Multi-Energy Low-Kiloelectron Volt versus Single-Energy Low-Kilovolt Images for Endoleak Detection at CT Angiography of the Aorta

Purpose To compare image quality, diagnostic performance, and conspicuity between single-energy and multi-energy images for endoleak detection at CT angiography (CTA) after endovascular aortic repair (EVAR). Materials and Methods In this single-center prospective randomized controlled trial, individuals undergoing CTA after EVAR between August 2020 and May 2022 were allocated to imaging using either low-kilovolt single-energy images (SEI; 80 kV, group A) or low-kiloelectron volt virtual monoenergetic images (VMI) at 40 and 50 keV from multi-energy CT (80/Sn150 kV, group B). Scan protocols were dose matched (volume CT dose index: mean, 4.5 mGy ± 1.8 [SD] vs 4.7 mGy ± 1.3, P = .41). Contrast-to-noise ratio (CNR) was measured. Two expert radiologists established the reference standard for the presence of endoleaks. Detection and conspicuity of endoleaks and subjective image quality were assessed by two different blinded radiologists. Interreader agreement was calculated. Nonparametric statistical tests were used. Results A total of 125 participants (mean age, 76 years ± 8; 103 men) were allocated to groups A (n = 64) and B (n = 61). CNR was significantly lower for 40-keV VMI (mean, 19.1; P = .048) and 50-keV VMI (mean, 16.8; P < .001) as compared with SEI (mean, 22.2). In total, 45 endoleaks were present (A: 23 vs B: 22). Sensitivity for endoleak detection was higher for SEI (82.6%, 19 of 23; P = .88) and 50-keV VMI (81.8%, 18 of 22; P = .90) as compared with 40-keV VMI (77.3%, 17 of 22). Specificity was comparable among groups (SEI: 92.7%, 38 of 41; both VMI energies: 92.3%, 35 of 38; P = .99), with an interreader agreement of 1. Conspicuity of endoleaks was comparable between SEI (median, 2.99) and VMI (both energies: median, 2.87; P = .04). Overall subjective image quality was rated significantly higher for SEI (median, 4 [IQR, 4-4) as compared with 40 and 50 keV (both energies: median, 4 [IQR, 3-4]; P < .001). Conclusion SEI demonstrated higher image quality and comparable diagnostic accuracy as compared with 50-keV VMI for endoleak detection at CTA after EVAR. Keywords: Aneurysms, CT, CT Angiography, Vascular, Aorta, Technology Assessment, Multidetector CT, Abdominal Aortic Aneurysms, Endoleaks, Perigraft Leak Supplemental material is available for this article. © RSNA, 2024.

Evaluating the Use of ChatGPT to Accurately Simplify Patient-centered Information about Breast Cancer Prevention and Screening

Purpose To evaluate the use of ChatGPT as a tool to simplify answers to common questions about breast cancer prevention and screening. Materials and Methods In this retrospective, exploratory study, ChatGPT was requested to simplify responses to 25 questions about breast cancer to a sixth-grade reading level in March and August 2023. Simplified responses were evaluated for clinical appropriateness. All original and simplified responses were assessed for reading ease on the Flesch Reading Ease Index and for readability on five scales: Flesch-Kincaid Grade Level, Gunning Fog Index, Coleman-Liau Index, Automated Readability Index, and the Simple Measure of Gobbledygook (ie, SMOG) Index. Mean reading ease, readability, and word count were compared between original and simplified responses using paired t tests. McNemar test was used to compare the proportion of responses with adequate reading ease (score of 60 or greater) and readability (sixth-grade level). Results ChatGPT improved mean reading ease (original responses, 46 vs simplified responses, 70; P < .001) and readability (original, grade 13 vs simplified, grade 8.9; P < .001) and decreased word count (original, 193 vs simplified, 173; P < .001). Ninety-two percent (23 of 25) of simplified responses were considered clinically appropriate. All 25 (100%) simplified responses met criteria for adequate reading ease, compared with only two of 25 original responses (P < .001). Two of the 25 simplified responses (8%) met criteria for adequate readability. Conclusion ChatGPT simplified answers to common breast cancer screening and prevention questions by improving the readability by four grade levels, though the potential to produce incorrect information necessitates physician oversight when using this tool. Keywords: Mammography, Screening, Informatics, Breast, Education, Health Policy and Practice, Oncology, Technology Assessment Supplemental material is available for this article. © RSNA, 2023.

Coronary CT Angiography-based Morphologic Index for Predicting Hemodynamically Significant Coronary Stenosis

Purpose To develop a new coronary CT angiography (CCTA)-based index, α×LL/MLD4, that considers lesion entrance angle (α) in addition to lesion length (LL) and minimal lumen diameter (MLD) and to evaluate its efficacy in predicting hemodynamically significant coronary stenosis compared with invasive coronary angiography (ICA)-derived fractional flow reserve (FFR). Materials and Methods This prospective study enrolled participants (September 2016-March 2020) from two centers who underwent CCTA followed by ICA (ClinicalTrials.gov identifier: NCT03054324). CCTA images were processed semiautomatically to measure LL, MLD, and α for calculating α×LL/MLD4. Diagnostic performance and accuracy of α×LL/MLD4 and LL/MLD4 in detecting hemodynamically significant coronary stenosis were compared against the reference standard (invasive FFR ≤ 0.80). Results In total, 133 participants (mean age, 63 years ± 9 [SD]; 99 [74%] men) with 210 stenosed coronary arteries were analyzed. Median α×LL/MLD4 was 54.0 degree/mm3 (IQR, 25.3-128.7) in participants with invasive FFR of 0.80 or less and 6.7 degree/mm3 (IQR, 3.3-12.8) in participants with invasive FFR of more than 0.80 (P < .001). The per-vessel accuracy, sensitivity, specificity, positive predictive value, and negative predictive value for discriminating ischemic lesions were 86.2%, 83.1%, 88.4%, 84.1%, and 87.7% for α×LL/MLD4 and 80.5%, 66.3%, 90.9%, 84.3%, and 78.6% for LL/MLD4, respectively. Area under the receiver operating characteristic curve for discriminating hemodynamically significant stenosis was 0.93 for α×LL/MLD4, which was significantly greater than the values of 0.84 for LL/MLD4 and 0.63 for diameter stenosis (both P < .001). Conclusion The new morphologic index, α×LL/MLD4, incorporating lesion entrance angle achieved higher diagnostic performance in detecting hemodynamically significant lesions compared with diameter stenosis and LL/MLD4. Keywords: CT Angiography, Cardiac, Coronary Arteries, Ischemia, Infarction, Technology Assessment Clinical trial registration no. NCT03054324 Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Fairbairn and Nørgaard in this issue.

