Primary Results of NRG-RTOG1106/ECOG-ACRIN 6697: A Randomized Phase II Trial of Individualized Adaptive (chemo)Radiotherapy Using Midtreatment 18F-Fluorodeoxyglucose Position Emission Tomography/Computed Tomography in Stage III Non-Small Cell Lung Cancer.

PURPOSE: NRG-RTOG0617 demonstrated a detrimental effect of uniform high-dose radiation in stage III non-small cell lung cancer. NRG-RTOG1106/ECOG-ACRIN6697 (ClinicalTrials.gov identifier: NCT01507428), a randomized phase II trial, studied whether midtreatment 18F-fluorodeoxyglucose position emission tomography/computed tomography (FDG-PET/CT) can guide individualized/adaptive dose-intensified radiotherapy (RT) to improve and predict outcomes in patients with this disease. MATERIALS AND METHODS: Patients fit for concurrent chemoradiation were randomly assigned (1:2) to standard (60 Gy/30 fractions) or FDG-PET-guided adaptive treatment, stratified by substage, primary tumor size, and histology. All patients had midtreatment FDG-PET/CT; adaptive arm patients had an individualized, intensified boost RT dose to residual metabolically active areas. The primary therapeutic end point was 2-year centrally reviewed freedom from local-regional progression (FFLP), defined as no progression in or near the planning target volume and/or regional nodes. FFLP was analyzed on a modified intent-to-treat population at a one-sided Z-test significance level of 0.15. The primary imaging end point was centrally reviewed change in SUVpeak from baseline to midtreatment; its association with FFLP was assessed using the two-sided Wald test on the basis of Cox regression. RESULTS: Of 138 patients enrolled, 127 were eligible. Adaptive-arm patients received a mean 71 Gy in 30 fractions, with mean lung dose 17.9 Gy. There was no significant difference in centrally reviewed 2-year FFLP (59.5% and 54.6% in standard and adaptive arms; P = .66). There were no significant differences in protocol-specified grade 3 toxicities, survival, or progression-free survival (P > .4). Median SUVpeak and metabolic tumor volume (MTV) in the adaptive arm decreased 49% and 54%, from pre-RT to mid-RT PET. However, ΔSUVpeak and ΔMTV were not associated with FFLP (hazard ratios, 0.997; P = .395 and .461). CONCLUSION: Midtreatment PET-adapted RT dose escalation as given in this study was safe and feasible but did not improve efficacy outcomes.

CTLESS: A scatter-window projection and deep learning-based transmission-less attenuation compensation method for myocardial perfusion SPECT.

Attenuation compensation (AC), while being beneficial for visual-interpretation tasks in myocardial perfusion imaging (MPI) by SPECT, typically requires the availability of a separate X-ray CT component, leading to additional radiation dose, higher costs, and potentially inaccurate diagnosis due to SPECT/CT misalignment. To address these issues, we developed a method for cardiac SPECT AC using deep learning and emission scatter-window photons without a separate transmission scan (CTLESS). In this method, an estimated attenuation map reconstructed from scatter-energy window projections is segmented into different regions using a multi-channel input multi-decoder network trained on CT scans. Pre-defined attenuation coefficients are assigned to these regions, yielding the attenuation map used for AC. We objectively evaluated this method in a retrospective study with anonymized clinical SPECT/CT stress MPI images on the clinical task of detecting defects with an anthropomorphic model observer. CTLESS yielded statistically non-inferior performance compared to a CT-based AC (CTAC) method and significantly outperformed a non-AC (NAC) method on this clinical task. Similar results were observed in stratified analyses with different sexes, defect extents and severities. The method was observed to generalize across two SPECT scanners, each with a different camera. In addition, CTLESS yielded similar performance as CTAC and outperformed NAC method on the metrics of root mean squared error and structural similarity index measure. Moreover, as we reduced the training dataset size, CTLESS yielded relatively stable AUC values and generally outperformed another DL-based AC method that directly estimated the attenuation coefficient within each voxel. These results demonstrate the capability of the CTLESS method for transmission-less AC in SPECT and motivate further clinical evaluation.

Prostate-Specific Membrane Antigen-Targeted Imaging and Its Correlation with HOXB13 Expression.

