CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images

Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase-polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method; however, its accuracy in detection is only ~70�75. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80�98, but similar accuracy of 70. To enhance the accuracy of CT imaging detection, we developed an open-source framework, CovidCTNet, composed of a set of deep learning algorithms that accurately differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 95 compared to radiologists (70). CovidCTNet is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. To facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and model parameter details as open-source. Open-source sharing of CovidCTNet enables developers to rapidly improve and optimize services while preserving user privacy and data ownership. © 2021, The Author(s)

Bridging pre-surgical endocrine therapy for breast cancer during the COVID-19 pandemic: outcomes from the B-MaP-C study

Purpose: The B-MaP-C study investigated changes to breast cancer care that were necessitated by the COVID-19 pandemic. Here we present a follow-up analysis of those patients commenced on bridging endocrine therapy (BrET), whilst they were awaiting surgery due to reprioritisation of resources. Methods: This multicentre, multinational cohort study recruited 6045 patients from the UK, Spain and Portugal during the peak pandemic period (Feb–July 2020). Patients on BrET were followed up to investigate the duration of, and response to, BrET. This included changes in tumour size to reflect downstaging potential, and changes in cellular proliferation (Ki67), as a marker of prognosis. Results: 1094 patients were prescribed BrET, over a median period of 53 days (IQR 32–81 days). The majority of patients (95.6%) had strong ER expression (Allred score 7–8/8). Very few patients required expedited surgery, due to lack of response (1.2%) or due to lack of tolerance/compliance (0.8%). There were small reductions in median tumour size after 3 months’ treatment duration; median of 4 mm [IQR − 20, 4]. In a small subset of patients ( n = 47), a drop in cellular proliferation (Ki67) occurred in 26 patients (55%), from high (Ki67 ≥ 10%) to low (< 10%), with at least one month’s duration of BrET. Discussion: This study describes real-world usage of pre-operative endocrine therapy as necessitated by the pandemic. BrET was found to be tolerable and safe. The data support short-term (≤ 3 months) usage of pre-operative endocrine therapy. Longer-term use should be investigated in future trials