A five-year, $2.7 million grant from the National Cancer Institute will help provide answers. The goal: Improve survival rates for lung cancer patients.
The question researchers want to answer: Are there new ways to determine the best treatment for these patients?
Relying only on radiological imaging of the lung could lead to false positives or negatives for lung cancer. Image credit: University of Washington
This is what UW Medicine researchers hope to discover through a five-year, $2.7 million grant from the National Cancer Institute.
“Diagnostic tests have known error rates,” said Dr. Farhood Farjah, associate professor of surgery, Division of Cardiothoracic Surgery, and associate medical director of the Surgical Outcomes Research Center at UW Medicine.
CT and PET scans both have false positives and negatives, he said. So if physicians rely on imaging alone, the cancer's stage, or determination of whether the disease has spread, might be classified incorrectly. That misclassification can inadvertently result int providing patients with either the wrong or less-than-optimal treatments, he said.
For example, understaging can lead to unnecessary surgery or omission of chemotherapy. Overstating can lead to unnecessary chemotherapy and omission of surgery. Either can lead to lower survival rates.
Imaging of part of the upper thorax, or chest cavity. Image credit: University of Washington
Biopsies on lymph nodes in the chest can reduce these errors. But they also can have drawbacks, including false negatives and procedure-related risks such as a collapsed lung, Farjah said.
While there are guidelines that help clinicians determine when to do biopsies, the evidence “is admittedly low,” he said.
“As well intentioned as the guidelines are, many smart clinicians will question and deviate from them,” Farjah said. Yet he said some patients who need biopsies aren’t getting them when necessary.
“The hallmark of clinical and scientific uncertainty is inexplicable variability in care,” he added.
When biopsies are needed, they help determine the next steps in treatment, such as surgery, radiation and chemotherapy.
Farjah and other researchers think that the path to a better diagnosis may lie in a micro statistical analysis with maximized safeguards to protect patient confidentiality. This would be conducted on 4,000 lung cancer patient records from Kaiser Permanente Northern California and Marshfield Clinic in Wisconsin.
Other medical systems and universities participating in the research are Medical University of South Carolina, and Kaiser Permanente Bernard J. Tyson School of Medicine.
Within UW Medicine, collaborators on the project include experts in thoracic surgery, pulmonary medicine, radiology, medical and radiation oncology, biostatistics and health economics. The researchers are looking for trends in patient treatments, survivability rates and whether current treatment guidelines were followed.
The study will test a risk prediction model that estimates the probability that a patient actually has cancer that has spread to the lymph nodes. This model might guide decisions on whether to conduct a biopsy if proven useful. The analytic tool will estimate outcomes under different treatment scenarios. Researchers will test if this tool works just as well with current guidelines for predicting long-term survival while reducing the number of unnecessary biopsies. The study also will determine if the statistical model can accurately predict the best treatment options.
“Where we can do better is doing fewer procedures on people who ultimately will not have any nodal disease,” Farjah said.
“Getting the right treatment is the best strategy for best outcomes,” he said, including long-term survival and providing a good quality of life.
While advances in screening and treatments often receive national attention, getting the correct staging of cancer “never feels like it gets enough of the spotlight,” Farjah said.
“Sometimes the most important things in our lives are right in front of us,” he said. “We’re just not paying attention.”
Source: University of Washington