Abstract
Objective: To demonstrate a data-driven method for personalizing lung cancer risk prediction using a large clinical dataset. Materials and Methods: An algorithm was used to categorize nodules found in the first screening year of the National Lung Screening Trial as malignant or nonmalignant. Risk of malignancy for nodules was calculated based on size criteria according to the Fleischner Society recommendations from 2005, along with the additional discriminators of pack-years smoking history, sex, and nodule location. Imaging follow-up recommendations were assigned according to Fleischner size category malignancy risk. Results: Nodule size correlated with malignancy risk as predicted by the Fleischner Society recommendations. With the additional discriminators of smoking history, sex, and nodule location, significant risk stratification was observed. For example, men with ≥60 pack-years smoking history and upper lobe nodules measuring >4 and <6mm demonstrated significantly increased risk of malignancy at 12.4% compared to the mean of 3.81% for similarly sized nodules (P<.0001). Based on personalized malignancy risk, 54% of nodules >4 and ≤6mm were reclassified to longer-term followup than recommended by Fleischner. Twenty-seven percent of nodules ≤4mm were reclassified to shorterterm follow-up. Discussion: Using available clinical datasets such as the National Lung Screening Trial in conjunction with locally collected datasets can help clinicians provide more personalized malignancy risk predictions and followup recommendations. Conclusion: By incorporating 3 demographic data points, the risk of lung nodule malignancy within the Fleischner categories can be considerably stratified and more personalized follow-up recommendations can be made.
Original language | English (US) |
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Pages (from-to) | 1046-1051 |
Number of pages | 6 |
Journal | Journal of the American Medical Informatics Association |
Volume | 24 |
Issue number | 6 |
DOIs | |
State | Published - Nov 1 2017 |
Externally published | Yes |
Keywords
- Cancer screening
- Clinical decision support
- Data mining
- Lung cancer
- Medical informatics
ASJC Scopus subject areas
- Health Informatics