MR Fingerprinting for Contrast Agent-free and Quantitative Characterization of Focal Liver Lesions

Purpose To evaluate the feasibility of liver MR fingerprinting (MRF) for quantitative characterization and diagnosis of focal liver lesions. Materials and Methods This single-site, prospective study included 89 participants (mean age, 62 years ± 15 [SD]; 45 women, 44 men) with various focal liver lesions who underwent MRI between October 2021 and August 2022. The participants underwent routine clinical MRI, non-contrast-enhanced liver MRF, and reference quantitative MRI with a 1.5-T MRI scanner. The bias and repeatability of the MRF measurements were assessed using linear regression, Bland-Altman plots, and coefficients of variation. The diagnostic capability of MRF-derived T1, T2, T2*, proton density fat fraction (PDFF), and a combination of these metrics to distinguish benign from malignant lesions was analyzed according to the area under the receiver operating characteristic curve (AUC). Results Liver MRF measurements showed moderate to high agreement with reference measurements (intraclass correlation = 0.94, 0.77, 0.45, and 0.61 for T1, T2, T2*, and PDFF, respectively), with underestimation of T2 values (mean bias in lesion = -0.5%, -29%, 5.8%, and -8.2% for T1, T2, T2*, and PDFF, respectively). The median coefficients of variation for repeatability of T1, T2, and T2* values were 2.5% (IQR, 3.6%), 3.1% (IQR, 5.6%), and 6.6% (IQR, 13.9%), respectively. After considering multicollinearity, a combination of MRF measurements showed a high diagnostic performance in differentiating benign from malignant lesions (AUC = 0.92 [95% CI: 0.86, 0.98]). Conclusion Liver MRF enabled the quantitative characterization of various focal liver lesions in a single breath-hold acquisition. Keywords: MR Imaging, Abdomen/GI, Liver, Imaging Sequences, Technical Aspects, Tissue Characterization, Technology Assessment, Diagnosis, Liver Lesions, MR Fingerprinting, Quantitative Characterization Supplemental material is available for this article. © RSNA, 2023.

StrainNet: Improved Myocardial Strain Analysis of Cine MRI by Deep Learning from DENSE

Purpose

To develop a three-dimensional (two dimensions + time) convolutional neural network trained with displacement encoding with stimulated echoes (DENSE) data for displacement and strain analysis of cine MRI.

Materials and Methods

In this retrospective multicenter study, a deep learning model (StrainNet) was developed to predict intramyocardial displacement from contour motion. Patients with various heart diseases and healthy controls underwent cardiac MRI examinations with DENSE between August 2008 and January 2022. Network training inputs were a time series of myocardial contours from DENSE magnitude images, and ground truth data were DENSE displacement measurements. Model performance was evaluated using pixelwise end-point error (EPE). For testing, StrainNet was applied to contour motion from cine MRI. Global and segmental circumferential strain (Ecc) derived from commercial feature tracking (FT), StrainNet, and DENSE (reference) were compared using intraclass correlation coefficients (ICCs), Pearson correlations, Bland-Altman analyses, paired t tests, and linear mixed-effects models.

Results

The study included 161 patients (110 men; mean age, 61 years ± 14 [SD]), 99 healthy adults (44 men; mean age, 35 years ± 15), and 45 healthy children and adolescents (21 males; mean age, 12 years ± 3). StrainNet showed good agreement with DENSE for intramyocardial displacement, with an average EPE of 0.75 mm ± 0.35. The ICCs between StrainNet and DENSE and FT and DENSE were 0.87 and 0.72, respectively, for global Ecc and 0.75 and 0.48, respectively, for segmental Ecc. Bland-Altman analysis showed that StrainNet had better agreement than FT with DENSE for global and segmental Ecc.

Conclusion

StrainNet outperformed FT for global and segmental Ecc analysis of cine MRI.Keywords: Image Postprocessing, MR Imaging, Cardiac, Heart, Pediatrics, Technical Aspects, Technology Assessment, Strain, Deep Learning, DENSE Supplemental material is available for this article. © RSNA, 2023.

Impact of Different Mammography Systems on Artificial Intelligence Performance in Breast Cancer Screening

Artificial intelligence (AI) tools may assist breast screening mammography programs, but limited evidence supports their generalizability to new settings. This retrospective study used a 3-year dataset (April 1, 2016-March 31, 2019) from a U.K. regional screening program. The performance of a commercially available breast screening AI algorithm was assessed with a prespecified and site-specific decision threshold to evaluate whether its performance was transferable to a new clinical site. The dataset consisted of women (aged approximately 50-70 years) who attended routine screening, excluding self-referrals, those with complex physical requirements, those who had undergone a previous mastectomy, and those who underwent screening that had technical recalls or did not have the four standard image views. In total, 55 916 screening attendees (mean age, 60 years ± 6 [SD]) met the inclusion criteria. The prespecified threshold resulted in high recall rates (48.3%, 21 929 of 45 444), which reduced to 13.0% (5896 of 45 444) following threshold calibration, closer to the observed service level (5.0%, 2774 of 55 916). Recall rates also increased approximately threefold following a software upgrade on the mammography equipment, requiring per-software version thresholds. Using software-specific thresholds, the AI algorithm would have recalled 277 of 303 (91.4%) screen-detected cancers and 47 of 138 (34.1%) interval cancers. AI performance and thresholds should be validated for new clinical settings before deployment, while quality assurance systems should monitor AI performance for consistency. Keywords: Breast, Screening, Mammography, Computer Applications-Detection/Diagnosis, Neoplasms-Primary, Technology Assessment Supplemental material is available for this article. © RSNA, 2023.

CT Angiography of the Aorta Using Photon-counting Detector CT with Reduced Contrast Media Volume

Purpose

To develop and evaluate a low-volume contrast media protocol for thoracoabdominal CT angiography (CTA) with photon-counting detector (PCD) CT.

Materials and Methods

This prospective study included consecutive participants (April-September 2021) who underwent CTA with PCD CT of the thoracoabdominal aorta and previous CTA with energy-integrating detector (EID) CT at equal radiation doses. In PCD CT, virtual monoenergetic images (VMI) were reconstructed in 5-keV intervals from 40 to 60 keV. Attenuation of the aorta, image noise, and contrast-to-noise ratio (CNR) were measured, and subjective image quality was rated by two independent readers. In the first group of participants, the same contrast media protocol was used for both scans. CNR gain in PCD CT compared with EID CT served as the reference for contrast media volume reduction in the second group. Noninferiority analysis was used to test noninferior image quality of the low-volume contrast media protocol with PCD CT.

Results

The study included 100 participants (mean age, 75 years ± 8 [SD]; 83 men). In the first group (n = 40), VMI at 50 keV provided the best trade-off between objective and subjective image quality, achieving 25% higher CNR compared with EID CT. Contrast media volume in the second group (n = 60) was reduced by 25% (52.5 mL). Mean differences in CNR and subjective image quality between EID CT and PCD CT at 50 keV were above the predefined boundaries of noninferiority (-0.54 [95% CI: -1.71, 0.62] and -0.36 [95% CI: -0.41, -0.31], respectively).

Conclusion

CTA of the aorta with PCD CT was associated with higher CNR, which was translated into a low-volume contrast media protocol demonstrating noninferior image quality compared with EID CT at the same radiation dose.Keywords: CT Angiography, CT-Spectral, Vascular, Aorta, Contrast Agents-Intravenous, Technology Assessment© RSNA, 2023See also the commentary by Dundas and Leipsic in this issue.

