The Rapidly-Developing Area of Radiocardiology: Principles, Complications and Applications of Radiotherapy on the Heart.

Ventricular arrhythmias are the leading cause of sudden cardiac death. Current treatment strategies for ventricular tachycardia, including antiarrhythmic drugs and catheter ablation, have limited efficacy in patients with structural heart disease. Noninvasive ablation with the use of externally applied radiation (cardiac radioablation) has emerged as a promising and novel approach to treating recurrent ventricular tachycardias. However, the heart is generally an "organ at risk" for radiation treatments, such that very little is known on the effects of radiotherapy on cardiac ultrastructure and electrophysiologic properties. Furthermore, there has been limited interaction between the fields of cardiology and radiation oncology and physics. The advent of cardiac radioablation will undoubtedly increase interactions between cardiologists, cardiac electrophysiologists, radiation oncologists and physicists. There is an important knowledge gap separating these specialties, but scientific developments, technical optimisation, and improvements depend on intense multidisciplinary collaboration. This manuscript seeks to review the basic of radiation physics and biology for cardiovascular specialists in an effort to facilitate constructive scientific and clinical collaborations to improve patient outcomes.

Natural Language Processing to Identify Pulmonary Nodules and Extract Nodule Characteristics From Radiology Reports.

BACKGROUND: There is an urgent need for population-based studies on managing patients with pulmonary nodules. RESEARCH QUESTION: Is it possible to identify pulmonary nodules and associated characteristics using an automated method? STUDY DESIGN AND METHODS: We revised and refined an existing natural language processing (NLP) algorithm to identify radiology transcripts with pulmonary nodules and greatly expanded its functionality to identify the characteristics of the largest nodule, when present, including size, lobe, laterality, attenuation, calcification, and edge. We compared NLP results with a reference standard of manual transcript review in a random test sample of 200 radiology transcripts. We applied the final automated method to a larger cohort of patients who underwent chest CT scan in an integrated health care system from 2006 to 2016, and described their demographic and clinical characteristics. RESULTS: In the test sample, the NLP algorithm had very high sensitivity (98.6%; 95% CI, 95.0%-99.8%) and specificity (100%; 95% CI, 93.9%-100%) for identifying pulmonary nodules. For attenuation, edge, and calcification, the NLP algorithm achieved similar accuracies, and it correctly identified the diameter of the largest nodule in 135 of 141 cases (95.7%; 95% CI, 91.0%-98.4%). In the larger cohort, the NLP found 217,771 reports with nodules among 717,304 chest CT reports (30.4%). From 2006 to 2016, the number of reports with nodules increased by 150%, and the mean size of the largest nodule gradually decreased from 11 to 8.9 mm. Radiologists documented the laterality and lobe (90%-95%) more often than the attenuation, calcification, and edge characteristics (11%-14%). INTERPRETATION: The NLP algorithm identified pulmonary nodules and associated characteristics with high accuracy. In our community practice settings, the documentation of nodule characteristics is incomplete. Our results call for better documentation of nodule findings. The NLP algorithm can be used in population-based studies to identify pulmonary nodules, avoiding labor-intensive chart review.

Decreased Medical Care During the COVID-19 Pandemic - A Comprehensive Analysis of Radiological Examinations.