Homeobox 13 (HOXB13) is an oncogenic transcription factor that directly regulates expression of folate hydrolase 1, which encodes prostate-specific membrane antigen (PSMA). HOXB13 is expressed in primary and metastatic prostate cancers (PCs) and promotes androgen-independent PC growth. Since HOXB13 promotes resistance to androgen receptor (AR)-targeted therapies and regulates the expression of folate hydrolase 1, we investigated whether SUVs on PSMA PET would correlate with HOXB13 expression. Methods: We analyzed 2 independent PC patient cohorts who underwent PSMA PET/CT for initial staging or for biochemical recurrence. In the discovery cohort, we examined the relationship between HOXB13, PSMA, and AR messenger RNA (mRNA) expression in prostate biopsy specimens from 179 patients who underwent PSMA PET/CT with 18F-piflufolastat. In the validation cohort, we confirmed the relationship between HOXB13, PSMA, and AR by comparing protein expression in prostatectomy and lymph node (LN) sections from 19 patients enrolled in 18F-rhPSMA-7.3 PET clinical trials. Correlation and association analyses were also used to confirm the relationship between the markers, LN positivity, and PSMA PET SUVs. Results: We observed a significant correlation between PSMA and HOXB13 mRNA (P < 0.01). The association between HOXB13 and 18F-piflufolastat SUVs was also significant (SUVmax, P = 0.0005; SUVpeak, P = 0.0006). Likewise, the PSMA SUVmax was significantly associated with the expression of HOXB13 protein in the 18F-rhPSMA-7.3 PET cohort (P = 0.008). Treatment-naïve patients with LN metastases demonstrated elevated HOXB13 and PSMA levels in their tumors as well as higher PSMA tracer uptake and low AR expression. Conclusion: Our findings demonstrate that HOXB13 correlates with PSMA expression and PSMA PET SUVs at the mRNA and protein levels. Our study suggests that the PSMA PET findings may reflect oncogenic HOXB13 transcriptional activity in PC, thus potentially serving as an imaging biomarker for more aggressive disease.

DEMIST: A Deep-Learning-Based Detection-Task-Specific Denoising Approach for Myocardial Perfusion SPECT.

There is an important need for methods to process myocardial perfusion imaging (MPI) single-photon emission computed tomography (SPECT) images acquired at lower-radiation dose and/or acquisition time such that the processed images improve observer performance on the clinical task of detecting perfusion defects compared to low-dose images. To address this need, we build upon concepts from model-observer theory and our understanding of the human visual system to propose a detection task-specific deep-learning-based approach for denoising MPI SPECT images (DEMIST). The approach, while performing denoising, is designed to preserve features that influence observer performance on detection tasks. We objectively evaluated DEMIST on the task of detecting perfusion defects using a retrospective study with anonymized clinical data in patients who underwent MPI studies across two scanners (N = 338). The evaluation was performed at low-dose levels of 6.25%, 12.5%, and 25% and using an anthropomorphic channelized Hotelling observer. Performance was quantified using area under the receiver operating characteristics curve (AUC). Images denoised with DEMIST yielded significantly higher AUC compared to corresponding low-dose images and images denoised with a commonly used task-agnostic deep learning-based denoising method. Similar results were observed with stratified analysis based on patient sex and defect type. Additionally, DEMIST improved visual fidelity of the low-dose images as quantified using root mean squared error and structural similarity index metric. A mathematical analysis revealed that DEMIST preserved features that assist in detection tasks while improving the noise properties, resulting in improved observer performance. The results provide strong evidence for further clinical evaluation of DEMIST to denoise low-count images in MPI SPECT.

Can patient-specific acquisition protocol improve performance on defect detection task in myocardial perfusion SPECT?

