Uncertainty quantification via localized gradients for deep learning-based medical image assessments.

Objective.Deep learning models that aid in medical image assessment tasks must be both accurate and reliable to be deployed within clinical settings. While deep learning models have been shown to be highly accurate across a variety of tasks, measures that indicate the reliability of these models are less established. Increasingly, uncertainty quantification (UQ) methods are being introduced to inform users on the reliability of model outputs. However, most existing methods cannot be augmented to previously validated models because they are not post hoc, and they change a model's output. In this work, we overcome these limitations by introducing a novel post hoc UQ method, termedLocal Gradients UQ, and demonstrate its utility for deep learning-based metastatic disease delineation.Approach.This method leverages a trained model's localized gradient space to assess sensitivities to trained model parameters. We compared the Local Gradients UQ method to non-gradient measures defined using model probability outputs. The performance of each uncertainty measure was assessed in four clinically relevant experiments: (1) response to artificially degraded image quality, (2) comparison between matched high- and low-quality clinical images, (3) false positive (FP) filtering, and (4) correspondence with physician-rated disease likelihood.Main results.(1) Response to artificially degraded image quality was enhanced by the Local Gradients UQ method, where the median percent difference between matching lesions in non-degraded and most degraded images was consistently higher for the Local Gradients uncertainty measure than the non-gradient uncertainty measures (e.g. 62.35% vs. 2.16% for additive Gaussian noise). (2) The Local Gradients UQ measure responded better to high- and low-quality clinical images (p< 0.05 vsp> 0.1 for both non-gradient uncertainty measures). (3) FP filtering performance was enhanced by the Local Gradients UQ method when compared to the non-gradient methods, increasing the area under the receiver operating characteristic curve (ROC AUC) by 20.1% and decreasing the false positive rate by 26%. (4) The Local Gradients UQ method also showed more favorable correspondence with physician-rated likelihood for malignant lesions by increasing ROC AUC for correspondence with physician-rated disease likelihood by 16.2%.Significance. In summary, this work introduces and validates a novel gradient-based UQ method for deep learning-based medical image assessments to enhance user trust when using deployed clinical models.

Models using comprehensive, lesion-level, longitudinal [68Ga]Ga-DOTA-TATE PET-derived features lead to superior outcome prediction in neuroendocrine tumor patients treated with [177Lu]Lu-DOTA-TATE.

PURPOSE: Somatostatin receptor (SSTR) imaging features are predictive of treatment outcome for neuroendocrine tumor (NET) patients receiving peptide receptor radionuclide therapy (PRRT). However, comprehensive (all metastatic lesions), longitudinal (temporal variation), and lesion-level measured features have never been explored. Such features allow for capturing the heterogeneity in disease response to treatment. Furthermore, models combining these features are lacking. In this work we evaluated the predictive power of comprehensive, longitudinal, lesion-level 68GA-SSTR-PET features combined with a multivariate linear regression (MLR) model. METHODS: This retrospective study enrolled NET patients treated with [177Lu]Lu-DOTA-TATE and imaged with [68Ga]Ga-DOTA-TATE at baseline and post-therapy. All lesions were segmented, anatomically labeled, and longitudinally matched. Lesion-level uptake and variation in uptake were measured. Patient-level features were engineered and selected for modeling of progression-free survival (PFS). The model was validated via concordance index, patient classification (ROC analysis), and survival analysis (Kaplan-Meier and Cox proportional hazards). The MLR was benchmarked against single feature predictions. RESULTS: Thirty-six NET patients were enrolled and stratified into poor and good responders (PFS ≥ 25 months). Four patient-level features were selected, the MLR concordance index was 0.826, and the AUC was 0.88 (0.85 specificity, 0.81 sensitivity). Survival analysis led to significant patient stratification (p<.001) and hazard ratio (3⨯10-5). Lastly, in a benchmark study, the MLR modeling approach outperformed all the single feature predictors. CONCLUSION: Comprehensive, lesion-level, longitudinal 68GA-SSTR-PET analysis, combined with MLR modeling, leads to excellent predictions of PRRT outcome in NET patients, outperforming non-comprehensive, patient-level, and single time-point feature predictions. MESSAGE: Neuroendocrine tumor, peptide receptor radionuclide therapy, Somatostatin Receptor Imaging, Outcome Prediction, Treatment Response Assessment.

Automatic Quantification of Serial PET/CT Images for Pediatric Hodgkin Lymphoma Patients Using a Longitudinally-Aware Segmentation Network.