"Are You a TA Practitioner, Then?" - Identity Constructions in Post-Normal Science

Technology assessment (TA) is a paradigmatic case for the manifold and, at times, ambiguous processes of identity formation of researchers in inter- and transdisciplinary settings. TA combines the natural, technical, and social sciences and follows the multiple missions of scientific analysis, public outreach, and policy advice. However, despite this diversity, it also constitutes a genuine community with its own discourses, conferences, and publications. To which extent "being a TA practitioner" also provides for a genuine scholarly identity is still unclear. Building on interviews with technology assessment practitioners at an academic TA institute, we ask what inter/trans/disciplinary identification patterns emerge in this field. Our analysis shows that TA practitioners adopt multiple identities, from "enthusiastic TA practitioner" to "strong interdisciplinarian" or "disciplinarian" - with distinct identity troubles inherent in all these options. We find that generational affiliation plays a vital role in identity formation. It relates to different primary orientations (towards research or advisory practices), inter/disciplinary backgrounds, contracting modes, and lengths of time spent at the TA institute. We conclude firstly, that disciplinary categories figure strongly in transdisciplinary identities; secondly, that the relation of chronos and identity warrants more substantial consideration: as time spent at a transdisciplinary institute as or as perceived options for "futuring one's identity"; thirdly, that our understanding of academic generations could profit from a more sociological conception; and, fourthly, that TA's multidisciplinary setup and threefold orientation towards science, society, and policy result in multiplying possible identities and thus making it difficult to form a stable community.

Application of a Domain-specific BERT for Detection of Speech Recognition Errors in Radiology Reports

PURPOSE

To develop radiology domain-specific bidirectional encoder representations from transformers (BERT) models that can identify speech recognition (SR) errors and suggest corrections in radiology reports.

MATERIALS AND METHODS

A pretrained BERT model, Clinical BioBERT, was further pretrained on a corpus of 114 008 radiology reports between April 2016 and August 2019 that were retrospectively collected from two hospitals. Next, the model was fine-tuned on a training dataset of generated insertion, deletion, and substitution errors, creating Radiology BERT. This model was retrospectively evaluated on an independent dataset of radiology reports with generated errors (n = 18 885) and on unaltered report sentences (n = 2000) and prospectively evaluated on true clinical SR errors (n = 92). Correction Radiology BERT was separately trained to suggest corrections for detected deletion and substitution errors. Area under the receiver operating characteristic curve (AUC) and bootstrapped 95% CIs were calculated for each evaluation dataset.

RESULTS

Radiology-specific BERT had AUC values of >.99 (95% CI: >0.99, >0.99), 0.94 (95% CI: 0.93, 0.94), 0.98 (95% CI: 0.98, 0.98), and 0.97 (95% CI: 0.97, 0.97) for detecting insertion, deletion, substitution, and all errors, respectively, on the independently generated test set. Testing on unaltered report impressions revealed a sensitivity of 82% (28 of 34; 95% CI: 70%, 93%) and specificity of 88% (1521 of 1728; 95% CI: 87%, 90%). Testing on prospective SR errors showed an accuracy of 75% (69 of 92; 95% CI: 65%, 83%). Finally, the correct word was the top suggestion for 45.6% (475 of 1041; 95% CI: 42.5%, 49.3%) of errors.

CONCLUSION

Radiology-specific BERT models fine-tuned on generated errors were able to identify SR errors in radiology reports and suggest corrections.Keywords: Computer Applications, Technology Assessment Supplemental material is available for this article. © RSNA, 2022See also the commentary by Abajian and Cheung in this issue.

Blockchain for SME Clusters: An Ideation using the Framework of Ostrom Commons Governance

Small and medium-sized enterprises (SMEs) organize themselves into clusters by sharing a set of limited resources to achieve the holistic success of the cluster. However, these SMEs often face conflicts and deadlock situations that hinder the fundamental operational dynamics of the cluster due to varied reasons, including lack of trust and transparency in interactions, lack of common consensus, and lack of accountability and non-repudiation. Blockchain technology brings trust, transparency, and traceability to systems, as demonstrated by previous research and practice. In this paper, we explore the role of blockchain technology in building a trustworthy yet collaborative environment in SME clusters through the principles of community self-governance based on the work of Nobel Laureate Elinor Ostrom. We develop and present a blockchain commons governance framework for the three main dimensions i.e., interaction, autonomy, and control, based on the theoretical premise of equivalence mapping and qualitative analysis. This paper examines the role of blockchain technology to act as a guiding mechanism and support the smooth functioning of SMEs for their holistic good. The study focuses on sustainability and improving productivity of SMEs operating in clusters under public and private partnership. This is the first study to address the operational challenges faced by SEMs in clusters by highlighting the dimensions of blockchain commons governance dimensions.

External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review

Purpose

To assess generalizability of published deep learning (DL) algorithms for radiologic diagnosis.

Materials and Methods

In this systematic review, the PubMed database was searched for peer-reviewed studies of DL algorithms for image-based radiologic diagnosis that included external validation, published from January 1, 2015, through April 1, 2021. Studies using nonimaging features or incorporating non-DL methods for feature extraction or classification were excluded. Two reviewers independently evaluated studies for inclusion, and any discrepancies were resolved by consensus. Internal and external performance measures and pertinent study characteristics were extracted, and relationships among these data were examined using nonparametric statistics.

Results

Eighty-three studies reporting 86 algorithms were included. The vast majority (70 of 86, 81%) reported at least some decrease in external performance compared with internal performance, with nearly half (42 of 86, 49%) reporting at least a modest decrease (≥0.05 on the unit scale) and nearly a quarter (21 of 86, 24%) reporting a substantial decrease (≥0.10 on the unit scale). No study characteristics were found to be associated with the difference between internal and external performance.

Conclusion

Among published external validation studies of DL algorithms for image-based radiologic diagnosis, the vast majority demonstrated diminished algorithm performance on the external dataset, with some reporting a substantial performance decrease.Keywords: Meta-Analysis, Computer Applications-Detection/Diagnosis, Neural Networks, Computer Applications-General (Informatics), Epidemiology, Technology Assessment, Diagnosis, Informatics Supplemental material is available for this article. © RSNA, 2022.

Reader Perceptions and Impact of AI on CT Assessment of Air Trapping

Quantitative imaging measurements can be facilitated by artificial intelligence (AI) algorithms, but how they might impact decision-making and be perceived by radiologists remains uncertain. After creation of a dedicated inspiratory-expiratory CT examination and concurrent deployment of a quantitative AI algorithm for assessing air trapping, five cardiothoracic radiologists retrospectively evaluated severity of air trapping on 17 examination studies. Air trapping severity of each lobe was evaluated in three stages: qualitatively (visually); semiquantitatively, allowing manual region-of-interest measurements; and quantitatively, using results from an AI algorithm. Readers were surveyed on each case for their perceptions of the AI algorithm. The algorithm improved interreader agreement (intraclass correlation coefficients: visual, 0.28; semiquantitative, 0.40; quantitative, 0.84; P < .001) and improved correlation with pulmonary function testing (forced expiratory volume in 1 second-to-forced vital capacity ratio) (visual r = -0.26, semiquantitative r = -0.32, quantitative r = -0.44). Readers perceived moderate agreement with the AI algorithm (Likert scale average, 3.7 of 5), a mild impact on their final assessment (average, 2.6), and a neutral perception of overall utility (average, 3.5). Though the AI algorithm objectively improved interreader consistency and correlation with pulmonary function testing, individual readers did not immediately perceive this benefit, revealing a potential barrier to clinical adoption. Keywords: Technology Assessment, Quantification © RSNA, 2021.

