The University of California San Francisco Adult Longitudinal Post-Treatment Diffuse Glioma (UCSF-ALPTDG) MRI Dataset.

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. The University of California San Francisco Adult Longitudinal Post-Treatment Diffuse Glioma (UCSF-ALPTDG) MRI dataset is a publicly available annotated dataset featuring multimodal brain MRIs from 298 patients with diffuse gliomas taken at two consecutive follow-ups (596 scans total), with corresponding clinical history and expert voxelwise annotations. ©RSNA, 2024.

The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) MRI Dataset.

Supplemental material is available for this article.

Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNA.

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. This article is simultaneously published in Insights into Imaging (DOI 10.1186/s13244-023-01541-3), Journal of Medical Imaging and Radiation Oncology (DOI 10.1111/1754-9485.13612), Canadian Association of Radiologists Journal (DOI 10.1177/08465371231222229), Journal of the American College of Radiology (DOI 10.1016/j.jacr.2023.12.005), and Radiology: Artificial Intelligence (DOI 10.1148/ryai.230513). Keywords: Artificial Intelligence, Radiology, Automation, Machine Learning Published under a CC BY 4.0 license. ©The Author(s) 2024. Editor's Note: The RSNA Board of Directors has endorsed this article. It has not undergone review or editing by this journal.

Developing, purchasing, implementing and monitoring AI tools in radiology: Practical considerations. A multi-society statement from the ACR, CAR, ESR, RANZCR & RSNA.

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools.

The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn).

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.

The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma.

Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal treatment monitoring; yet automated, objective, and quantitative tools for non-invasive assessment of meningiomas on mpMRI are lacking. The BraTS meningioma 2023 challenge will provide a community standard and benchmark for state-of-the-art automated intracranial meningioma segmentation models based on the largest expert annotated multilabel meningioma mpMRI dataset to date. Challenge competitors will develop automated segmentation models to predict three distinct meningioma sub-regions on MRI including enhancing tumor, non-enhancing tumor core, and surrounding nonenhancing T2/FLAIR hyperintensity. Models will be evaluated on separate validation and held-out test datasets using standardized metrics utilized across the BraTS 2023 series of challenges including the Dice similarity coefficient and Hausdorff distance. The models developed during the course of this challenge will aid in incorporation of automated meningioma MRI segmentation into clinical practice, which will ultimately improve care of patients with meningioma.

Deep Learning to Simulate Contrast-enhanced Breast MRI of Invasive Breast Cancer.

Background There is increasing interest in noncontrast breast MRI alternatives for tumor visualization to increase the accessibility of breast MRI. Purpose To evaluate the feasibility and accuracy of generating simulated contrast-enhanced T1-weighted breast MRI scans from precontrast MRI sequences in biopsy-proven invasive breast cancer with use of deep learning. Materials and Methods Women with invasive breast cancer and a contrast-enhanced breast MRI examination that was performed for initial evaluation of the extent of disease between January 2015 and December 2019 at a single academic institution were retrospectively identified. A three-dimensional, fully convolutional deep neural network simulated contrast-enhanced T1-weighted breast MRI scans from five precontrast sequences (T1-weighted non-fat-suppressed [FS], T1-weighted FS, T2-weighted FS, apparent diffusion coefficient, and diffusion-weighted imaging). For qualitative assessment, four breast radiologists (with 3-15 years of experience) blinded to whether the method of contrast was real or simulated assessed image quality (excellent, acceptable, good, poor, or unacceptable), presence of tumor enhancement, and maximum index mass size by using 22 pairs of real and simulated contrast-enhanced MRI scans. Quantitative comparison was performed using whole-breast similarity and error metrics and Dice coefficient analysis of enhancing tumor overlap. Results Ninety-six MRI examinations in 96 women (mean age, 52 years ± 12 [SD]) were evaluated. The readers assessed all simulated MRI scans as having the appearance of a real MRI scan with tumor enhancement. Index mass sizes on real and simulated MRI scans demonstrated good to excellent agreement (intraclass correlation coefficient, 0.73-0.86; P < .001) without significant differences (mean differences, -0.8 to 0.8 mm; P = .36-.80). Almost all simulated MRI scans (84 of 88 [95%]) were considered of diagnostic quality (ratings of excellent, acceptable, or good). Quantitative analysis demonstrated strong similarity (structural similarity index, 0.88 ± 0.05), low voxel-wise error (symmetric mean absolute percent error, 3.26%), and Dice coefficient of enhancing tumor overlap of 0.75 ± 0.25. Conclusion It is feasible to generate simulated contrast-enhanced breast MRI scans with use of deep learning. Simulated and real contrast-enhanced MRI scans demonstrated comparable tumor sizes, areas of tumor enhancement, and image quality without significant qualitative or quantitative differences. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Slanetz in this issue. An earlier incorrect version appeared online. This article was corrected on January 17, 2023.

