Clinical situations for which 3D printing is considered an appropriate representation or extension of data contained in a medical imaging examination: pediatric congenital heart disease conditions.

BACKGROUND: The use of medical 3D printing (focusing on anatomical modeling) has continued to grow since the Radiological Society of North America's (RSNA) 3D Printing Special Interest Group (3DPSIG) released its initial guideline and appropriateness rating document in 2018. The 3DPSIG formed a focused writing group to provide updated appropriateness ratings for 3D printing anatomical models across a variety of congenital heart disease. Evidence-based- (where available) and expert-consensus-driven appropriateness ratings are provided for twenty-eight congenital heart lesion categories. METHODS: A structured literature search was conducted to identify all relevant articles using 3D printing technology associated with pediatric congenital heart disease indications. Each study was vetted by the authors and strength of evidence was assessed according to published appropriateness ratings. RESULTS: Evidence-based recommendations for when 3D printing is appropriate are provided for pediatric congenital heart lesions. Recommendations are provided in accordance with strength of evidence of publications corresponding to each cardiac clinical scenario combined with expert opinion from members of the 3DPSIG. CONCLUSIONS: This consensus appropriateness ratings document, created by the members of the RSNA 3DPSIG, provides a reference for clinical standards of 3D printing for pediatric congenital heart disease clinical scenarios.

METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII.

PURPOSE: To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research quality of radiomics studies. METHODS: We conducted an online modified Delphi study with a group of international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members to identify the items to be voted; and Stage#3, four rounds of the modified Delphi exercise by panelists to determine the items eligible for the METRICS and their weights. The consensus threshold was 75%. Based on the median ranks derived from expert panel opinion and their rank-sum based conversion to importance scores, the category and item weights were calculated. RESULT: In total, 59 panelists from 19 countries participated in selection and ranking of the items and categories. Final METRICS tool included 30 items within 9 categories. According to their weights, the categories were in descending order of importance: study design, imaging data, image processing and feature extraction, metrics and comparison, testing, feature processing, preparation for modeling, segmentation, and open science. A web application and a repository were developed to streamline the calculation of the METRICS score and to collect feedback from the radiomics community. CONCLUSION: In this work, we developed a scoring tool for assessing the methodological quality of the radiomics research, with a large international panel and a modified Delphi protocol. With its conditional format to cover methodological variations, it provides a well-constructed framework for the key methodological concepts to assess the quality of radiomic research papers. CRITICAL RELEVANCE STATEMENT: A quality assessment tool, METhodological RadiomICs Score (METRICS), is made available by a large group of international domain experts, with transparent methodology, aiming at evaluating and improving research quality in radiomics and machine learning. KEY POINTS: • A methodological scoring tool, METRICS, was developed for assessing the quality of radiomics research, with a large international expert panel and a modified Delphi protocol. • The proposed scoring tool presents expert opinion-based importance weights of categories and items with a transparent methodology for the first time. • METRICS accounts for varying use cases, from handcrafted radiomics to entirely deep learning-based pipelines. • A web application has been developed to help with the calculation of the METRICS score ( https://metricsscore.github.io/metrics/METRICS.html ) and a repository created to collect feedback from the radiomics community ( https://github.com/metricsscore/metrics ).

Clinical situations for which 3D Printing is considered an appropriate representation or extension of data contained in a medical imaging examination: vascular conditions.

BACKGROUND: Medical three-dimensional (3D) printing has demonstrated utility and value in anatomic models for vascular conditions. A writing group composed of the Radiological Society of North America (RSNA) Special Interest Group on 3D Printing (3DPSIG) provides appropriateness recommendations for vascular 3D printing indications. METHODS: A structured literature search was conducted to identify all relevant articles using 3D printing technology associated with vascular indications. Each study was vetted by the authors and strength of evidence was assessed according to published appropriateness ratings. RESULTS: Evidence-based recommendations for when 3D printing is appropriate are provided for the following areas: aneurysm, dissection, extremity vascular disease, other arterial diseases, acute venous thromboembolic disease, venous disorders, lymphedema, congenital vascular malformations, vascular trauma, vascular tumors, visceral vasculature for surgical planning, dialysis access, vascular research/development and modeling, and other vasculopathy. Recommendations are provided in accordance with strength of evidence of publications corresponding to each vascular condition combined with expert opinion from members of the 3DPSIG. CONCLUSION: This consensus appropriateness ratings document, created by the members of the 3DPSIG, provides an updated reference for clinical standards of 3D printing for the care of patients with vascular conditions.