Myocardial perfusion imaging using single-photon emission computed tomography (SPECT), or myocardial perfusion SPECT (MPS) is a widely used clinical imaging modality for the diagnosis of coronary artery disease. Current clinical protocols for acquiring and reconstructing MPS images are similar for most patients. However, for patients with outlier anatomical characteristics, such as large breasts, images acquired using conventional protocols are often sub-optimal in quality, leading to degraded diagnostic accuracy. Solutions to improve image quality for these patients outside of increased dose or total acquisition time remain challenging. Thus, there is an important need for new methodologies to improve image quality for such patients. One approach to improving this performance is adapting the image acquisition protocol specific to each patient. For this study, we first designed and implemented a personalized patient-specific protocol-optimization strategy, which we term precision SPECT (PRESPECT). This strategy integrates ideal observer theory with the constraints of tomographic reconstruction to optimize the acquisition time for each projection view, such that MPS defect detection performance is maximized. We performed a clinically realistic simulation study on patients with outlier anatomies on the task of detecting perfusion defects on various realizations of low-dose scans by an anthropomorphic channelized Hotelling observer. Our results show that using PRESPECT led to improved performance on the defect detection task for the considered patients. These results provide evidence that personalization of MPS acquisition protocol has the potential to improve defect detection performance, motivating further research to design optimal patient-specific acquisition and reconstruction protocols for MPS, as well as developing similar approaches for other medical imaging modalities.

Clinical validation of the PrecivityAD2 blood test: A mass spectrometry-based test with algorithm combining %p-tau217 and Aß42/40 ratio to identify presence of brain amyloid.

BACKGROUND: With the availability of disease-modifying therapies for Alzheimer's disease (AD), it is important for clinicians to have tests to aid in AD diagnosis, especially when the presence of amyloid pathology is a criterion for receiving treatment. METHODS: High-throughput, mass spectrometry-based assays were used to measure %p-tau217 and amyloid beta (Aß)42/40 ratio in blood samples from 583 individuals with suspected AD (53% positron emission tomography [PET] positive by Centiloid > 25). An algorithm (PrecivityAD2 test) was developed using these plasma biomarkers to identify brain amyloidosis by PET. RESULTS: The area under the receiver operating characteristic curve (AUC-ROC) for %p-tau217 (0.94) was statistically significantly higher than that for p-tau217 concentration (0.91). The AUC-ROC for the PrecivityAD2 test output, the Amyloid Probability Score 2, was 0.94, yielding 88% agreement with amyloid PET. Diagnostic performance of the APS2 was similar by ethnicity, sex, age, and apoE4 status. DISCUSSION: The PrecivityAD2 blood test showed strong clinical validity, with excellent agreement with brain amyloidosis by PET.

Need for Objective Task-Based Evaluation of Image Segmentation Algorithms for Quantitative PET: A Study with ACRIN 6668/RTOG 0235 Multicenter Clinical Trial Data.

Reliable performance of PET segmentation algorithms on clinically relevant tasks is required for their clinical translation. However, these algorithms are typically evaluated using figures of merit (FoMs) that are not explicitly designed to correlate with clinical task performance. Such FoMs include the Dice similarity coefficient (DSC), the Jaccard similarity coefficient (JSC), and the Hausdorff distance (HD). The objective of this study was to investigate whether evaluating PET segmentation algorithms using these task-agnostic FoMs yields interpretations consistent with evaluation on clinically relevant quantitative tasks. Methods: We conducted a retrospective study to assess the concordance in the evaluation of segmentation algorithms using the DSC, JSC, and HD and on the tasks of estimating the metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of primary tumors from PET images of patients with non-small cell lung cancer. The PET images were collected from the American College of Radiology Imaging Network 6668/Radiation Therapy Oncology Group 0235 multicenter clinical trial data. The study was conducted in 2 contexts: (1) evaluating conventional segmentation algorithms, namely those based on thresholding (SUVmax40% and SUVmax50%), boundary detection (Snakes), and stochastic modeling (Markov random field-Gaussian mixture model); (2) evaluating the impact of network depth and loss function on the performance of a state-of-the-art U-net-based segmentation algorithm. Results: Evaluation of conventional segmentation algorithms based on the DSC, JSC, and HD showed that SUVmax40% significantly outperformed SUVmax50%. However, SUVmax40% yielded lower accuracy on the tasks of estimating MTV and TLG, with a 51% and 54% increase, respectively, in the ensemble normalized bias. Similarly, the Markov random field-Gaussian mixture model significantly outperformed Snakes on the basis of the task-agnostic FoMs but yielded a 24% increased bias in estimated MTV. For the U-net-based algorithm, our evaluation showed that although the network depth did not significantly alter the DSC, JSC, and HD values, a deeper network yielded substantially higher accuracy in the estimated MTV and TLG, with a decreased bias of 91% and 87%, respectively. Additionally, whereas there was no significant difference in the DSC, JSC, and HD values for different loss functions, up to a 73% and 58% difference in the bias of the estimated MTV and TLG, respectively, existed. Conclusion: Evaluation of PET segmentation algorithms using task-agnostic FoMs could yield findings discordant with evaluation on clinically relevant quantitative tasks. This study emphasizes the need for objective task-based evaluation of image segmentation algorithms for quantitative PET.

