Comparing the prognostic performance of iBOX and biopsy-proven acute rejection for long-term kidney graft survival.

Biopsy-proven acute rejection (BPAR) occurs in approximately 10% of kidney transplant recipients in the first year, making superiority trials unfeasible. iBOX, a quantitative composite of estimated glomerular filtration rate, proteinuria, antihuman leukocyte antigen donor-specific antibody, and + full/- abbreviated kidney histopathology, is a new proposed surrogate endpoint. BPAR's prognostic ability was compared with iBOX in a pooled cohort of 1534 kidney transplant recipients from 4 data sets, including 2 prospective randomized controlled trials. Discrimination analyses showed mean c-statistic differences between both iBOX compared with BPAR of 0.25 (95% confidence interval: 0.17-0.32) for full iBOX and 0.24 (95% confidence interval: 0.16-0.32) for abbreviated iBOX, indicating statistically significantly higher c-statistic values for the iBOX prognosis of death-censored graft survival. Mean (± standard error) c-statistics were 0.81 ± 0.03 for full iBOX, 0.80 ± 0.03 for abbreviated iBOX, and 0.57 ± 0.03 for BPAR. In calibration analyses, predicted graft loss events from both iBOX models were not significantly different from those observed. However, for BPAR, the predicted events were significantly (P < .01) different (observed: 64; predicted: 70; full iBOX: 76; abbreviated iBOX: 173 BPAR). IBOX at 1-year posttransplant is superior to BPAR in the first year posttransplant in graft loss prognostic performance, providing valuable additional information and facilitating the demonstration of superiority of novel immunosuppressive regimens.

Intraprocedural C-arm dual-phase cone-beam enhancement patterns correlate with tumor absorbed dose after radioembolization.

BACKGROUND: Recent studies have shown a clear relationship between absorbed dose and tumor response to treatment after hepatic radioembolization. These findings help to create more personalized treatment planning and dosimetry. However, crucial to this goal is the ability to predict the dose distribution prior to treatment. The microsphere distribution is ultimately determined by (i) the hepatic vasculature and the resulting blood flow dynamics and (ii) the catheter position. PURPOSE: To show that pretreatment, intra-procedural imaging of blood flow patterns, as quantified by catheter-directed intra-arterial contrast enhancement, correlate with posttreatment microsphere accumulation and, consequently, absorbed dose. MATERIALS AND METHODS: Patients who participated in a clinical trial (NCT01177007) and for whom both a pretreatment dual-phase contrast-enhanced cone-beam CT (CBCT) and a posttreatment 90Y PET/CT scan were available were included in this retrospective study. Tumors and perfused volumes were manually delineated on the CBCT by an experienced radiologist. The mean, sum, and standard deviation of the voxels in each volume were recorded. The delineations were transferred to the PET-based absorbed dose maps by coregistration of the corresponding CTs. Linear multiple regression was used to correlate pretreatment CBCT enhancement to posttreatment 90Y PET/CT-based absorbed dose in each region. Leave-one-out cross-validation and Bland-Altman analyses were performed on the predicted versus measured absorbed doses. RESULTS: Nine patients, with a total of 23 tumors were included. All presented with hepatocellular carcinoma (HCC). Visually, all patients had a clear correspondence between CBCT enhancement and absorbed dose. The correlation between CBCT enhancement and posttherapy absorbed tumor dose based was strong (R2 = 0.91), and moderate for the non-tumor liver tissue (R2 = 0.61). Limits of agreement were approximately ±55 Gray for tumor tissue. CONCLUSION: There is a linear relationship between pretreatment blood dynamics in HCC tumors and posttreatment absorbed dose, which, if shown to be generalizable, allows for pretreatment tumor absorbed dose prediction.

Girth-based administered activity for pediatric 99m Tc-DMSA SPECT.

