Automatic machine learning based prediction of cardiovascular events in lung cancer screening data

Calcium burden determined in CT images acquired in lung cancer screening is a strong predictor of cardiovascular events (CVEs). This study investigated whether subjects undergoing such screening who are at risk of a CVE can be identified using automatic image analysis and subject characteristics. Moreover, the study examined whether these individuals can be identified using solely image information, or if a combination of image and subject data is needed.

A set of 3559 male subjects undergoing Dutch-Belgian lung cancer screening trial was included. Low-dose non-ECG synchronized chest CT images acquired at baseline were analyzed (1834 scanned in the University Medical Center Groningen, 1725 in the University Medical Center Utrecht). Aortic and coronary calcifications were identified using previously developed automatic algorithms. A set of features describing number, volume and size distribution of the detected calcifications was computed. Age of the participants was extracted from image headers. Features describing participants' smoking status, smoking history and past CVEs were obtained. CVEs that occurred within three years after the imaging were used as outcome.

Support vector machine classification was performed employing different feature sets using sets of only image features, or a combination of image and subject related characteristics. Classification based solely on the image features resulted in the area under the ROC curve (A(z)) of 0.69. A combination of image and subject features resulted in an A(z) of 0.71.

The results demonstrate that subjects undergoing lung cancer screening who are at risk of CVE can be identified using automatic image analysis. Adding subject information slightly improved the performance.