Methodology for evaluation of tumor cell density (TCD) and allocation of radiotherapy response categories. (A) Virtual graticule of approximately 300 points applied to tumor area on a digital H&E section. (B) Demonstration of TCD point scoring. (C) Box and whisker plot to show the relationship between ΔTCD and American Joint Committee on Cancer (AJCC) Tumour Regression Grade System (TRG). (D, E) Allocation of three radiotherapy response groups according to change in TCD (ΔTCD) for short-course radiotherapy (SCRT) (D) and long-course chemoradiotherapy (LCRT) (E). The Kruskall-Wallis test was used for statistical comparisons. Image courtesy of BMJ
Can you tell us about your studies at Imperial?
After obtaining a medical degree from the University of Crete (Greece), I decided to move to the UK for postgraduate studies. I wanted to do a PhD in a Chemical Engineering department in order to delve into the field of tissue engineering. Tissue engineering is an interdisciplinary field, working towards the development of artificial tissues for regenerative medicine purposes. It was regenerative medicine that fascinated me since medical school and influenced my decision to do a PhD in this area, to learn how the principles of engineering can be applied to artificial tissue development. I strongly believe that the regeneration of damaged tissues in the lab is the future of medicine.
[December 29, 2020]
Volpara Health s AI-powered Breast Density Software Central to New Breast Density Studies
SILICON VALLEY, Calif., Dec. 29, 2020 /PRNewswire/ Volpara Health announced two new research studies using AI-powered software to score breast density objectively and consistently to evaluate its impact in mammography and breast cancer risk assessment.
Volpara
®Density⢠software uses a combination of x-ray physics and machine learning to generate an accurate volumetric measure of breast composition and remains the density measurement tool of choice for breast cancer research. With the two studies, which were presented at the virtual 106
th Annual Radiological Society of North America (RSNA) meeting, Volpara has reached another research milestone with more than 300 journal articles and conference proceedings, including more than 150 peer-reviewed papers.Â
On Investors’ Radar: Cantor Fitzgerald’s $70 “BUY” Recommendation for Nanox
As 2021 brings promises of market increases, one of the leading financial firms recommends investors put Nanox’s ground-breaking product on their radar with a $70 target. By IBTimes Staff Reporter Nanox’s innovative x-ray tube Photo: Nanox.vision
As the economy begins to reverse course and increases in the market continue, it is time for investors to review which start-up and leading companies to finance in 2021. While the stock market was lackluster during the 2020 coronavirus pandemic, the coming year will see unprecedented developments in start-up innovation and technologies.
On December 3rd, investors and interested businesses were able to view the 2020 Radiological Society of North America (RSNA) Virtual Annual Meeting, which held a live demonstration of new upcoming and innovative products. The online webinar featured over 11,000 international participants and attendees, in
RSNA Announces Publication of First MIDRC-RICORD COVID-19 Data Set
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December 21, 2020 The Radiological Society of North America (RSNA) and the RSNA COVID-19 AI Task Force announced that the first annotated data set from the RSNA International COVID-19 Open Radiology Database (RICORD) has been published by The Cancer Imaging Archive (TCIA).
The COVID-19 pandemic is a global public health crisis. Although prediction models for COVID-19 imaging have been developed to support medical decision making, the lack of a diverse annotated data set has hindered the capabilities of these models. RSNA launched RICORD in mid-2020 with the goal of building the largest open database of anonymized COVID-19 medical images in the world. It is being made freely available to the global research and education communities to gain new insights, apply new tools such as artificial intelligence and deep learning, and accelerate clinical recognition of this novel disease.