BACKGROUND: Pediatric molecular imaging requires a balance between administering an activity that will yield sufficient diagnostic image quality while maintaining patient radiation exposure at acceptable levels. In current clinical practice, this balance is arrived at by the current North American Consensus Guidelines in which patient weight is used to recommend the administered activity (AA). PURPOSE: We have previously demonstrated that girth (waist circumference at the level of the kidneys) is better at equalizing image quality than patient weight for pediatric Tc-99m DMSA renal function imaging. However, the correlation between image quality (IQ), AA, and patient girth has not been rigorously and systematically developed. In this work, we generate a series of curves showing the tradeoff between AA and IQ as a function of patient girth, providing the data for standards bodies to develop the next generation of dosing guideline for pediatric DMSA SPECT. METHODS: An anthropomorphic phantom series that included variations in age (5, 10, and 15 years), gender (M, F), local body morphometry (5, 10, 50, 90, and 95th girth percentiles), and kidney size (±15% standard size), was used to generate realistic SPECT projections. A fixed and clinically challenging defect-to-organ volume percentage (0.49% of renal cortex value) was used to model a focal defect with zero uptake (i.e., full local loss of renal function). Task-based IQ assessment methods were used to rigorously measure IQ in terms of renal perfusion defect detectability. This assessment was performed at multiple count levels (corresponding to various AAs) for groups of patients that had similar girths and defect sizes. Receiver-operating characteristics (ROC) analysis was applied; the area under the ROC curve (AUC) was used as a figure-of-merit for task performance. Curves showing the tradeoff between AUC and AA were generated for these groups of phantoms. RESULTS: Overall, the girth-based dosing method suggested different amounts of AA compared to weight-based dosing for the phantoms that had a relatively large body weight but a small girth or phantoms with relatively small bodyweight but large girth. Reductions of AA to 62.9% compared to weight-based dosing guidelines can potentially be realized while maintaining a baseline (AUC = 0.80) IQ for certain 15-year-olds who have a relatively small girth and large defect size. Note that the task-based IQ results are heavily dependent on the simulated defect size for the defect detection task and the appropriate AUC value must be decided by the physicians for this diagnostic task. These results are based purely on simulation and are subject to future clinical validation. CONCLUSIONS: The study provides simulation-based IQ-AA data for a girth-based dosing method for pediatric renal SPECT, suggesting that patient waist circumference at the level of kidneys should be considered in selecting the AA needed to achieve an acceptable IQ. This data may be useful for standards bodies to develop girth-based dosing guidelines.

Qualifying a Novel Clinical Trial Endpoint (iBOX) Predictive of Long-Term Kidney Transplant Outcomes.

New immunosuppressive therapies that improve long-term graft survival are needed in kidney transplant. Critical Path Institute's Transplant Therapeutics Consortium received a qualification opinion for the iBOX Scoring System as a novel secondary efficacy endpoint for kidney transplant clinical trials through European Medicines Agency's qualification of novel methodologies for drug development. This is the first qualified endpoint for any transplant indication and is now available for use in kidney transplant clinical trials. Although the current efficacy failure endpoint has typically shown the noninferiority of therapeutic regimens, the iBOX Scoring System can be used to demonstrate the superiority of a new immunosuppressive therapy compared to the standard of care from 6 months to 24 months posttransplant in pivotal or exploratory drug therapeutic studies.

Qualifying a novel clinical trial endpoint (iBOX) predictive of long-term kidney transplant outcomes.

New immunosuppressive therapies that improve long-term graft survival are needed in kidney transplant. Critical Path Institute's Transplant Therapeutics Consortium received a qualification opinion for the iBOX Scoring System as a novel secondary efficacy endpoint for kidney transplant clinical trials through European Medicines Agency's qualification of novel methodologies for drug development. This is the first qualified endpoint for any transplant indication and is now available for use in kidney transplant clinical trials. Although the current efficacy failure endpoint has typically shown the noninferiority of therapeutic regimens, the iBOX Scoring System can be used to demonstrate the superiority of a new immunosuppressive therapy compared to the standard of care from 6 months to 24 months posttransplant in pivotal or exploratory drug therapeutic studies.

Longitudinal estimated glomerular filtration rate (eGFR) modeling in long-term renal function to inform clinical trial design in kidney transplantation.