Purpose: Automatic quantification of longitudinal changes in PET scans for lymphoma patients has proven challenging, as residual disease in interim-therapy scans is often subtle and difficult to detect. Our goal was to develop a longitudinally-aware segmentation network (LAS-Net) that can quantify serial PET/CT images for pediatric Hodgkin lymphoma patients. Materials and Methods: This retrospective study included baseline (PET1) and interim (PET2) PET/CT images from 297 patients enrolled in two Children's Oncology Group clinical trials (AHOD1331 and AHOD0831). LAS-Net incorporates longitudinal cross-attention, allowing relevant features from PET1 to inform the analysis of PET2. Model performance was evaluated using Dice coefficients for PET1 and detection F1 scores for PET2. Additionally, we extracted and compared quantitative PET metrics, including metabolic tumor volume (MTV) and total lesion glycolysis (TLG) in PET1, as well as qPET and ΔSUVmax in PET2, against physician measurements. We quantified their agreement using Spearman's ρ correlations and employed bootstrap resampling for statistical analysis. Results: LAS-Net detected residual lymphoma in PET2 with an F1 score of 0.606 (precision/recall: 0.615/0.600), outperforming all comparator methods (P<0.01). For baseline segmentation, LAS-Net achieved a mean Dice score of 0.772. In PET quantification, LAS-Net's measurements of qPET, ΔSUVmax, MTV and TLG were strongly correlated with physician measurements, with Spearman's ρ of 0.78, 0.80, 0.93 and 0.96, respectively. The performance remained high, with a slight decrease, in an external testing cohort. Conclusion: LAS-Net achieved high performance in quantifying PET metrics across serial scans, highlighting the value of longitudinal awareness in evaluating multi-time-point imaging datasets.

A Four-Arm Randomized Clinical Trial of Topical Pain Control for Sentinel Node Radiotracer Injections in Patients with Breast Cancer.

BACKGROUND: Radioactive tracer injections for breast cancer sentinel lymph node mapping can be painful. In this randomized trial, we compared four approaches to topical pain control for radiotracer injections. METHODS: Breast cancer patients were randomized (9 April 2021-8 May 2022) to receive the institutional standard of ice prior to injection (n = 44), or one of three treatments: ice plus a vibrating distraction device (Buzzy®; n = 39), 4% lidocaine patch (n = 44), or 4% lidocaine patch plus ice plus Buzzy® (n = 40). Patients completed the Wong-Baker FACES® pain score (primary outcome) and a satisfaction with pain control received scale (secondary). Nuclear medicine technologists (n = 8) rated perceived pain control and ease of administration for each patient. At study conclusion, technologists rank-ordered treatments. Data were analyzed as intention-to-treat. Wilcoxon rank-sum tests were used to compare pain scores of control versus pooled treatment arms (primary) and then control to each treatment arm individually (secondary). RESULTS: There were no differences in pain scores between the control and treatment groups, both pooled and individually. Eighty-five percent of patients were 'satisfied/very satisfied' with treatment received, with no differences between groups. No differences in providers' perceptions of pain were observed, although providers perceived treatments involving Buzzy© more difficult to administer (p < 0.001). Providers rated lidocaine patch as the easiest, with ice being second. CONCLUSION: In this randomized trial, no differences in patient-reported pain or satisfaction with treatment was observed between ice and other topical treatments. Providers found treatments using Buzzy® more difficult to administer. Given patient satisfaction and ease of administration, ice is a reasonable standard.

An automated methodology for whole-body, multimodality tracking of individual cancer lesions.

Objective. Manual analysis of individual cancer lesions to assess disease response is clinically impractical and requires automated lesion tracking methodologies. However, no methodology has been developed for whole-body individual lesion tracking, across an arbitrary number of scans, and acquired with various imaging modalities.Approach. This study introduces a lesion tracking methodology and benchmarked it using 2368Ga-DOTATATE PET/CT and PET/MR images of eight neuroendocrine tumor patients. The methodology consists of six steps: (1) alignment of multiple scans via image registration, (2) body-part labeling, (3) automatic lesion-wise dilation, (4) clustering of lesions based on local lesion shape metrics, (5) assignment of lesion tracks, and (6) output of a lesion graph. Registration performance was evaluated via landmark distance, lesion matching accuracy was evaluated between each image pair, and lesion tracking accuracy was evaluated via identical track ratio. Sensitivity studies were performed to evaluate the impact of lesion dilation (fixed versus automatic dilation), anatomic location, image modalities (inter- versus intra-modality), registration mode (direct versus indirect registration), and track size (number of time-points and lesions) on lesion matching and tracking performance.Main results. Manual contouring yielded 956 lesions, 1570 lesion-matching decisions, and 493 lesion tracks. The median residual registration error was 2.5 mm. The automatic lesion dilation led to 0.90 overall lesion matching accuracy, and an 88% identical track ratio. The methodology is robust regarding anatomic locations, image modalities, and registration modes. The number of scans had a moderate negative impact on the identical track ratio (94% for 2 scans, 91% for 3 scans, and 81% for 4 scans). The number of lesions substantially impacted the identical track ratio (93% for 2 nodes versus 54% for ≥5 nodes).Significance. The developed methodology resulted in high lesion-matching accuracy and enables automated lesion tracking in PET/CT and PET/MR.

