Intervention for critical aortic stenosis in Hutchinson-Gilford progeria syndrome.

Hutchinson-Gilford Progeria Syndrome (HGPS) is an ultra-rare genetic premature aging disease that is historically fatal in teenage years, secondary to severe accelerated atherosclerosis. The only approved treatment is the farnesyltransferase inhibitor lonafarnib, which improves vascular structure and function, extending average untreated lifespan of 14.5 years by 4.3 years (30%). With this longer lifespan, calcific aortic stenosis (AS) was identified as an emerging critical risk factor for cardiac death in older patients. Intervention to relieve critical AS has the potential for immediate improvement in healthspan and lifespan. However, HGPS patient-device size mismatch, pervasive peripheral arterial disease, skin and bone abnormalities, and lifelong failure to thrive present unique challenges to intervention. An international group of experts in HGPS, pediatric and adult cardiology, cardiac surgery, and pediatric critical care convened to identify strategies for successful treatment. Candidate procedures were evaluated by in-depth examination of 4 cases that typify HGPS clinical pathology. Modified transcatheter aortic valve replacement (TAVR) and left ventricular Apico-Aortic Conduit (AAC) placement were deemed high risk but viable options. Two cases received TAVR and 2 received AAC post-summit. Three were successful and 1 patient died perioperatively due to cardiovascular disease severity, highlighting the importance of intervention timing and comparative risk stratification. These breakthrough interventions for treating critical aortic stenosis in HGPS patients could rewrite the current clinical perspective on disease course by greatly improving late-stage quality of life and increasing lifespan. Expanding worldwide medical and surgical competency for this ultra-rare disease through expert information-sharing could have high impact on treatment success.

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.

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

BACKGROUND: Medical three dimensional (3D) printing is performed for neurosurgical and otolaryngologic conditions, but without evidence-based guidance on clinical appropriateness. A writing group composed of the Radiological Society of North America (RSNA) Special Interest Group on 3D Printing (SIG) provides appropriateness recommendations for neurologic 3D printing conditions. METHODS: A structured literature search was conducted to identify all relevant articles using 3D printing technology associated with neurologic and otolaryngologic conditions. Each study was vetted by the authors and strength of evidence was assessed according to published guidelines. RESULTS: Evidence-based recommendations for when 3D printing is appropriate are provided for diseases of the calvaria and skull base, brain tumors and cerebrovascular disease. Recommendations are provided in accordance with strength of evidence of publications corresponding to each neurologic condition combined with expert opinion from members of the 3D printing SIG. CONCLUSIONS: This consensus guidance document, created by the members of the 3D printing SIG, provides a reference for clinical standards of 3D printing for neurologic conditions.

Generalization optimizing machine learning to improve CT scan radiomics and assess immune checkpoint inhibitors' response in non-small cell lung cancer: a multicenter cohort study.

Background: Recent developments in artificial intelligence suggest that radiomics may represent a promising non-invasive biomarker to predict response to immune checkpoint inhibitors (ICIs). Nevertheless, validation of radiomics algorithms in independent cohorts remains a challenge due to variations in image acquisition and reconstruction. Using radiomics, we investigated the importance of scan normalization as part of a broader machine learning framework to enable model external generalizability to predict ICI response in non-small cell lung cancer (NSCLC) patients across different centers. Methods: Radiomics features were extracted and compared from 642 advanced NSCLC patients on pre-ICI scans using established open-source PyRadiomics and a proprietary DeepRadiomics deep learning technology. The population was separated into two groups: a discovery cohort of 512 NSCLC patients from three academic centers and a validation cohort that included 130 NSCLC patients from a fourth center. We harmonized images to account for variations in reconstruction kernel, slice thicknesses, and device manufacturers. Multivariable models, evaluated using cross-validation, were used to estimate the predictive value of clinical variables, PD-L1 expression, and PyRadiomics or DeepRadiomics for progression-free survival at 6 months (PFS-6). Results: The best prognostic factor for PFS-6, excluding radiomics features, was obtained with the combination of Clinical + PD-L1 expression (AUC = 0.66 in the discovery and 0.62 in the validation cohort). Without image harmonization, combining Clinical + PyRadiomics or DeepRadiomics delivered an AUC = 0.69 and 0.69, respectively, in the discovery cohort, but dropped to 0.57 and 0.52, in the validation cohort. This lack of generalizability was consistent with observations in principal component analysis clustered by CT scan parameters. Subsequently, image harmonization eliminated these clusters. The combination of Clinical + DeepRadiomics reached an AUC = 0.67 and 0.63 in the discovery and validation cohort, respectively. Conversely, the combination of Clinical + PyRadiomics failed generalizability validations, with AUC = 0.66 and 0.59. Conclusion: We demonstrated that a risk prediction model combining Clinical + DeepRadiomics was generalizable following CT scan harmonization and machine learning generalization methods. These results had similar performances to routine oncology practice using Clinical + PD-L1. This study supports the strong potential of radiomics as a future non-invasive strategy to predict ICI response in advanced NSCLC.