Kidney transplantation is the preferred treatment for individuals with end-stage kidney disease. From a modeling perspective, our understanding of kidney function trajectories after transplantation remains limited. Current modeling of kidney function post-transplantation is focused on linear slopes or percent decline and often excludes the highly variable early timepoints post-transplantation, where kidney function recovers and then stabilizes. Using estimated glomerular filtration rate (eGFR), a well-known biomarker of kidney function, from an aggregated dataset of 4904 kidney transplant patients including both observational studies and clinical trials, we developed a longitudinal model of kidney function trajectories from time of transplant to 6 years post-transplant. Our model is a nonlinear, mixed-effects model built in NONMEM that captured both the recovery phase after kidney transplantation, where the graft recovers function, and the long-term phase of stabilization and slow decline. Model fit was assessed using diagnostic plots and individual fits. Model performance, assessed via visual predictive checks, suggests accurate model predictions of eGFR at the median and lower 95% quantiles of eGFR, ranges which are of critical clinical importance for assessing loss of kidney function. Various clinically relevant covariates were also explored and found to improve the model. For example, transplant recipients of deceased donors recover function more slowly after transplantation and calcineurin inhibitor use promotes faster long-term decay. Our work provides a generalizable, nonlinear model of kidney allograft function that will be useful for estimating eGFR up to 6 years post-transplant in various clinically relevant populations.

An International Study of Factors Affecting Variability of Dosimetry Calculations, Part 2: Overall Variabilities in Absorbed Dose.

Dosimetry for personalized radiopharmaceutical therapy has gained considerable attention. Many methods, tools, and workflows have been developed to estimate absorbed dose (AD). However, standardization is still required to reduce variability of AD estimates across centers. One effort for standardization is the Society of Nuclear Medicine and Molecular Imaging 177Lu Dosimetry Challenge, which comprised 5 tasks (T1-T5) designed to assess dose estimate variability associated with the imaging protocol (T1 vs. T2 vs. T3), segmentation (T1 vs. T4), time integration (T4 vs. T5), and dose calculation (T5) steps of the dosimetry workflow. The aim of this work was to assess the overall variability in AD calculations for the different tasks. Methods: Anonymized datasets consisting of serial planar and quantitative SPECT/CT scans, organ and lesion contours, and time-integrated activity maps of 2 patients treated with 177Lu-DOTATATE were made available globally for participants to perform dosimetry calculations and submit their results in standardized submission spreadsheets. The data were carefully curated for formal mistakes and methodologic errors. General descriptive statistics for ADs were calculated, and statistical analysis was performed to compare the results of different tasks. Variability in ADs was measured using the quartile coefficient of dispersion. Results: ADs to organs estimated from planar imaging protocols (T2) were lower by about 60% than those from pure SPECT/CT (T1), and the differences were statistically significant. Importantly, the average differences in dose estimates when at least 1 SPECT/CT acquisition was available (T1, T3, T4, T5) were within ±10%, and the differences with respect to T1 were not statistically significant for most organs and lesions. When serial SPECT/CT images were used, the quartile coefficients of dispersion of ADs for organs and lesions were on average less than 20% and 26%, respectively, for T1; 20% and 18%, respectively, for T4 (segmentations provided); and 10% and 5%, respectively, for T5 (segmentation and time-integrated activity images provided). Conclusion: Variability in ADs was reduced as segmentation and time-integration data were provided to participants. Our results suggest that SPECT/CT-based imaging protocols generate more consistent and less variable results than planar imaging methods. Effort at standardizing segmentation and fitting should be made, as this may substantially reduce variability in ADs.

Lack of repeatability of radiomic features derived from PET scans: Results from a 18 F-DCFPyL test-retest cohort.

OBJECTIVES: PET-based radiomic metrics are increasingly utilized as predictive image biomarkers. However, the repeatability of radiomic features on PET has not been assessed in a test-retest setting. The prostate-specific membrane antigen-targeted compound 18 F-DCFPyL is a high-affinity, high-contrast PET agent that we utilized in a test-retest cohort of men with metastatic prostate cancer (PC). METHODS: Data of 21 patients enrolled in a prospective clinical trial with histologically proven PC underwent two 18 F-DCFPyL PET scans within 7 days, using identical acquisition and reconstruction parameters. Sites of disease were segmented and a set of 29 different radiomic parameters were assessed on both scans. We determined repeatability of quantification by using Pearson's correlations, within-subject coefficient of variation (wCOV), and Bland-Altman analysis. RESULTS: In total, 230 lesions (177 bone, 38 lymph nodes, 15 others) were assessed on both scans. For all investigated radiomic features, a broad range of inter-scan correlation was found (r, 0.07-0.95), with acceptable reproducibility for entropy and homogeneity (wCOV, 16.0% and 12.7%, respectively). On Bland-Altman analysis, no systematic increase or decrease between the scans was observed for either parameter (±1.96 SD: 1.07/-1.30, 0.23/-0.18, respectively). The remaining 27 tested radiomic metrics, however, achieved unacceptable high wCOV (≥21.7%). CONCLUSION: Many common radiomic features derived from a test-retest PET study had poor repeatability. Only Entropy and homogeneity achieved good repeatability, supporting the notion that those image biomarkers may be incorporated in future clinical trials. Those radiomic features based on high frequency aspects of images appear to lack the repeatability on PET to justify further study.

