El Centre de Recerca i Difusió de la Imatge (CRDI) i la Guia de Fons en Imatge de la ciutat de Girona

In 1982 with 6,000 photo, the Girona City Council created the Photographic Archive of Girona (Spain). Since then special works on treatment and description of photographic have been made

Prediction of advanced colonic neoplasm in symptomatic patients: a scoring system to prioritize colonoscopy (COLONOFIT study).

Background: Fast-track colonoscopy to detect patients with colorectal cancer based on high-risk symptoms is associated with low sensitivity and specificity. The aim was to derive a predictive score of advanced colonic neoplasia in symptomatic patients in fast-track programs. Methods: All patients referred for fast-track colonoscopy were evaluated. Faecal immunological haemoglobin test (3 samples; positive> 4 μg Hb/g), and a survey to register clinical variables of interest were performed. Colorectal cancer and advanced adenoma were considered as advanced colonic neoplasia. A sample size of 600 and 500 individuals were calculated for each phase 1 and phase 2 of the study, respectively (Phase 1, derivation and Phase 2, validation cohort). A Bayesian logistic regression analysis was used to derive a predictive score. Results: 1495 patients were included. Age (OR, 21), maximum faecal-Hb value (OR, 2.3), and number of positive samples (OR, 28) presented the highest ORs predictive of advanced colonic neoplasia. The additional significant predictive variables adjusted for age and faecal-Hb variables in Phase 1 were previous colonoscopy (last 5 years) and smoking (no, ex/active). With these variables a predictive score of advanced colonic neoplasia was derived. Applied to Phase 2, patients with a Score > 20 had an advanced colonic neoplasia probability of 66% (colorectal cancer, 32%), while those with a Score ≤ 10, a probability of 10% (colorectal cancer, 1%). Prioritizing patients with Score > 10, 49.4% of patients would be referred for fast-track colonoscopy, diagnosing 98.3% of colorectal cancers and 77% of advanced adenomas. Conclusions: A scoring system was derived and validated to prioritize fast-track colonoscopies according to risk, which was efficient, simple, and robust

Development and external validation of a faecal immunochemical test-based prediction model for colorectal cancer detection in symptomatic patients

Background: Risk prediction models for colorectal cancer (CRC) detection in symptomatic patients based on available biomarkers may improve CRC diagnosis. Our aim was to develop, compare with the NICE referral criteria and externally validate a CRC prediction model, COLONPREDICT, based on clinical and laboratory variables. Methods: This prospective cross-sectional study included consecutive patients with gastrointestinal symptoms referred for colonoscopy between March 2012 and September 2013 in a derivation cohort and between March 2014 and March 2015 in a validation cohort. In the derivation cohort, we assessed symptoms and the NICE referral criteria, and determined levels of faecal haemoglobin and calprotectin, blood haemoglobin, and serum carcinoembryonic antigen before performing an anorectal examination and a colonoscopy. A multivariate logistic regression analysis was used to develop the model with diagnostic accuracy with CRC detection as the main outcome. Results: We included 1572 patients in the derivation cohort and 1481 in the validation cohorts, with a 13.6 % and 9. 1 % CRC prevalence respectively. The final prediction model included 11 variables: age (years) (odds ratio [OR] 1.04, 95 % confidence interval [CI] 1.02-1.06), male gender (OR 2.2, 95 % CI 1.5-3.4), faecal haemoglobin >= 20 mu g/g (OR 17.0, 95 % CI 10.0-28.6), blood haemoglobin = 3 ng/mL (OR 4.5, 95 % CI 3.0-6.8), acetylsalicylic acid treatment (OR 0.4, 95 % CI 0.2-0.7), previous colonoscopy (OR 0.1, 95 % CI 0.06-0.2), rectal mass (OR 14.8, 95 % CI 5.3-41.0), benign anorectal lesion (OR 0.3, 95 % CI 0.2-0.4), rectal bleeding (OR 2.2, 95 % CI 1.4-3.4) and change in bowel habit (OR 1.7, 95 % CI 1.1-2.5). The area under the curve (AUC) was 0.92 (95 % CI 0.91-0.94), higher than the NICE referral criteria (AUC 0.59, 95 % CI 0.55-0.63; p < 0.001). On the basis of the thresholds with 90 % (5.6) and 99 % (3.5) sensitivity, we divided the derivation cohort into three risk groups for CRC detection: high (30.9 % of the cohort, positive predictive value [PPV] 40.7 %, 95 % CI 36.7-45.9 %), intermediate (29.5 %, PPV 4.4 %, 95 % CI 2.8-6.8 %) and low (39.5 %, PPV 0.2 %, 95 % CI 0.0-1.1 %). The discriminatory ability was equivalent in the validation cohort (AUC 0.92, 95 % CI 0.90-0.94; p = 0.7). Conclusions: COLONPREDICT is a highly accurate prediction model for CRC detection.This study was funded by a grant from Instituto de Salud Carlos III (PI11/00094). JC and VH have received an intensification grant through the European Commission funded "BIOCAPS" project (FP-7-REGPOT 2012-2013-1, Grant agreement no. FP7-316265). The validation cohort recruitment was funded by a grant from Fundacio de la Marato TV3 2012 (785/U/2013). The funding institutions had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the article for publication