Evaluation of Data-Driven Rigid Motion Correction in Clinical Brain PET Imaging.

Head motion during brain PET imaging can significantly degrade the quality of the reconstructed image, leading to reduced diagnostic value and inaccurate quantitation. A fully data-driven motion correction approach was recently demonstrated to produce highly accurate motion estimates (<1 mm) with high temporal resolution (≥1 Hz), which can then be used for a motion-corrected reconstruction. This can be applied retrospectively with no impact on the clinical image acquisition protocol. We present a reader-based evaluation and an atlas-based quantitative analysis of this motion correction approach within a clinical cohort. Methods: Clinical patient data were collected over 2019-2020 and processed retrospectively. Motion was estimated using image-based registration on reconstructions of ultrashort frames (0.6-1.8 s), after which list-mode reconstructions that were fully motion-corrected were performed. Two readers graded the motion-corrected and uncorrected reconstructions. An atlas-based quantitative analysis was performed. Paired Wilcoxon tests were used to test for significant differences in reader scores and SUVs between reconstructions. The Levene test was used to determine whether motion correction had a greater impact on quantitation in the presence of motion than when motion was low. Results: Fifty standard clinical 18F-FDG brain PET datasets (age range, 13-83 y; mean ± SD, 59 ± 20 y; 27 women) from 3 scanners were collected. The reader study showed a significantly different, diagnostically relevant improvement by motion correction when motion was present (P = 0.02) and no impact in low-motion cases. Eight percent of all datasets improved from diagnostically unacceptable to acceptable. The atlas-based analysis demonstrated a significant difference between the motion-corrected and uncorrected reconstructions in cases of high motion for 7 of 8 regions of interest (P < 0.05). Conclusion: The proposed approach to data-driven motion estimation and correction demonstrated a clinically significant impact on brain PET image reconstruction.

Image intensity histograms as imaging biomarkers: application to immune-related colitis.

Purpose.To investigate image intensity histograms as a potential source of useful imaging biomarkers in both a clinical example of detecting immune-related colitis (irColitis) in18F-FDG PET/CT images of immunotherapy patients and an idealized case of classifying digital reference objects (DRO).Methods.Retrospective analysis of bowel18F-FDG uptake in N = 40 patients receiving immune checkpoint inhibitors was conducted. A CNN trained to segment the bowel was used to generate the histogram of bowel18F-FDG uptake, and percentiles of the histogram were considered as potential metrics for detecting inflammation associated with irColitis. A model of the colon was also considered using cylindrical DRO. Classification of DRO with different intensity distributions was undertaken under varying geometry and noise settings.Results.The most predictive biomarker of irColitis was the 95th percentile of the bowel SUV histogram (SUV95%). Patients later diagnosed with irColitis had a significantly higher increase in SUV95%from baseline to first on-treatment PET than patients who did not experience irColitis (p = 0.02). An increase in SUV95%> + 40% separated pre-irColitis change from normal variability with a sensitivity of 75% and specificity of 88%. Furthermore, histogram percentiles were ideal metrics for classifying 'hot center' and 'cold center' DRO, and were robust to varying DRO geometry and noise, and to the presence of spoiler volumes unrelated to the detection task.Conclusions.The 95th percentile of the bowel SUV histogram was the optimal metric for detecting irColitis on18F-FDG PET/CT. Image intensity histograms are a promising source of imaging biomarkers for clinical tasks.

Development and validation of a longitudinal soft-tissue metastatic lesion matching algorithm.