A task-specific deep-learning-based denoising approach for myocardial perfusion SPECT.

Deep-learning (DL)-based methods have shown significant promise in denoising myocardial perfusion SPECT images acquired at low dose. For clinical application of these methods, evaluation on clinical tasks is crucial. Typically, these methods are designed to minimize some fidelity-based criterion between the predicted denoised image and some reference normal-dose image. However, while promising, studies have shown that these methods may have limited impact on the performance of clinical tasks in SPECT. To address this issue, we use concepts from the literature on model observers and our understanding of the human visual system to propose a DL-based denoising approach designed to preserve observer-related information for detection tasks. The proposed method was objectively evaluated on the task of detecting perfusion defect in myocardial perfusion SPECT images using a retrospective study with anonymized clinical data. Our results demonstrate that the proposed method yields improved performance on this detection task compared to using low-dose images. The results show that by preserving task-specific information, DL may provide a mechanism to improve observer performance in low-dose myocardial perfusion SPECT.

Need for objective task-based evaluation of AI-based segmentation methods for quantitative PET.

Artificial intelligence (AI)-based methods are showing substantial promise in segmenting oncologic positron emission tomography (PET) images. For clinical translation of these methods, assessing their performance on clinically relevant tasks is important. However, these methods are typically evaluated using metrics that may not correlate with the task performance. One such widely used metric is the Dice score, a figure of merit that measures the spatial overlap between the estimated segmentation and a reference standard (e.g., manual segmentation). In this work, we investigated whether evaluating AI-based segmentation methods using Dice scores yields a similar interpretation as evaluation on the clinical tasks of quantifying metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of primary tumor from PET images of patients with non-small cell lung cancer. The investigation was conducted via a retrospective analysis with the ECOG-ACRIN 6668/RTOG 0235 multi-center clinical trial data. Specifically, we evaluated different structures of a commonly used AI-based segmentation method using both Dice scores and the accuracy in quantifying MTV/TLG. Our results show that evaluation using Dice scores can lead to findings that are inconsistent with evaluation using the task-based figure of merit. Thus, our study motivates the need for objective task-based evaluation of AI-based segmentation methods for quantitative PET.

Amyloid Positron Emission Tomography and Subsequent Health Care Use Among Medicare Beneficiaries With Mild Cognitive Impairment or Dementia.

Importance: Results of amyloid positron emission tomography (PET) have been shown to change the management of patients with mild cognitive impairment (MCI) or dementia who meet Appropriate Use Criteria (AUC). Objective: To determine if amyloid PET is associated with reduced hospitalizations and emergency department (ED) visits over 12 months in patients with MCI or dementia. Design, Setting, and Participants: This nonrandomized controlled trial analyzed participants in the Imaging Dementia-Evidence for Amyloid Scanning (IDEAS) study, an open-label, multisite, longitudinal study that enrolled participants between February 2016 and December 2017 and followed up through December 2018. These participants were recruited at 595 clinical sites that provide specialty memory care across the US. Eligible participants were Medicare beneficiaries 65 years or older with a diagnosis of MCI or dementia within the past 24 months who met published AUC for amyloid PET. Each IDEAS study participant was matched to a control Medicare beneficiary who had not undergone amyloid PET. Data analysis was conducted on December 13, 2022. Exposure: Participants underwent amyloid PET at imaging centers. Main Outcomes and Measures: The primary end points were the proportions of patients with 12-month inpatient hospital admissions and ED visits. One of 4 secondary end points was the rate of hospitalizations and rate of ED visits in participants with positive vs negative amyloid PET results. Health care use was ascertained from Medicare claims data. Results: The 2 cohorts (IDEAS study participants and controls) each comprised 12 684 adults, including 6467 females (51.0%) with a median (IQR) age of 77 (73-81) years. Over 12 months, 24.0% of the IDEAS study participants were hospitalized, compared with 25.1% of the matched control cohort, for a relative reduction of -4.49% (97.5% CI, -9.09% to 0.34%). The 12-month ED visit rates were nearly identical between the 2 cohorts (44.8% in both IDEAS study and control cohorts) for a relative reduction of -0.12% (97.5% CI, -3.19% to 3.05%). Both outcomes fell short of the prespecified effect size of 10% or greater relative reduction. Overall, 1467 of 6848 participants (21.4%) with positive amyloid PET scans were hospitalized within 12 months compared with 1081 of 4209 participants (25.7%) with negative amyloid PET scans (adjusted odds ratio, 0.83; 95% CI, 0.78-0.89). Conclusions and Relevance: Results of this nonrandomized controlled trial showed that use of amyloid PET was not associated with a significant reduction in 12-month hospitalizations or ED visits. Rates of hospitalization were lower in patients with positive vs negative amyloid PET results.