Radiological Society of North America (RSNA) 3D Printing Special Interest Group (SIG) clinical situations for which 3D printing is considered an appropriate representation or extension of data contained in a medical imaging examination: breast conditions.

The use of medical 3D printing has expanded dramatically for breast diseases. A writing group composed of the Radiological Society of North America (RSNA) Special Interest Group on 3D Printing (SIG) provides updated appropriateness criteria for breast 3D printing in various clinical scenarios. Evidence-based appropriateness criteria are provided for the following clinical scenarios: benign breast lesions and high-risk breast lesions, breast cancer, breast reconstruction, and breast radiation (treatment planning and radiation delivery).

Ontologies in the New Computational Age of Radiology: RadLex for Semantics and Interoperability in Imaging Workflows.

From basic research to the bedside, precise terminology is key to advancing medicine and ensuring optimal and appropriate patient care. However, the wide spectrum of diseases and their manifestations superimposed on medical team-specific and discipline-specific communication patterns often impairs shared understanding and the shared use of common medical terminology. Common terms are currently used in medicine to ensure interoperability and facilitate integration of biomedical information for clinical practice and emerging scientific and educational applications alike, from database integration to supporting basic clinical operations such as billing. Such common terminologies can be provided in ontologies, which are formalized representations of knowledge in a particular domain. Ontologies unambiguously specify common concepts and describe the relationships between those concepts by using a form that is mathematically precise and accessible to humans and machines alike. RadLex® is a key RSNA initiative that provides a shared domain model, or ontology, of radiology to facilitate integration of information in radiology education, clinical care, and research. As the contributions of the computational components of common radiologic workflows continue to increase with the ongoing development of big data, artificial intelligence, and novel image analysis and visualization tools, the use of common terminologies is becoming increasingly important for supporting seamless computational resource integration across medicine. This article introduces ontologies, outlines the fundamental semantic web technologies used to create and apply RadLex, and presents examples of RadLex applications in everyday radiology and research. It concludes with a discussion of emerging applications of RadLex, including artificial intelligence applications. © RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Utility and Costs During the Initial Year of 3D Printing in an Academic Hospital.

OBJECTIVE: There is a paucity of utility and cost data regarding the launch of 3D printing in a hospital. The objective of this project is to benchmark utility and costs for radiology-based in-hospital 3D printing of anatomic models in a single, adult academic hospital. METHODS: All consecutive patients for whom 3D printed anatomic models were requested during the first year of operation were included. All 3D printing activities were documented by the 3D printing faculty and referring specialists. For patients who underwent a procedure informed by 3D printing, clinical utility was determined by the specialist who requested the model. A new metric for utility termed Anatomic Model Utility Points with range 0 (lowest utility) to 500 (highest utility) was derived from the specialist answers to Likert statements. Costs expressed in United States dollars were tallied from all 3D printing human resources and overhead. Total costs, focused costs, and outsourced costs were estimated. The specialist estimated the procedure room time saved from the 3D printed model. The time saved was converted to dollars using hospital procedure room costs. RESULTS: The 78 patients referred for 3D printed anatomic models included 11 clinical indications. For the 68 patients who had a procedure, the anatomic model utility points had an overall mean (SD) of 312 (57) per patient (range, 200-450 points). The total operation cost was $213,450. The total cost, focused costs, and outsourced costs were $2,737, $2,180, and $2,467 per model, respectively. Estimated procedure time saved had a mean (SD) of 29.9 (12.1) min (range, 0-60 min). The hospital procedure room cost per minute was $97 (theoretical $2,900 per patient saved with model). DISCUSSION: Utility and cost benchmarks for anatomic models 3D printed in a hospital can inform health care budgets. Realizing pecuniary benefit from the procedure time saved requires future research.