TransMorph: Transformer for unsupervised medical image registration.

In the last decade, convolutional neural networks (ConvNets) have been a major focus of research in medical image analysis. However, the performances of ConvNets may be limited by a lack of explicit consideration of the long-range spatial relationships in an image. Recently, Vision Transformer architectures have been proposed to address the shortcomings of ConvNets and have produced state-of-the-art performances in many medical imaging applications. Transformers may be a strong candidate for image registration because their substantially larger receptive field enables a more precise comprehension of the spatial correspondence between moving and fixed images. Here, we present TransMorph, a hybrid Transformer-ConvNet model for volumetric medical image registration. This paper also presents diffeomorphic and Bayesian variants of TransMorph: the diffeomorphic variants ensure the topology-preserving deformations, and the Bayesian variant produces a well-calibrated registration uncertainty estimate. We extensively validated the proposed models using 3D medical images from three applications: inter-patient and atlas-to-patient brain MRI registration and phantom-to-CT registration. The proposed models are evaluated in comparison to a variety of existing registration methods and Transformer architectures. Qualitative and quantitative results demonstrate that the proposed Transformer-based model leads to a substantial performance improvement over the baseline methods, confirming the effectiveness of Transformers for medical image registration.

Improved accuracy of S-value-based dosimetry: a guide to transition from Cristy-Eckerman to ICRP adult phantoms.

BACKGROUND: In 2016, the International Commission on Radiological Protection (ICRP) published the results of Monte Carlo simulations performed using updated and anatomically realistic voxelized phantoms. The resulting specific absorbed fractions are based on more realistic human anatomy than those computed in the stylized, geometrical Cristy-Eckerman (CE) phantom. Despite this development, the ICRP-absorbed fractions have not been widely adopted for radiopharmaceutical dosimetry. To help make the transition, we have established a correspondence between source and target tissues defined in the CE phantom and those defined in the ICRP phantoms. RESULTS: The ICRP phantom has 79 source regions and 43 target regions in comparison with the 23 source and 18 target tissue regions defined in the CE phantom. The ICRP phantom provides tissue regions with greater anatomical detail. Some of this additional detail is focused on radiation protection and dosimetry of inhaled/ingested radioactivity. Some, but not all, of this detail is useful and appropriate for radiopharmaceutical therapy. We have established the correspondence between CE and ICRP phantom source and target regions and attempted to highlight the ICRP source tissues relevant to radiopharmaceutical therapy (RPT). This paper provides tables and figures highlighting the correspondences established. CONCLUSION: The results provide assistance in transitioning from CE-stylized phantoms to the anatomically accurate voxelized ICRP phantoms. It provides specific guidance for porting the total absorbed activity for regions as defined in the CE phantom to regions within the ICRP phantoms.

An International Study of Factors Affecting Variability of Dosimetry Calculations, Part 1: Design and Early Results of the SNMMI Dosimetry Challenge.

