Holistic Person-Centered Care in Radiotherapy: Protocol for a Scoping Review

BACKGROUND

Several types of health care professionals are responsible for the care of patients with cancer throughout their engagement with the health care system. One such type is the radiotherapist. The radiotherapist not only administers treatment but is also directly involved with the patient during treatment. Despite this direct contact with the patient, the narrative tends to focus more on technical tasks than the actual patient. This task-focused interaction is often due to the highly sophisticated equipment and complex radiotherapy treatment processes involved. This often results in not meeting the psychosocial needs of the patient, and patients have acknowledged noncompliance and delayed treatment as a result.

OBJECTIVE

The scoping review aims to explore, chart, and map the available literature on holistic person-centered care in radiotherapy and to identify and present key concepts, definitions, methodologies, knowledge gaps, and evidence related to holistic person-centered care in radiotherapy.

METHODS

This protocol was developed using previously described methodological frameworks for scoping studies. The review will include both peer-reviewed and gray literature regarding holistic, person-centered care in radiotherapy. A comprehensive search strategy has been developed for MEDLINE (Ovid), which will be translated into the other included databases: Scopus, CINAHL (EBSCO), MEDLINE (PubMed), Embase (Elsevier), Cochrane Library, and the Directory of Open Access Journals. Gray literature searching will include Google (Google Books and Google Scholar), ProQuest, the WorldWideScience website, the OpenGrey website, and various university dissertation and thesis repositories. The title and abstract screening, full-text review, and relevant data extraction will be performed independently by all 3 reviewers using the Covidence (Veritas Health Innovation) software, which will also be used to guide the resolution of conflicts. Sources selected will be imported into ATLAS.ti (ATLAS.ti Scientific Software Development GmbH) for analysis, which will consist of content analysis, narrative analysis, and descriptive synthesis. Results will be presented using narrative, diagrammatic, and tabular formats.

RESULTS

The review is expected to identify research gaps that will inform current and future holistic, person-centered care in radiotherapy. The review commenced in November 2023, and the formal literature search was completed by the end of February 2024. Final results are expected to be published in a peer-reviewed journal by 2025.

CONCLUSIONS

The findings of this review are expected to provide a wide variety of strategies aimed at providing holistic, person-centered care in radiotherapy, as well as to identify some gaps in the literature. These findings will be used to inform future studies aimed at designing, developing, evaluating, and implementing strategies toward improved holistic, person-centered care in radiotherapy.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/51338.

Factors Contributing to Disproportionate Burnout in Women Breast Imaging Radiologists: A Review

Physician burnout continues to increase in prevalence and disproportionately affects women physicians. Breast imaging is a woman-dominated subspeciality, and therefore, worsening burnout among women physicians may have significant repercussions on the future of the breast imaging profession. Systemic and organizational factors have been shown to be the greatest contributors to burnout beyond individual factors. Based on the Mayo Model, we review the evidence regarding the 7 major organizational contributors to physician burnout and their potential disproportionate impacts on women breast radiologists. The major organizational factors discussed are work-life integration, control and flexibility, workload and job demands, efficiency and resources, finding meaning in work, social support and community at work, and organizational culture and values. We also propose potential strategies for institutions and practices to mitigate burnout in women breast imaging radiologists. Many of these strategies could also benefit men breast imaging radiologists, who are at risk for burnout as well.

Assessing radiologists' and radiographers' perceptions on artificial intelligence integration: opportunities and challenges

OBJECTIVES

The objective of this study was to evaluate radiologists' and radiographers' opinions and perspectives on artificial intelligence (AI) and its integration into the radiology department. Additionally, we investigated the most common challenges and barriers that radiologists and radiographers face when learning about AI.

METHODS

A nationwide, online descriptive cross-sectional survey was distributed to radiologists and radiographers working in hospitals and medical centres from May 29, 2023 to July 30, 2023. The questionnaire examined the participants' opinions, feelings, and predictions regarding AI and its applications in the radiology department. Descriptive statistics were used to report the participants' demographics and responses. Five-points Likert-scale data were reported using divergent stacked bar graphs to highlight any central tendencies.

