Vanderbilt Ingram Cancer Center Archives
Jul. 16, 2020—After serving 16 years as associate director for Research Education at Vanderbilt-Ingram Cancer Center (VICC), Ann Richmond, PhD, Ingram Professor of Cancer Research, is stepping down from the leadership post.
Friedman named associate director for Community Science and Health Outcomes at Vanderbilt-Ingram Cancer Center
Jul. 2, 2020—Debra Friedman, MD, MS, E. Bronson Ingram Chair of Pediatric Oncology, is expanding her leadership role in improving cancer outcomes both within and beyond the Vanderbilt-Ingram Cancer Center (VICC) catchment area. She has been named associate director of Community Science and Health Outcomes.
Jul. 2, 2020—The Latino community is affected disproportionately by COVID-19. News outlets report leaders and lawmakers calling its impact “catastrophic.” In the state of Tennessee, one-third of residents who test positive for the coronavirus are Hispanic, even though only 5.6 percent of the population is Hispanic.
May. 26, 2020—People with thoracic cancers sickened by COVID-19 were especially vulnerable to deaths with a 35% mortality rate, according to early results from TERAVOLT, a global consortium that tracks outcomes among this vulnerable patient population.
May. 21, 2020—More than a dozen representatives of Vanderbilt University Medical Center helped plan and made a commitment to help carry out the strategy for how Tennessee will prevent cancer and minimize its burden on state residents.
May. 7, 2020—by Tom Wilemon Computed tomography scans for people at risk for lung cancer lead to earlier diagnoses and improve survival rates, but they can also lead to overtreatment when suspicious nodules turn out to be benign. A study published in American Journal of Respiratory and Critical Care Medicine indicates that an artificial intelligence strategy can correctly assess and categorize these indeterminate pulmonary nodules (IPNs). When compared to the conventional risk models clinicians currently use, the algorithm developed by the team of researchers in a very large dataset (15,693 nodules) reclassified IPNs into low-risk or high-risk categories in over a third of cancers and benign nodules. “These results suggest the potential clinical utility of this deep learning algorithm to revise the probability of cancer among IPNs aiming to decrease invasive procedures and shorten time to diagnosis,” said Pierre Massion, MD, Cornelius Vanderbilt Chair in Medicine at Vanderbilt University, the study’s lead author. Currently, clinicians refer to guidelines issued by the American College of Radiology and the American College of Chest Physicians. Adherence to these guidelines can be variable, and how patient cases are classified can be subjective. With the goal of providing clinicians with an unbiased assessment tool, the researchers developed an algorithm based on datasets from the National Lung Screening Trial, Vanderbilt University Medical Center and Oxford University Hospital. Their study is the first to validate a risk stratification tool on multiple independent cohorts and to show reclassification performance that is significantly superior to existing risk models. With IPNs, clinicians are often faced with the dilemma of weighing whether to advise a patient to undergo an invasive surgical procedure, which may be unnecessary, against a watch-and-wait strategy, which may result in delaying needed cancer treatment. A definitive diagnosis of an IPN can take up to two years. Better assessment tools are needed by clinicians as screenings for patients at risk for lung cancer increase. Lung cancer is the leading cause of cancer-related death in the United States and globally. The overall five-year survival rate is 21.7%, but it is much greater (92%) for those patients who receive an early diagnosis of stage IA1 non-small cell cancer. n
Apr. 30, 2020—A multi-institutional consortium led by Vanderbilt-Ingram Cancer Center (VICC) is collecting data on cancer patients with COVID-19 as part of a rapid effort to understand the unique effects the coronavirus has on this vulnerable population.