Accelerated Breast Diffusion-weighted Imaging Using Multiband Sensitivity Encoding with the CAIPIRINHA Method: Clinical Experience at 3 T

Purpose To examine the clinical value of multiband (MB) sensitivity encoding (SENSE)-accelerated diffusion-weighted imaging (DWI) for breast imaging by performing quantitative and qualitative comparisons with conventional diffusion-weighted echo-planar imaging, or conventional DWI (cDWI). Materials and Methods In this prospective study (ClinicalTrials.gov identifier NCT03607552), women with breast cancer were recruited from July 2018 to July 2019 to undergo additional MB SENSE DWI during clinical 3-T breast MRI examinations. The cDWI and MB SENSE DWI acquisitions were assessed both quantitatively and qualitatively. Regions of interest were defined for tumorous and normal tissue, and the tumor apparent diffusion coefficient (ADC), contrast-to-noise ratio (CNR), and signal index (SI) were calculated for both DWI methods. Three readers independently reviewed the two acquisitions side by side and provided relative image quality scores. Tumor ADC, CNR, and SI measures were compared between cDWI and MB SENSE DWI acquisitions by using a paired t test, and reader preferences were evaluated by using the sign test. Results The study included 38 women (median age, 48 years; range, 28-83 years). Overall agreement was good between cDWI and MB SENSE DWI tumor ADC measures (intraclass correlation coefficient, 0.87 [95% CI: 0.75, 0.94]), and no differences were evident in the ADC (median, 0.93 × 10^-3 mm²/sec vs 0.87 ×10^-3 mm²/sec; P = .50), CNR (2.2 vs 2.3; P = .17), or SI (9.2 vs 9.2; P = .23) measurements. The image quality of cDWI and MB SENSE DWI acquisitions were considered equal for 51% of images (58 of 114), whereas MB SENSE DWI was preferred more often than cDWI (37% [42 of 114] vs 12% [14 of 114]; P < .001). The preference for MB SENSE DWI was most often attributed to better fat suppression. Conclusion MB SENSE can be used to accelerate breast DWI acquisition times without compromising the image quality or the fidelity of quantitative ADC measurements. Keywords: MR-Diffusion-weighted Imaging, Breast, Comparative Studies, Technology Assessment Clinical trial registration no. NCT03607552 © RSNA, 2022.

Multicriteria decision analysis in health care decision in oncology: a systematic review

INTRODUCTION

Multicriteria decision analysis (MCDA) has been used to inform health decisions in health technology assessments (HTA) processes. This is particularly important to complex treatment decisions in oncology.

AREAS COVERED

Five databases (PubMed, EMBASE, LILACS, Web of Science and CRD's NHS Economic Evaluation Database) were searched for studies comparing health technologies in oncology, involving the concept MCDA. The ISPOR MCDA Good Practices Guidelines were used to assess the reporting quality. Study selection, appraisal, and data extraction were performed by two reviewers. Fifteen studies were included. The main decision problem was related to health technology assessment of cancer treatments. Clinicians and public health experts were the most frequent stakeholders. The most frequently included criteria comprised therapeutic benefit, and socio-economic impact. Value measurement approach, direct rating techniques, and additive model for aggregation were used in most studies. Uncertainty analysis revealed the impact of posology and costs on the studies' results. All studies showed some level of overlapping decision criteria.

EXPERT OPINION

There is considerable diversity of methods in MCDA for healthcare decision-making in oncology. The evidence presented can serve as a resource when considering which stakeholders, criteria, and techniques to include in future MCDA studies in oncology.

Validation of Deep Learning-based Augmentation for Reduced 18F-FDG Dose for PET/MRI in Children and Young Adults with Lymphoma

Purpose

To investigate if a deep learning convolutional neural network (CNN) could enable low-dose fluorine 18 (¹⁸F) fluorodeoxyglucose (FDG) PET/MRI for correct treatment response assessment of children and young adults with lymphoma.

Materials and Methods

In this secondary analysis of prospectively collected data (ClinicalTrials.gov identifier: NCT01542879), 20 patients with lymphoma (mean age, 16.4 years ± 6.4 [standard deviation]) underwent ¹⁸F-FDG PET/MRI between July 2015 and August 2019 at baseline and after induction chemotherapy. Full-dose ¹⁸F-FDG PET data (3 MBq/kg) were simulated to lower ¹⁸F-FDG doses based on the percentage of coincidence events (representing simulated 75%, 50%, 25%, 12.5%, and 6.25% ¹⁸F-FDG dose [hereafter referred to as 75%_Sim, 50%_Sim, 25%_Sim, 12.5%_Sim, and 6.25%_Sim, respectively]). A U.S. Food and Drug Administration-approved CNN was used to augment input simulated low-dose scans to full-dose scans. For each follow-up scan after induction chemotherapy, the standardized uptake value (SUV) response score was calculated as the maximum SUV (SUV_max) of the tumor normalized to the mean liver SUV; tumor response was classified as adequate or inadequate. Sensitivity and specificity in the detection of correct response status were computed using full-dose PET as the reference standard.

Results

With decreasing simulated radiotracer doses, tumor SUV_max increased. A dose below 75%_Sim of the full dose led to erroneous upstaging of adequate responders to inadequate responders (43% [six of 14 patients] for 75%_Sim; 93% [13 of 14 patients] for 50%_Sim; and 100% [14 of 14 patients] below 50%_Sim; P < .05 for all). CNN-enhanced low-dose PET/MRI scans at 75%_Sim and 50%_Sim enabled correct response assessments for all patients. Use of the CNN augmentation for assessing adequate and inadequate responses resulted in identical sensitivities (100%) and specificities (100%) between the assessment of 100% full-dose PET, augmented 75%_Sim, and augmented 50%_Sim images.

Conclusion

CNN enhancement of PET/MRI scans may enable 50% ¹⁸F-FDG dose reduction with correct treatment response assessment of children and young adults with lymphoma.Keywords: Pediatrics, PET/MRI, Computer Applications Detection/Diagnosis, Lymphoma, Tumor Response, Whole-Body Imaging, Technology AssessmentClinical trial registration no: NCT01542879 Supplemental material is available for this article. © RSNA, 2021.

Imaging AI in Practice: A Demonstration of Future Workflow Using Integration Standards

Artificial intelligence (AI) tools are rapidly being developed for radiology and other clinical areas. These tools have the potential to dramatically change clinical practice; however, for these tools to be usable and function as intended, they must be integrated into existing radiology systems. In a collaborative effort between the Radiological Society of North America, radiologists, and imaging-focused vendors, the Imaging AI in Practice (IAIP) demonstrations were developed to show how AI tools can generate, consume, and present results throughout the radiology workflow in a simulated clinical environment. The IAIP demonstrations highlight the critical importance of semantic and interoperability standards, as well as orchestration profiles for successful clinical integration of radiology AI tools. Keywords: Computer Applications-General (Informatics), Technology Assessment © RSNA, 2021.