Metastatic cancer presents with many, sometimes hundreds of metastatic lesions through the body, which often respond heterogeneously to treatment. Therefore, lesion-level assessment is necessary for a complete understanding of disease response. Lesion-level assessment typically requires manual matching of corresponding lesions, which is a tedious, subjective, and error-prone task. This study introduces a fully automated algorithm for matching of metastatic lesions in longitudinal medical images. The algorithm entails four steps: (1) image registration, (2) lesion dilation, (3) lesion clustering, and (4) linear assignment. In step (1), 3D deformable registration is used to register the scans. In step (2), lesion contours are conformally dilated. In step (3), lesion clustering is evaluated based on local metrics. In step (4), matching is assigned based on non-greedy cost minimization. The algorithm was optimized (e.g. choice of deformable registration algorithm, dilatation size) and validated on 140 scan-pairs of 32 metastatic cancer patients from two independent clinical trials, who received longitudinal PET/CT scans as part of their treatment response assessment. Registration error was evaluated using landmark distance. A sensitivity study was performed to evaluate the optimal lesion dilation magnitude. Lesion matching performance accuracy was evaluated for all patients and for a subset with high disease burden. Two investigated deformable registration approaches (whole body deformable and articulated deformable registrations) led to similar performance with the overall registration accuracy between 2.3 and 2.6 mm. The optimal dilation magnitude of 25 mm yielded almost a perfect matching accuracy of 0.98. No significant matching accuracy decrease was observed in the subset of patients with high lesion disease burden. In summary, lesion matching using our new algorithm was highly accurate and a significant improvement, when compared to previously established methods. The proposed method enables accurate automated metastatic lesion matching in whole-body longitudinal scans.

Initial Experience With Low-Dose 18F-Fluorodeoxyglucose Positron Emission Tomography/Magnetic Resonance Imaging With Deep Learning Enhancement.

OBJECTIVE: To demonstrate the utility of deep learning enhancement (DLE) to achieve diagnostic quality low-dose positron emission tomography (PET)/magnetic resonance (MR) imaging. METHODS: Twenty subjects with known Crohn disease underwent simultaneous PET/MR imaging after intravenous administration of approximately 185 MBq of 18F-fluorodeoxyglucose (FDG). Five image sets were generated: (1) standard-of-care (reference), (2) low-dose (ie, using 20% of PET counts), (3) DLE-enhanced low-dose using PET data as input, (4) DLE-enhanced low-dose using PET and MR data as input, and (5) DLE-enhanced using no PET data input. Image sets were evaluated by both quantitative metrics and qualitatively by expert readers. RESULTS: Although low-dose images (series 2) and images with no PET data input (series 5) were nondiagnostic, DLE of the low-dose images (series 3 and 4) achieved diagnostic quality images that scored more favorably than reference (series 1), both qualitatively and quantitatively. CONCLUSIONS: Deep learning enhancement has the potential to enable a 90% reduction of radiotracer while achieving diagnostic quality images.

Measuring Glucose Uptake in Primary Invasive Breast Cancer Using Simultaneous Time-of-Flight Breast PET/MRI: A Method Comparison Study with Prone PET/CT.

Purpose: To compare the measurement of glucose uptake in primary invasive breast cancer using simultaneous, time-of-flight breast PET/MRI with prone time-of-flight PET/CT. Materials and Methods: In this prospective study, women with biopsy-proven invasive breast cancer undergoing preoperative breast MRI from 2016 to 2018 were eligible. Participants who had fasted underwent prone PET/CT of the breasts approximately 60 minutes after injection of 370 MBq (10 mCi) fluorine 18 fluorodeoxyglucose (18F-FDG) followed by prone PET/MRI using standard clinical breast MRI sequences performed simultaneously with PET acquisition. Volumes of interest were drawn for tumors and contralateral normal breast fibroglandular tissue to calculate standardized uptake values (SUVs). Spearman correlation, Wilcoxon signed ranked test, Mann-Whitney test, and Bland-Altman analyses were performed. Results: Twenty-three women (mean age, 50 years; range, 33-70 years) were included. Correlation between tumor uptake values measured with PET/MRI and PET/CT was strong (r s = 0.95-0.98). No difference existed between modalities for tumor maximum SUV (SUVmax) normalized to normal breast tissue SUVmean (normSUVmax) (P = .58). The least amount of measurement bias was observed with normSUVmax, +3.86% (95% limits of agreement: -28.92, +36.64). Conclusion: These results demonstrate measurement agreement between PET/CT, the current reference standard for tumor glucose uptake quantification, and simultaneous time-of-flight breast 18F-FDG PET/MRI.Keywords: Breast, Comparative Studies, PET/CT, PET/MR Supplemental material is available for this article. © RSNA, 2021See also the commentary by Mankoff and Surti in this issue.

Comparison of 11 automated PET segmentation methods in lymphoma.

