Published onJuly 7, 2020
For any tumor that's accessible by scalpel, physicians often take what's called a biopsy, snipping away a tiny portion of the mass.
From that scrap, doctors glean as much information about the tumor as they can through two distinct methods. Imaging captures the physical likeness of the tumor cells, which can help scientists understand disease severity, among other things. And through molecular analysis, scientists can analyze the tissue to find drivers of disease on the cellular level.
These two modes of investigation are usually conducted separately, but James Zou, PhD, an assistant professor of biomedical data science at Stanford, has laid the groundwork for doctors to learn a tumor's spatial and genomic properties simultaneously.
"We thought, 'How can we use machine learning to combine both of these, so that we make the most out of these biopsies?'" Zou told me.
Published onJune 24, 2020
Current methods used to diagnose and treat depression are imprecise at best, relying largely on subjective answers to survey questions, said Leanne Williams, PhD, Stanford professor of psychiatry. At worse, these approaches can result in treatment choices that further postpone a patient's recovery as the disease progresses.
"Currently treatments are a trial and error process," Williams said. "It's one size fits all. If the first treatment doesn't work, a second gets tried. We need a more precise tool to pick the best treatment option first."
Williams and her collaborators set out to define a more effective model, which they hope can be used in clinics soon. In a recent study, they deployed an algorithm to interpret brainwave patterns unique to individuals with depression, with the goal of better pinpointing which symptoms change with treatment.
"We know that depression is very heterogenous, and that there are at least 1,000 unique combinations of symptoms that can be diagnosed as depression," said Williams, who is the director of Stanford's Center for Precision Mental Health and Wellness. "We've found that brainwave measurements can be used to help identify which particular symptoms change with antidepressant treatment and which do not."
Center for Digital Health Newsletter
Decoding Digital Health
The Association of American Medical Colleges (AAMC) has announced that the Stanford Center for Digital Health (CDH) was one of six finalists for the 2018 AAMC Innovations in Research and Research Education Award.
According to the AAMC, the goal of this year’s awards program was “to highlight innovative institutional models [that] promote tech transfer, entrepreneurship and research or research education partnerships with the private sector.” The focus of the award was a great fit for Stanford CDH, which, as stated in the abstract for the award application, “works to find synergies and create collaborations between Stanford Medicine and digital health companies” with the goal of creating “cutting-edge advancements at the intersection of health care and technology.”
Mintu Turakhia, MD, associate professor of cardiovascular medicine; Euan Ashley, MD, professor of cardiovascular medicine and pathology; Ken Mahaffey, MD, professor of cardiovascular medicine; and Marco Perez, MD, assistant professor of cardiovascular medicine submitted the Stanford CDH abstract and application. It focused on several key areas for the CDH: enabling research; tailored education and community building; and leading flagship studies. It also pointed to the Apple Heart Study and MyHeartCounts apps as examples of “ground-breaking digital health clinical trials and cohort studies [that are] the first of [their] kind, having made major methodological advancements in scaling clinical trials digitally and virtually, and in the evaluation of apps, sensors, and the broad class of digital therapeutics."