Prevalence of Venovenous Shunting and High-Output State Quantified with 4D Flow MRI in Patients with Fontan Circulation

PURPOSE

To assess the ability of four-dimensional (4D) flow MRI to quantify flow volume of the Fontan circuit, including the frequency and hemodynamic contribution of systemic-to-pulmonary venovenous collateral vessels.

MATERIALS AND METHODS

In this retrospective study, patients with Fontan circulation were included from three institutions (2017-2021). Flow measurements were performed at several locations along the circuit by two readers, and collateral shunt volumes were quantified. The frequency of venovenous collaterals and structural defects were tabulated from concurrent MR angiography, contemporaneous CT, or catheter angiography and related to Fontan clinical status. Statistical analysis included Pearson and Spearman correlation and Bland-Altman analysis.

RESULTS

Seventy-five patients (mean age, 20 years; range, 5-58 years; 46 female and 29 male patients) were included. Interobserver agreement was high for aortic output, pulmonary arteries, pulmonary veins, superior vena cava (Glenn shunt), and inferior vena cava (Fontan conduit) (range, ρ = 0.913-0.975). Calculated shunt volume also showed strong agreement, on the basis of the difference between aortic and pulmonary flow (ρ = 0.935). A total of 37 of 75 (49%) of the patients exhibited shunts exceeding 1.00 L/min, 81% (30 of 37) of whom had pulmonary venous or atrial flow volume step-ups and corresponding venovenous collaterals. A total of 12% of patients (nine of 75) exhibited a high-output state (>4 L/min/m2), most of whom had venovenous shunts exceeding 30% of cardiac output.

CONCLUSION

Fontan flow and venovenous shunting can be reliably quantified at 4D flow MRI; high-output states were found in a higher proportion of patients than expected, among whom venovenous collaterals were common and constituted a substantial proportion of cardiac output.Keywords: Pediatrics, MR Angiography, Cardiac, Technology Assessment, Hemodynamics/Flow Dynamics, Congenital Supplemental material is available for this article. © RSNA, 2021.

Assessing the Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging

PURPOSE

To evaluate the trustworthiness of saliency maps for abnormality localization in medical imaging.

MATERIALS AND METHODS

Using two large publicly available radiology datasets (Society for Imaging Informatics in Medicine-American College of Radiology Pneumothorax Segmentation dataset and Radiological Society of North America Pneumonia Detection Challenge dataset), the performance of eight commonly used saliency map techniques were quantified in regard to (a) localization utility (segmentation and detection), (b) sensitivity to model weight randomization, (c) repeatability, and (d) reproducibility. Their performances versus baseline methods and localization network architectures were compared, using area under the precision-recall curve (AUPRC) and structural similarity index measure (SSIM) as metrics.

RESULTS

All eight saliency map techniques failed at least one of the criteria and were inferior in performance compared with localization networks. For pneumothorax segmentation, the AUPRC ranged from 0.024 to 0.224, while a U-Net achieved a significantly superior AUPRC of 0.404 (P < .005). For pneumonia detection, the AUPRC ranged from 0.160 to 0.519, while a RetinaNet achieved a significantly superior AUPRC of 0.596 (P <.005). Five and two saliency methods (of eight) failed the model randomization test on the segmentation and detection datasets, respectively, suggesting that these methods are not sensitive to changes in model parameters. The repeatability and reproducibility of the majority of the saliency methods were worse than localization networks for both the segmentation and detection datasets.

CONCLUSION

The use of saliency maps in the high-risk domain of medical imaging warrants additional scrutiny and recommend that detection or segmentation models be used if localization is the desired output of the network.Keywords: Technology Assessment, Technical Aspects, Feature Detection, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2021.

Analysis and Evaluation of a Deep Learning Reconstruction Approach with Denoising for Orthopedic MRI

PURPOSE

To evaluate two settings (noise reduction of 50% or 75%) of a deep learning (DL) reconstruction model relative to each other and to conventional MR image reconstructions on clinical orthopedic MRI datasets.

MATERIALS AND METHODS

This retrospective study included 54 patients who underwent two-dimensional fast spin-echo MRI for hip (n = 22; mean age, 44 years ± 13 [standard deviation]; nine men) or shoulder (n = 32; mean age, 56 years ± 17; 17 men) conditions between March 2019 and June 2020. MR images were reconstructed with conventional methods and the vendor-provided and commercially available DL model applied with 50% and 75% noise reduction settings (DL 50 and DL 75, respectively). Quantitative analytics, including relative anatomic edge sharpness, relative signal-to-noise ratio (rSNR), and relative contrast-to-noise ratio (rCNR) were computed for each dataset. In addition, the image sets were randomized, blinded, and presented to three board-certified musculoskeletal radiologists for ranking based on overall image quality and diagnostic confidence. Statistical analysis was performed with a nonparametric hypothesis comparing derived quantitative metrics from each reconstruction approach. In addition, inter- and intrarater agreement analysis was performed on the radiologists' rankings.

RESULTS

Both denoising settings of the DL reconstruction showed improved edge sharpness, rSNR, and rCNR relative to the conventional reconstructions. The reader rankings demonstrated strong agreement, with both DL reconstructions outperforming the conventional approach (Gwet agreement coefficient = 0.98). However, there was lower agreement between the readers on which DL reconstruction denoising setting produced higher-quality images (Gwet agreement coefficient = 0.31 for DL 50 and 0.35 for DL 75).

CONCLUSION

The vendor-provided DL MRI reconstruction showed higher edge sharpness, rSNR, and rCNR in comparison with conventional methods; however, optimal levels of denoising may need to be further assessed.Keywords: MRI Reconstruction Method, Deep Learning, Image Analysis, Signal-to-Noise Ratio, MR-Imaging, Neural Networks, Hip, Shoulder, Physics, Observer Performance, Technology Assessment Supplemental material is available for this article. © RSNA, 2021.

Evaluation of Coronary Plaques and Stents with Conventional and Photon-counting CT: Benefits of High-Resolution Photon-counting CT

PURPOSE

To compare the performance of energy-integrating detector (EID) CT, photon-counting detector CT (PCCT), and high-resolution PCCT (HR-PCCT) for the visualization of coronary plaques and reduction of stent artifacts in a phantom model.

MATERIALS AND METHODS

An investigational scanner with EID and PCCT subsystems was used to image a coronary artery phantom containing cylindrical probes simulating different plaque compositions. The phantom was imaged with and without coronary stents using both subsystems. Images were reconstructed with a clinical cardiac kernel and an additional HR-PCCT kernel. Regions of interest were drawn around probes and evaluated for in-plane diameter and a qualitative comparison by expert readers. A linear mixed-effects model was used to compare the diameter results, and a Shrout-Fleiss intraclass correlation coefficient was used to assess consistency in the reader study.