In this work, we present details and initial results from a 177Lu dosimetry challenge that has been designed to collect data from the global nuclear medicine community aiming at identifying, understanding, and quantitatively characterizing the consequences of the various sources of variability in dosimetry. Methods: The challenge covers different approaches to performing dosimetry: planar, hybrid, and pure SPECT. It consists of 5 different and independent tasks to measure the variability of each step in the dosimetry workflow. Each task involves the calculation of absorbed doses to organs and tumors and was meant to be performed in sequential order. The order of the tasks is such that results from a previous one would not affect subsequent ones. Different sources of variability are removed as the participants advance through the challenge by giving them the data required to begin the calculations at different steps of the dosimetry workflow. Data from 2 patients after a therapeutic administration of 177Lu-DOTATATE were used for this study. The data are hosted in Deep Blue Data, a data repository service run by the University of Michigan. Participants submit results in standardized spreadsheets and with a short description summarizing their methods. Results: In total, 178 participants have signed up for the challenge, and 119 submissions have been received. Sixty percent of submissions have used voxelized dose methods, with 47% of those using commercial software. In initial analysis, the volume of organs showed a variability of up to 49.8% whereas for lesions this was up to 176%. Variability in time-integrated activity was up to 192%. Mean absorbed doses varied up to 57.7%. Segmentation is the step that required the longest time to complete, with a median of 43 min. The median total time to perform the full calculation was 89 min. Conclusion: To advance dosimetry and encourage its routine use in radiopharmaceutical therapy applications, it is critical that dosimetry results be reproducible across centers. Our initial results provide insights into the variability associated with performing dose calculations. It is expected that this dataset, including results from future stages, will result in efforts to standardize and harmonize methods and procedures.

Imaging and dosimetry for alpha-particle emitter radiopharmaceutical therapy: improving radiopharmaceutical therapy by looking into the black box.

Radiopharmaceutical therapy using α-particle emitting radionuclides (αRPT) is a novel treatment modality that delivers highly potent alpha-particles to cancer cells or their environment. We review the advantages and challenges of imaging and dosimetry in implementing αRPT for cancer patients.

Automated segmentation of lung, liver, and liver tumors from Tc-99m MAA SPECT/CT images for Y-90 radioembolization using convolutional neural networks.

PURPOSE: 90 Y selective internal radiation therapy (SIRT) has become a safe and effective treatment option for liver cancer. However, segmentation of target and organ-at-risks is labor-intensive and time-consuming in 90 Y SIRT planning. In this study, we developed a convolutional neural network (CNN)-based method for automated lungs, liver, and tumor segmentation on 99m Tc-MAA SPECT/CT images for 90 Y SIRT planning. METHODS: 99m Tc-MAA SPECT/CT images and corresponding clinical segmentations were retrospectively collected from 56 patients who underwent 90 Y SIRT. The collected data were used to train three CNN-based segmentation algorithms for lungs, liver, and tumor segmentation. Segmentation performance was evaluated using the Dice similarity coefficient (DSC), surface DSC, and average symmetric surface distance (ASSD). Dosimetric parameters (volume, counts, and lung shunt fraction) were measured from the segmentation results and were compared with clinical reference segmentations. RESULTS: The evaluation results show that the method can accurately segment lungs, liver, and tumor with median [interquartile range] DSCs of 0.98 [0.97-0.98], 0.91 [0.83-0.93], and 0.85 [0.71-0.88]; surface DSCs of 0.99 [0.97-0.99], 0.86 [0.77-0.93], and 0.85 [0.62-0.93], and ASSDs of 0.91 [0.69-1.5], 4.8 [2.6-8.4], and 4.7 [3.5-9.2] mm, respectively. Dosimetric parameters from the three segmentation networks show relationship with those from the reference segmentations. The overall segmentation took about 1 min per patient on an NVIDIA RTX-2080Ti GPU. CONCLUSION: This work presents CNN-based algorithms to segment lungs, liver, and tumor from 99m Tc-MAA SPECT/CT images. The results demonstrated the potential of the proposed CNN-based segmentation method for assisting 90 Y SIRT planning while drastically reducing operator time.

Real-time quantitation of thyroidal radioiodine uptake in thyroid disease with monitoring by a collar detection device.

Radioactive iodine (RAI) is safe and effective in most patients with hyperthyroidism but not all individuals are cured by the first dose, and most develop post-RAI hypothyroidism. Postoperative RAI therapy for remnant ablation is successful in 80-90% of thyroid cancer patients and sometimes induces remission of nonresectable cervical and/or distant metastatic disease but the effective tumor dose is usually not precisely known and must be moderated to avoid short- and long-term adverse effects on other tissues. The Collar Therapy Indicator (COTI) is a radiation detection device embedded in a cloth collar secured around the patient's neck and connected to a recording and data transmission box. In previously published experience, the data can be collected at multiple time points, reflecting local cervical RAI exposure and correlating well with conventional methods. We evaluated the real-time uptake of RAI in patients with hyperthyroid Graves' disease and thyroid cancer. We performed a pilot feasibility prospective study. Data were analyzed using R© (version 4.0.3, The R Foundation for Statistical Computing, 2020), and Python (version 3.6, Matplotlib version 3.0.3). The COTI was able to provide a quantitative temporal pattern of uptake within the thyroid in persons with Graves' disease and lateralized the remnant tissue in persons with thyroid cancer. The study has demonstrated that the portable collar radiation detection device outside of a healthcare facility is accurate and feasible for use after administration of RAI for diagnostic studies and therapy to provide a complete collection of fractional target radioactivity data compared to that traditionally acquired with clinic-based measurements at one or two time-points.Clinical Trials Registration NCT03517579, DOR 5/7/2018.