Inter-observer variability of expert-derived morphologic risk predictors in aortic dissection.

OBJECTIVES: Establishing the reproducibility of expert-derived measurements on CTA exams of aortic dissection is clinically important and paramount for ground-truth determination for machine learning. METHODS: Four independent observers retrospectively evaluated CTA exams of 72 patients with uncomplicated Stanford type B aortic dissection and assessed the reproducibility of a recently proposed combination of four morphologic risk predictors (maximum aortic diameter, false lumen circumferential angle, false lumen outflow, and intercostal arteries). For the first inter-observer variability assessment, 47 CTA scans from one aortic center were evaluated by expert-observer 1 in an unconstrained clinical assessment without a standardized workflow and compared to a composite of three expert-observers (observers 2-4) using a standardized workflow. A second inter-observer variability assessment on 30 out of the 47 CTA scans compared observers 3 and 4 with a constrained, standardized workflow. A third inter-observer variability assessment was done after specialized training and tested between observers 3 and 4 in an external population of 25 CTA scans. Inter-observer agreement was assessed with intraclass correlation coefficients (ICCs) and Bland-Altman plots. RESULTS: Pre-training ICCs of the four morphologic features ranged from 0.04 (-0.05 to 0.13) to 0.68 (0.49-0.81) between observer 1 and observers 2-4 and from 0.50 (0.32-0.69) to 0.89 (0.78-0.95) between observers 3 and 4. ICCs improved after training ranging from 0.69 (0.52-0.87) to 0.97 (0.94-0.99), and Bland-Altman analysis showed decreased bias and limits of agreement. CONCLUSIONS: Manual morphologic feature measurements on CTA images can be optimized resulting in improved inter-observer reliability. This is essential for robust ground-truth determination for machine learning models. KEY POINTS: • Clinical fashion manual measurements of aortic CTA imaging features showed poor inter-observer reproducibility. • A standardized workflow with standardized training resulted in substantial improvements with excellent inter-observer reproducibility. • Robust ground truth labels obtained manually with excellent inter-observer reproducibility are key to develop reliable machine learning models.

Medical 3D Printing Dimensional Accuracy for Multi-pathological Anatomical Models 3D Printed Using Material Extrusion.

Medical 3D printing of anatomical models is being increasingly applied in healthcare facilities. The accuracy of such 3D-printed anatomical models is an important aspect of their overall quality control. The purpose of this research was to test whether the accuracy of a variety of anatomical models 3D printed using Material Extrusion (MEX) lies within a reasonable tolerance level, defined as less than 1-mm dimensional error. Six medical models spanning across anatomical regions (musculoskeletal, neurological, abdominal, cardiovascular) and sizes (model volumes ranging from ~ 4 to 203 cc) were chosen for the primary study. Three measurement landing blocks were strategically designed within each of the six medical models to allow high-resolution caliper measurements. An 8-cc reference cube was printed as the 7th model in the primary study. In the secondary study, the effect of model rotation and scale was assessed using two of the models from the first study. All models were 3D printed using an Ultimaker 3 printer in triplicates. All absolute measurement errors were found to be less than 1 mm with a maximum error of 0.89 mm. The maximum relative error was 2.78%. The average absolute error was 0.26 mm, and the average relative error was 0.71% in the primary study, and the results were similar in the secondary study with an average absolute error of 0.30 mm and an average relative error of 0.60%. The relative errors demonstrated certain patterns in the data, which were explained based on the mechanics of MEX 3D printing. Results indicate that the MEX process, when carefully assessed on a case-by-case basis, could be suitable for the 3D printing of multi-pathological anatomical models for surgical planning if an accuracy level of 1 mm is deemed sufficient for the application.

Evaluation of a deep learning method for the automated detection of supraspinatus tears on MRI.