RESULTS

Comparing in-plane diameter to the physical dimension for nonstented and stented phantoms, measurements of the HR-PCCT images were more accurate (nonstented: 4.4% ± 1.1 [standard deviation], stented: -9.4% ± 4.6) than EID (nonstented: 15.5% ± 4.0, stented: -19.5% ± 5.8) and PCCT (nonstented: 19.4% ± 2.5, stented: -18.3% ± 4.4). Our analysis of variance found diameter measurements to be different across image groups for both nonstented and stented cases (P < .001). HR-PCCT showed less change on average in percent stenosis due to the addition of a stent (-5.5%) than either EID (+90.5%) or PCCT (+313%). For both nonstented and stented phantoms, observers rated the HR-PCCT images as having higher plaque conspicuity and as being the image type that was least impacted by stent artifacts, with a high level of agreement (interclass correlation coefficient = 0.85).

CONCLUSION

Despite increased noise, HR-PCCT images were able to better visualize coronary plaques and reduce stent artifacts compared with EID or PCCT reconstructions.Keywords: CT-Spectral Imaging (Dual Energy), Phantom Studies, Cardiac, Physics, Technology Assessment© RSNA, 2021.

Assisted versus Manual Interpretation of Low-Dose CT Scans for Lung Cancer Screening: Impact on Lung-RADS Agreement

Purpose To compare the inter- and intraobserver agreement and reading times achieved when assigning Lung Imaging Reporting and Data System (Lung-RADS) categories to baseline and follow-up lung cancer screening studies by using a dedicated CT lung screening viewer with integrated nodule detection and volumetric support with those achieved by using a standard picture archiving and communication system (PACS)-like viewer. Materials and Methods Data were obtained from the National Lung Screening Trial (NLST). By using data recorded by NLST radiologists, scans were assigned to Lung-RADS categories. For each Lung-RADS category (1 or 2, 3, 4A, and 4B), 40 CT scans (20 baseline scans and 20 follow-up scans) were randomly selected for 160 participants (median age, 61 years; interquartile range, 58-66 years; 61 women) in total. Seven blinded observers independently read all CT scans twice in a randomized order with a 2-week washout period: once by using the standard PACS-like viewer and once by using the dedicated viewer. Observers were asked to assign a Lung-RADS category to each scan and indicate the risk-dominant nodule. Inter- and intraobserver agreement was analyzed by using Fleiss κ values and Cohen weighted κ values, respectively. Reading times were compared by using a Wilcoxon signed rank test. Results The interobserver agreement was moderate for the standard viewer and substantial for the dedicated viewer, with Fleiss κ values of 0.58 (95% CI: 0.55, 0.60) and 0.66 (95% CI: 0.64, 0.68), respectively. The intraobserver agreement was substantial, with a mean Cohen weighted κ value of 0.67. The median reading time was significantly reduced from 160 seconds with the standard viewer to 86 seconds with the dedicated viewer (P < .001). Conclusion Lung-RADS interobserver agreement increased from moderate to substantial when using the dedicated CT lung screening viewer. The median reading time was substantially reduced when scans were read by using the dedicated CT lung screening viewer. Keywords: CT, Thorax, Lung, Computer Applications-Detection/Diagnosis, Observer Performance, Technology Assessment Supplemental material is available for this article. © RSNA, 2021.

Assessment of Early Response to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer Using Amide Proton Transfer-weighted Chemical Exchange Saturation Transfer MRI: A Pilot Study

Purpose To determine if amide proton transfer-weighted chemical exchange saturation transfer (APT_W CEST) MRI is useful in the early assessment of treatment response in persons with triple-negative breast cancer (TNBC). Materials and Methods In this prospective study, a total of 51 participants (mean age, 51 years [range, 26-79 years]) with TNBC were included who underwent APT_W CEST MRI with 0.9- and 2.0-µT saturation power performed at baseline, after two cycles (C2), and after four cycles (C4) of neoadjuvant systemic therapy (NAST). Imaging was performed between January 31, 2019, and November 11, 2019, and was a part of a clinical trial (registry number NCT02744053). CEST MR images were analyzed using two methods-magnetic transfer ratio asymmetry (MTR_asym) and Lorentzian line shape fitting. The APT_W CEST signals at baseline, C2, and C4 were compared for 51 participants to evaluate the saturation power levels and analysis methods. The APT_W CEST signals and their changes during NAST were then compared for the 26 participants with pathology reports for treatment response assessment. Results A significant APT_W CEST signal decrease was observed during NAST when acquisition at 0.9-µT saturation power was paired with Lorentzian line shape fitting analysis and when the acquisition at 2.0 µT was paired with MTR_asym analysis. Using 0.9-µT saturation power and Lorentzian line shape fitting, the APT_W CEST signal at C2 was significantly different from baseline in participants with pathologic complete response (pCR) (3.19% vs 2.43%; P = .03) but not with non-pCR (2.76% vs 2.50%; P > .05). The APT_W CEST signal change was not significant between pCR and non-pCR at all time points. Conclusion Quantitative APT_W CEST MRI depended on optimizing acquisition saturation powers and analysis methods. APT_W CEST MRI monitored treatment effects but did not differentiate participants with TNBC who had pCR from those with non-pCR. © RSNA, 2021 Clinical trial registration no. NCT02744053 Supplemental material is available for this article.Keywords Molecular Imaging-Cancer, Molecular Imaging-Clinical Translation, MR-Imaging, Breast, Technical Aspects, Tumor Response, Technology Assessment.

Do We Expect More from Radiology AI than from Radiologists?

The expectations of radiology artificial intelligence do not match expectations of radiologists in terms of performance and explainability.

Accelerated in Vivo Cardiac Diffusion-Tensor MRI Using Residual Deep Learning-based Denoising in Participants with Obesity

Purpose

To develop and assess a residual deep learning algorithm to accelerate in vivo cardiac diffusion-tensor MRI (DT-MRI) by reducing the number of averages while preserving image quality and DT-MRI parameters.

Materials and Methods

In this prospective study, a denoising convolutional neural network (DnCNN) for DT-MRI was developed; a total of 26 participants, including 20 without obesity (body mass index [BMI] < 30 kg/m²; mean age, 28 years ± 3 [standard deviation]; 11 women) and six with obesity (BMI ≥ 30 kg/m²; mean age, 48 years ± 11; five women), were recruited from June 19, 2019, to July 29, 2020. DT-MRI data were constructed at four averages (4Av), two averages (2Av), and one average (1Av) without and with the application of the DnCNN (4Av_DnCNN, 2Av_DnCNN, 1Av_DnCNN). All data were compared against the reference DT-MRI data constructed at eight averages (8Av). Image quality, characterized by using the signal-to-noise ratio (SNR) and structural similarity index (SSIM), and the DT-MRI parameters of mean diffusivity (MD), fractional anisotropy (FA), and helix angle transmurality (HAT) were quantified.