The University of California San Francisco Preoperative Diffuse Glioma MRI Dataset.

Supplemental material is available for this article. Keywords: Informatics, MR Diffusion Tensor Imaging, MR Perfusion, MR Imaging, Neuro-Oncology, CNS, Brain/Brain Stem, Oncology, Radiogenomics, Radiology-Pathology Integration © RSNA, 2022.

Machine Learning Tools for Image-Based Glioma Grading and the Quality of Their Reporting: Challenges and Opportunities.

Technological innovation has enabled the development of machine learning (ML) tools that aim to improve the practice of radiologists. In the last decade, ML applications to neuro-oncology have expanded significantly, with the pre-operative prediction of glioma grade using medical imaging as a specific area of interest. We introduce the subject of ML models for glioma grade prediction by remarking upon the models reported in the literature as well as by describing their characteristic developmental workflow and widely used classifier algorithms. The challenges facing these models-including data sources, external validation, and glioma grade classification methods -are highlighted. We also discuss the quality of how these models are reported, explore the present and future of reporting guidelines and risk of bias tools, and provide suggestions for the reporting of prospective works. Finally, this review offers insights into next steps that the field of ML glioma grade prediction can take to facilitate clinical implementation.

Evaluating Artificial Intelligence Systems to Guide Purchasing Decisions.

Many radiologists are considering investments in artificial intelligence (AI) to improve the quality of care for our patients. This article outlines considerations for the purchasing process beginning with performance evaluation. Practices should decide whether there is a need to independently verify performance or accept vendor-provided data. Successful implementations will consider who will receive AI results, how results will be presented, and the impact on efficiency. The article provides education on infrastructure considerations including the benefits and drawbacks of best-of-breed and platform approaches in addition to highly specialized server requirements like graphical processing unit availability. Finally, the article presents financial and quality and safety considerations, some of which are unique to AI. Examples include whether additional revenue could be obtained, as in the case of mammography, and whether an AI model unintentionally leads to reinforcing healthcare disparities.

What Physicians and Health Organizations Should Know About Mandated Imaging Appropriate Use Criteria.

The Appropriate Use Criteria Program, enacted by the Centers for Medicare & Medicaid Services in response to the Protecting Access to Medicare Act of 2014 (PAMA), aims to reduce inappropriate and unnecessary imaging by mandating use of clinical decision support (CDS) by all providers who order advanced imaging examinations (magnetic resonance imaging; computed tomography; and nuclear medicine studies, including positron emission tomography). Beginning 1 January 2020, documentation of an interaction with a certified CDS system using approved appropriate use criteria will be required on all Medicare claims for advanced imaging in all emergency department patients and outpatients as a prerequisite for payment. The Appropriate Use Criteria Program will initially cover 8 priority clinical areas, including several (such as headache and low back pain) commonly encountered by internal medicine providers. All providers and organizations that order and provide advanced imaging must understand program requirements and their options for compliance strategies. Substantial resources and planning will be needed to comply with PAMA regulations and avoid unintended negative consequences on workflow and payments. However, robust evidence supporting the desired outcome of reducing inappropriate use of advanced imaging is lacking.