Medical 3D Printing Using Desktop Inverted Vat Photopolymerization: Background, Clinical Applications, and Challenges.

Medical 3D printing is a complex, highly interdisciplinary, and revolutionary technology that is positively transforming the care of patients. The technology is being increasingly adopted at the Point of Care (PoC) as a consequence of the strong value offered to medical practitioners. One of the key technologies within the medical 3D printing portfolio enabling this transition is desktop inverted Vat Photopolymerization (VP) owing to its accessibility, high quality, and versatility of materials. Several reports in the peer-reviewed literature have detailed the medical impact of 3D printing technologies as a whole. This review focuses on the multitude of clinical applications of desktop inverted VP 3D printing which have grown substantially in the last decade. The principles, advantages, and challenges of this technology are reviewed from a medical standpoint. This review serves as a primer for the continually growing exciting applications of desktop-inverted VP 3D printing in healthcare.

3D printing exposure and perception in radiology residency: survey results of radiology chief residents.

RATIONALE AND OBJECTIVES: The purpose of this study is to summarize a survey of radiology chief residents focused on 3D printing in radiology. MATERIALS AND METHODS: An online survey was distributed to chief residents in North American radiology residencies by subgroups of the Association of University Radiologists. The survey included a subset of questions focused on the clinical use of 3D printing and perceptions of the role of 3D printing and radiology. Respondents were asked to define the role of 3D printing at their institution and asked about the potential role of clinical 3D printing in radiology and radiology residencies. RESULTS: 152 individual responses from 90 programs were provided, with a 46% overall program response rate (n = 90/194 radiology residencies). Most programs had 3D printing at their institution (60%; n = 54/90 programs). Among the institutions that perform 3D printing, 33% (n = 18/54) have structured opportunities for resident contribution. Most residents (60%; n = 91/152 respondents) feel they would benefit from 3D printing exposure or educational material. 56% of residents (n = 84/151) believed clinical 3D printing should be centered in radiology departments. 22% of residents (n = 34/151) believed it would increase communication and improve relationships between radiology and surgery colleagues. A minority (5%; 7/151) believe 3D printing is too costly, time-consuming, or outside a radiologist's scope of practice. CONCLUSIONS: A majority of surveyed chief residents in accredited radiology residencies believe they would benefit from exposure to 3D printing in residency. 3D printing education and integration would be a valuable addition to current radiology residency program curricula.

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).

2022 use of coronary computed tomographic angiography for patients presenting with acute chest pain to the emergency department: An expert consensus document of the Society of cardiovascular computed tomography (SCCT): Endorsed by the American College of Radiology (ACR) and North American Society for cardiovascular Imaging (NASCI).

Coronary computed tomography angiography (CTA) improves the quality of care for patients presenting with acute chest pain (ACP) to the emergency department (ED), particularly in patients with low to intermediate likelihood of acute coronary syndrome (ACS). The Society of Cardiovascular Computed Tomography Guidelines Committee was formed to develop recommendations for acquiring, interpreting, and reporting of coronary CTA to ensure appropriate, safe, and efficient use of this modality. Because of the increasing use of coronary CTA testing for the evaluation of ACP patients, the Committee has been charged with the development of the present document to assist physicians and technologists. These recommendations were produced as an educational tool for practitioners evaluating acute chest pain patients in the ED, in the interest of developing systematic standards of practice for coronary CTA based on the best available data or broad expert consensus. Due to the highly variable nature of medical care, approaches to patient selection, preparation, protocol selection, interpretation or reporting that differs from these guidelines may represent an appropriate variation based on a legitimate assessment of an individual patient's needs.