OBJECTIVE: To evaluate if deep learning is a feasible approach for automated detection of supraspinatus tears on MRI. MATERIALS AND METHODS: A total of 200 shoulder MRI studies performed between 2015 and 2019 were retrospectively obtained from our institutional database using a balanced random sampling of studies containing a full-thickness tear, partial-thickness tear, or intact supraspinatus tendon. A 3-stage pipeline was developed comprised of a slice selection network based on a pre-trained residual neural network (ResNet); a segmentation network based on an encoder-decoder network (U-Net); and a custom multi-input convolutional neural network (CNN) classifier. Binary reference labels were created following review of radiologist reports and images by a radiology fellow and consensus validation by two musculoskeletal radiologists. Twenty percent of the data was reserved as a holdout test set with the remaining 80% used for training and optimization under a fivefold cross-validation strategy. Classification and segmentation accuracy were evaluated using area under the receiver operating characteristic curve (AUROC) and Dice similarity coefficient, respectively. Baseline characteristics in correctly versus incorrectly classified cases were compared using independent sample t-test and chi-squared. RESULTS: Test sensitivity and specificity of the classifier at the optimal Youden's index were 85.0% (95% CI: 62.1-96.8%) and 85.0% (95% CI: 62.1-96.8%), respectively. AUROC was 0.943 (95% CI: 0.820-0.991). Dice segmentation accuracy was 0.814 (95% CI: 0.805-0.826). There was no significant difference in AUROC between 1.5 T and 3.0 T studies. Sub-analysis showed superior sensitivity on full-thickness (100%) versus partial-thickness (72.5%) subgroups. DATA CONCLUSION: Deep learning is a feasible approach to detect supraspinatus tears on MRI.

A Metrics-Based Research Salary Award System and Its 9-Year Impact on Publication Productivity.

RATIONALE AND OBJECTIVES: Although metrics-based systems may incentivize academic output, no prior studies have evaluated the impact on publication metrics in academic radiology. This study presents a metrics-based system of awarding research protected time, and retrospectively evaluates its 9-year impact on publication productivity and impact factor. MATERIALS AND METHODS: Based on a metrics-based algorithm to award department funded Research Protected Time (RPT), metrics pre-RPT (2003-2009) and during the RPT period (2010-2018) from an academic radiology department were retrospectively analyzed to test the hypothesis that the RPT program resulted in higher publication productivity and journal impact factor at the departmental level and for faculty members receiving the award. Comparison was made between (1) pre-RPT and RPT periods and (2) during the RPT period, between RPT and non-RPT faculty members, for annual publication productivity normalized to faculty count (Student's t test) and median impact factor (Wilcoxon rank sum test). RESULTS: For the evaluation period of 2003-2018, 724 unique publications were identified: 15% (107/724) pre-RPT period and 85% (617/724) RPT period. Normalized annual publication productivity was higher during the RPT period compared to the Pre-RPT period (1.2 vs. 0.3, p = 0.002), and within the RPT period, higher among faculty who received RPT vs. non-RPT faculty (3.5 vs. 0.4, p = 0.002). Median impact factor was higher during the RPT period compared to pre-RPT period (2.843 vs. 2.322, p = 0.044), and within the RPT period, higher in RPT vs. non-RPT faculty (3.016 vs. 2.346, p < 0.001). CONCLUSION: The implementation of a metrics-based system of funded, research protected time, was associated with increased publication productivity and increased impact factor.

A systematic evaluation of medical 3D printing accuracy of multi-pathological anatomical models for surgical planning manufactured in elastic and rigid material using desktop inverted vat photopolymerization.