Discrepant Assessments of Progressive Disease in Clinical Trials between Routine Clinical Reads and Formal RECIST 1.1 Interpretations.

Purpose To analyze the frequency of discrepant interpretations of progressive disease (PD) between routine clinical and formal Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 interpretations in patients enrolled in solid tumor clinical trials and investigate the causes of discordance. Materials and Methods This retrospective study included patients in solid tumor clinical trials undergoing imaging response assessments based on RECIST 1.1 from January to July 2021. Routine clinical interpretations (RCIs) performed as part of standard workflow and not requiring formal use of any established response criteria were compared with separate local core laboratory interpretations (CLIs) by specially trained radiologists who used software that tracks target lesion measurements, changes in nontarget lesions, and appearance of new lesions longitudinally. The comparison focused on discordant interpretations of PD. Results Among 1053 patients who had both RCIs and CLIs performed, PD was diagnosed on one or both reads in 327 patients (median age, 63.6 [range, 22.4-83.2] years; 57.8% female patients). The RCIs and CLIs agreed with PD status in 65% (213 of 327) of assessments. In 32% (105 of 327) of assessments, RCIs overdiagnosed PD when CLIs diagnosed stable disease, and in 3% (nine of 327), CLIs diagnosed PD when RCIs diagnosed stable disease. Reasons for discrepant RCIs of PD included erroneous target lesion measurements (58%, 61 of 105), erroneous diagnosis of nontarget progression (30%, 32 of 105), and misclassification of new lesions as cancer (11%, 12 of 105). Most patients (93%, 98 of 105) with RCI overdiagnosis of PD remained in the clinical trial for one or more treatment cycles. Conclusion PD was frequently overdiagnosed on RCIs versus formal RECIST 1.1 CLIs which could result in patients removed from the clinical trial inappropriately. Keywords: Oncology, Cancer, Tumor Response, MR Imaging, CT © RSNA, 2023 See also commentary by Margolis and Ruchalski in this issue.

Use of 18 F-PSMA PET to Distinguish Cerebral Radiation Necrosis From Tumor Recurrence.

ABSTRACT: Brain metastasis in prostate adenocarcinoma is extremely rare and usually arises in the setting of widespread osseous and visceral metastases. Surgical resection and radiation therapy, including stereotactic radiosurgery, are the mainstays of treatment for brain metastasis. Radiation necrosis is a common complication of radiotherapy for brain metastasis, and distinguishing it from tumor recurrence by MRI is difficult because of overlapping findings. We present a 73-year-old man with prostate cancer with a solitary brain metastasis where PET with 18 F-piflufolostat helped detect disease recurrence in the setting of ambiguous MRI findings.

Development and task-based evaluation of a scatter-window projection and deep learning-based transmission-less attenuation compensation method for myocardial perfusion SPECT.

Attenuation compensation (AC) is beneficial for visual interpretation tasks in single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). However, traditional AC methods require the availability of a transmission scan, most often a CT scan. This approach has the disadvantage of increased radiation dose, increased scanner costs, and the possibility of inaccurate diagnosis in cases of misregistration between the SPECT and CT images. Further, many SPECT systems do not include a CT component. To address these issues, we developed a Scatter-window projection and deep Learning-based AC (SLAC) method to perform AC without a separate transmission scan. To investigate the clinical efficacy of this method, we then objectively evaluated the performance of this method on the clinical task of detecting perfusion defects on MPI in a retrospective study with anonymized clinical SPECT/CT stress MPI images. The proposed method was compared with CT-based AC (CTAC) and no-AC (NAC) methods. Our results showed that the SLAC method yielded an almost overlapping receiver operating characteristic (ROC) plot and a similar area under the ROC (AUC) to the CTAC method on this task. These results demonstrate the capability of the SLAC method for transmission-less AC in SPECT and motivate further clinical evaluation.

