Author Interviews, Cancer Research, Gender Differences, JAMA / 02.11.2019
Racial & Ethnic Cancer Survivors Less Able to See Health Care Providers Who Share Their Culture
MedicalResearch.com Interview with:
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Dr. Nina Niu Sanford[/caption]
Nina Niu Sanford, M.D.
Assistant Professor
Dedman Family Scholar in Clinical Care
UT Southwestern Department of Radiation Oncology
Dallas TX
MedicalResearch.com: What is the background for this study?
Response: Minority racial/ethnic groups present at later stages of cancer and have worse stage-specific survival rates. Cultural competency represents a single element within the dynamic and trans-disciplinary field of health disparities, but is an important modifiable factor for both providers and health organizations that could be associated with disparities in cancer outcomes.
There have been longstanding initiatives and training requirements in medical education specifically designed to improve provider cultural competency over the past couple of decades, and the American Society of Clinical Oncology (ASCO) has recently outlined goals for improving cultural competency within its policy statement on cancer disparities.
Moreover, ASCO health disparity policies have recently highlighted the association between racial/ethnic disparities in cancer outcomes and a “lack of access to high-quality care that is understanding and respectful of diverse traditions and cultures plays a significant role.” Given the above, we wished to assess access to culturally competent providers among patients with cancer by race/ethnicity.
Dr. Nina Niu Sanford[/caption]
Nina Niu Sanford, M.D.
Assistant Professor
Dedman Family Scholar in Clinical Care
UT Southwestern Department of Radiation Oncology
Dallas TX
MedicalResearch.com: What is the background for this study?
Response: Minority racial/ethnic groups present at later stages of cancer and have worse stage-specific survival rates. Cultural competency represents a single element within the dynamic and trans-disciplinary field of health disparities, but is an important modifiable factor for both providers and health organizations that could be associated with disparities in cancer outcomes.
There have been longstanding initiatives and training requirements in medical education specifically designed to improve provider cultural competency over the past couple of decades, and the American Society of Clinical Oncology (ASCO) has recently outlined goals for improving cultural competency within its policy statement on cancer disparities.
Moreover, ASCO health disparity policies have recently highlighted the association between racial/ethnic disparities in cancer outcomes and a “lack of access to high-quality care that is understanding and respectful of diverse traditions and cultures plays a significant role.” Given the above, we wished to assess access to culturally competent providers among patients with cancer by race/ethnicity.
Dr. Helen Marsden PhD
Skin Analytics Limited
London, United Kingdom
MedicalResearch.com: What is the background for this study?
Response: In this technology age, with the explosion of interest and applications using Artificial Intelligence, it is easy to accept the output of a technology-based test - such as a smartphone app designed to identify skin cancer - without thinking too much about it. In reality, technology is only as good as the way it has been developed, tested and validated. In particular, AI algorithms are prone to a lack of “generalisation” - i.e. their performance drops when presented with data it has not seen before. In the medical field, and particularly in areas where AI is being developed to direct a patient’s diagnosis or care, this is particularly problematic. Inappropriate diagnosis or advice to patients can lead to false reassurance, heightened concern and pressure on NHS services, or worse. It is concerning, therefore, that there are a large number of smartphone apps available that provide an assessment of skin lesions, including some that provide an estimate of the probability of malignancy, that have not been assessed for diagnostic accuracy.
Skin Analytics has developed an AI-based algorithm, named: Deep Ensemble for Recognition of Malignancy (DERM), for use as a decision support tool for healthcare providers. DERM determines the likelihood of skin cancer from dermoscopic images of skin lesions. It was developed using deep learning techniques that identify and assess features of these lesions which are associated with melanoma, using over 7,000 archived dermoscopic images. Using these images, it was shown to identify melanoma with similar accuracy to specialist physicians. However, to prove the algorithm could be used in a real life clinical setting, Skin Analytics set out to conduct a clinical validation study.
Dr. Qing Chen[/caption]
Qing Chen, M.D., Ph.D.
Assistant Professor, Immunology, Microenvironment & Metastasis Program
Scientific Director, Imaging Facility
The Wistar Institute
MedicalResearch.com: What is the background for this study?
Response: We are focusing on how a specific type of brain cells, astrocytes, helps the cancer cells from melanoma and breast cancer to form metastatic lesions.