PURPOSE: The dimensional accuracy of three-dimensional (3D) printed anatomical models is essential to correctly understand spatial relationships and enable safe presurgical planning. Most recent accuracy studies focused on 3D printing of a single pathology for surgical planning. This study evaluated the accuracy of medical models across multiple pathologies, using desktop inverted vat photopolymerization (VP) to 3D print anatomic models using both rigid and elastic materials. METHODS: In the primary study, we 3D printed seven models (six anatomic models and one reference cube) with volumes ranging from ~2 to ~209 cc. The anatomic models spanned multiple pathologies (neurological, cardiovascular, abdominal, musculoskeletal). Two solid measurement landing blocks were strategically created around the pathology to allow high-resolution measurement using a digital micrometer and/or caliper. The physical measurements were compared to the designed dimensions, and further analysis was conducted regarding the observed patterns in accuracy. All of the models were printed in three resins: Elastic, Clear, and Grey Pro in the primary experiments. A full factorial block experimental design was employed and a total of 42 models were 3D printed in 21 print runs. In the secondary study, we 3D printed two of the anatomic models in triplicates selected from the previous six to evaluate the effect of 0.1 mm vs 0.05 mm layer height on the accuracy. RESULTS: In the primary experiment, all dimensional errors were less than 1 mm. The average dimensional error across the 42 models was 0.238  ±  0.219 mm and the relative error was 1.10  ±  1.13%. Results from the secondary experiments were similar with an average dimensional error of 0.252  ±  0.213 mm and relative error of 1.52%  ±  1.28% across 18 models. There was a statistically significant difference in the relative errors between the Elastic resin and Clear resin groups. We explained this difference by evaluating inverted VP 3D printing peel forces. There was a significant difference between the Solid and Hollow group of models. There was a significant difference between measurement landing blocks oriented Horizontally and Vertically. In the secondary experiments, there was no difference in accuracy between the 0.10 and 0.05 mm layer heights. CONCLUSIONS: The maximum measured error was less than 1 mm across all models, and the mean error was less than 0.26mm. Therefore, inverted VP 3D printing technology is suitable for medical 3D printing if 1 mm is considered the cutoff for clinical use cases. The 0.1 mm layer height is suitable for 3D printing accurate anatomical models for presurgical planning in a majority of cases. Elastic models, models oriented horizontally, and models that are hollow tend to have relatively higher deviation as seen from experimental results and mathematical model predictions. While clinically insignificant using a 1 mm cutoff, further research is needed to better understand the complex physical interactions in VP 3D printing which influence model accuracy.

A guideline for 3D printing terminology in biomedical research utilizing ISO/ASTM standards.

First patented in 1986, three-dimensional (3D) printing, also known as additive manufacturing or rapid prototyping, now encompasses a variety of distinct technology types where material is deposited, joined, or solidified layer by layer to create a physical object from a digital file. As 3D printing technologies continue to evolve, and as more manuscripts describing these technologies are published in the medical literature, it is imperative that standardized terminology for 3D printing is utilized. The purpose of this manuscript is to provide recommendations for standardized lexicons for 3D printing technologies described in the medical literature. For all 3D printing methods, standard general ISO/ASTM terms for 3D printing should be utilized. Additional, non-standard terms should be included to facilitate communication and reproducibility when the ISO/ASTM terms are insufficient in describing expository details. By aligning to these guidelines, the use of uniform terms for 3D printing and the associated technologies will lead to improved clarity and reproducibility of published work which will ultimately increase the impact of publications, facilitate quality improvement, and promote the dissemination and adoption of 3D printing in the medical community.

Improved Appropriateness of Advanced Diagnostic Imaging After Implementation of Clinical Decision Support Mechanism.

The Protecting Access to Medicare Act (PAMA) mandates clinical decision support mechanism (CDSM) consultation for all advanced imaging. There are a growing number of studies examining the association of CDSM use with imaging appropriateness, but a paucity of multicenter data. This observational study evaluates the association between changes in advanced imaging appropriateness scores with increasing provider exposure to CDSM. Each provider's first 200 consecutive anonymized requisitions for advanced imaging (CT, MRI, ultrasound, nuclear medicine) using a single CDSM (CareSelect, Change Healthcare) between January 1, 2017 and December 31, 2019 were collected from 288 US institutions. Changes in imaging requisition proportions among four appropriateness categories ("usually appropriate" [green], "may be appropriate" [yellow], "usually not appropriate" [red], and unmapped [gray]) were evaluated in relation to the chronological order of the requisition for each provider and total provider exposure to CDSM using logistic regression fits and Wald tests. The number of providers and requisitions included was 244,158 and 7,345,437, respectively. For 10,123 providers with ≥ 200 requisitions (2,024,600 total requisitions), the fraction of green, yellow, and red requisitions among the last 10 requisitions changed by +3.0% (95% confidence interval +2.6% to +3.4%), -0.8% (95% CI -0.5% to -1.1%), and -3.0% (95% CI 3.3% to -2.7%) in comparison with the first 10, respectively. Providers with > 190 requisitions had 8.5% (95% CI 6.3% to 10.7%) more green requisitions, 2.3% (0.7% to 3.9%) fewer yellow requisitions, and 0.5% (95% CI -1.0% to 2.0%) fewer red (not statistically significant) requisitions relative to providers with ≤ 10 requisitions. Increasing provider exposure to CDSM is associated with improved appropriateness scores for advanced imaging requisitions.

