Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography.

PURPOSE: Low-dose computed tomography (LDCT) for lung cancer screening is effective, although most eligible people are not being screened. Tools that provide personalized future cancer risk assessment could focus approaches toward those most likely to benefit. We hypothesized that a deep learning model assessing the entire volumetric LDCT data could be built to predict individual risk without requiring additional demographic or clinical data. METHODS: We developed a model called Sybil using LDCTs from the National Lung Screening Trial (NLST). Sybil requires only one LDCT and does not require clinical data or radiologist annotations; it can run in real time in the background on a radiology reading station. Sybil was validated on three independent data sets: a heldout set of 6,282 LDCTs from NLST participants, 8,821 LDCTs from Massachusetts General Hospital (MGH), and 12,280 LDCTs from Chang Gung Memorial Hospital (CGMH, which included people with a range of smoking history including nonsmokers). RESULTS: Sybil achieved area under the receiver-operator curves for lung cancer prediction at 1 year of 0.92 (95% CI, 0.88 to 0.95) on NLST, 0.86 (95% CI, 0.82 to 0.90) on MGH, and 0.94 (95% CI, 0.91 to 1.00) on CGMH external validation sets. Concordance indices over 6 years were 0.75 (95% CI, 0.72 to 0.78), 0.81 (95% CI, 0.77 to 0.85), and 0.80 (95% CI, 0.75 to 0.86) for NLST, MGH, and CGMH, respectively. CONCLUSION: Sybil can accurately predict an individual's future lung cancer risk from a single LDCT scan to further enable personalized screening. Future study is required to understand Sybil's clinical applications. Our model and annotations are publicly available.[Media: see text].

External Validation of a Deep Learning Model for Predicting Mammographic Breast Density in Routine Clinical Practice.

RATIONALE AND OBJECTIVES: Federal legislation requires patient notification of dense mammographic breast tissue because increased density is a marker of breast cancer risk and can limit the sensitivity of mammography. As previously described, we clinically implemented our deep learning model at the academic breast imaging practice where the model was developed with high clinical acceptance. Our objective was to externally validate our deep learning model on radiologist breast density assessments in a community breast imaging practice. MATERIALS AND METHODS: Our deep learning model was implemented at a dedicated breast imaging practice staffed by both academic and community breast imaging radiologists in October 2018. Deep learning model assessment of mammographic breast density was presented to the radiologist during routine clinical practice at the time of mammogram interpretation. We identified 2174 consecutive screening mammograms after implementation of the deep learning model. Radiologist agreement with the model's assessment was measured and compared across radiologist groups. RESULTS: Both academic and community radiologists had high clinical acceptance of the deep learning model's density prediction, with 94.9% (academic) and 90.7% (community) acceptance for dense versus nondense categories (p < 0.001). The proportion of mammograms assessed as dense by all radiologists decreased from 47.0% before deep learning model implementation to 41.0% after deep learning model implementation (p < 0.001). CONCLUSION: Our deep learning model had a high clinical acceptance rate among both academic and community radiologists and reduced the proportion of mammograms assessed as dense. This is an important step to validating our deep learning model prior to potential widespread implementation.