MRI scarcity in low- and middle-income countries

Reliability of Magseed® marking before neoadjuvant systemic therapy with subsequent contrast-enhanced mammography in patients with non-palpable breast cancer lesions after treatment: the MAGMA study

PURPOSE

To assess the reliability of excising residual breast cancer lesions after neoadjuvant systemic therapy (NAST) using a previously localized paramagnetic seed (Magseed®) and the subsequent use of contrast-enhanced spectral mammography (CESM) to evaluate response.

METHODS

Observational, prospective, multicenter study including adult women (> 18 years) with invasive breast carcinoma undergoing NAST between January 2022 and February 2023 with non-palpable tumor lesions at surgery. Radiologists marked tumors with Magseed® during biopsy before NAST, and surgeons excised tumors guided by the Sentimag® magnetometer. CESMs were performed before and after NAST to evaluate tumor response (Response Evaluation Criteria for Solid Tumors [RECIST]). We considered intraoperative, surgical, and CESM-related variables and histological response.

RESULTS

We analyzed 109 patients (median [IQR] age of 55.0 [46.0, 65.0] years). Magseed® was retrieved from breast tumors in all surgeries (100%; 95% CI 95.47-100.0%) with no displacement and was identified by radiology in 106 patients (97.24%), a median (IQR) of 176.5 (150.0, 216.3) days after marking. Most surgeries (94.49%) were conservative; they lasted a median (IQR) of 22.5 (14.75, 40.0) min (95% CI 23.59-30.11 min). Most dissected tumor margins (93.57%) were negative, and few patients (5.51%) needed reintervention. Magseed® was identified using CESM in all patients (100%); RECIST responses correlated with histopathological evaluations of dissected tumors using the Miller-Payne response grade (p < 0.0001) and residual lesion diameter (p < 0.0001). Also 69 patients (63.3%) answered a patient's satisfaction survey and 98.8% of them felt very satisfied with the entire procedure.

CONCLUSION

Long-term marking of breast cancer lesions with Magseed® is a reliable and feasible method in patients undergoing NAST and may be used with subsequent CESM.

Machine-learning models for diagnosis of rotator cuff tears in osteoporosis patients based on anteroposterior X-rays of the shoulder joint

OBJECTIVE

This study aims to diagnose Rotator Cuff Tears (RCT) and classify the severity of RCT in patients with Osteoporosis (OP) through the analysis of shoulder joint anteroposterior (AP) X-ray-based localized proximal humeral bone mineral density (BMD) measurements and clinical information based on machine learning (ML) models.

METHODS

A retrospective cohort of 89 patients was analyzed, including 63 with both OP and RCT (OPRCT) and 26 with OP only. The study analyzed a series of shoulder radiographs from April 2021 to April 2023. Grayscale values were measured after plotting ROIs based on AP X-rays of shoulder joint. Five kinds of ML models were developed and compared based on their performance in predicting the occurrence and severity of RCT from ROIs' greyscale values and clinical information (age, gender, advantage side, lumbar BMD, and acromion morphology (AM)). Further analysis using SHAP values illustrated the significant impact of selected features on model predictions.

RESULTS

R1-6 had a positive correlation with BMD respectively. The nine variables, including greyscale R1-6, age, BMD, and AM, were used in the prediction models. The RF model was determined to be superior in effectively diagnosing RCT in OP patients, with high AUC scores of 0.998, 0.889, and 0.95 in the training, validation, and testing sets, respectively. SHAP values revealed that the most influential factors on the diagnostic outcomes were the grayscale values of all cancellous bones in ROIs. A column-line graph prediction model based on nine variables was constructed, and DCA curves indicated that RCT prediction in OP patients was favored based on this model. Furthermore, the RF model was also the most superior in predicting the types of RCT within the OPRCT group, with an accuracy of 86.364% and 73.684% in the training and test sets, respectively. SHAP values indicated that the most significant factor affecting the predictive outcomes was the AM, followed by the grayscale values of the greater tubercle, among others.

CONCLUSIONS

ML models, particularly the RF algorithm, show significant promise in diagnosing RCT occurrence and severity in OP patients using conventional shoulder X-rays based on the nine variables. This method presents a cost-effective, accessible, and non-invasive diagnostic strategy that has the potential to substantially enhance the early detection and management of RCT in OP patient population.