Dosimetric impact of Ac-227 in accelerator-produced Ac-225 for alpha-emitter radiopharmaceutical therapy of patients with hematological malignancies: a pharmacokinetic modeling analysis.

BACKGROUND: Actinium-225 is an alpha-particle emitter under investigation for use in radiopharmaceutical therapy. To address limited supply, accelerator-produced 225Ac has been recently made available. Accelerator-produced 225Ac via 232Th irradiation (denoted 225/7Ac) contains a low percentage (0.1-0.3%) of 227Ac (21.77-year half-life) activity at end of bombardment. Using pharmacokinetic modeling, we have examined the dosimetric impact of 227Ac on the use of accelerator-produced 225Ac for radiopharmaceutical therapy. We examine the contribution of 227Ac and its daughters to tissue absorbed doses. The dosimetric analysis was performed for antibody-conjugated 225/7Ac administered intravenously to treat patients with hematological cancers. Published pharmacokinetic models are used to obtain the distribution of 225/7Ac-labeled antibody and also the distribution of either free or antibody-conjugated 227Th. RESULTS: Based on our modeling, the tissue specific absorbed dose from 227Ac would be negligible in the context of therapy, less than 0.02 mGy/MBq for the top 6 highest absorbed tissues and less than 0.007 mGy/MBq for all other tissues. Compared to that from 225Ac, the absorbed dose from 227Ac makes up a very small component (less than 0.04%) of the total absorbed dose delivered to the 6 highest dose tissues: red marrow, spleen, endosteal cells, liver, lungs and kidneys when accelerator produced 225/7Ac-conjugated anti-CD33 antibody is used to treat leukemia patients. For all tissues, the dominant contributor to the absorbed dose arising from the 227Ac is 227Th, the first daughter of 227Ac which has the potential to deliver absorbed dose both while it is antibody-bound and while it is free. CONCLUSIONS: These results suggest that the absorbed dose arising from 227Ac to normal organs would be negligible for an 225/7Ac-labeled antibody that targets hematological cancer.

Prospective SPECT-CT Organ Dosimetry-Driven Radiation-Absorbed Dose Escalation Using the In-111 (111In)/Yttrium 90 (90Y) Ibritumomab Tiuxetan (Zevalin®) Theranostic Pair in Patients with Lymphoma at Myeloablative Dose Levels.