Results

No differences were found in image quality or DT-MRI parameters between the accelerated 4Av_DnCNN DT-MRI and the reference 8Av DT-MRI data for the SNR (29.1 ± 2.7 vs 30.5 ± 2.9), SSIM (0.97 ± 0.01), MD (1.3 µm²/msec ± 0.1 vs 1.31 µm²/msec ± 0.11), FA (0.32 ± 0.05 vs 0.30 ± 0.04), or HAT (1.10°/% ± 0.13 vs 1.11°/% ± 0.09). The relationship of a higher MD and lower FA and HAT in individuals with obesity compared with individuals without obesity in reference 8Av DT-MRI measurements was retained in 4Av_DnCNN and 2Av_DnCNN DT-MRI measurements but was not retained in 4Av or 2Av DT-MRI measurements.

Conclusion

Cardiac DT-MRI can be performed at an at least twofold-accelerated rate by using DnCNN to preserve image quality and DT-MRI parameter quantification.Keywords: Adults, Cardiac, Obesity, Technology Assessment, MR-Diffusion Tensor Imaging, Heart, Tissue CharacterizationSupplemental material is available for this article.© RSNA, 2021.

Feasibility of Simulated Postcontrast MRI of Glioblastomas and Lower-Grade Gliomas by Using Three-dimensional Fully Convolutional Neural Networks

PURPOSE

To evaluate the feasibility and accuracy of simulated postcontrast T1-weighted brain MR images generated by using precontrast MR images in patients with brain glioma.

MATERIALS AND METHODS

In this retrospective study, a three-dimensional deep convolutional neural network was developed to simulate T1-weighted postcontrast images from eight precontrast sequences in 400 patients (mean age, 57 years; 239 men; from 2015 to 2020), including 332 with glioblastoma and 68 with lower-grade gliomas. Performance was evaluated by using quantitative image similarity and error metrics and enhancing tumor overlap analysis. Performance was also assessed on a multicenter external dataset (n = 286 from the 2019 Multimodal Brain Tumor Segmentation Challenge; mean age, 60 years; ratio of men to women unknown) by using transfer learning. A subset of cases was reviewed by neuroradiologist readers to assess whether simulated images affected the ability to determine the tumor grade.

RESULTS

Simulated whole-brain postcontrast images were both qualitatively and quantitatively similar to the real postcontrast images in terms of quantitative image similarity (structural similarity index of 0.84 ± 0.05), pixelwise error (symmetric mean absolute percent error of 3.65%), and enhancing tumor compartment overlap (Dice coefficient, 0.65 ± 0.25). Similar results were achieved with the external dataset (Dice coefficient, 0.62 ± 0.27). There was no difference in the ability of the neuroradiologist readers to determine the tumor grade in real versus simulated images (accuracy, 87.7% vs 90.6%; P = .87).

CONCLUSION

The developed model was capable of producing simulated postcontrast T1-weighted MR images that were similar to real acquired images as determined by both quantitative analysis and radiologist assessment.Keywords: MR-Contrast Agent, MR-Imaging, CNS, Brain/Brain Stem, Contrast Agents-Intravenous, Neoplasms-Primary, Experimental Investigations, Technology Assessment, Supervised Learning, Transfer Learning, Convolutional Neural Network, Deep Learning Algorithms, Machine Learning Algorithms Supplemental material is available for this article. © RSNA, 2021.

A Framework for Sharing Radiation Dose Distribution Maps in the Electronic Medical Record for Improving Multidisciplinary Patient Management

Radiation oncology practices use a suite of dedicated software and hardware that are not common to other medical subspecialties, making radiation treatment history inaccessible to colleagues. A radiation dose distribution map is generated for each patient internally that allows for visualization of the dose given to each anatomic structure volumetrically; however, this crucial information is not shared systematically to multidisciplinary medical, surgery, and radiology colleagues. A framework was developed in which dose distribution volumes are uploaded onto the medical center's picture archiving and communication system (PACS) to rapidly retrieve and review exactly where, when, and to what dose a lesion or structure was treated. The ability to easily visualize radiation therapy information allows radiology clinics to incorporate radiation dose into image interpretation without direct access to radiation oncology planning software and data. Tumor board discussions are simplified by incorporating radiation therapy information collectively in real time, and daily onboard imaging can also be uploaded while a patient is still undergoing radiation therapy. Placing dose distribution information into PACS facilitates central access into the electronic medical record and provides a succinct visual summary of a patient's radiation history for all medical providers. More broadly, the radiation dose map provides greater visibility and facilitates incorporation of a patient's radiation history to improve oncologic decision making and patient outcomes. Keywords: Brain/Brain Stem, CNS, MRI, Neuro-Oncology, Radiation Effects, Radiation Therapy, Radiation Therapy/Oncology, Radiosurgery, Skull Base, Spine, Technology Assessment Supplemental material is available for this article. © RSNA, 2021 See also commentary by Khandelwal and Scarboro in this issue.

Principles of pharmacoeconomic analysis: the case of pharmacist-led interventions

In the past years, several factors such as evidence-based healthcare culture, quality-linked incentives, and patient-centered actions, associated with an important increase of financial constraints and pressures on healthcare budgets, resulted in a growing interest by policy-makers in enlarging pharmacists' roles in care. Numerous studies have demonstrated positive therapeutic outcomes associated with pharmaceutical services in a wide array of diseases. Yet, the evidence of the economic impact of the pharmacist in decreasing total health expenditures, unnecessary care, and societal costs relies on well-performed, reliable, and transparent economic evaluations, which are scarce. Pharmacoeconomics is a branch of health economics that usually focuses on balancing the costs and benefits of an intervention towards the use of limited resources, aiming at maximizing value to patients, healthcare payers and society through data driven decision making. These decisions can be guide by a health technology assessment (HTA) process that inform governmental players about medical, social, and economic implications of development, diffusion, and use of health technologies - including clinical pharmacy interventions. This paper aims to provide an overview of the important concepts in costing in healthcare, including studies classification according to the type of analysis method (e.g. budget-impact analysis, cost-minimization analysis, cost-effectiveness analysis, cost-utility analysis), types of costs (e.g. direct, indirect and intangible costs) and outcomes (e.g. events prevented, quality adjusted life year - QALY, disability adjusted life year - DALY). Other key components of an economic evaluation such as the models' perspective, time horizon, modelling approaches (e.g. decision trees or simulation models as the Markov model) and sensitivity analysis are also briefly covered. Finally, we discuss the methodological issues for the identification, measurement and valuation of costs and benefits of pharmacy services, and suggest some recommendations for future studies, including the use of Value of Assessment Frameworks.

Impact of the Fourth Industrial Revolution on the Health Sector: A Qualitative Study

Objectives

The Fourth Industrial Revolution is changing the way health is understood, transforming the methods of treatment and diagnosis as well as the relationship between health professionals and patients and altering the management and organization of health systems. The main objective of this study was to explore the impact that the Fourth Industrial Revolution is having on the health sector.

Methods

Conducting interviews consisting of four questions with 10 professionals who had experience working in the health sector to gain their insights and to obtain information to meet the general objective of the study as well as its specific objectives.

Results

From the analysis of the respondents’ responses, it was possible to create five dimensions developed by the topics most addressed by respondents, namely, impact on healthcare efficiency and effectiveness, impact on government action, impact on human resources, impact on health system organization, and financial impact on the health sector.