Segmentation of lymphoma lesions in FDG PET/CT images is critical in both assessing individual lesions and quantifying patient disease burden. Simple thresholding methods remain common despite the large heterogeneity in lymphoma lesion location, size, and contrast. Here, we assess 11 automated PET segmentation methods for their use in two scenarios: individual lesion segmentation and patient-level disease quantification in lymphoma. Lesions on 18F-FDG PET/CT scans of 90 lymphoma patients were contoured by a nuclear medicine physician. Thresholding, active contours, clustering, adaptive region-growing, and convolutional neural network (CNN) methods were implemented on all physician-identified lesions. Lesion-level segmentation was evaluated using multiple segmentation performance metrics (Dice, Hausdorff Distance). Patient-level quantification of total disease burden (SUVtotal) and metabolic tumor volume (MTV) was assessed using Spearman's correlation coefficients between the segmentation output and physician contours. Lesion segmentation and patient quantification performance was compared to inter-physician agreement in a subset of 20 patients segmented by a second nuclear medicine physician. In total, 1223 lesions with median tumor-to-background ratio of 4.0 and volume of 1.8 cm3, were evaluated. When assessed for lesion segmentation, a 3D CNN, DeepMedic, achieved the highest performance across all evaluation metrics. DeepMedic, clustering methods, and an iterative threshold method had lesion-level segmentation performance comparable to the degree of inter-physician agreement. For patient-level SUVtotal and MTV quantification, all methods except 40% and 50% SUVmax and adaptive region-growing achieved a performance that was similar the agreement of the two physicians. Multiple methods, including a 3D CNN, clustering, and an iterative threshold method, achieved both good lesion-level segmentation and patient-level quantification performance in a population of 90 lymphoma patients. These methods are thus recommended over thresholding methods such as 40% and 50% SUVmax, which were consistently found to be significantly outside the limits defined by inter-physician agreement.

Exploring Spatial-Temporal Changes in 18F-Sodium Fluoride PET/CT and Circulating Tumor Cells in Metastatic Castration-Resistant Prostate Cancer Treated With Enzalutamide.

PURPOSE: Intrapatient treatment response heterogeneity is under-recognized. Quantitative total bone imaging (QTBI) using 18F-NaF positron emission tomography/computed tomography (PET/CT) scans is a tool that allows characterization of interlesional treatment response heterogeneity in bone. Understanding spatial-temporal response is important to identify individuals who may benefit from treatment beyond progression. PATIENTS AND METHODS: Men with progressive metastatic castration-resistant prostate cancer (mCRPC) with at least two lesions on bone scintigraphy were enrolled and treated with enzalutamide 160 mg daily (ClinicalTrials.gov identifier: NCT02384382). 18F-NaF PET/CT scans were obtained at baseline (PET1), week 13 (PET2), and at the time of prostate-specific antigen (PSA) progression, standard radiographic or clinical progression, or at 2 years without progression (PET3). QTBI was used to determine lesion-level response. The primary end point was the proportion of men with at least one responding bone lesion on PET3 using QTBI. RESULTS: Twenty-three men were enrolled. Duration on treatment ranged from 1.4 to 34.1 months. In general, global standardized uptake value (SUV) metrics decreased while on enzalutamide (PET2) and increased at the time of progression (PET3). The most robust predictor of PSA progression was change in SUVhetero (PET1 to PET3; hazard ratio, 3.88; 95% CI, 1.24 to 12.1). Although overall functional disease burden improved during enzalutamide treatment, an increase in total burden (SUVtotal) was seen at the time of progression, as measured by 18F-NaF PET/CT. All (22/22) evaluable men had at least one responding bone lesion at PET3 using QTBI. CONCLUSION: We found that the proportion of progressing lesions was low, indicating that a substantial number of lesions appear to continue to benefit from enzalutamide beyond progression. Selective targeting of nonresponding lesions may be a reasonable approach to extend benefit.

Current Applications for Nuclear Medicine Imaging in Pulmonary Disease.

PURPOSE OF REVIEW: The main goal of the article is to familiarize the reader with commonly and uncommonly used nuclear medicine procedures that can significantly contribute to improved patient care. The article presents examples of specific modality utilization in the chest including assessment of lung ventilation and perfusion, imaging options for broad range of infectious and inflammatory processes, and selected aspects of oncologic imaging. In addition, rapidly developing new techniques utilizing molecular imaging are discussed. RECENT FINDINGS: The article describes nuclear medicine imaging modalities including gamma camera, SPECT, PET, and hybrid imaging (SPECT/CT, PET/CT, and PET/MR) in the context of established and emerging clinical applications. Areas of potential future development in nuclear medicine are discussed with emphasis on molecular imaging and implementation of new targeted tracers used in diagnostics and therapeutics (theranostics). SUMMARY: Nuclear medicine and molecular imaging provide many unique and novel options for the diagnosis and treatment of pulmonary diseases. This article reviews current applications for nuclear medicine and molecular imaging and selected future applications for radiopharmaceuticals and targeted molecular imaging techniques.

