A274 IMPACT OF FIT CUT-OFFS VALUES ON MISSED COLORECTAL CANCER AND HIGH-RISK LESIONS
Notice bibliographique
Résumé
Colorectal cancer (CRC) is the third most common cancer worldwide and carries a high mortality rate. Early screening has decreased morbidity and mortality from CRC. The fecal immunochemical test (FIT) uses antibodies against human hemoglobin and gives a quantitative value depending on the amount of hemoglobin detected in stool samples. The FIT is used in Canada as the standard for CRC screening of average risk populations. A lower numerical FIT is more sensitive for detecting “high-risk” lesions (HRL) but increases the number of colonoscopies that are performed; therefore, adjusting the positive value threshold allows screening programs to change the sensitivity and specificity of the test for detecting HRL and CRC 1. Determine the correlation between numerical FIT and high-risk colonoscopy findings 2. Determine the correlation between numerical FIT and different types of HRL Our study was a retrospective, population-based cohort study that identified patients aged 50–74 years old who underwent colonoscopy as part of the Edmonton SCOPE program, a regional CRC screening program for patients who were FIT positive. FIT positive was defined as a value ≥75 ng/mL. Demographic data, including age, sex, and family history of CRC as well as colonoscopy findings, including number of polyps, HRL, or CRC was collected. HRL were defined as: polyp size ≥1 cm, ≥3 polyps that were tubular or sessile serrated adenomas (SSA), villous pathology, or high-grade dysplasia (HGD). Numerical FIT was then correlated with CRC and HRL Between January-December 2017, a total of 2369 patients underwent colonoscopy for FIT positivity. Males comprised 60.6% of patients with a mean age of 60.6 ± 7.0 years. Multivariable analysis, adjusting for age, sex, and family history, revealed each increase of 50 ng/mL resulted in a 3% increase in CRC or HRL detected by colonoscopy. Increased numerical FIT correlated with increased CRC as well as HGD and larger polyps (≥2 cm) but not with large hyperplastic polyps, villous polyps, tubular adenomas, or SSAs. When modeling a cut-off of ≥75 ng/mL to ≥100 ng/mL there was a 7.3% (2.0–17.6, CI 95%) increase in CRC miss rates and a 17.9% (13.4–20.6, CI 95%) increase in HRL miss rates; this was even more significant when the cut-off was increased from ≥75 ng/mL to ≥175 ng/mL with an increase in miss rates to 20.0% (10.4–33.0, CI 95%) for CRC and 44.1% (40.8–47.4, CI 95%) for HRL [Table 1]. Number needed to scope decreased by an average of 2.8 scopes for each increase in 25 ng/L for CRC but decreased minimally for HRL Although increasing the FIT cut-off value decreases the total number of colonoscopies performed, it significantly increases the rate of missed CRC and HRL. Additionally, numerical FIT appears to correlate with CRC and specific types of HRL including HGD and large polyps. Table 1. Detection rates, miss rates, and number needed to scope (NNS) for colorectal cancers and high-risk lesions at various FIT cut-offs. None
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».