A systematic review of the incidence and outcomes of ICD-11 defined stroke

BACKGROUND

The World Health Organisation has expanded the definition of stroke to include people with symptoms less than 24 h if they have evidence of stroke on neuroimaging. The impact is that people previously diagnosed as having a transient ischaemic attack (TIA) would now be considered to have had a stroke. This change will impact incidence and outcomes of stroke and increase eligibility for secondary prevention. We aimed to evaluate the new ICD-11 criteria retrospectively to previous TIA studies to understand the change in incidence and outcomes of this type of stroke.

METHODS

We conducted a systematic review of observational studies of the incidence and outcomes of clinically defined TIA. We searched PubMed, EMBASE, and Google Scholar from inception to 23rd May 2023. Study quality was assessed using a risk of bias tool for prevalence studies.

FINDINGS

Our review included 25 studies. The rate of scan positivity for stroke among those with clinically defined TIA was 24 %, (95 % CI, 16-33 %) but with high heterogeneity (I2 = 100 %, p <0.001). Sensitivity analyses provided evidence that heterogeneity could be explained by methodology and recruitment method. The scan positive rate when examining only studies at low risk of bias was substantially lower, at 13 % (95 % CI, 11-15 %, I2 = 0, p = 0.77). We estimate from population-based incidence studies that ICD-11 would result in an increase stroke incidence between 4.8 and 10.5 per 100,000 persons/year. Of those with DWI-MRI evidence of stroke, 6 % (95 % CI, 3-11 %) developed a recurrent stroke in the subsequent 90 days, but with substantial heterogeneity (I2 = 67 %, p = 0.02).

CONCLUSION

The impact of the ICD-11 change in stroke definition on incidence and outcomes may have been overestimated by individual studies. Community-based stroke services with access to DWI MRI are likely to accurately diagnose greater numbers of people with mild ICD-11 stroke, increasing access to effective prevention.

Building Radiology Equity: Themes from the 2023 RAD-AID Conference on International Radiology and Global Health

Low- and middle-income countries are significantly impacted by the global scarcity of medical imaging services. Medical imaging is an essential component for diagnosis and guided treatment, which is needed to meet the current challenges of increasing chronic diseases and preparedness for acute-care response. We present some key themes essential for improving global health equity, which were discussed at the 2023 RAD-AID Conference on International Radiology and Global Health. They include (1) capacity building, (2) artificial intelligence, (3) community-based patient navigation, (4) organizational design for multidisciplinary global health strategy, (5) implementation science, and (6) innovation. Although not exhaustive, these themes should be considered influential as we guide and expand global health radiology programs in low- and middle-income countries in the coming years.

Intelligent digital tools for screening of brain connectivity and dementia risk estimation in people affected by mild cognitive impairment: the AI-Mind clinical study protocol

More than 10 million Europeans show signs of mild cognitive impairment (MCI), a transitional stage between normal brain aging and dementia stage memory disorder. The path MCI takes can be divergent; while some maintain stability or even revert to cognitive norms, alarmingly, up to half of the cases progress to dementia within 5 years. Current diagnostic practice lacks the necessary screening tools to identify those at risk of progression. The European patient experience often involves a long journey from the initial signs of MCI to the eventual diagnosis of dementia. The trajectory is far from ideal. Here, we introduce the AI-Mind project, a pioneering initiative with an innovative approach to early risk assessment through the implementation of advanced artificial intelligence (AI) on multimodal data. The cutting-edge AI-based tools developed in the project aim not only to accelerate the diagnostic process but also to deliver highly accurate predictions regarding an individual's risk of developing dementia when prevention and intervention may still be possible. AI-Mind is a European Research and Innovation Action (RIA H2020-SC1-BHC-06-2020, No. 964220) financed between 2021 and 2026. First, the AI-Mind Connector identifies dysfunctional brain networks based on high-density magneto- and electroencephalography (M/EEG) recordings. Second, the AI-Mind Predictor predicts dementia risk using data from the Connector, enriched with computerized cognitive tests, genetic and protein biomarkers, as well as sociodemographic and clinical variables. AI-Mind is integrated within a network of major European initiatives, including The Virtual Brain, The Virtual Epileptic Patient, and EBRAINS AISBL service for sensitive data, HealthDataCloud, where big patient data are generated for advancing digital and virtual twin technology development. AI-Mind's innovation lies not only in its early prediction of dementia risk, but it also enables a virtual laboratory scenario for hypothesis-driven personalized intervention research. This article introduces the background of the AI-Mind project and its clinical study protocol, setting the stage for future scientific contributions.