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.

CAD-RADS™ 2.0 - 2022 Coronary Artery Disease - Reporting and Data System.: An expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Cardiology (ACC), the American College of Radiology (ACR) and the North America Society of Cardiovascular Imaging (NASCI).

Coronary Artery Disease Reporting and Data System (CAD-RADS) was created to standardize reporting system for patients undergoing coronary CT angiography (CCTA) and to guide possible next steps in patient management. The goal of this updated 2022 CAD-RADS 2.0 is to improve the initial reporting system for CCTA by considering new technical developments in Cardiac CT, including data from recent clinical trials and new clinical guidelines. The updated CAD-RADS classification will follow an established framework of stenosis, plaque burden, and modifiers, which will include assessment of lesion-specific ischemia using CT fractional-flow-reserve (CT-FFR) or myocardial CT perfusion (CTP), when performed. Similar to the method used in the original CAD-RADS version, the determinant for stenosis severity classification will be the most severe coronary artery luminal stenosis on a per-patient basis, ranging from CAD-RADS 0 (zero) for absence of any plaque or stenosis to CAD-RADS 5 indicating the presence of at least one totally occluded coronary artery. Given the increasing data supporting the prognostic relevance of coronary plaque burden, this document will provide various methods to estimate and report total plaque burden. The addition of P1 to P4 descriptors are used to denote increasing categories of plaque burden. The main goal of CAD-RADS, which should always be interpreted together with the impression found in the report, remains to facilitate communication of test results with referring physicians along with suggestions for subsequent patient management. In addition, CAD-RADS will continue to provide a framework of standardization that may benefit education, research, peer-review, artificial intelligence development, clinical trial design, population health and quality assurance with the ultimate goal of improving patient care.

ACR Appropriateness Criteria® Suspected Pulmonary Embolism: 2022 Update.

Pulmonary embolism (PE) remains a common and important clinical condition that cannot be accurately diagnosed on the basis of signs, symptoms, and history alone. The diagnosis of PE has been facilitated by technical advancements and multidetector CT pulmonary angiography, which is the major diagnostic modality currently used. Ventilation and perfusion scans remain largely accurate and useful in certain settings. MR angiography can be useful in some clinical scenarios and lower-extremity ultrasound can substitute by demonstrating deep vein thrombosis; however, if negative, further studies to exclude PE are indicated. In all cases, correlation with the clinical status, particularly with risk factors, improves not only the accuracy of diagnostic imaging but also overall utilization. Other diagnostic tests have limited roles. The ACR Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer-reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances in which peer-reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

CAD-RADS™ 2.0 - 2022 Coronary Artery Disease - Reporting and Data System An Expert Consensus Document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Cardiology (ACC), the American College of Radiology (ACR) and the North America Society of Cardiovascular Imaging (NASCI).

Coronary Artery Disease Reporting and Data System (CAD-RADS) was created to standardize reporting system for patients undergoing coronary CT angiography (CCTA) and to guide possible next steps in patient management. The goal of this updated 2022 CAD-RADS 2.0 is to improve the initial reporting system for CCTA by considering new technical developments in Cardiac CT, including data from recent clinical trials and new clinical guidelines. The updated CAD-RADS classification will follow an established framework of stenosis, plaque burden, and modifiers, which will include assessment of lesion-specific ischemia using CT fractional-flow-reserve (CT-FFR) or myocardial CT perfusion (CTP), when performed. Similar to the method used in the original CAD-RADS version, the determinant for stenosis severity classification will be the most severe coronary artery luminal stenosis on a per-patient basis, ranging from CAD-RADS 0 (zero) for absence of any plaque or stenosis to CAD-RADS 5 indicating the presence of at least one totally occluded coronary artery. Given the increasing data supporting the prognostic relevance of coronary plaque burden, this document will provide various methods to estimate and report total plaque burden. The addition of P1 to P4 descriptors are used to denote increasing categories of plaque burden. The main goal of CAD-RADS, which should always be interpreted together with the impression found in the report, remains to facilitate communication of test results with referring physicians along with suggestions for subsequent patient management. In addition, CAD-RADS will continue to provide a framework of standardization that may benefit education, research, peer-review, artificial intelligence development, clinical trial design, population health and quality assurance with the ultimate goal of improving patient care. Keywords: Coronary Artery Disease, Coronary CTA, CAD-RADS, Reporting and Data System, Stenosis Severity, Report Standardization Terminology, Plaque Burden, Ischemia Supplemental material is available for this article. This article is published synchronously in Radiology: Cardiothoracic Imaging, Journal of Cardiovascular Computed Tomography, JACC: Cardiovascular Imaging, Journal of the American College of Radiology, and International Journal for Cardiovascular Imaging. © 2022 Society of Cardiovascular Computed Tomography. Published by RSNA with permission.