DEMIST: A deep-learning-based task-specific denoising approach for myocardial perfusion SPECT.

There is an important need for methods to process myocardial perfusion imaging (MPI) SPECT images acquired at lower radiation dose and/or acquisition time such that the processed images improve observer performance on the clinical task of detecting perfusion defects. To address this need, we build upon concepts from model-observer theory and our understanding of the human visual system to propose a Detection task-specific deep-learning-based approach for denoising MPI SPECT images (DEMIST). The approach, while performing denoising, is designed to preserve features that influence observer performance on detection tasks. We objectively evaluated DEMIST on the task of detecting perfusion defects using a retrospective study with anonymized clinical data in patients who underwent MPI studies across two scanners (N = 338). The evaluation was performed at low-dose levels of 6.25%, 12.5% and 25% and using an anthropomorphic channelized Hotelling observer. Performance was quantified using area under the receiver operating characteristics curve (AUC). Images denoised with DEMIST yielded significantly higher AUC compared to corresponding low-dose images and images denoised with a commonly used task-agnostic DL-based denoising method. Similar results were observed with stratified analysis based on patient sex and defect type. Additionally, DEMIST improved visual fidelity of the low-dose images as quantified using root mean squared error and structural similarity index metric. A mathematical analysis revealed that DEMIST preserved features that assist in detection tasks while improving the noise properties, resulting in improved observer performance. The results provide strong evidence for further clinical evaluation of DEMIST to denoise low-count images in MPI SPECT.

Diagnostic Performance and Safety of 18F-rhPSMA-7.3 Positron Emission Tomography in Men With Suspected Prostate Cancer Recurrence: Results From a Phase 3, Prospective, Multicenter Study (SPOTLIGHT).

PURPOSE: SPOTLIGHT (NCT04186845) evaluated diagnostic performance and safety of radiohybrid 18F-rhPSMA-7.3, a novel high-affinity positron emission tomography radiopharmaceutical. MATERIALS AND METHODS: Men with prostate cancer recurrence underwent positron emission tomography/CT 50-70 minutes after intravenous administration of 296±20% MBq 18F-rhPSMA-7.3. To assess the coprimary end points (verified detection rate and combined region-level positive predictive value), 3 blinded, independent central readers evaluated the scans. Verified detection rate is equivalent to the overall detection rate × positive predictive value. Standard of truth was established for each patient using histopathology or confirmatory imaging. Statistical thresholds (lower bounds of the confidence intervals) of 36.5% and 62.5% were prespecified for verified detection rate and combined region-level positive predictive value, respectively. Additional end points included detection rate, verified detection rate, and combined region-level positive predictive value in patients with histopathology standard of truth, and safety. RESULTS: The overall 18F-rhPSMA-7.3 detection rate among all 389 patients with an evaluable scan was 83% (majority read). Among the 366 patients (median prostate-specific antigen 1.27 ng/mL) for whom a standard of truth (histopathology [n=69]/confirmatory imaging only [n=297]) was available, verified detection rate ranged from 51% (95% CI 46.1-56.6) to 54% (95% CI 48.8-59.3), exceeding the prespecified statistical threshold. Combined region-level positive predictive value ranged from 46% (95% CI 42.0-50.3) to 60% (95% CI 55.1-65.5) across the readers, not meeting the threshold. In the subset of patients with histopathology standard of truth, the verified detection rate and combined region-level positive predictive value were both above the prespecified thresholds (majority read, 81% [95% CI 69.9-89.6] and 72% [95% CI 62.5-80.7], respectively). No significant safety concerns were identified. CONCLUSIONS: 18F-rhPSMA-7.3 offers a clinically meaningful verified detection rate for localization of recurrent prostate cancer. Despite missing the coprimary end point of combined region-level positive predictive value, the totality of the data support the potential clinical utility of 18F-rhPSMA-7.3.

