Diffusion-weighted imaging complements T2-weighted MRI for tumour response assessment in squamous anal carcinoma.

OBJECTIVES: A published tumour regression grade (TRG) score for squamous anal carcinoma treated with definitive chemoradiotherapy based on T2-weighted MRI yields a high proportion of indeterminate responses (TRG-3). We investigate whether the addition of diffusion-weighted imaging (DWI) improves tumour response assessment in the early post treatment period. MATERIALS AND METHODS: This retrospective observational study included squamous anal carcinoma patients undergoing MRI before and within 3 months of completing chemoradiotherapy from 2009 to 2020. Four independent radiologists (1-20 years' experience) scored MRI studies using a 5-point TRG system (1 = complete response; 5 = no response) based on T2-weighted sequences alone, and then after a 12-week washout period, using a 5-point DWI-TRG system based on T2-weighted and DWI. Scoring confidence was recorded on a 5-point scale (1 = low; 5 = high) for each reading and compared using the Wilcoxon test. Indeterminate scores (TRG-3) from each reading session were compared using the McNemar test. Interobserver agreement was assessed using kappa statistics. RESULTS: Eighty-five patients were included (mean age, 59 years ± 12 [SD]; 55 women). T2-weighted TRG-3 scores from all readers combined halved from 24% (82/340) to 12% (41/340) with DWI (p < 0.001). TRG-3 scores changed most frequently (41%, 34/82) to DWI-TRG-2 (excellent response). Complete tumour response was recorded clinically in 77/85 patients (91%). Scoring confidence increased using DWI (p < 0.001), with scores of 4 or 5 in 84% (287/340). Interobserver agreement remained fair to moderate (kappa range, 0.28-0.58). CONCLUSION: DWI complements T2-weighted MRI by reducing the number of indeterminate tumour responses (TRG-3). DWI increases radiologist's scoring confidence. CLINICAL RELEVANCE STATEMENT: Diffusion-weighted imaging improves T2-weighted tumour response assessment in squamous anal cancer, halving the number of indeterminate responses in the early post treatment period, and increases radiologists' confidence. KEY POINTS: Tumour response based on T2-weighted MRI is often indeterminate in squamous anal carcinoma. Diffusion-weighted imaging alongside T2-weighted MRI halved indeterminate tumour regression grade scores assigned by four radiologists from 24 to 12%. Scoring confidence of expert and non-expert radiologists increased with the inclusion of diffusion-weighted imaging.

Assessment of Technical Skills in Axillary Lymph Node Dissection.

OBJECTIVE: A simulator to enable safe practice and assessment of ALND has been designed, and face, content and construct validity has been investigated. SUMMARY AND BACKGROUND DATA: The reduction in the number of ALNDs conducted has led to decreased resident exposure and confidence. METHODS: A cross-sectional multicenter observational study was carried out between July 2017 and August 2018. Following model development, 30 surgeons of varying experience (n = "experts," n = 11 "senior residents," and n = 10 "junior residents") were asked to perform a simulated ALND. Face and content validity questionnaires were administered immediately after ALND. All ALND procedures were retrospectively assessed by 2 attending breast surgeons, blinded to operator identity, using a video-based assessment tool, and an end product assessment tool. RESULTS: Statistically significant differences between groups were observed across all operative subphases on the axillary clearance assessment tool (P < 0.001). Significant differences between groups were observed for overall procedure quality (P < 0.05) and total number of lymph nodes harvested (P < 0.001). However, operator grade could not be distinguished across other end product variables such as axillary vein damage (P = 0.864) and long thoracic nerve injury (P = 0.094). Overall, participants indicated that the simulator has good anatomical (median score >7) and procedural realism (median score >7). CONCLUSIONS: Video-based analysis demonstrates construct validity for ALND assessment. Given reduced ALND exposure, this simulation is a useful adjunct for both technical skills training and formative Deanery or Faculty administered assessments.

Comparison of the diagnostic performance and impact on management of 18F-FDG PET/CT and whole-body MRI in multiple myeloma.