PURPOSE: We prospectively evaluated the feasibility of SPECT-CT/planar organ dosimetry-based radiation dose escalation radioimmunotherapy in patients with recurrent non-Hodgkin's lymphoma using the theranostic pair of 111In and 90Y anti-CD20 ibritumomab tiuxetan (Zevalin®) at myeloablative radiation-absorbed doses with autologous stem cell support. We also assessed acute non-hematopoietic toxicity and early tumor response in this two-center outpatient study. METHODS: 24 patients with CD20-positive relapsed or refractory rituximab-sensitive, low-grade, mantle cell, or diffuse large-cell NHL, with normal organ function, platelet counts > 75,000/mm3, and <35% tumor involvement in the marrow were treated with Rituximab (375 mg/m2) weekly for 4 consecutive weeks, then one dose of cyclophosphamide 2.5 g/m2 with filgrastim 10 mcg/kg/day until stem cell collection. Of these, 18 patients with successful stem cell collection (at least 2 × 106 CD34 cells/kg) proceeded to RIT. A dosimetric administration of 111In ibritumomab tiuxetan (185 MBq) followed by five sequential quantitative planar and one SPECT/CT scan was used to determine predicted organ radiation-absorbed dose. Two weeks later, 90Y ibritumomab tiuxetan was administered in an outpatient setting at a cohort- and patient-specific predicted organ radiation-absorbed dose guided by a Continuous Response Assessment (CRM) methodology with the following cohorts for dose escalation: 14.8 MBq/kg, and targeted 18, 24, 28, and 30.5 Gy to the liver. Autologous stem cell infusion occurred when the estimated marrow radiation-absorbed dose rate was predicted to be <1 cGy/h. Feasibility, short-term toxicities, and tumor response were assessed. RESULTS: Patient-specific hybrid SPECT/CT + planar organ dosimetry was feasible in all 18 cases and used to determine the patient-specific therapeutic dose and guide dose escalation (26.8 ± 7.3 MBq/kg (mean), 26.3 MBq/kg (median) of 90Y (range: 12.1-41.4 MBq/kg)) of ibritumomab tiuxetan that was required to deliver 10 Gy to the liver. Infused stem cells engrafted rapidly. The most common treatment-related toxicities were hematological and were reversible following stem cell infusion. No significant hepatotoxicity was seen. One patient died from probable treatment-related causes-pneumonia at day 27 post-transplant. One patient at dose level 18 Gy developed myelodysplastic syndrome (MDS), 4 patients required admission post-90Y RIT for febrile neutropenia, 16/18 patients receiving 90Y ibritumomab tiuxetan (89%) responded to the therapy, with 13 CR (72%) and 3/18 PR (17%), at 60 days post-treatment. Two patients had progressive disease at sixty days. One patient was lost to follow-up. Median time to progression was estimated to be at least 13 months. MTD to the liver is greater than 28 Gy, but the MTD was not reached as the study was terminated due to unexpected discontinuation of availability of the therapeutic agent. CONCLUSIONS: Patient-specific outpatient 90Y ibritumomab tiuxetan RIT with myeloablative doses of RIT up to a targeted 30.5 Gy to the liver is feasible, guided by prospective SPECT/CT + planar imaging with the theranostic pair of 111In and 90Y anti-CD20, with outpatient autologous stem cell transplant support. Administered activity over 5 times the standard FDA-approved activity was well-tolerated. The non-hematopoietic MTD in this study exceeds 28 Gy to the liver. Initial tumor responses were common at all dose levels. This study supports the feasibility of organ dosimetry-driven patient-specific dose escalation in the treatment of NHL with stem cell transplant and provides additional information on the radiation tolerance of the normal liver to radiopharmaceutical therapy.

Renal 99mTc-DMSA pharmacokinetics in pediatric patients.

99mTc-DMSA is one of the most commonly used pediatric nuclear medicine imaging agents. Nevertheless, there are no pharmacokinetic (PK) models for 99mTc-DMSA in children, and currently available pediatric dose estimates for 99mTc-DMSA use pediatric S values with PK data derived from adults. Furthermore, the adult PK data were collected in the mid-70's using quantification techniques and instrumentation available at the time. Using pediatric imaging data for DMSA, we have obtained kinetic parameters for DMSA that differ from those applicable to adults. METHODS: We obtained patient data from a retrospective re-evaluation of clinically collected pediatric SPECT images of 99mTc-DMSA in 54 pediatric patients from Boston's Children Hospital (BCH), ranging in age from 1 to 16 years old. These were supplemented by prospective data from twenty-three pediatric patients (age range: 4 months to 6 years old). RESULTS: In pediatric patients, the plateau phase in fractional kidney uptake occurs at a fractional uptake value closer to 0.3 than the value of 0.5 reported by the International Commission on Radiological Protection (ICRP) for adult patients. This leads to a 27% lower time-integrated activity coefficient in pediatric patients than in adults. Over the age range examined, no age dependency in uptake fraction at the clinical imaging time was observed. Female pediatric patients had a 17% higher fractional kidney uptake at the clinical imaging time than males (P < 0.001). CONCLUSIONS: Pediatric 99mTc-DMSA kinetics differ from those reported for adults and should be considered in pediatric patient dosimetry. Alternatively, the differences obtained in this study could reflect improved quantification methods and the need to re-examine DMSA kinetics in adults.

Learning fuzzy clustering for SPECT/CT segmentation via convolutional neural networks.