[124I]CLR1404 PET/CT in High-Grade Primary and Metastatic Brain Tumors.

PURPOSE: There is a continuous search for imaging techniques with high sensitivity and specificity for brain tumors. Positron emission tomography (PET) imaging has shown promise, though many PET agents either have a low tumor specificity or impractical physical half-lives. [124I]CLR1404 is a small molecule alkylphosphocholine analogue that is thought to bind to plasma membrane lipid rafts and has shown high tumor-to-background ratios (TBR) in a previous pilot study in brain tumor patients. This study attempts to define the clinical value of [124I]CLR1404 PET/CT (aka CLR124). PROCEDURES: Adult patients with new or suspected recurrence of high-grade primary or metastatic brain tumors (N = 27) were injected with [124I]CLR1404 followed by PET/CT at 6, 24, and 48 h. Standard uptake values (SUV) and TBR values were calculated for all time points. Uptake of [124I]CLR1404 was qualitatively assessed, compared with magnetic resonance imaging (MRI), and correlated with clinical outcome. Final diagnosis (N = 25) was established based on surgically resected tissue or long-term follow-up. RESULTS: Positive uptake with high TBR was detected in all but one patient with a final diagnosis of primary/recurrent brain tumor (12/13) and in less than half of patients with treatment-related changes (5/12). Concordance between [124I]CLR1404 uptake and contrast enhancement on MRI was seen in < 40 %, with no concordance between T2-hyperintensities and uptake. No significant difference in overall outcome was found between patients with and without [124I]CLR1404 uptake. CONCLUSIONS: The uptake pattern in these patients suggests a very high sensitivity of [124I]CLR1404 PET/CT for diagnosing tumor tissue; however, tumor specificity needs to be further defined. Relative lack of concordance with standard MRI characteristics suggests that [124I]CLR1404 PET/CT provides additional information about brain tumors compared to MRI alone, potentially improving clinical decision-making.

Convolutional Neural Networks for Automated PET/CT Detection of Diseased Lymph Node Burden in Patients with Lymphoma.

PURPOSE: To automatically detect lymph nodes involved in lymphoma on fluorine 18 (18F) fluorodeoxyglucose (FDG) PET/CT images using convolutional neural networks (CNNs). MATERIALS AND METHODS: In this retrospective study, baseline disease of 90 patients with lymphoma was segmented on 18F-FDG PET/CT images (acquired between 2005 and 2011) by a nuclear medicine physician. An ensemble of three-dimensional patch-based, multiresolution pathway CNNs was trained using fivefold cross-validation. Performance was assessed using the true-positive rate (TPR) and number of false-positive (FP) findings. CNN performance was compared with agreement between physicians by comparing the annotations of a second nuclear medicine physician to the first reader in 20 of the patients. Patient TPR was compared using Wilcoxon signed rank tests. RESULTS: Across all 90 patients, a range of 0-61 nodes per patient was detected. At an average of four FP findings per patient, the method achieved a TPR of 85% (923 of 1087 nodes). Performance varied widely across patients (TPR range, 33%-100%; FP range, 0-21 findings). In the 20 patients labeled by both physicians, a range of 1-49 nodes per patient was detected and labeled. The second reader identified 96% (210 of 219) of nodes with an additional 3.7 per patient compared with the first reader. In the same 20 patients, the CNN achieved a 90% (197 of 219) TPR at 3.7 FP findings per patient. CONCLUSION: An ensemble of three-dimensional CNNs detected lymph nodes at a performance nearly comparable to differences between two physicians' annotations. This preliminary study is a first step toward automated PET/CT assessment for lymphoma.© RSNA, 2020.

Impact of Anatomic Location of Bone Metastases on Prognosis in Metastatic Castration-Resistant Prostate Cancer.