OBJECTIVES: As a cross-section discipline within the hospital infrastructure, radiological departments might be able to provide important information regarding the impact of the COVID-19 pandemic on healthcare. The goal of this study was to quantify changes in medical care during the first wave of the pandemic using radiological examinations as a comprehensive surrogate marker and to determine potential future workload. METHODS: A retrospective analysis of all radiological examinations during the first wave of the pandemic was performed. The number of examinations was compared to time-matched control periods. Furthermore, an in-depth analysis of radiological examinations attributed to various medical specialties was conducted and postponed examinations were extrapolated to calculate additional workload in the near future. RESULTS: A total of 596,760 examinations were analyzed. Overall case volumes decreased by an average of 41 % during the shutdown compared to the control period. The most affected radiological modalities were sonography (-54 %), X-ray (-47 %) followed by MRI (-42 %). The most affected medical specialty was trauma and orthopedics (-60 % case volume) followed by general surgery (-49 %). Examination numbers increased during the post-shutdown period leading to a predicted additional workload of up to 22 %. CONCLUSION: This study shows a marked decrease in radiological examinations in total and among several core medical specialties, indicating a significant reduction in medical care during the first COVID-19 shutdown. KEY POINTS: · Number of radiological examinations decreased by 41 % during the first wave of the COVID-19 pandemic.. · Several core medical specialties were heavily affected with a reduction of case volumes up to 60 %.. · When extrapolating postponed examinations to the near future, the overall workload for radiological departments might increase up to 22 %.. CITATION FORMAT: · Fleckenstein FN, Maleitzke T, Böning G et al. Decreased Medical Care During the COVID-19 Pandemic - A Comprehensive Analysis of Radiological Examinations. Fortschr Röntgenstr 2021; 193: 937 - 946.

Radiomics to better characterize small renal masses.

PURPOSE: Radiomics is a specific field of medical research that uses programmable recognition tools to extract objective information from standard images to combine with clinical data, with the aim of improving diagnostic, prognostic, and predictive accuracy beyond standard visual interpretation. We performed a narrative review of radiomic applications that may support improved characterization of small renal masses (SRM). The main focus of the review was to identify and discuss methods which may accurately differentiate benign from malignant renal masses, specifically between renal cell carcinoma (RCC) subtypes and from angiomyolipoma without visible fat (fat-poor AML) and oncocytoma. Furthermore, prediction of grade, sarcomatoid features, and gene mutations would be of importance in terms of potential clinical utility in prognostic stratification and selecting personalised patient management strategies. METHODS: A detailed search of original articles was performed using the PubMed-MEDLINE database until 20 September 2020 to identify the English literature relevant to radiomics applications in renal tumour assessment. In total, 42 articles were included in the analysis in 3 main categories related to SRM: prediction of benign versus malignant SRM, subtypes, and nuclear grade, and other features of aggressiveness. CONCLUSION: Overall, studies reported the superiority of radiomics over expert radiological assessment, but were mainly of retrospective design and therefore of low-quality evidence. However, it is clear that radiomics is an attractive modality that has the potential to improve the non-invasive diagnostic accuracy of SRM imaging and prediction of its natural behaviour. Further prospective validation studies of radiomics are needed to augment management algorithms of SRM.

Update on ACR TI-RADS: Successes, Challenges, and Future Directions, From the AJR Special Series on Radiology Reporting and Data Systems.

The American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS) is an ultrasound-based risk stratification system (RSS) for thyroid nodules that was released in 2017. Since publication, research has shown that ACR TI-RADS has a higher specificity than other RSSs and reduces the number of unnecessary biopsies of benign nodules compared with other systems by 19.9-46.5%. The risk of missing significant cancers using ACR TI-RADS is mitigated by the follow-up recommendations for nodules that do not meet criteria for biopsy. In practice, after a nodule's ultrasound features have been enumerated, the ACR TI-RADS points-based approach leads to clear management recommendations. Practices seeking to implement ACR TI-RADS must engage their radiologists in understanding how the system addresses the problems of thyroid cancer overdiagnosis and unnecessary surgeries by reducing unnecessary biopsies. This review compares ACR TI-RADS to other RSSs and explores key clinical questions faced by practices considering its implementation. We also address the challenge of reducing interobserver variability in assigning ultrasound features. Finally, we highlight emerging imaging techniques and recognize the ongoing international effort to develop a system that harmonizes multiple RSSs, including ACR TI-RADS.

Radiological honeycombing: pitfalls in idiopathic pulmonary fibrosis diagnosis.