RESULTS

Responses were collected from 258 participants, revealing a positive attitude towards implementing AI. Both radiologists and radiographers predicted breast imaging would be the subspecialty most impacted by the AI revolution. MRI, mammography, and CT were identified as the primary modalities with significant importance in the field of AI application. The major barrier encountered by radiologists and radiographers when learning about AI was the lack of mentorship, guidance, and support from experts.

CONCLUSION

Participants demonstrated a positive attitude towards learning about AI and implementing it in the radiology practice. However, radiologists and radiographers encounter several barriers when learning about AI, such as the absence of experienced professionals support and direction.

ADVANCES IN KNOWLEDGE

Radiologists and radiographers reported several barriers to AI learning, with the most significant being the lack of mentorship and guidance from experts, followed by the lack of funding and investment in new technologies.

Pre-operative Assessment of Shoulder Pathologies on MRI by a Radiologist and an Orthopaedic Surgeon

Introduction

Pathologies of the shoulder, i.e. rotator cuff tears and labral injuries are very common. Most patients receive MRI examination prior to surgery. A correct assessment of pathologies is significant for a detailed patient education and planning of surgery.

Materials and methods

Sixty-nine patients were identified, who underwent both, a standardised shoulder MRI and following arthroscopic shoulder surgery in our hospital. For this retrospective comparative study, the MRIs were pseudonymised and evaluated separately by an orthopaedic surgeon and a radiologist. A third rater evaluated images and reports of shoulder surgery, which served as positive control. Results of all raters were then compared. The aim was an analysis of agreement rates of diagnostic accuracy of preoperative MRI by a radiologist and an orthopaedic surgeon.

Results

The overall agreement with positive control of detecting transmural cuff tears was high (84% and 89%) and lower for partial tears (70-80%). Subscapularis tears were assessed with moderate rates of agreement (60 - 70%) compared to intra-operative findings. Labral pathologies were detected mostly correctly. SLAP lesions and pulley lesions of the LHB were identified with only moderate agreement (66.4% and 57.2%) and had a high inter-rater disagreement.

Conclusion

This study demonstrated that tears of the rotator cuff (supraspinatus, infraspinatus) and labral pathologies can be assessed in non-contrast pre-operative shoulder MRI images with a high accuracy. This allows a detailed planning of surgery and aftercare. Pathologies of the subscapularis tendon, SLAP lesions and biceps instabilities are more challenging to detect correctly. There were only small differences between a radiologic and orthopaedic interpretation of the images.

Occupational Stress Levels among Radiologists and Radiographers in Hungary during the COVID-19 Era

The COVID-19 pandemic has heightened stress levels, potentially affecting the occupational wellbeing of radiographers and radiologists. Our study aimed to assess occupational stress levels within the radiology department and identify contributing factors. A cross-sectional survey was conducted between September and November 2022, with participants comprising radiographers and radiologists affiliated with the Hungarian Society of Radiographers and the Hungarian Society of Radiologists. The online survey collected socio-demographic and COVID-19 data, and the participants completed an effort-reward imbalance questionnaire. The analysis of 406 responses revealed significantly higher effort-reward imbalance (ERI) levels among the radiologists compared to the radiographers (p < 0.05). The healthcare professionals with over 30 years of experience exhibited significantly lower ERI levels than those with 1-9 years, 10-19 years, or 20-29 years of experience (p < 0.05). Additionally, the individuals aged 31-40 demonstrated higher ERI levels compared to their counterparts aged 19-30, 41-50, and over 51 (p < 0.05). The respondents cohabiting with a spouse/partner reported significantly higher stress levels than their single colleagues (p < 0.05), while the dog owners exhibited significantly lower ERI levels (p < 0.05). Elevated occupational stress highlights specific groups requiring targeted interventions to reduce stress and mitigate burnout among radiologists and radiographers.

