In patients with stage I lung cancer, adding noncancerous features to CT chest imaging better predicts overall survival than clinical features alone, according to an article published online in the American Journal of Roentgenology.
Modeling incorporating noncancerous imaging features captured on chest computed tomography (CT) along with clinical features, when calculated before stereotactic body radiation therapy (SBRT) is administered, improves survival prediction, compared to modeling that relies solely on clinical features, report the authors.
“The focus of the study was to look at the environment in which the cancer lives,” says senior author Florian J. Fintelmann, MD, a radiologist at Massachusetts General Hospital and an associate professor of radiology at Harvard Medical School, both in Boston. “This is looking at parameters such as aortic diameter, body composition – that is, the quantification and characterization of adipose tissue and muscle – calcifications of the coronary arteries and quantification of emphysema.”
CT images are used by radiation oncologists to determine where to deliver the radiation. “There’s more information from these images that we can use,” he said.
Survival estimates in patients with State I lung cancer now depend on biological age, Eastern Cooperative Oncology Group (ECOG) score and the presence of comorbidities, Fintelmann said.
This retrospective study involved 282 patients with a median age of 75 years. There were 168 women and 114 men. All patients had stage I lung cancer and were treated with SBRT between January 2009 and June 2017.
Researchers analyzed breast images before CT treatment. They assessed coronary artery calcium score (CAC) (see image above), pulmonary artery (PA) to aorta ratio, emphysema, and various measures of body composition (skeletal muscle and adipose tissue). They developed a statistical model to link clinical and imaging features to overall survival.
An elevated CAC score (11-399: HR, 1.83 [95% confidence interval, 1.15-2.91]† 400: HR, 1.63 [95% CI, 1.01-2.63]), increased PA-to-aortic ratio (HR, 1.33 [95% CI, 1.16-1.52]per 0.1 unit gain) and thoracic skeletal muscle decreased (HR, 0.88 [95% CI, 0.79-0.98]per 10 cm2/m2 increase) were independently associated with shorter overall survival, the researchers observed.
In addition, the 5-year overall survival was superior to the model that included clinical and imaging features and inferior to the model limited to clinical features only. Of all features, the PA-to-aortic ratio was the most predictive factor for overall survival.
In this single-center study of stage I lung cancer patients undergoing SBRT, increased CAC score, increased PA-to-aortic ratio, and decreased thoracic skeletal muscle index were independently predictive of poorer overall survival.
“Our modeling shows that these image features add so much more [to predicting overall survival]” said Fintelmann. “The strength of this study is that we show the usefulness [of the model] and how it exceeds the clinical risk prediction that is currently the standard of care. We think this will benefit patients in the sense that they can advise and better advise them on their medical decisions.”
This proof-of-concept study requires external validation, Fintelmann emphasized. “External data for validation is the next step,” he said, noting that he and fellow researchers welcome data input from other researchers.
Elsie Nguyen, MD, FRCPC, FNASCI, associate professor of radiology, University of Toronto, responded by email that the study shows that imaging functions complement clinical data in predicting overall survival.
“This study demonstrates the value of extracting non-cancer-related computed tomography imaging features to build a model that can better predict overall survival compared to clinical parameters alone (such as age, performance status, and co-morbidities) for stage I lung cancer patients treated with SBRT,” Nguyen wrote.
“Coronary artery calcium score, pulmonary artery-to-aorta ratio, and sarcopenia independently predicted overall survival,” she wrote. “These results are not surprising, as the prognostic value of each of these imaging features has already been established in the literature.”
Nguyen pointed to the power of the sum of these image features to predict overall survival.
“However, the results of this study show promising results that support the idea that combining clinical and imaging data points can help build a more accurate prediction model for overall survival,” she wrote. “This is analogous to the Brock University (in St. Catharines, Ontario) solitary pulmonary nodule calculator that calculates malignancy risk based on both clinical and imaging data points. However, external validation of these study results in other centers is required first.”
Fintelmann and Nguyen have not disclosed any relevant financial relationships.
This article originally appeared on MDedge.com, part of the Medscape Professional Network.