Lung Cancer Detection and Classification Using Deep Learning
has been shown to reduce mortality by 20–43% and is now included in US screening guidelines. These CT studies can be performed as part of routine screening, for example, studies performed under the National Lung Cancer Screening trial.
Improving the sensitivity and specificity of lung cancer screening is imperative because of the high clinical and financial costs of missed diagnosis, late diagnosis and unnecessary biopsy procedures resulting from false negatives and false positives The clinical state of the art for diagnosing lung cancer is using the ACR Lung-RADS standard which tries to help a radiologist report in a consistent way and help them decide what is the malignancy risk (and therefore the protocol of treatment).
Despite improved consistency, persistent inter-grader variability and incomplete characterization of comprehensive imaging findings remain as limitations of Lung-RADS. These limitations suggest opportunities for more sophisticated systems to improve performance and inter-reader consistency. We aim to develop a deep learning algorithm for risk prediction, while improving accuracy, consistency and adoption of lung cancer screening.
Specific aim 1: Use deep learning techniques to predict malignancy probability and risk bucket classification from lung CT studies. This would allow for risk categorization of patients being screened and guide the most appropriate surveillance and management.
Specific aim 2: Apply deep learning techniques to detect malignant nodules and regions of concern within CT images (localization). Along with aim 1, this would allow to replicate a more complete part of a radiologist's workflow.
David Chettrit, Zebra Medical Vision Ltd.