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Smart use of FIT: Combining FIT with other
parameters
Evelien Dekker
AMC Amsterdam
the Netherlands
Pre-selection in screening by FIT
• Positive FIT followed by colonoscopy
FIT + colonoscopy
• FIT has low burden, high participation-rate, cheap
• FIT limited sensitivity (& specificity)
• for CRC 75%
• for advanced adenomas 33%
Other parameters: risk factors?
• Many known risk factors for CRC
• Risk factors could be useful for optimizing pre-selection of screening by FIT
• Increase sensitivity: decrease missed lesions
• Increase specificity: decrease number of false positives
Age
Age
• Risk for CRC increases with age
• Currently only age is applied in screening programs
– Deciding who to screen
– UK: method
Gender
• Men have higher prevalence of AA & CRC1,2
• At every FIT cut-off: men have higher positivity rate,
higher sensitivity and lower specificity - higher
detection rates and higher miss rates3
• Tailored cut-off based on gender?
• Lower cut-off in women to level sensitivity – or lower
cut-off in men to level miss rates..
1Regula NEJM 2006, 2Brenner Gut 2007, 3Grobbee UEG Journal 2016
Gender
Familial risk for CRC
Familial risk for CRC
General population 4%
1 FDR >45 yrs 6%
1 FDR & 1 SDR 8%
1 FDR <45 yrs 10%
≥2 FDRs >50 yrs 17%
Hereditary syndromes (adenomatous polyposis, Lynch)
25-100%
For high-risk cancer syndromes (Lynch, Familial CRC):
colonoscopy-based surveillance programs
However, diagnosis often missed..
And families with lower risk (i.e. no Lynch or FCC) not
included in guidelines
Positive family history for CRC
Potential strategies:
Ask FIT+ patients at intake colonoscopy
Include with FIT also (extensive) questionnaire on
family history and decide:
FCC or Lynch: colonoscopy
No FCC or Lynch: FIT-result decisive
Include as risk-factor in model with FIT
Include family history for CRC
Smoking
Diet
BMI...
......
Other risk factors..?
FIT-based prediction model
FIT-based prediction model
• Identify individuals at high risk for CRC on the basis of FIT-result and risk factors
• Increase the sensitivity of pre-selection for CRC screening
• Increase the diagnostic yield of CRC screening... and cost-effectiveness
What is the gain in accuracy of a risk model with FIT plus risk factors in the pre-selection for colonoscopy in population screening for CRC, relative to FIT only?
FIT-based prediction model
1236 colonoscopy participants
COCOS-trial
Stegeman et al, Cancer Epid 2013
Risk factors CRC: questionnaire
Personal Characteristics Age
Behavioral characteristics Physical activity
Sex Smoking
First degree relatives with CRC
BMI
Medication Alcohol
Hormone Replacement Therapy for women
Nutritional characteristics Fiber intake
Regular aspirin/NSAID use
Calcium intake
1236 colonoscopy participants
1022 (83%) completed
questionnaire and FIT
82 (8%) advanced neoplasia at
colonoscopy
COCOS-trial
Stegeman et al, Cancer Epid 2013
Variable Diagnostic Odds Ratio (CI)
Male versus Female 1.21 (0.80-1.82)
Alcohol in units 1.01 (0.99-1.03)
NSAID 0.85 (0.51-1.44)
Sleep in hours 1.08 (0.90-1.31)
BMI 1.01 (0.96-1.06)
Physical Activity 0.82 (0.53-1.26)
Hormonal status (females) 0.35 (0.05-2.62)
Red meat 0.95 (0.85-1.06)
Stegeman et al, Cancer Epid 2013
COCOS: results
Variable Diagnostic Odds Ratio
Smoking (current/past) 1.97 (1.21-3.23)
Age (per year) 1.05 (1.02-1.09)
Calcium (per mg) 0.99 (0.98-0.99)
Family history (per family member) 1.50 (1.06-2.12)
FIT 7.93 (4.92 -12.78)
Stegeman et al, Cancer Epid 2013
COCOS: results
– Multivariate logistic regression
– Calculate risk of advanced neoplasia
COCOS: ROC
AUC FIT: 0.69 AUC model: 0.76
Stegeman et al, Gut 2014
COCOS: reclassification
• At cut-off 50 ng/ml buffer = 10 ugHb/g faeces
• 102 FIT-positives (10%)
• With same number of risk-positive persons: 25 other persons invited
• 5 extra cases of advanced neoplasia
Stegeman et al, Gut 2014
COCOS: sensitivities
For advanced neoplasia:
• FIT at cut-off 10 ugHb/g faeces: 32%
• Risk-model: 40%
Stegeman et al, Gut 2014
Other prediction models
Ladabaum Cancer 2016, Yeoh Gut 2011, Imperiale Gastro 2013, Kaminski Gastro 2013
• NCI risk score - for man and for women
• Asia-Pacific Colorectal Screening Score
• Other scoring-systems
All using clinical data & questionnaire data, no FIT
FIT-based prediction model
• FIT low burden, done at home, high participation-rates
• Risk-factors could increase yield of FIT-based screening program, but..
Participation is crucial for a population-based screening program!
Prediction model: participation?
• Age, gender: via municipal office..
• Information from questionnaires: low burden, understandable for everyone, etc.. depending on implementation!
– Postal mail
– Online
– App
– Via family doctor
– ..?
In conclusion.. smart use of FIT
• Combining FIT with other risk-factors can improve yield of FIT-based screening program
Evelien Dekker
In conclusion.. smart use of FIT
• Combining FIT with other risk-factors can improve yield of FIT-based screening program
• Cheap!
Evelien Dekker
In conclusion.. smart use of FIT
• Combining FIT with other risk-factors can improve yield of FIT-based screening program
• Cheap!
• Optimal prediction-model to be established
Evelien Dekker
In conclusion.. smart use of FIT
• Combining FIT with other risk-factors can improve yield of FIT-based screening program
• Cheap!
• Optimal prediction-model to be established
• Implementation is crucial!
Evelien Dekker
In conclusion - smart use of FIT
• Combining FIT with other risk-factors can improve yield of FIT-based screening program
• Cheap!
• Optimal prediction-model to be established
• Implementation is crucial!
• Future: combine FIT with other non-invasive tests, e.g. molecular stool-tests, blood test, genetic information (saliva?), microbiome information...
Evelien Dekker
Ideal combination?!