Visa Opportunities for International Medical Graduates Applying for U.S. Academic Radiology Department Faculty Positions: A National Survey

BACKGROUND. International medical graduates (IMGs) are a source of physicians who could help alleviate radiologist workforce shortages in the United States. However, IMGs may face barriers in obtaining appropriate visas (e.g., H-1B or O-1 visas) to allow faculty employment. OBJECTIVE. The purpose of this study was to assess the policies and experiences of U.S. academic radiology departments in offering visas to IMGs applying for faculty positions. METHODS. A web-based survey on policies and experiences in offering visas to IMG faculty candidates was distributed to chairs of U.S. radiology departments with a diagnostic radiology training program recognized by the National Resident Matching Program. Individual survey questions were optional. The initial survey and subsequent reminders were sent from October 7, 2022, through November 7, 2022. RESULTS. The survey response rate was 81% (143/177). A total of 24% (28/115), 38% (44/115), 17% (20/115), and 20% (23/115) of departments offered H-1B visas to IMG faculty frequently, sometimes, rarely, and never, respectively; 3% (3/113), 27% (31/113), 22% (25/113), and 48% (54/113) of departments offered O-1 visas frequently, sometimes, rarely, and never, respectively. However, 41% (46/113) and 5% (6/113) of departments had default policies of offering H-1B and O-1 visas for IMG faculty candidates, respectively. The most common reasons given for why departments did not offer visas included, for both H-1B and O-1 visas, the time-consuming process, lack of reliability of candidates' starting time, and the expense of the visa application; for O-1 visas, the reasons given also included lack of expertise. A total of 15% (16/108) of departments set their own visa policies, 75% (81/108) followed institutional policies, and 10% (11/108) followed policies set by other entities (e.g., state government). CONCLUSION. Although to at least some extent most U.S. academic radiology departments offer H-1B and O-1 visas for IMGs seeking faculty positions, use of such visas typically is not the departments' default policy. A variety of barriers contributed to visas not being offered. The departments' visa policies were primarily determined at the institutional level. CLINICAL IMPACT. The identified barriers faced by U.S. academic radiology departments in offering visas to IMG faculty candidates impact the role of IMGs in helping to address radiologist workforce shortages.

Ultrasonographic diagnosis of ovarian tumors through the deep convolutional neural network

OBJECTIVES

The objective of this study was to develop and validate an ovarian tumor ultrasonographic diagnostic model based on deep convolutional neural networks (DCNN) and compare its diagnostic performance with that of human experts.

MATERIAL AND METHODS

We collected 486 ultrasound images of 192 women with malignant ovarian tumors and 617 ultrasound images of 213 women with benign ovarian tumors, all confirmed by pathological examination. The image dataset was split into a training set and a validation set according to a 7:3 ratio. We selected 5 DCNNs to develop our model: MobileNet, Xception, Inception, ResNet and DenseNet. We compared the performance of the five models through the area under the curve (AUC), sensitivity, specificity, and accuracy. We then randomly selected 200 images from the validation set as the test set. We asked three expert radiologists to diagnose the images to compare the performance of radiologists and the DCNN model.

RESULTS

In the validation set, AUC of DenseNet was 0.997 while AUC was 0.988 of ResNet, 0.987 of Inception, 0.968 of Xception and 0.836 of MobileNet. In the test set, the accuracy was 0.975 with the DenseNet model versus 0.825 (p < 0.0001) with the radiologists, and sensitivity was 0.975 versus 0.700 (p < 0.0001), and specificity was 0.975 versus 0.908 (p < 0.001).

CONCLUSIONS

DensNet performed better than other DCNNs and expert radiologists in identifying malignant ovarian tumors from benign ovarian tumors based on ultrasound images, a finding that needs to be further explored in clinical trials.

A Multi-Disciplinary MRI Assessment May Optimize the Evaluation of Chondral Lesions in Acute Ankle Fractures: A Prospective Study

Chondral lesions (CL) in the ankle following acute fractures are frequently overlooked immediately after the injury or diagnosed at a later stage, leading to persistent symptoms despite successful surgery. The literature presents a wide range of discrepancies in the reported incidence of CLs in acute ankle fractures. The objective of this prospective study is to provide a precise assessment of the occurrence of chondral lesions (CLs) in acute ankle fractures through MRI scans conducted immediately after the trauma and prior to scheduled surgery. Furthermore, the study aims to highlight the disparities in the interpretation of these MRI scans, particularly concerning the size and extent of chondral damage, between radiologists and orthopedic surgeons. Over the period of three years, all patients presenting with an unstable ankle fracture that underwent operative treatment were consecutively included in this single-center prospective study. Preoperative MRIs were obtained for all included patients within 10 days of the trauma and were evaluated by a trauma surgeon and a radiologist specialized in musculoskeletal MRI blinded to each other's results. The location of the lesions was documented, as well as their size and ICRS classification. Correlations and kappa coefficients as well as the p-values were calculated. A total of 65 patients were included, with a mean age of 41 years. The evaluation of the orthopedic surgeon showed CLs in 52.3% of patients. CLs occurred mainly on the tibial articular surface (70.6%). Most talar lesions were located laterally (11.2%). The observed CLs were mainly ICRS grade 4. According to the radiologist, 69.2% of the patients presented with CLs. The most common location was the talar dome (48.9%), especially laterally. Most detected CLs were graded ICRS 3a. The correlation between the two observers was weak/fair regarding the detection and classification of CLs and moderate regarding the size of the detected CLs. To enhance the planning of surgical treatment for ankle chondral lesions (CLs), it may be beneficial to conduct an interdisciplinary preoperative assessment of the performed scans. This collaborative approach can optimize the evaluation of ankle CLs and improve overall treatment strategies.