Association between misty mesentery with baseline or new diagnosis of cancer: a matched cohort study.

We compared the prevalence of a baseline diagnosis of cancer in patients with and without misty mesentery (MM) and determined its association with the development of a new cancer. This was a retrospective, HIPAA-compliant, IRB-approved case-control study of 148 cases and 4:1 age- and gender-matched controls. Statistical tests included chi-square, t-test, hazard models, and C-statistic. Patients with MM were less likely to have cancer at baseline (RR=0.74, p=0.003), but more likely to develop a new malignancy on follow-up (RR=2.13, p=0.003; survival analysis HR 1.74, p=0.05). MM may confer an increased probability of later developing cancer, particularly genitourinary tumors.

Can Radiologists Learn From Airport Baggage Screening?: A Survey About Using Fictional Patients for Quality Assurance.

RATIONALE AND OBJECTIVES: For both airport baggage screeners and radiologists, low target prevalence is associated with low detection rate, a phenomenon known as "prevalence effect." In airport baggage screening, the target prevalence is artificially increased with fictional weapons that are digitally superimposed on real baggage. This strategy improves the detection rate of real weapons and also allows airport supervisors to monitor screener performance. A similar strategy using fictional patients could be applied in radiology. The purpose of this study was twofold: (1) to review the psychophysics literature regarding low target prevalence and (2) to survey radiologists' attitudes toward using fictional patients as a quality assurance tool. MATERIALS AND METHODS: We reviewed the psychophysics literature on low target prevalence and airport x-ray baggage screeners. An online survey was e-mailed to all members of the Association of University Radiologists to determine their attitudes toward using fictional patients in radiology. RESULTS: Of the 1503 Association of University Radiologists member recipients, there were 153 respondents (10% response rate). When asked whether the use of fictional patients was a good idea, the responses were as follows: disagree (44%), neutral (25%), and agree (31%). The most frequent concern was the time taken away from doing clinical work (89% of the respondents). CONCLUSIONS: The psychophysics literature supports the use of fictional targets to mitigate the prevalence effect. However, the use of fictional patients is not a popular idea among academic radiologists.

Correlate: A PACS- and EHR-integrated Tool Leveraging Natural Language Processing to Provide Automated Clinical Follow-up.

A major challenge for radiologists is obtaining meaningful clinical follow-up information for even a small percentage of cases encountered and dictated. Traditional methods, such as keeping medical record number follow-up lists, discussing cases with rounding clinical teams, and discussing cases at tumor boards, are effective at keeping radiologists informed of clinical outcomes but are time intensive and provide follow-up for a small subset of cases. To this end, the authors developed a picture archiving and communication system-accessible electronic health record (EHR)-integrated program called Correlate, which allows the user to easily enter free-text search queries regarding desired clinical follow-up information, with minimal interruption to the workflow. The program uses natural language processing (NLP) to process the query and parse relevant future clinical data from the EHR. Results are ordered in terms of clinical relevance, and the user is e-mailed a link to results when these are available for viewing. A customizable personal database of queries and results is also maintained for convenient future access. Correlate aids radiologists in efficiently obtaining useful clinical follow-up information that can improve patient care, help keep radiologists integrated with other specialties and referring physicians, and provide valuable experiential learning. The authors briefly review the history of automated clinical follow-up tools and discuss the design and function of the Correlate program, which uses NLP to perform intelligent prospective searches of the EHR. © RSNA, 2017.

Contrast Enhanced Ultrasound as a Radiation-Free Alternative to Fluoroscopic Nephrostogram for Evaluating Ureteral Patency.