CAD-RADS™ 2.0 - 2022 Coronary Artery Disease-Reporting and Data System: An Expert Consensus Document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Cardiology (ACC), the American College of Radiology (ACR), and the North America Society of Cardiovascular Imaging (NASCI).

Coronary Artery Disease Reporting and Data System (CAD-RADS) was created to standardize reporting system for patients undergoing coronary CT angiography (CCTA) and to guide possible next steps in patient management. The goal of this updated 2022 CAD-RADS 2.0 is to improve the initial reporting system for CCTA by considering new technical developments in cardiac CT, including data from recent clinical trials and new clinical guidelines. The updated CAD-RADS classification will follow an established framework of stenosis, plaque burden, and modifiers, which will include assessment of lesion-specific ischemia using CT fractional-flow-reserve (CT-FFR) or myocardial CT perfusion (CTP), when performed. Similar to the method used in the original CAD-RADS version, the determinant for stenosis severity classification will be the most severe coronary artery luminal stenosis on a per-patient basis, ranging from CAD-RADS 0 (zero) for absence of any plaque or stenosis to CAD-RADS 5 indicating the presence of at least one totally occluded coronary artery. Given the increasing data supporting the prognostic relevance of coronary plaque burden, this document will provide various methods to estimate and report total plaque burden. The addition of P1 to P4 descriptors are used to denote increasing categories of plaque burden. The main goal of CAD-RADS, which should always be interpreted together with the impression found in the report, remains to facilitate communication of test results with referring physicians along with suggestions for subsequent patient management. In addition, CAD-RADS will continue to provide a framework of standardization that may benefit education, research, peer-review, artificial intelligence development, clinical trial design, population health and quality assurance with the ultimate goal of improving patient care.

CAD-RADS™ 2.0 - 2022 Coronary Artery Disease-Reporting and Data System: An Expert Consensus Document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Cardiology (ACC), the American College of Radiology (ACR), and the North America Society of Cardiovascular Imaging (NASCI).

Coronary Artery Disease Reporting and Data System (CAD-RADS) was created to standardize reporting system for patients undergoing coronary CT angiography (CCTA) and to guide possible next steps in patient management. The goal of this updated 2022 CAD-RADS 2.0 is to improve the initial reporting system for CCTA by considering new technical developments in Cardiac CT, including data from recent clinical trials and new clinical guidelines. The updated CAD-RADS classification will follow an established framework of stenosis, plaque burden, and modifiers, which will include assessment of lesion-specific ischemia using CT fractional-flow-reserve (CT-FFR) or myocardial CT perfusion (CTP), when performed. Similar to the method used in the original CAD-RADS version, the determinant for stenosis severity classification will be the most severe coronary artery luminal stenosis on a per-patient basis, ranging from CAD-RADS 0 (zero) for absence of any plaque or stenosis to CAD-RADS 5 indicating the presence of at least one totally occluded coronary artery. Given the increasing data supporting the prognostic relevance of coronary plaque burden, this document will provide various methods to estimate and report total plaque burden. The addition of P1 to P4 descriptors are used to denote increasing categories of plaque burden. The main goal of CAD-RADS, which should always be interpreted together with the impression found in the report, remains to facilitate communication of test results with referring physicians along with suggestions for subsequent patient management. In addition, CAD-RADS will continue to provide a framework of standardization that may benefit education, research, peer-review, artificial intelligence development, clinical trial design, population health and quality assurance with the ultimate goal of improving patient care.

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.