BACKGROUND: Whole-body assessments of 18F-NaF positron emission tomography (PET)/computed tomography (CT) provide promising quantitative imaging biomarkers of metastatic castration-resistant prostate cancer (mCRPC). This study investigated whether the distribution of metastases across anatomic regions is prognostic of progression-free survival. PATIENTS AND METHODS: Fifty-four mCRPC patients with osseous metastases received baseline NaF PET/CT. Patients received chemotherapy (n = 16) or androgen receptor pathway inhibitors (n = 38). Semiautomated analysis using Quantitative Total Bone Imaging software extracted imaging metrics for the whole, axial, and appendicular skeleton as well as 11 skeletal regions. Five PET metrics were extracted for each region: number of lesions (NL), standardized maximum uptake value (SUVmax), average uptake (SUVmean), sum of uptake (SUVtotal), and diseased fraction of the skeleton (volume fraction). Progression included that discovered by clinical, biochemical, or radiographic means. Univariate and multivariate Cox proportional hazard regression analyses were performed between imaging metrics and progression-free survival, and were assessed according to their hazard ratios (HR) and concordance (C)-indices. RESULTS: The strongest univariate models of progression-free survival were pelvic NL and SUVmax with HR = 1.80 (NL: false discovery rate adjusted P = .001, SUVmax: adjusted P = .001). Three other region-specific metrics (axial NL: HR = 1.59, adjusted P = .02, axial SUVmax: HR = 1.61, adjusted P = .02, and skull SUVmax: HR = 1.58, adjusted P = .04) were found to be stronger prognosticators relative to their whole-body counterparts. Multivariate model including region-specific metrics (C-index = 0.727) outperformed that of whole-body metrics (C-index = 0.705). The best performance was obtained when region-specific and whole-body metrics were included (C-index = 0.742). CONCLUSION: Quantitative characterization of metastatic spread by anatomic location on NaF PET/CT enhances potential prognostication. Further study is warranted to optimize the prognostic and predictive value of NaF PET/CT in mCRPC patients.

FLT PET/CT imaging of metastatic prostate cancer patients treated with pTVG-HP DNA vaccine and pembrolizumab.

BACKGROUND: Immunotherapy has demonstrated remarkable success in treating different cancers. Nonetheless, a large number of patients do not respond, many respond without immediate changes detectable with conventional imaging, and many have unusual immune-related adverse events that cannot be predicted in advance. In this exploratory study, we investigate how 3'-Deoxy-3'-18F-fluorothymidine (FLT) positron emission tomography (PET) measurements of tumor and immune cell proliferation might be utilized as biomarkers in immunotherapy. METHODS: Seventeen patients with metastatic castrate resistant prostate cancer were treated with combination pTVG-HP DNA vaccine and pembrolizumab. Patients underwent baseline and 12-week FLT PET/CT scans. FLT PET standardized uptake values (SUVs) were extracted from tumors, non-metastatic lymph nodes, spleen, bone marrow, pancreas, and thyroid to quantify cell proliferation in these tissues. Regional immune cell responses to pTVG-HP DNA vaccine were assessed by comparing FLT uptake changes in vaccine draining and non-draining lymph nodes. Cox proportional hazards regression was utilized to relate FLT uptake and other clinical markers (PSA and tumor size) to progression-free survival. Area under receiver operating characteristic (AUC) curves and concordance indices were used to assess the predictive capabilities of FLT uptake. RESULTS: Changes in FLT uptake in vaccine draining lymph nodes were significantly greater than changes in non-draining lymph nodes (P = 0.02), suggesting a regional immune response to vaccination. However, the changes in FLT uptake in lymph nodes were not significantly predictive of progression-free survival. Increases in tumor FLT uptake were significantly predictive of shorter progression-free survival (concordance index = 0.83, P < 0.01). Baseline FLT uptake in the thyroid was significantly predictive of whether or not a patient would subsequently experience a thyroid-related adverse event (AUC = 0.97, P < 0.01). CONCLUSIONS: FLT PET uptake was significantly predictive of progression-free survival and the occurrence of adverse events relating to thyroid function. The results suggest FLT PET imaging has potential as a biomarker in immunotherapy, providing a marker of tumor and immune responses, and as a possible means of anticipating specific immune-related adverse events. TRIAL REGISTRATION: NCT02499835 .

Response-to-repeatability of quantitative imaging features for longitudinal response assessment.

Quantitative imaging biomarkers (QIBs) are often selected and ranked based on their repeatability performance. In the context of treatment response assessment, however, one must also consider how sensitive a QIB is to measuring changes in the tumour. This work introduces response-to-repeatability ratio (R/R), which weighs the ability of a QIB to detect significant changes with respect to its measurement repeatability and applies it to the case of PET texture features. R/R is evaluated as the proportion of measurable changes from baseline to follow-up for each candidate QIB. We analyse 47 texture features extracted from lesions in bone-metastatic prostate cancer patients who received double baseline and/or baseline to treatment follow-up 18F-NaF PET/CT scans. R/R evaluates the proportion of follow-up changes outside of the 95% limits of agreement (LOA) defined by test-retest values. Intraclass correlation coefficient (ICC) and coefficient of variation (CV) are calculated for each feature. Relationship between ICC and R/R are evaluated with the Spearman's correlation coefficient. R/R varied significantly across texture features: 41/47 (87%) features demonstrated R/R > 5%; 21/47 (45%) features demonstrated R/R > 10%, and 11/47 (23%) features demonstrated R/R > 20%. LOA of features ranged from [0.998, 1.001] to [0.22, 4.86]. Repeatability alone did not qualify a feature for its efficacy at detecting measurable change at follow-up, as shown by weak correlations between R/R and both CV and ICC (ρ = 0.23 and ρ = 0.40, respectively). Three features demonstrated excellent ICC (ICC > 0.75) and R/R greater than that of SUVmax (R/R = 41.8%): skewness (ICC = 0.92, R/R = 75.4%), kurtosis (ICC = 0.88, R/R = 47.0%) and diagonal moment (ICC = 0.88, R/R = 45.5%). R/R characterizes the sensitivity of candidate QIBs to detect measurable changes at follow-up. R/R supplements existing precision performance metrics (e.g. CV, ICC, and LOA) as an index to assess the utility of QIBs for response assessment.