INTRODUCTION: High-Resolution Computed Tomography (HRCT) plays a pivotal role in the diagnosis of Idiopathic Pulmonary Fibrosis (IPF). First, it establishes the presence of lung fibrosis. Second, it allows the recognition of specific patterns, namely typical and probable Usual Interstitial Pneumonia (UIP) pattern obviating the need for tissue confirmation in the appropriate clinical context. AREAS COVERED: Acknowledging the extreme versatility of modern radiology and the heavy burden of knowledge the modern radiologist has to cope with, this review addresses the diagnostic pitfalls of honeycombing in IPF diagnosis. This review focuses on two areas: i) when honeycombing is actually present but there are other findings that should raise suspicion of an alternative diagnosis and ii) when honeycombing is misdiagnosed, focusing on the commonest radiographic patterns that are responsible for this confusion. EXPERT OPINION: It is pivotal to establish the actual presence of honeycombing. Even then, the distribution of honeycombing or the presence of other findings could be suggestive of alternative diagnoses. Reviewing older images can be extremely helpful in reaching the correct diagnosis.

Early-Stage Radiology Volume Effects and Considerations with the Coronavirus Disease 2019 (COVID-19) Pandemic: Adaptations, Risks, and Lessons Learned.

OBJECTIVE: The coronavirus disease 2019 (COVID-19) pandemic resulted in significant loss of radiologic volume as a result of shelter-at-home mandates and delay of non-time-sensitive imaging studies to preserve capacity for the pandemic. We analyze the volume-related impact of the COVID-19 pandemic on six academic medical systems (AMSs), three in high COVID-19 surge (high-surge) and three in low COVID-19 surge (low-surge) regions, and a large national private practice coalition. We sought to assess adaptations, risks of actions, and lessons learned. METHODS: Percent change of 2020 volume per week was compared with the corresponding 2019 volume calculated for each of the 14 imaging modalities and overall total, outpatient, emergency, and inpatient studies in high-surge AMSs and low-surge AMSs and the practice coalition. RESULTS: Steep examination volume drops occurred during week 11, with slow recovery starting week 17. The lowest total AMS volume drop was 40% compared with the same period the previous year, and the largest was 70%. The greatest decreases were seen with screening mammography and dual-energy x-ray absorptiometry scans, and the smallest decreases were seen with PET/CT, x-ray, and interventional radiology. Inpatient volume was least impacted compared with outpatient or emergency imaging. CONCLUSION: Large percentage drops in volume were seen from weeks 11 through 17, were seen with screening studies, and were larger for the high-surge AMSs than for the low-surge AMSs. The lowest drops in volume were seen with modalities in which delays in imaging had greater perceived adverse consequences.

Analysis of magnetic resonance imaging features of ovarian thecoma.

To investigate the magnetic resonance imaging (MRI) findings in ovarian thecoma and improve preoperative diagnostic accuracy.Retrospective analysis was performed on 45 patients with surgically and pathologically confirmed ovarian thecoma. Patients were grouped into those with maximum lesion diameter ≥5 cm and <5 cm. Diagnostic scores (up to 6 points) were evaluated on the basis of MRI performance.The ≥5 cm group contained 36 cases (cystic necrosis, 32 cases) with the following findings: T1WI: isointense signal, 22 cases; slightly hypointense signal, 14 cases; T2WI: isointense signal, 6 cases; slightly hypointense signal, 21 cases; slightly hyperintense signal, 9 cases; Diffusion-weighted imaging (DWI): hyperintense signal, 23 cases; mixed hyperintense signal, 13 cases; slight enhancement on dynamic enhanced scans; pelvic fluid accumulation, 31 cases. The diagnostic score evaluations yielded 6 points in 31 cases, 5 points in 1 case, 4 points in 2 cases, and 3 points in 2 cases. The <5 cm group contained 9 cases (cystic necrosis, 3 cases) with the following findings: T1WI: isointense signal, 3 cases; slightly hypointense signal, 6 cases; T2WI: isointense signal, 2 cases; slightly hypointense signal, 4 cases; slightly hyperintense signal, 3 cases; DWI, hyperintense signal; slight enhancement in 8 cases and significant enhancement in 1 case; pelvic fluid accumulation, 4 cases. The diagnostic score evaluations yielded 6 points in 3 cases, 5 points in 1 case, 4 points in 4 cases, and 3 points in 1 case. (iii) Incidence of pelvic fluid accumulation and cystic necrosis differed depending on the size of the lesion (P = .007, .000).Larger lesions show hyperintense or mixed hyperintense signals on DWI along with pelvic fluid and cystic necrosis; whereas, smaller lesions show a hyperintense signal on DWI, cystic necrosis is rare. MRI characteristics along with the patient age and laboratory findings can improve the accuracy of preoperative diagnosis of these lesions.

