Mentorship Interest in Breast Imaging: Survey Results From the Society of Breast Imaging

OBJECTIVE

This study assessed mentorship interest within the breast radiologist community to guide development of a mentorship program through the Society of Breast Imaging (SBI).

METHODS

A 19-question survey developed by the SBI mentorship committee was distributed electronically to its members March 16, 2021, to May 7, 2021, to gauge interest in forming a society-sponsored mentorship program. Responses were analyzed, with subgroups compared using chi-square analysis.

RESULTS

There was an 18% response rate (598/3277), and 65% (381/588) professed interest in an SBI-sponsored mentorship. Respondents were evenly distributed between academic (241/586, 41%) and private practice (242/586, 41%). Most were breast imaging fellowship-trained (355/593, 60%) and identified as female (420/596, 70%). For practice years, 50% (293/586) were late career (11+ years) with the remainder early-mid career (201/586, 34%) or trainees (92/586, 16%). For mentorship content areas, work/life balance was the most popular choice (275/395, 70%) followed by leadership (234/395, 59%). Most respondents were not currently mentors (279/377, 74%) or mentees (284/337, 84%). Those interested in a mentorship relationship were statistically younger (<45 years old, 234/381, 61% vs 31/207, 15%, P < 0.00001), female (289/381, 76% vs 123/207, 59%, P = 0.00003), academics (189/381, 50% vs 48/207, 23%, P < 0.00001), identified as a racial/ethnic minority (138/381, 64% vs 121/297, 15%, P < 0.00001), and fellowship-trained (262/381, 69% vs 88/207, 43%, P < 0.00001).

CONCLUSION

There is demand, especially among the society's young and minority members, for an SBI-sponsored mentorship program. Work/life balance and leadership were the most popular choices for guidance.

How are countries supporting health workers? Data from the COVID-19 Health System Response Monitor

Background

Health workers have been at the forefront of treating and caring for patients with COVID-19. They were often under immense pressure to care for severely ill patients with a new disease, under strict hygiene conditions and with lockdown measures creating practical barriers to working. This study aims to explore the range of mental health, financial and other practical support measures that 36 countries in Europe and Canada have put in place to support health workers and enable them to do their job.

Methods

We use data extracted from the COVID-19 Health Systems Response Monitor (HSRM). We only consider initiatives implemented outside of clinical settings where COVID-19 patients are treated, and therefore exclude workplace provisions such as availability of personal protective equipment, working time limits or mandatory rest periods.

Results

We show that countries have implemented a range of measures, ranging from mental health and well-being support initiatives, to providing bonuses and temporary salary increases. Practical measures such as childcare provision and free transport and accommodation have also been implemented to ensure health workers can get to their workplace and have their children looked after. Other initiatives such as offering continuing professional development credits for knowledge learnt during the crisis were also offered in some countries, albeit less frequently.

Conclusions

While a large number of initiatives have been introduced, often as ad-hoc measures, their effectiveness in helping staff is unknown in most countries. The effectiveness of these initiatives should be evaluated to inform future crisis responses and strategies for health workforce development.

Diagnostic value of radiomics and machine learning with dynamic contrast-enhanced magnetic resonance imaging for patients with atypical ductal hyperplasia in predicting malignant upgrade

Purpose

To investigate whether radiomics features extracted from magnetic resonance imaging (MRI) of patients with biopsy-proven atypical ductal hyperplasia (ADH) coupled with machine learning can differentiate high-risk lesions that will upgrade to malignancy at surgery from those that will not, and to determine if qualitatively and semi-quantitatively assessed imaging features, clinical factors, and image-guided biopsy technical factors are associated with upgrade rate.

Methods

This retrospective study included 127 patients with 139 breast lesions yielding ADH at biopsy who were assessed with multiparametric MRI prior to biopsy. Two radiologists assessed all lesions independently and with a third reader in consensus according to the BI-RADS lexicon. Univariate analysis and multivariate modeling were performed to identify significant radiomic features to be included in a machine learning model to discriminate between lesions that upgraded to malignancy on surgery from those that did not.

Results

Of 139 lesions, 28 were upgraded to malignancy at surgery, while 111 were not upgraded. Diagnostic accuracy was 53.6%, specificity 79.2%, and sensitivity 15.3% for the model developed from pre-contrast features, and 60.7%, 86%, and 22.8% for the model developed from delta radiomics datasets. No significant associations were found between any radiologist-assessed lesion parameters and upgrade status. There was a significant correlation between the number of specimens sampled during biopsy and upgrade status (p = 0.003).

Conclusion

Radiomics analysis coupled with machine learning did not predict upgrade status of ADH. The only significant result from this analysis is between the number of specimens sampled during biopsy procedure and upgrade status at surgery.