A model for lung cancer risk prediction that combines deep learning features from the Sybil model with clinical and epidemiological factors.
First, you need to process a low-dose CT image of the subject to be analyzed using the Sybil model. Then, record the resulting 6-year lung cancer risk prediction value and use it as input in the program below.
To run Sybil-Epi, download the sybil_epi.py file from this repository and run it as indicated below:
python sybil_epi.py --age 66.08055556 --bmi 29.64582054 --copd 0 --education 6 --ethnicity White --family_history 0 --personal_history 1 --smoking_duration 43 --smoking_intensity 0.8 --smoking_quit 0 --smoking_status 0 --risk_sybil_6_year 0.034103291
The subject used in the example above presents the following factor values1:
| Factor | Value |
|---|---|
| Age (years) | 66.08055556 |
| BMI (kg/m2) | 29.64582054 |
| COPD (0-no, 1-yes) | 0 |
| Education level2 | 6 |
| Ethnicity | White |
| Family lung cancer history (0-no, 1-yes) | 0 |
| Personal cancer history (0-no, 1-yes) | 1 |
| Smoking duration (years) | 43 |
| Smoking intensity (cigarrettes per day) | 0.8 |
| Smoking quit time (years) | 0 |
| Smoking status (0-former, 1-current) | 0 |
| 6-year Risk Sybil3 | 0.034103291 |
Further details on how to use sybil_epi.py can be obtained with the command
python sybil_epi.py -h
1All factors were measured using the units indicated in the PLCOm2012 model.
2Education was measured in six ordinal levels: less than high-school graduate (level 1), high-school graduate (level 2), some training after high school (level 3), some college (level 4), college graduate (level 5), and postgraduate or professional degree (level 6).
3The 6-year Risk Sybil value can be calculated from a single low-dose CT image, analyzed using the Sybil model.