Need for objective task-based evaluation of deep learning-based denoising methods: A study in the context of myocardial perfusion SPECT.

BACKGROUND: Artificial intelligence-based methods have generated substantial interest in nuclear medicine. An area of significant interest has been the use of deep-learning (DL)-based approaches for denoising images acquired with lower doses, shorter acquisition times, or both. Objective evaluation of these approaches is essential for clinical application. PURPOSE: DL-based approaches for denoising nuclear-medicine images have typically been evaluated using fidelity-based figures of merit (FoMs) such as root mean squared error (RMSE) and structural similarity index measure (SSIM). However, these images are acquired for clinical tasks and thus should be evaluated based on their performance in these tasks. Our objectives were to: (1) investigate whether evaluation with these FoMs is consistent with objective clinical-task-based evaluation; (2) provide a theoretical analysis for determining the impact of denoising on signal-detection tasks; and (3) demonstrate the utility of virtual imaging trials (VITs) to evaluate DL-based methods. METHODS: A VIT to evaluate a DL-based method for denoising myocardial perfusion SPECT (MPS) images was conducted. To conduct this evaluation study, we followed the recently published best practices for the evaluation of AI algorithms for nuclear medicine (the RELAINCE guidelines). An anthropomorphic patient population modeling clinically relevant variability was simulated. Projection data for this patient population at normal and low-dose count levels (20%, 15%, 10%, 5%) were generated using well-validated Monte Carlo-based simulations. The images were reconstructed using a 3-D ordered-subsets expectation maximization-based approach. Next, the low-dose images were denoised using a commonly used convolutional neural network-based approach. The impact of DL-based denoising was evaluated using both fidelity-based FoMs and area under the receiver operating characteristic curve (AUC), which quantified performance on the clinical task of detecting perfusion defects in MPS images as obtained using a model observer with anthropomorphic channels. We then provide a mathematical treatment to probe the impact of post-processing operations on signal-detection tasks and use this treatment to analyze the findings of this study. RESULTS: Based on fidelity-based FoMs, denoising using the considered DL-based method led to significantly superior performance. However, based on ROC analysis, denoising did not improve, and in fact, often degraded detection-task performance. This discordance between fidelity-based FoMs and task-based evaluation was observed at all the low-dose levels and for different cardiac-defect types. Our theoretical analysis revealed that the major reason for this degraded performance was that the denoising method reduced the difference in the means of the reconstructed images and of the channel operator-extracted feature vectors between the defect-absent and defect-present cases. CONCLUSIONS: The results show the discrepancy between the evaluation of DL-based methods with fidelity-based metrics versus the evaluation on clinical tasks. This motivates the need for objective task-based evaluation of DL-based denoising approaches. Further, this study shows how VITs provide a mechanism to conduct such evaluations computationally, in a time and resource-efficient setting, and avoid risks such as radiation dose to the patient. Finally, our theoretical treatment reveals insights into the reasons for the limited performance of the denoising approach and may be used to probe the effect of other post-processing operations on signal-detection tasks.

Need for objective task-based evaluation of AI-based segmentation methods for quantitative PET.

Artificial intelligence (AI)-based methods are showing substantial promise in segmenting oncologic positron emission tomography (PET) images. For clinical translation of these methods, assessing their performance on clinically relevant tasks is important. However, these methods are typically evaluated using metrics that may not correlate with the task performance. One such widely used metric is the Dice score, a figure of merit that measures the spatial overlap between the estimated segmentation and a reference standard (e.g., manual segmentation). In this work, we investigated whether evaluating AI-based segmentation methods using Dice scores yields a similar interpretation as evaluation on the clinical tasks of quantifying metabolic tumor volume (MTV) and total lesion glycolysis (TLG) of primary tumor from PET images of patients with non-small cell lung cancer. The investigation was conducted via a retrospective analysis with the ECOG-ACRIN 6668/RTOG 0235 multi-center clinical trial data. Specifically, we evaluated different structures of a commonly used AI-based segmentation method using both Dice scores and the accuracy in quantifying MTV/TLG. Our results show that evaluation using Dice scores can lead to findings that are inconsistent with evaluation using the task-based figure of merit. Thus, our study motivates the need for objective task-based evaluation of AI-based segmentation methods for quantitative PET.