Applying Modern Virtual and Augmented Reality Technologies to Medical Images and Models.

Recent technological innovations have created new opportunities for the increased adoption of virtual reality (VR) and augmented reality (AR) applications in medicine. While medical applications of VR have historically seen greater adoption from patient-as-user applications, the new era of VR/AR technology has created the conditions for wider adoption of clinician-as-user applications. Historically, adoption to clinical use has been limited in part by the ability of the technology to achieve a sufficient quality of experience. This article reviews the definitions of virtual and augmented reality and briefly covers the history of their development. Currently available options for consumer-level virtual and augmented reality systems are presented, along with a discussion of technical considerations for their adoption in the clinical environment. Finally, a brief review of the literature of medical VR/AR applications is presented prior to introducing a comprehensive conceptual framework for the viewing and manipulation of medical images in virtual and augmented reality. Using this framework, we outline considerations for placing these methods directly into a radiology-based workflow and show how it can be applied to a variety of clinical scenarios.

Outcomes after Surgery for Early Stage Breast Cancer in Women Staged With Preoperative Breast Magnetic Resonance Imaging According to Breast Tissue Density.

PURPOSE: To determine surgical outcomes and breast cancer disease-free survival outcomes of women with early stage breast cancer with and without use of preoperative breast MRI according to breast tissue density. METHODS: Women with early stage breast cancer diagnosed from 2004 to 2009 were classified into 2 groups: 1) those with dense and heterogeneously dense breasts (DB); 2) those with nondense breasts (NDB) (scattered fibroglandular and fatty replaced tissue). The 2 groups were reviewed to determine who underwent preoperative MRI. Breast tissue density was determined with mammography according to ACR BI-RADS. Patients were compared according to tumor size, grade, stage, and treatment. Survival analysis was performed using Kaplan-Meier estimates. RESULTS: In total, 261 patients with mean follow-up of 85 months (25-133) were included: 156 DB and 105 NDB. Disease-free survival outcomes were better in the DB group in patients with MRI than in those without MRI: patients with MRI had significantly fewer local recurrences (P < 0.016) and metachronous contralateral breast cancers (P < 0.001), but this was not the case in the NDB group. Mastectomies were higher in the DB group with preoperative MRI than in those without MRI (P < 0.01), as it was in the NDB group (P > 0.05). CONCLUSIONS: Preoperative breast MRI was associated with reduced local recurrence and metachronous contralateral cancers in the DB group, but not in the NDB group; however, the DB patients with MRI had higher mastectomy rates.

Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology.

Artificial intelligence (AI) is rapidly moving from an experimental phase to an implementation phase in many fields, including medicine. The combination of improved availability of large datasets, increasing computing power, and advances in learning algorithms has created major performance breakthroughs in the development of AI applications. In the last 5 years, AI techniques known as deep learning have delivered rapidly improving performance in image recognition, caption generation, and speech recognition. Radiology, in particular, is a prime candidate for early adoption of these techniques. It is anticipated that the implementation of AI in radiology over the next decade will significantly improve the quality, value, and depth of radiology's contribution to patient care and population health, and will revolutionize radiologists' workflows. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI working group with the mandate to discuss and deliberate on practice, policy, and patient care issues related to the introduction and implementation of AI in imaging. This white paper provides recommendations for the CAR derived from deliberations between members of the AI working group. This white paper on AI in radiology will inform CAR members and policymakers on key terminology, educational needs of members, research and development, partnerships, potential clinical applications, implementation, structure and governance, role of radiologists, and potential impact of AI on radiology in Canada.