Quantification of bone flare on 18F-NaF PET/CT in metastatic castration-resistant prostate cancer.

BACKGROUND: Bone flare has been observed on 99mTc-MDP bone scans of patients with metastatic castration-resistant prostate cancer (mCRPC). This exploratory study investigates bone flare in mCRPC patients receiving androgen receptor (AR) inhibitors using 18F-NaF PET/CT. METHODS: Twenty-nine mCRPC patients undergoing AR-inhibiting therapy (abiraterone, orteronel, enzalutamide) received NaF PET/CT scans at baseline, week 6, and week 12 of treatment. SUV metrics were extracted globally for each patient (SUV) and for each individual lesion (iSUV). Bone flare was defined as increasing SUV metrics or lesion number at week 6 followed by subsequent week 12 decrease. Differences in metrics across timepoints were compared using Wilcoxon tests. Cox proportional hazard regression was conducted between global metrics and progression-free survival (PFS). RESULTS: Total SUV was most sensitive for flare detection and was identified in 14/23 (61%) patients receiving CYP17A1-inhibitors (abiraterone, orteronel), and not identified in any of six patients receiving enzalutamide. The appearance of new lesions did not account for initial increases in SUV metrics. iSUV metrics followed patient-level trends: bone flare positive patients showed a median of 72% (range: 0-100%) of lesions with total iSUV flare. Increasing mean SUV at week 6 correlated with extended PFS (HR = 0.58, p = 0.02). CONCLUSION: NaF PET bone flare was present on 61% of mCRPC patients in the first 6 weeks of treatment with CYP17A1-inhibitors. Characterization provided in this study suggests favorable PFS in patients showing bone flare. This characterization of NaF flare is important for guiding treatment assessment schedules to better distinguish between patients showing bone flare and those truly progressing, and should be performed for all emerging mCRPC treatments and imaging agents.

A statistically optimized regional thresholding method (SORT) for bone lesion detection in 18F-NaF PET/CT imaging.

Identification of individual lesions on 18F-NaF PET bone scans is a time-consuming and often subjective process that makes accurate characterization of disease burden challenging. Current automated methods either underestimate disease or struggle with high false positive rates. We developed a statistically optimized regional thresholding (SORT) method that optimizes detection of bone lesions. This study assessed 18F-NaF PET/CT scans of 37 bone metastatic prostate cancer patients. Each PET image was divided into 19 skeletal regions. Areas of disease in each skeletal region were identified by an experienced nuclear medicine physician. A region of interest (ROI) was placed at each disease location and local maxima were extracted for both healthy and diseased ROIs. Secondary physician review was performed after identification of suspicious local maxima. Region-specific SUV thresholds were determined based on receiver operating characteristic (ROC) analysis optimized for detection of malignant disease. The detection performance of the SORT thresholds were compared to commonly used SUV > 10 g ml-1 (SUV10) and SUV > 15 g ml-1 (SUV15) global thresholds. The sensitivity of the SORT thresholds to various factors was evaluated, such as the number of subjects evaluated or image reconstruction settings. 1751 lesions were manually identified by the nuclear medicine physician. SORT identified different thresholds in each skeletal region (SUV range: 3-13 g ml-1). Region-specific SORT thresholding resulted in higher sensitivity (95.8%) than commonly used global thresholds (82.8% for SUV10 and 58.4% for SUV15) while maintaining a high specificity (97.1%, compared to 97.3% for SUV10 and 100.0% for SUV15). Factors, such as reconstruction settings, had minimal impact on threshold optimization, resulting in an average change of 10% (range: 2%-17%) in thresholds for each factor. Region-specific SUV thresholding of NaF PET images for bone lesion detection in metastatic prostate patients was found to be superior to current global thresholding methods.