PURPOSE: Quantitative bone single-photon emission computed tomography (QBSPECT) has the potential to provide a better quantitative assessment of bone metastasis than planar bone scintigraphy due to its ability to better quantify activity in overlapping structures. An important element of assessing the response of bone metastasis is accurate image segmentation. However, limited by the properties of QBSPECT images, the segmentation of anatomical regions-of-interests (ROIs) still relies heavily on the manual delineation by experts. This work proposes a fast and robust automated segmentation method for partitioning a QBSPECT image into lesion, bone, and background. METHODS: We present a new unsupervised segmentation loss function and its semi- and supervised variants for training a convolutional neural network (ConvNet). The loss functions were developed based on the objective function of the classical Fuzzy C-means (FCM) algorithm. The first proposed loss function can be computed within the input image itself without any ground truth labels, and is thus unsupervised; the proposed supervised loss function follows the traditional paradigm of the deep learning-based segmentation methods and leverages ground truth labels during training. The last loss function is a combination of the first and the second and includes a weighting parameter, which enables semi-supervised segmentation using deep learning neural network. EXPERIMENTS AND RESULTS: We conducted a comprehensive study to compare our proposed methods with ConvNets trained using supervised, cross-entropy and Dice loss functions, and conventional clustering methods. The Dice similarity coefficient (DSC) and several other metrics were used as figures of merit as applied to the task of delineating lesion and bone in both simulated and clinical SPECT/CT images. We experimentally demonstrated that the proposed methods yielded good segmentation results on a clinical dataset even though the training was done using realistic simulated images. On simulated SPECT/CT, the proposed unsupervised model's accuracy was greater than the conventional clustering methods while reducing computation time by 200-fold. For the clinical QBSPECT/CT, the proposed semi-supervised ConvNet model, trained using simulated images, produced DSCs of 0.75 and 0.74 for lesion and bone segmentation in SPECT, and a DSC of 0.79 bone segmentation of CT images. These DSCs were larger than that for standard segmentation loss functions by > 0.4 for SPECT segmentation, and > 0.07 for CT segmentation with P-values < 0.001 from a paired t-test. CONCLUSIONS: A ConvNet-based image segmentation method that uses novel loss functions was developed and evaluated. The method can operate in unsupervised, semi-supervised, or fully-supervised modes depending on the availability of annotated training data. The results demonstrated that the proposed method provides fast and robust lesion and bone segmentation for QBSPECT/CT. The method can potentially be applied to other medical image segmentation applications.

DeepAMO: a multi-slice, multi-view anthropomorphic model observer for visual detection tasks performed on volume images.

Purpose: We propose a deep learning-based anthropomorphic model observer (DeepAMO) for image quality evaluation of multi-orientation, multi-slice image sets with respect to a clinically realistic 3D defect detection task. Approach: The DeepAMO is developed based on a hypothetical model of the decision process of a human reader performing a detection task using a 3D volume. The DeepAMO is comprised of three sequential stages: defect segmentation, defect confirmation (DC), and rating value inference. The input to the DeepAMO is a composite image, typical of that used to view 3D volumes in clinical practice. The output is a rating value designed to reproduce a human observer's defect detection performance. In stages 2 and 3, we propose: (1) a projection-based DC block that confirms defect presence in two 2D orthogonal orientations and (2) a calibration method that "learns" the mapping from the features of stage 2 to the distribution of observer ratings from the human observer rating data (thus modeling inter- or intraobserver variability) using a mixture density network. We implemented and evaluated the DeepAMO in the context of Tc 99 m -DMSA SPECT imaging. A human observer study was conducted, with two medical imaging physics graduate students serving as observers. A 5 × 2 -fold cross-validation experiment was conducted to test the statistical equivalence in defect detection performance between the DeepAMO and the human observer. We also compared the performance of the DeepAMO to an unoptimized implementation of a scanning linear discriminant observer (SLDO). Results: The results show that the DeepAMO's and human observer's performances on unseen images were statistically equivalent with a margin of difference ( Δ AUC ) of 0.0426 at p < 0.05 , using 288 training images. A limited implementation of an SLDO had a substantially higher AUC (0.99) compared to the DeepAMO and human observer. Conclusion: The results show that the DeepAMO has the potential to reproduce the absolute performance, and not just the relative ranking of human observers on a clinically realistic defect detection task, and that building conceptual components of the human reading process into deep learning-based models can allow training of these models in settings where limited training images are available.