A review of original articles published in the emerging field of radiomics.

PURPOSE: To determine the characteristics of and trends in research in the emerging field of radiomics through bibliometric and hotspot analyses of relevant original articles published between 2013 and 2018. METHODS: We evaluated 553 original articles concerning radiomics, published in a total of 61 peer-reviewed journals between 2013 and 2018. The following information was retrieved for each article: radiological subspecialty, imaging technique(s), machine learning technique(s), sample size, study setting and design, statistical result(s), study purpose, software used for feature calculation, funding declarations, author number, first author's affiliation, study origin, and journal name. Qualitative and quantitative analyses were performed for the manually extracted data for identification and visualization of the trends in radiomics research. RESULTS: The annual growth rate in the number of published papers was 177.82% (p < 0.001). The characteristics and trends of research hotspots in the field of radiomics were clarified and visualized in this study. It was found that the field of radiomics is at a more mature stage for lung, breast, and prostate cancers than for other sites. Radiomics studies primarily focused on radiological characterization (215) and monitoring (182). Logistic regression and LASSO were the two most commonly used techniques for feature selection. Non-clinical researchers without a medical background dominated radiomics studies (70.52%), the vast majority of which only highlighted positive results (97.80%) while downplaying negative findings. CONCLUSIONS: The reporting of quantifiable knowledge about the characteristics and trajectories of radiomics can inform researchers about the gaps in the field of radiomics and guide its future direction.

Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century.

Advances in computing hardware and software platforms have led to the recent resurgence in artificial intelligence (AI) touching almost every aspect of our daily lives by its capability for automating complex tasks or providing superior predictive analytics. AI applications are currently spanning many diverse fields from economics to entertainment, to manufacturing, as well as medicine. Since modern AI's inception decades ago, practitioners in radiological sciences have been pioneering its development and implementation in medicine, particularly in areas related to diagnostic imaging and therapy. In this anniversary article, we embark on a journey to reflect on the learned lessons from past AI's chequered history. We further summarize the current status of AI in radiological sciences, highlighting, with examples, its impressive achievements and effect on re-shaping the practice of medical imaging and radiotherapy in the areas of computer-aided detection, diagnosis, prognosis, and decision support. Moving beyond the commercial hype of AI into reality, we discuss the current challenges to overcome, for AI to achieve its promised hope of providing better precision healthcare for each patient while reducing cost burden on their families and the society at large.

Open access image repositories: high-quality data to enable machine learning research.

Originally motivated by the need for research reproducibility and data reuse, large-scale, open access information repositories have become key resources for training and testing of advanced machine learning applications in biomedical and clinical research. To be of value, such repositories must provide large, high-quality data sets, where quality is defined as minimising variance due to data collection protocols and data misrepresentations. Curation is the key to quality. We have constructed a large public access image repository, The Cancer Imaging Archive, dedicated to the promotion of open science to advance the global effort to diagnose and treat cancer. Drawing on this experience and our experience in applying machine learning techniques to the analysis of radiology and pathology image data, we will review the requirements placed on such information repositories by state-of-the-art machine learning applications and how these requirements can be met.