PURPOSE: Comparative data on the impact of imaging on management is lacking for multiple myeloma. This study compared the diagnostic performance and impact on management of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) and whole-body magnetic resonance imaging (WBMRI) in treatment-naive myeloma. METHODS: Forty-six patients undergoing 18F-FDG PET/CT and WBMRI were reviewed by a nuclear medicine physician and radiologist, respectively, for the presence of myeloma bone disease. Blinded clinical and imaging data were reviewed by two haematologists in consensus and management recorded following clinical data ± 18F-FDG PET/CT or WBMRI. Bone disease was defined using International Myeloma Working Group (IMWG) criteria and a clinical reference standard. Per-patient sensitivity for lesion detection was established. McNemar test compared management based on clinical assessment ± 18F-FDG PET/CT or WBMRI. RESULTS: Sensitivity for bone lesions was 69.6% (32/46) for 18F-FDG PET/CT (54.3% (25/46) for PET component alone) and 91.3% (42/46) for WBMRI. 27/46 (58.7%) of cases were concordant. In 19/46 patients (41.3%) WBMRI detected more focal bone lesions than 18F-FDG PET/CT. Based on clinical data alone, 32/46 (69.6%) patients would have been treated. Addition of 18F-FDG PET/CT to clinical data increased this to 40/46 (87.0%) patients (p = 0.02); and WBMRI to clinical data to 43/46 (93.5%) patients (p = 0.002). The difference in treatment decisions was not statistically significant between 18F-FDG PET/CT and WBMRI (p = 0.08). CONCLUSION: Compared to 18F-FDG PET/CT, WBMRI had a higher per patient sensitivity for bone disease. However, treatment decisions were not statistically different and either modality would be appropriate in initial staging, depending on local availability and expertise.

Outcome of TORS to tongue base and epiglottis in patients with OSA intolerant of conventional treatment.

PURPOSE: Transoral robotic surgery (TORS) of the tongue base with or without epiglottoplasty represents a novel treatment for obstructive sleep apnea (OSA). The objective was to evaluate the clinical efficacy of TORS of the tongue base with or without epiglottoplasty in patients who had not tolerated or complied with conventional treatment (continuous positive airway pressure or oral appliance). METHODS: Four-year prospective case series. The primary outcome measure was the apnea-hypopnea index (AHI) in combination with the Epworth Sleepiness Score (ESS). Mean oxygen saturation levels (SaO2) before and after TORS on respective sleep studies were also recorded. Secondary outcome measures included operative time and complications. Patient reported outcome measures (PROMs) assessed included voice, swallow and quality of life. RESULTS: Fourteen patients underwent TORS for tongue base reduction with ten having additional wedge epiglottoplasty. A 64 % success rate was achieved with a normal post-operative sleep study in 36 % of cases at 6 months. There was a 51 % reduction in the mean AHI (36.3 ± 21.4 to 21.2 ± 24.6, p = 0.02) and a sustained reduction in the mean Epworth Sleepiness Score (p = 0.002). Mean SaO2 significantly increased after surgery compared to pre-operative values (92.9 ± 1.8 to 94.3 ± 2.5, p = 0.005). Quality of life showed a sustained improvement 3 months following surgery (p = 0.01). No major complications occurred. CONCLUSIONS: TORS of the tongue base with or without epiglottoplasty represents a promising treatment option with minimal morbidity for selected patients with OSA. Long-term prospective comparative evaluation is necessary to validate the findings of this study.

Development and validation of open-source deep neural networks for comprehensive chest x-ray reading: a retrospective, multicentre study.