Conclusions

Although the Fourth Industrial Revolution is still at an early stage, it has been concluded that it is having a major positive impact on the health sector. For the effective and efficient implementation of these disruptive technologies, a global interaction between governments, health professionals, stakeholders, and society is essential to make this change possible.

Measuring the Intention of Using Augmented Reality Technology in the Health Domain

Augmented Reality technology can provide useful tools and devices to support healthcare services. The aim of this study is to investigate the intention of IT and health care scientists' to use Augmented Reality technology in Healthcare. A survey was conducted using a questionnaire based on a theoretical research model. According to the results, the participants seem to have positive perception about using the Augmented Reality technology in health domain, and they intend to use it.

AGING AND HEALTH TECHNOLOGY ASSESSMENT: AN IDEA WHOSE TIME HAS COME

OBJECTIVES

With the increase in technologies to support an aging population, health technology assessment (HTA) of aging-related technologies warrants special consideration. At Health Technology Assessment international (HTAi) 2016 and HTAi 2017, an international panel explored interests in HTA focused on aging.

METHODS

Panelists from five countries shared the state of aging and HTA in their countries. Opportunities were provided for participants to discuss and rate the themes identified by the panelists.

RESULTS

In 2016, the highest ranked themes were: (i) identifying unmet needs of older adults that could be met by technology-how can HTA help?; (ii) differences in assessment of aging-related technologies-what is the scope?; and (iii) involvement of older adults and caregivers. These themes became the starting point for discussion in 2017, for which the highest ranked themes were: (i) identification of challenges in HTA and aging; and (ii) approaches to advancing effectiveness of HTA for aging.

CONCLUSION

These discussions allowed for examination of future directions for HTA and aging: engagement of older adults to inform the agenda of HTA and the broader public policy enterprise; a systems approach to thinking about needs of older persons should support the type and level of care desired by the individual rather than the health institutions, and HTA should reflect these desires when evaluating technological aides; and there is potential for health information systems and "big data" to support HTA activities that assess usability of technologies for older adults. We hope to build on the momentum of this community to continue exploring opportunities for aging and HTA.

Responsible pricing in value-based assessment of cancer drugs: real-world data are an inevitable addition to select meaningful new cancer treatments

Recently, NICE was given the task of governing the Cancer Drug Fund (CDF) in the UK as the latter was criticized for allowing too many insufficiently tested drugs to be covered [1, 2]. The CDF was initiated in 2012, but immediately received criticism from several health economists because of the rather strict coverage criteria that are commonly used by NICE for most other health services in the NHS. This led to questions about the use of different reimbursement criteria (why have a different fund otherwise?) for expensive cancer drugs. Such a separate fund would potentially take away large amounts of the collective health budget. This led to questions about the use of different reimbursement criteria (why have a different fund otherwise?) for expensive cancer drugs compared to other technologies. This is just one example of discussions that are taking place in many countries on the issue of drug coverage policies. This development takes place against a background of increasingly intense discussion on pricing and affordability of (new) cancer drugs, the responsible behavior of pharmaceutical companies that spend public resources for R&D, and the lack of transparency in pricing and R&D expenditure in combination with profit margins of sometimes up to 20%. We argue that Real-World Evidence (RWE) may play a much greater and, on occasion, pivotal role in developing sustainable cancer care, because it allows much better estimates of actual drug use and costs and increases transparency in health outcomes.

Augmented Reality in Nursing: Designing a Framework for a Technology Assessment

During the last decade, research emphasized the wide range of possibilities of augmented reality (AR). At the same time, Information Technology usage in nursing increased. The question occurs if AR can have reasonable fields of application in nursing. Nursing hinges strongly on the emotional and physical relationship between patients and their nurses. This may lead to ethical conflicts while implementing AR leading to special challenges. Therefore the realization of a technology assessment (TA) seams to be reasonable. We designed a framework for a TA of AR in nursing through workshops with nursing scientists and practical partners. The framework is designed to address ethical aspects of AR in nursing through techno-ethical scenarios, a plausibility assessment, and a participatory approach.

Cost-effectiveness modelling in diagnostic imaging: a stepwise approach

Abstract

Diagnostic imaging (DI) is the fastest growing sector in medical expenditures and takes a central role in medical decision-making. The increasing number of various and new imaging technologies induces a growing demand for cost-effectiveness analysis (CEA) in imaging technology assessment. In this article we provide a comprehensive framework of direct and indirect effects that should be considered for CEA in DI, suitable for all imaging modalities. We describe and explain the methodology of decision analytic modelling in six steps aiming to transfer theory of CEA to clinical research by demonstrating key principles of CEA in a practical approach. We thereby provide radiologists with an introduction to the tools necessary to perform and interpret CEA as part of their research and clinical practice.

Key Points

• DI influences medical decision making, affecting both costs and health outcome.

• This article provides a comprehensive framework for CEA in DI.

• A six-step methodology for conducting and interpreting cost-effectiveness modelling is proposed.

Utility of a genomic-based, personalized medicine test in patients presenting with symptoms suggesting coronary artery disease

PURPOSE

Better methods are needed to assess patients presenting with symptoms suggestive of obstructive coronary artery disease (CAD). We hypothesized that the use of a gene expression score (GES) would lead to a change in the diagnostic evaluation.

METHODS

The Primary Care Providers Use of a Gene Expression Test in Coronary Artery Disease Diagnosis (IMPACT-PCP) trial (clinical trial identifier NCT01594411, clinicaltrials.gov) was a prospective study of stable, nonacute, nondiabetic patients presenting with chest pain and related symptoms at 4 primary care practices. All patients underwent GES testing, with clinicians documenting their planned diagnostic strategy both before and after GES. The GES was derived from a peripheral blood draw measuring expression of 23 genes and has been shown to have a 96% negative predictive value for excluding the diagnosis of obstructive CAD.

RESULTS

Of the 251 study patients, 140 were women (56%); the participants had a mean age of 56 years (standard deviation, 13.0) and a mean body mass index of 30 mg/kg(2) (standard deviation, 6.7). The mean GES was 16 (range, 1-38), and 127 patients (51%) had a low GES ([ltqeu]15). A change in the diagnostic testing pattern before and after GES testing was noted in 145 of 251 patients (58% observed vs. 10% predefined expected change; P < .001).

CONCLUSIONS

Incorporation of the GES into the diagnostic workup showed clinical utility above and beyond conventional clinical factors by optimizing the patient's diagnostic evaluation.

Longitudinal disease detection rates for the evaluation of disease detection technologies with application in high-risk breast cancer screening

CONTEXT

This study presents a longitudinal simulation of disease screening at a variety of different test sensitivities.

AIMS

It is demonstrated that the difference between the performance of high quality tests and poor quality tests are relatively small in terms of the commonly used longitudinally measured disease detection rate.

STATISTICAL ANALYSIS

This simulation study is focused on the screening of patients at high-risk for breast cancer and thus used plausible rates of new cases of disease and initial disease prevalence for this population.

RESULTS AND CONCLUSIONS

The effects of varying the rate at which the disease enters the population and the initial disease prevalence is also discussed and was determined to not affect this study's conclusions regarding the inappropriateness of the use of the longitudinally measured disease detection rate for the evaluation of screening technologies.