (predictive study) and a univariate Kaplan-Meier analysis (prognostic study). RESULTS: Among the 61 initial features, 28 were not correlated. From these 28 features, the best subset of complementary features found using the RF classifier to predict response was composed of 2 features: metabolic tumor volume (MTV) and homogeneity from the co-occurrence matrix. The corresponding predictive value (AUC = 0.836 ± 0.105, Se = 82 ± 9%, Sp = 91 ± 12%) was higher than the best predictive results found using the Mann-Whitney test: busyness from the gray level difference matrix (P < 0.0001, AUC = 0.810, Se = 66%, Sp = 88%). The best prognostic subset found using RF was composed of 3 features: MTV and 2 clinical features (WHO status and nutritional risk index) (AUC = 0.822 ± 0.059, Se = 79 ± 9%, Sp = 95 ± 6%), while no feature was significantly prognostic according to the Kaplan-Meier analysis. CONCLUSIONS: The RF classifier can improve predictive and prognostic values compared to the Mann-Whitney U test and the univariate Kaplan-Meier survival analysis when applied to several tens of features in a limited patient database.

Reproducibility of the adaptive thresholding calibration procedure for the delineation of 18F-FDG-PET-positive lesions.

OBJECTIVE: The aim of the study was to evaluate the robustness of the calibration procedure against the counting statistics and lesion volumes when using an adaptive thresholding method for the delineation of 2-[18F]fluoro-2-deoxyglucose (18F-FDG)-PET-positive tissue. MATERIALS AND METHODS: Three data sets obtained from physical and simulated images of a phantom containing hot spheres of known volume and contrast were used to study the robustness of the calibration procedure against the counting statistics and range of volumes and contrasts for a given PET model. The mathematical expression of the adaptive thresholding method used corresponds to a linear relationship between the optimal threshold value and the inverse of the local contrast. Robustness was evaluated by testing whether the slopes and intercepts of the linear expression found under two experimental conditions were significantly different (P<0.05). RESULTS: It was found that the calibration step was not sensitive to the PET device for the studied PET model, nor to the counting statistics for a signal-to-noise ratio higher than 5.7. No statistical difference was found in the calibration step when using a wide range of volumes (0.2-200 ml) and contrasts (2.0-20.6) or more restricted ones (0.43-97.3 ml and 2.0-7.7, respectively). Therefore, a calibration procedure using limited experimental conditions can be applied to a wider range of volumes and contrasts. CONCLUSION: These results show that the manufacturer could propose simulated or experimental raw data corresponding to a given PET model with high counting statistics, allowing each clinical center to reconstruct calibration images according to the algorithm parameters used in the clinic.

Multidimensional B-spline parameterization of the detection probability of PET systems to improve the efficiency of Monte Carlo simulations.

Accurate modeling of system response and scatter distribution is crucial for image reconstruction in emission tomography. Monte Carlo simulations are very well suited to calculate these quantities. However, Monte Carlo simulations are also slow and many simulated counts are needed to provide a sufficiently exact estimate of the detection probabilities. In order to overcome these problems, we propose to split the simulation into two parts, the detection system and the object to be imaged (the patient). A so-called 'virtual boundary' that separates these two parts is introduced. Within the patient, particles are simulated conventionally. Whenever a photon reaches the virtual boundary, its detection probability is calculated analytically by evaluating a multi-dimensional B-spline that depends on the photon position, direction and energy. The unknown B-spline knot values that define this B-spline are fixed by a prior 'pre-' simulation that needs to be run once for each scanner type. After this pre-simulation, the B-spline model can be used in any subsequent simulation with different patients. We show that this approach yields accurate results when simulating the Biograph 16 HiREZ PET scanner with Geant4 Application for Emission Tomography (GATE). The execution time is reduced by a factor of about 22 x (scanner with voxelized phantom) to 30 x (empty scanner) with respect to conventional GATE simulations of same statistical uncertainty. The pre-simulation and calculation of the B-spline knots values could be performed within half a day on a medium-sized cluster.