PURPOSE: We compared contrast enhanced ultrasound and fluoroscopic nephrostography in the evaluation of ureteral patency following percutaneous nephrolithotomy. MATERIALS AND METHODS: This prospective cohort, noninferiority study was performed after obtaining institutional review board approval. We enrolled eligible patients with kidney and proximal ureteral stones who underwent percutaneous nephrolithotomy at our center. On postoperative day 1 patients received contrast enhanced ultrasound and fluoroscopic nephrostogram within 2 hours of each other to evaluate ureteral patency, which was the primary outcome of this study. RESULTS: A total of 92 pairs of imaging studies were performed in 82 patients during the study period. Five study pairs were excluded due to technical errors that prevented imaging interpretation. Females slightly predominated over males with a mean ± SD age of 50.5 ± 15.9 years and a mean body mass index of 29.6 ± 8.6 kg/m2. Of the remaining 87 sets of studies 69 (79.3%) demonstrated concordant findings regarding ureteral patency for the 2 imaging techniques and 18 (20.7%) were discordant. The nephrostomy tube was removed on the same day in 15 of the 17 patients who demonstrated antegrade urine flow only on contrast enhanced ultrasound and they had no subsequent adverse events. No adverse events were noted related to ultrasound contrast injection. While contrast enhanced ultrasound used no ionizing radiation, fluoroscopic nephrostograms provided a mean radiation exposure dose of 2.8 ± 3.7 mGy. CONCLUSIONS: A contrast enhanced ultrasound nephrostogram can be safely performed to evaluate for ureteral patency following percutaneous nephrolithotomy. This imaging technique was mostly concordant with fluoroscopic findings. Most discordance was likely attributable to the higher sensitivity for patency of contrast enhanced ultrasound compared to fluoroscopy.

Beyond Prostate Adenocarcinoma: Expanding the Differential Diagnosis in Prostate Pathologic Conditions.

Recent advances in magnetic resonance (MR) imaging of the prostate gland have dramatically improved the ability to detect and stage adenocarcinoma of the prostate, one of the most frequently diagnosed cancers in men and one of the most frequently diagnosed pathologic conditions of the prostate gland. A wide variety of nonadenocarcinoma diseases can also be seen with MR imaging, ranging from benign to malignant diseases, as well as infectious and inflammatory manifestations. Many of these diseases have distinctive imaging features that allow differentiation from prostate acinar adenocarcinoma. Early recognition of these entities produces a more accurate differential diagnosis and may enable more expeditious clinical workup. Benign neoplasms of the prostate include plexiform neurofibroma and cystadenoma, both of which demonstrate distinctive imaging features. Stromal neoplasms of uncertain malignant potential are rare tumors of uncertain malignant potential that are often difficult to distinguish at imaging from more-malignant prostate sarcomas. Other malignant neoplasms of the prostate include urothelial carcinoma, primary prostatic carcinoid, carcinosarcoma, endometrioid or ductal adenocarcinoma, and mucinous adenocarcinoma. Prostatic infections can lead to abscesses of pyogenic, tuberculous, or fungal origins. Finally, miscellaneous idiopathic disorders of the prostate include amyloidosis, exophytic benign prostatic hyperplasia, and various congenital cysts. Considerable overlap can exist in the clinical history and imaging findings associated with these prostate pathologic conditions, and biopsy is often required for ultimate confirmation of the diagnosis. However, many diagnoses, including cystadenoma, mucinous adenocarcinoma, sarcoma, and abscesses, have distinct imaging features, which can enable the informed radiologist to identify the diagnosis and recommend appropriate clinical workup and management. (©)RSNA, 2016.

Pancreatic imaging mimics: part 1, imaging mimics of pancreatic adenocarcinoma.

OBJECTIVE: The purpose of this article is to describe the imaging features of diseases that may closely simulate pancreatic adenocarcinoma, either radiologically or pathologically. CONCLUSION: Neoplastic and inflammatory diseases that can closely simulate pancreatic adenocarcinoma include neuroendocrine tumor, metastasis to the pancreas, lymphoma, groove pancreatitis, autoimmune pancreatitis, and focal chronic pancreatitis. Atypical imaging findings that should suggest diagnoses other than adenocarcinoma include the absence of significant duct dilatation, incidental detection, hypervascularity, large size (> 5 cm), IV tumor thrombus, and intralesional ducts or cysts.