Evaluating Efficiency of Time Use and Operational Costs in a Breast Clinic Workflow: A Comparative Analysis Between Automated Breast Ultrasound and Handheld Ultrasound

Objective

The aim of this study was to evaluate efficiency of time use for radiologists and operational costs of automated breast ultrasound (ABUS) versus handheld breast ultrasound (HHUS).

Materials and Methods

This study was approved by the Institutional Review Board, and informed consent was waived. One hundred and fifty-three patients, aged 21-81 years, underwent both ABUS and HHUS. The time required for the ABUS scanning and radiologist interpretation and the combined scanning and interpretation time for HHUS were recorded for screening and diagnostic exams. One-Way ANOVA test was used to compare the methods, and Cohen Kappa statistics were used to achieve the agreement levels. Finally, the cost of the methods and return of interest were compared by completing a cost analysis.

Results

The overall mean ± standard deviation examination time required for ABUS examination was 676.2±145.42 seconds while mean scan time performed by radiographers was 411.76±67.79 seconds, and the mean radiologist time was 234.01±81.88 seconds. The overall mean examination time required for HHUS was 452.52±171.26 seconds, and the mean scan time and radiologist time were 419.62±143.24 seconds. The reduced time translated into savings of 7.369 TL/month, and savings of 22% in operational costs was achieved with ABUS.

Conclusion

The radiologist's time was reduced with ABUS in both screening and diagnostic scenarios. Although a second-look HHUS is required for diagnostic cases, ABUS still saves radiologists time by enabling a focused approach instead of a complete evaluation of both breasts. Thus, ABUS appears to save both medical staff time and operational costs.

Analyzing Barriers and Enablers for the Acceptance of Artificial Intelligence Innovations into Radiology Practice: A Scoping Review

OBJECTIVES

This scoping review was conducted to determine the barriers and enablers associated with the acceptance of artificial intelligence/machine learning (AI/ML)-enabled innovations into radiology practice from a physician's perspective.

METHODS

A systematic search was performed using Ovid Medline and Embase. Keywords were used to generate refined queries with the inclusion of computer-aided diagnosis, artificial intelligence, and barriers and enablers. Three reviewers assessed the articles, with a fourth reviewer used for disagreements. The risk of bias was mitigated by including both quantitative and qualitative studies.

RESULTS

An electronic search from January 2000 to 2023 identified 513 studies. Twelve articles were found to fulfill the inclusion criteria: qualitative studies (n = 4), survey studies (n = 7), and randomized controlled trials (RCT) (n = 1). Among the most common barriers to AI implementation into radiology practice were radiologists' lack of acceptance and trust in AI innovations; a lack of awareness, knowledge, and familiarity with the technology; and perceived threat to the professional autonomy of radiologists. The most important identified AI implementation enablers were high expectations of AI's potential added value; the potential to decrease errors in diagnosis; the potential to increase efficiency when reaching a diagnosis; and the potential to improve the quality of patient care.

CONCLUSIONS

This scoping review found that few studies have been designed specifically to identify barriers and enablers to the acceptance of AI in radiology practice. The majority of studies have assessed the perception of AI replacing radiologists, rather than other barriers or enablers in the adoption of AI. To comprehensively evaluate the potential advantages and disadvantages of integrating AI innovations into radiology practice, gathering more robust research evidence on stakeholder perspectives and attitudes is essential.

