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UNASSIGNED: Diffusion-weighted imaging (DWI) is widely used in neuroradiology or abdominal imaging but not yet implemented in the diagnosis of musculoskeletal tumors.
UNASSIGNED: This study aimed to evaluate how including diffusion imaging in the MRI protocol for patients with musculoskeletal tumors affects the agreement between radiologists and non-radiologist.
UNASSIGNED: Thirty-nine patients with musculoskeletal tumors (Ewing sarcoma, osteosarcoma, and benign tumors) consulted at our institution were included. Three raters with different experience levels evaluated examinations blinded to all clinical data. The final diagnosis was determined by consensus. MRI examinations were split into 1) conventional sequences and 2) conventional sequences combined with DWI. We evaluated the presence or absence of diffusion restriction, solid nature, necrosis, deep localization, and diameter >4 cm as known radiological markers of malignancy. Agreement between raters was evaluated using Gwet\'s AC1 coefficients and interpreted according to Landis and Koch.
UNASSIGNED: The lowest agreement was for diffusion restriction in both groups of raters. Agreement among all raters ranged from 0.51 to 0.945, indicating moderate to almost perfect agreement, and 0.772-0.965 among only radiologists indicating substantial to almost perfect agreement.
UNASSIGNED: The agreement in evaluating diffusion-weighted MRI sequences was lower than that for conventional MRI sequences, both among radiologists and non-radiologist and among radiologists alone. This indicates that assessing diffusion imaging is more challenging, and experience may impact the agreement.

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.

OBJECTIVE: To noninvasively detect prostate cancer and predict the Gleason grade using single-modality T2-weighted imaging with a deep-learning approach.
METHODS: Patients with prostate cancer, confirmed by histopathology, who underwent magnetic resonance imaging examinations at our hospital during September 2015-June 2022 were retrospectively included in an internal dataset. An external dataset from another medical center and a public challenge dataset were used for external validation. A deep-learning approach was designed for prostate cancer detection and Gleason grade prediction. The area under the curve (AUC) was calculated to compare the model performance.
RESULTS: For prostate cancer detection, the internal datasets comprised data from 195 healthy individuals (age: 57.27 ± 14.45 years) and 302 patients (age: 72.20 ± 8.34 years) diagnosed with prostate cancer. The AUC of our model for prostate cancer detection in the validation set (n = 96, 19.7%) was 0.918. For Gleason grade prediction, datasets comprising data from 283 of 302 patients with prostate cancer were used, with 227 (age: 72.06 ± 7.98 years) and 56 (age: 72.78 ± 9.49 years) patients being used for training and testing, respectively. The external and public challenge datasets comprised data from 48 (age: 72.19 ± 7.81 years) and 91 patients (unavailable information on age), respectively. The AUC of our model for Gleason grade prediction in the training set (n = 227) was 0.902, whereas those of the validation (n = 56), external validation (n = 48), and public challenge validation sets (n = 91) were 0.854, 0.776, and 0.838, respectively.
CONCLUSIONS: Through multicenter dataset validation, our proposed deep-learning method could detect prostate cancer and predict the Gleason grade better than human experts.
UNASSIGNED: Precise prostate cancer detection and Gleason grade prediction have great significance for clinical treatment and decision making.
CONCLUSIONS: Prostate segmentation is easier to annotate than prostate cancer lesions for radiologists. Our deep-learning method detected prostate cancer and predicted the Gleason grade, outperforming human experts. Non-invasive Gleason grade prediction can reduce the number of unnecessary biopsies.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

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.
UNASSIGNED: DERR1-10.2196/51338.

UNASSIGNED: Imaging is indispensable to the diagnostic and treatment process. By facilitating access to rapid timely image interpretation, teleradiology plays a prominent role in improving access, quality of critical care, and management of the patients in intensive care units (ICU). The aim of the study is to investigate the role of teleradiology in ICU patient care and management.
UNASSIGNED: In our study, a total of 22,081 studies of a cohort of 14,900 patients which had been transmitted from intensive care units of 80 hospitals located across the United States of America through a teleradiology reporting workflow, were interpreted by the American Board Certified Radiologists empanelled by a teleradiology service provider, located in India.
UNASSIGNED: Among all modalities, the highest percentage of studies performed were computed tomography scan (47%) followed by radiographs (37.22%). Out of 22,081 cases under the study, 16,582 cases were reported during nighttime with a mean turnaround time (TAT) of 46.66 minutes 95% CI (46.27-47.04) while 5,499 cases were reported during daytime with a mean TAT of 44.66 minutes 95% CI (45.40-43.92).
UNASSIGNED: Setting up teleradiology service connectivity with a teleradiology service provider located in India, providing high-quality diagnostic interpretations and lower turnaround time with the ICUs in the US hospitals reduces the interval to intervention time and leads to efficient patient care management. Moreover, it also provides time advantage for US hospitals when on-site radiologists at night are unable to provide immediate coverage.
UNASSIGNED: The ICU teleradiology service model designed in the study would greatly help overcome the shortfall of radiologists in the hospitals, provide better patient management and care by quality reporting in short turnaround time, not only during daytime but also in the night hours or on holidays when on-site radiologists are unable to provide immediate coverage.
UNASSIGNED: Rao P, Mathur N, Kalyanpur A. Utilization of Teleradiology by Intensive Care Units: A Cohort Study. Indian J Crit Care Med 2024;28(1):20-25.

OBJECTIVE: 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.
CONCLUSIONS: 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.
CONCLUSIONS: 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.

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.

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