An artificial intelligence framework able to detect malignant lung nodules on chest computed tomography (CT) scans with performance meeting or exceeding that of expert radiologists is reported this week in Nature Medicine. This deep-learning model offers an automated image evaluation system to enhance the accuracy of early lung cancer diagnosis that could inform clinical interventions.
Lung cancer is the most common cause of cancer-related death in the United States, resulting in an estimated 160,000 deaths in 2018. Large clinical trials across the United States and Europe have shown that chest screening can identify the cancer and reduce death rates. However, high error rates and the limited applicability of this approach, alongside other clinical factors, mean that many lung cancers are being detected at advanced stages when they are hard to treat.
Daniel Tse and colleagues developed a deep-learning model and trained it on 42,290 CT scan images to predict the malignancy of lung nodules without the need for human involvement. They found that the artificial-intelligence-powered system was able to spot minuscule malignant lung nodules with an accuracy of 94% in 6,716 test cases. The model outperformed all six radiologists when previous CT imaging was not available and performed as well as the radiologists when there was prior imaging.
The authors caution that these findings need to be clinically validated in large patient populations, but they suggest this model may assist in improving the management and outcome of patients with lung cancer.