Corrigendum: Visual expertise modulates resting-state brain network dynamics in radiologists: a degree centrality analysis

[This corrects the article DOI: 10.3389/fnins.2023.1152619.].

Visual expertise modulates resting-state brain network dynamics in radiologists: a degree centrality analysis

Visual expertise reflects accumulated experience in reviewing domain-specific images and has been shown to modulate brain function in task-specific functional magnetic resonance imaging studies. However, little is known about how visual experience modulates resting-state brain network dynamics. To explore this, we recruited 22 radiology interns and 22 matched healthy controls and used resting-state functional magnetic resonance imaging (rs-fMRI) and the degree centrality (DC) method to investigate changes in brain network dynamics. Our results revealed significant differences in DC between the RI and control group in brain regions associated with visual processing, decision making, memory, attention control, and working memory. Using a recursive feature elimination-support vector machine algorithm, we achieved a classification accuracy of 88.64%. Our findings suggest that visual experience modulates resting-state brain network dynamics in radiologists and provide new insights into the neural mechanisms of visual expertise.

Changes in Radiology Due to Artificial Intelligence That Can Attract Medical Students to the Specialty

The role of artificial intelligence (AI) in radiology has grown exponentially in the recent years. One of the primary worries by medical students is that AI will cause the roles of a radiologist to become automated and thus obsolete. Therefore, there is a greater hesitancy by medical students to choose radiology as a specialty. However, it is in this time of change that the specialty needs new thinkers and leaders. In this succinct viewpoint, 2 medical students involved in AI and 2 radiologists specializing in AI or clinical informatics posit that not only are these fears false, but the field of radiology will be transformed in such a way due to AI that there will be novel reasons to choose radiology. These new factors include greater impact on patient care, new space for innovation, interdisciplinary collaboration, increased patient contact, becoming master diagnosticians, and greater opportunity for global health initiatives, among others. Finally, since medical students view mentorship as a critical resource when deciding their career path, medical educators must also be cognizant of these changes and not give much credence to the prevalent fearmongering. As the field and practice of radiology continue to undergo significant change due to AI, it is urgent and necessary for the conversation to expand from expert to expert to expert to student. Medical students should be encouraged to choose radiology specifically because of the changes brought on by AI rather than being deterred by it.

Deep Learning to Detect Pancreatic Cystic Lesions on Abdominal Computed Tomography Scans: Development and Validation Study

BACKGROUND

Pancreatic cystic lesions (PCLs) are frequent and underreported incidental findings on computed tomography (CT) scans and can evolve to pancreatic cancer-the most lethal cancer, with less than 5 months of life expectancy.

OBJECTIVE

The aim of this study was to develop and validate an artificial deep neural network (attention gate U-Net, also named "AGNet") for automated detection of PCLs. This kind of technology can help radiologists to cope with an increasing demand of cross-sectional imaging tests and increase the number of PCLs incidentally detected, thus increasing the early detection of pancreatic cancer.

METHODS

We adapted and evaluated an algorithm based on an attention gate U-Net architecture for automated detection of PCL on CTs. A total of 335 abdominal CTs with PCLs and control cases were manually segmented in 3D by 2 radiologists with over 10 years of experience in consensus with a board-certified radiologist specialized in abdominal radiology. This information was used to train a neural network for segmentation followed by a postprocessing pipeline that filtered the results of the network and applied some physical constraints, such as the expected position of the pancreas, to minimize the number of false positives.

RESULTS

Of 335 studies included in this study, 297 had a PCL, including serous cystadenoma, intraductal pseudopapillary mucinous neoplasia, mucinous cystic neoplasm, and pseudocysts . The Shannon Index of the chosen data set was 0.991 with an evenness of 0.902. The mean sensitivity obtained in the detection of these lesions was 93.1% (SD 0.1%), and the specificity was 81.8% (SD 0.1%).

CONCLUSIONS

This study shows a good performance of an automated artificial deep neural network in the detection of PCL on both noncontrast- and contrast-enhanced abdominal CT scans.