BACKGROUND: Artificial intelligence (AI) systems for automated chest x-ray interpretation hold promise for standardising reporting and reducing delays in health systems with shortages of trained radiologists. Yet, there are few freely accessible AI systems trained on large datasets for practitioners to use with their own data with a view to accelerating clinical deployment of AI systems in radiology. We aimed to contribute an AI system for comprehensive chest x-ray abnormality detection. METHODS: In this retrospective cohort study, we developed open-source neural networks, X-Raydar and X-Raydar-NLP, for classifying common chest x-ray findings from images and their free-text reports. Our networks were developed using data from six UK hospitals from three National Health Service (NHS) Trusts (University Hospitals Coventry and Warwickshire NHS Trust, University Hospitals Birmingham NHS Foundation Trust, and University Hospitals Leicester NHS Trust) collectively contributing 2 513 546 chest x-ray studies taken from a 13-year period (2006-19), which yielded 1 940 508 usable free-text radiological reports written by the contemporary assessing radiologist (collectively referred to as the "historic reporters") and 1 896 034 frontal images. Chest x-rays were labelled using a taxonomy of 37 findings by a custom-trained natural language processing (NLP) algorithm, X-Raydar-NLP, from the original free-text reports. X-Raydar-NLP was trained on 23 230 manually annotated reports and tested on 4551 reports from all hospitals. 1 694 921 labelled images from the training set and 89 238 from the validation set were then used to train a multi-label image classifier. Our algorithms were evaluated on three retrospective datasets: a set of exams sampled randomly from the full NHS dataset reported during clinical practice and annotated using NLP (n=103 328); a consensus set sampled from all six hospitals annotated by three expert radiologists (two independent annotators for each image and a third consultant to facilitate disagreement resolution) under research conditions (n=1427); and an independent dataset, MIMIC-CXR, consisting of NLP-annotated exams (n=252 374). FINDINGS: X-Raydar achieved a mean AUC of 0·919 (SD 0·039) on the auto-labelled set, 0·864 (0·102) on the consensus set, and 0·842 (0·074) on the MIMIC-CXR test, demonstrating similar performance to the historic clinical radiologist reporters, as assessed on the consensus set, for multiple clinically important findings, including pneumothorax, parenchymal opacification, and parenchymal mass or nodules. On the consensus set, X-Raydar outperformed historical reporter balanced accuracy with significance on 27 of 37 findings, was non-inferior on nine, and inferior on one finding, resulting in an average improvement of 13·3% (SD 13·1) to 0·763 (0·110), including a mean 5·6% (13·2) improvement in critical findings to 0·826 (0·119). INTERPRETATION: Our study shows that automated classification of chest x-rays under a comprehensive taxonomy can achieve performance levels similar to those of historical reporters and exhibit robust generalisation to external data. The open-sourced neural networks can serve as foundation models for further research and are freely available to the research community. FUNDING: Wellcome Trust.

Correlation between whole skeleton dual energy CT calcium-subtracted attenuation and bone marrow infiltration in multiple myeloma.

OBJECTIVES: Objective evaluation of the extent of skeletal marrow involvement in multiple myeloma remains a clinical gap for CT. We aimed to develop a quantitative segmentation pipeline for dual energy CT and to assess whether quantified whole skeleton calcium-subtracted attenuation values correlate with biopsy-derived bone marrow infiltration in multiple myeloma. METHODS: Consecutive prospective patients with suspected/established myeloma underwent dual source CT from the skull vertex to proximal tibia. Whole skeleton segmentation was performed for 120 kVp-equivalent images as follows: following Hounsfield unit (HU) thresholding, a Chan-Vese morphological operation was implemented to generate a whole skeleton segmentation mask. This mask was then applied to corresponding whole skeleton material decomposition calcium-subtracted maps, generating whole skeleton HU values. Associations with biopsy-derived bone marrow plasma cell infiltration percentage were assessed with Spearman's rank correlation; significance was at 5%. RESULTS: 21 patients (12 females; median (IQR) 67 (61, 73) years) were included; 16 patients had osteolytic bone lesions; 15 patients underwent bone marrow biopsy. Segmentation and quantification were feasible in all patients. Median (IQR) of the average skeletal calcium-subtracted attenuation was -59.9 HU (-66.3, -51.8HU). There was a positive correlation with bone marrow plasma cell infiltration percentage (Spearman's rho: + 0.79, p < 0.001). CONCLUSION: Whole skeleton calcium-subtracted attenuation is associated with the degree of bone marrow infiltration by plasma cells, providing an objective measure of marrow involvement with the potential to allow earlier detection of disease.

Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey.

OBJECTIVES: To explore the attitudes of United Kingdom (UK) medical students regarding artificial intelligence (AI), their understanding, and career intention towards radiology. We also examine the state of education relating to AI amongst this cohort. METHODS: UK medical students were invited to complete an anonymous electronic survey consisting of Likert and dichotomous questions. RESULTS: Four hundred eighty-four responses were received from 19 UK medical schools. Eighty-eight percent of students believed that AI will play an important role in healthcare, and 49% reported they were less likely to consider a career in radiology due to AI. Eighty-nine percent of students believed that teaching in AI would be beneficial for their careers, and 78% agreed that students should receive training in AI as part of their medical degree. Only 45 students received any teaching on AI; none of the students received such teaching as part of their compulsory curriculum. Statistically, students that did receive teaching in AI were more likely to consider radiology (p = 0.01) and rated more positively to the questions relating to the perceived competence in the post-graduation use of AI (p = 0.01-0.04); despite this, a large proportion of students in the taught group reported a lack of confidence and understanding required for the critical use of healthcare AI tools. CONCLUSIONS: UK medical students understand the importance of AI and are keen to engage. Medical school training on AI should be expanded and improved. Realistic use cases and limitations of AI must be presented to students so they will not feel discouraged from pursuing radiology.