European trends in radiology: investigating factors affecting the number of examinations and the effective dose.

The advances in technology have led to a growing trend in population exposure to radiation emerging from the invention of high-dose procedures. It is, for example, estimated that annually 1.2% of cancers are induced by radiological scans in Norway. This study aims to investigate and discuss the frequency and dose trends of radiological examinations in Europe. European Commission (EC) launched projects to gain information for medical exposures in 2004 and 2011. In this study, the European Commission Radiation Protection (RP) reports No. 154 and 180 have been reviewed. The RP 154 countries' data were extracted from both reports, and the average variation trend of the number of examinations and effective doses were studied. According to the results, plain radiography and fluoroscopy witnessed a reduction in the frequency and effective dose per examination. Nevertheless, European collective dose encountered an average increase of 23%, which resulted from a growing tendency for implementation of high-dose procedures such as CT scans and interventional examinations. It is worth noting that most of the CT procedures have undergone an increase in effective dose per examination. Although demand and dose per examination in some radiological procedures (such as intravenous urography (IVU) have been reduced, population collective dose is still rising due to the increasing demand for CT scan procedures. Even though the individual risks are not considerable, it can, in a large scale, threaten the health of the people at the present time. Due to this fact, better justification should be addressed so as to reduce population exposure.

Radiology-Pathology Correlation to Facilitate Peer Learning: An Overview Including Recent Artificial Intelligence Methods.

Correlation of pathology reports with radiology examinations has long been of interest to radiologists and helps to facilitate peer learning. Such correlation also helps meet regulatory requirements, ensures quality, and supports multidisciplinary conferences and patient care. Additional offshoots of such correlation include evaluating for and ensuring concordance of pathology results with radiology interpretation and procedures as well as ensuring specimen adequacy after biopsy. For much of the history of radiology, this correlation has been done manually, which is time consuming and cumbersome and provides coverage of only a fraction of radiology examinations performed. Electronic storage and indexing of radiology and pathology information laid the foundation for easier access and for the development of automated artificial intelligence methods to match pathology information with radiology reports. More recent techniques have resulted in near comprehensive coverage of radiology examinations with methods to present results and solicit feedback from end users. Newer deep learning language modeling techniques will advance these methods by providing more robust automated and comprehensive radiology-pathology correlation with the ability to rapidly, flexibly, and iteratively tune models to site and user preference.

Imaging Quality Control in the Era of Artificial Intelligence.

The advent of artificial intelligence (AI) promises to have a transformational impact on quality in medicine, including in radiology. However, experience has shown that quality tools alone are often not sufficient to bring about consistent excellent performance. Specifically, rather than assuming outcome targets are consistently met, in quality control, managers assume that wide variation is likely present unless proven otherwise with objective performance data. In this article, we discuss what we consider to be the eight essential elements required to achieve comprehensive process control, necessary to deliver consistent quality in radiology: a process control framework, performance measures, performance standards and targets, monitoring applications, prediction models, optimization models, feedback mechanisms, and accountability mechanisms. We consider these elements to be universally applicable, including in the application of AI-based models. We also discuss how the lack of specific elements of a quality control program can hinder widespread quality control efforts. We illustrate the concept using the example of a CT radiation dose optimization and process control program previously developed by one of the authors and provide several examples of how AI-based tools might be used for quality control in radiology.

Radiogenomics: bridging imaging and genomics.

From diagnostics to prognosis to response prediction, new applications for radiomics are rapidly being developed. One of the fastest evolving branches involves linking imaging phenotypes to the tumor genetic profile, a field commonly referred to as "radiogenomics." In this review, a general outline of radiogenomic literature concerning prominent mutations across different tumor sites will be provided. The field of radiogenomics originates from image processing techniques developed decades ago; however, many technical and clinical challenges still need to be addressed. Nevertheless, increasingly accurate and robust radiogenomic models are being presented and the future appears to be bright.