Immersive Virtual Reality for Patient-Specific Preoperative Planning: A Systematic Review

Background. Immersive virtual reality (iVR) facilitates surgical decision-making by enabling surgeons to interact with complex anatomic structures in realistic 3-dimensional environments. With emerging interest in its applications, its effects on patients and providers should be clarified. This systematic review examines the current literature on iVR for patient-specific preoperative planning. Materials and Methods. A literature search was performed on five databases for publications from January 1, 2000 through March 21, 2021. Primary studies on the use of iVR simulators by surgeons at any level of training for patient-specific preoperative planning were eligible. Two reviewers independently screened titles, abstracts, and full texts, extracted data, and assessed quality using the Quality Assessment Tool for Studies with Diverse Designs (QATSDD). Results were qualitatively synthesized, and descriptive statistics were calculated. Results. The systematic search yielded 2,555 studies in total, with 24 full-texts subsequently included for qualitative synthesis, representing 264 medical personnel and 460 patients. Neurosurgery was the most frequently represented discipline (10/24; 42%). Preoperative iVR did not significantly improve patient-specific outcomes of operative time, blood loss, complications, and length of stay, but may decrease fluoroscopy time. In contrast, iVR improved surgeon-specific outcomes of surgical strategy, anatomy visualization, and confidence. Validity, reliability, and feasibility of patient-specific iVR models were assessed. The mean QATSDD score of included studies was 32.9%. Conclusions. Immersive VR improves surgeon experiences of preoperative planning, with minimal evidence for impact on short-term patient outcomes. Future work should focus on high-quality studies investigating long-term patient outcomes, and utility of preoperative iVR for trainees.

[The comparative analysis of professional standards of specialists with higher and secondary medical professional education]

In 2020, the Mintrud of Russia approved a number of professional standards for specialists with secondary professional medical education. The implementation of professional standards is called to actualize outdated normative base concerning functions of medical workers, including assurance of separation of labor functions and actions of physicians and medical nurses, facilitation of development of job descriptions, and minimizing number of conflicts that occur during process of work activities at personnel functions crossing. The medical organization, focusing on requirements established by professional standards, can more competently develop personnel policy, make timely changes in staff list, establish progressive remuneration system. In this regard, it is useful to learn to what extent approved professional standards facilitate solution of practical problems of medical organizations.The article presents results of comparative analysis of three pairs of professional standards for paramedical personnel and specialists with higher education in comparable specialties. Certain contradictions and inaccuracies were esnablished too.

Radiologist versus Non-Radiologist Detection of Lymph Node Metastasis in Papillary Thyroid Carcinoma by Ultrasound: A Meta-Analysis

Papillary thyroid carcinoma (PTC) is the most common thyroid cancer worldwide and is known to spread to adjacent neck lymphatics. Lymph node metastasis (LNM) is a known predictor of disease recurrence and is an indicator for aggressive resection. Our study aims to determine if ultrasound sonographers’ degree of training influences overall LNM detection. PubMed, Embase, and Scopus articles were searched and screened for relevant articles. Two investigators independently screened and extracted the data. Diagnostic test parameters were determined for all studies, studies reported by radiologists, and studies reported by non-radiologists. The total sample size amounted to 5768 patients and 10,030 lymph nodes. Radiologists performed ultrasounds in 18 studies, while non-radiologists performed ultrasounds in seven studies, corresponding to 4442 and 1326 patients, respectively. The overall sensitivity of LNM detection by US was 59% (95%CI = 58–60%), and the overall specificity was 85% (95%CI = 84–86%). The sensitivity and specificity of US performed by radiologists were 58% and 86%, respectively. The sensitivity and specificity of US performed by non-radiologists were 62% and 78%, respectively. Summary receiver operating curve (sROC) found radiologists and non-radiologists to detect LNM on US with similar accuracy (p = 0.517). Our work suggests that both radiologists and non-radiologists alike detect overall LNM with high accuracy on US.

Deep Learning System Boosts Radiologist Detection of Intracranial Hemorrhage

BACKGROUND

Intracranial hemorrhage (ICH) requires emergent medical treatment for positive outcomes. While previous artificial intelligence (AI) solutions achieved rapid diagnostics, none were shown to improve the performance of radiologists in detecting ICHs. Here, we show that the Caire ICH artificial intelligence system enhances a radiologist's ICH diagnosis performance.

METHODS

A dataset of non-contrast-enhanced axial cranial computed tomography (CT) scans (n=532) were labeled for the presence or absence of an ICH. If an ICH was detected, its ICH subtype was identified. After a washout period, the three radiologists reviewed the same dataset with the assistance of the Caire ICH system. Performance was measured with respect to reader agreement, accuracy, sensitivity, and specificity when compared to the ground truth, defined as reader consensus.

RESULTS

Caire ICH improved the inter-reader agreement on average by 5.76% in a dataset with an ICH prevalence of 74.3%. Further, radiologists using Caire ICH detected an average of 18 more ICHs and significantly increased their accuracy by 6.15%, their sensitivity by 4.6%, and their specificity by 10.62%. The Caire ICH system also improved the radiologist's ability to accurately identify the ICH subtypes present.

CONCLUSION

The Caire ICH device significantly improves the performance of a cohort of radiologists. Such a device has the potential to be a tool that can improve patient outcomes and reduce misdiagnosis of ICH.

Team Approach to Improving Radiologist Wellness: A Case-Based Methodology

Radiologist wellness is important on an individual and group/institutional level and helps to promote a strong and healthy working environment, which can improve radiologist retention and engagement. This paper will discuss case examples of radiologist wellness improvements in a single academic institution over a 3-year period using the DMAIC (Define, Measure, Analyze, Improve, and Control) model. Leveraging this framework led to the implementation of reading room assistants, reduction in work-related injuries by improvements in ergonomics, and the formation of a faculty mentorship program.

Community-based Lung Cancer Screening Results in Relation to Patient and Radiologist Characteristics: The PROSPR Consortium

Rationale: Lung-RADS classification was developed to standardize reporting and management of lung cancer screening using low-dose computed tomographic (LDCT) imaging. Although variation in Lung-RADS distribution between healthcare systems has been reported, it is unclear if this is explained by patient characteristics, radiologist experience with lung cancer screening, or other factors. Objectives: Our objective was to determine if patient or radiologist factors are associated with Lung-RADS score. Methods: In the Population-based Research to Optimize the Screening Process (PROSPR) Lung consortium, we conducted a study of patients who received their first screening LDCT imaging at one of the five healthcare systems in the PROSPR Lung Research Center from May 1, 2014, through December 31, 2017. Data on LDCT scans, patient factors, and radiologist characteristics were obtained via electronic health records. LDCT scan findings were categorized using Lung-RADS (negative [1], benign [2], probably benign [3], or suspicious [4]). We used generalized estimating equations with a multinomial distribution to compare the odds of Lung-RADS 3, and separately Lung-RADS 4, versus Lung-RADS 1 or 2 and estimated adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between Lung-RADS assignment and patient and radiologist characteristics. Results: Analyses included 8,556 patients; 24% were assigned Lung-RADS 1, 60% Lung-RADS 2, 10% Lung-RADS 3, and 5% Lung-RADS 4. Age was positively associated with Lung-RADS 3 (OR, 1.02; 95% CI, 1.01-1.03) and 4 (OR, 1.03; 95% CI, 1.01-1.05); chronic obstructive pulmonary disease (COPD) was positively associated with Lung-RADS 4 (OR, 1.78; 95% CI, 1.45-2.20); obesity was inversely associated with Lung-RADS 3 (OR, 0.70; 95% CI, 0.58-0.84) and 4 (OR, 0.58; 95% CI, 0.45-0.75). There was no association between sex, race, ethnicity, education, or smoking status and Lung-RADS assignment. Radiologist volume of interpreting screening LDCT scans, years in practice, and thoracic specialty were also not associated with Lung-RADS assignment. Conclusions: Healthcare systems that are comprised of patients with an older age distribution or higher levels of COPD will have a greater proportion of screening LDCT scans with Lung-RADS 3 or 4 findings and should plan for additional resources to support appropriate and timely management of noted positive findings.

COVID-19 Pandemic: An Indian radiologist' perspective

During the COVID-19 pandemic, differences in health-care system and policies among countries worldwide meant that each country had to come up with their own strategies for containment, diagnosis, and treatment of the disease - "no one size fits all." India being the second populous country in the world with modern and traditional systems of health care has its own challenges to face during the pandemic. Among the increased cacophony of information regarding the COVID-19 disease and controversies surrounding the usage of various radiological modalities for its diagnosis, we are trying to present a sane perspective from an Indian radiologist viewpoint. Knowing the strengths and shortcomings of the Indian health-care system, we have suggested plausible solutions which may be the answers to the issues raised by the Indian media.