Predictive nomogram for leprosy using genetic and epidemiological risk factors in Southwestern China: Case-control and prospective analyses.

BACKGROUND: There is a high incidence of leprosy among house-contacts compared with the general population. We aimed to establish a predictive model using these genetic factors along with epidemiological factors to predict leprosy risk of leprosy household contacts (HHCs). METHODS: Weighted genetic risk score (wGRS) encompassing genome wide association studies (GWAS) variants and five non-genetic factors were examined in a case-control design associated with leprosy risk including 589 cases and 647 controls from leprosy HHCs. We constructed a risk prediction nomogram and evaluated its performance by concordance index (C-index) and calibration curve. The results were validated using bootstrap resampling with 1000 resamples and a prospective design including 1100 HHCs of leprosy patients. FINDING: The C-index for the risk model was 0·792 (95% confidence interval [CI] 0·768-0·817), and was confirmed to be 0·780 through bootstrapping validation. The calibration curve for the probability of leprosy showed good agreement between the prediction of the nomogram and actual observation. HHCs were then divided into the low-risk group (nomogram score ≤ 81) and the high-risk group (nomogram score > 81). In prospective analysis, 12 of 1100 participants had leprosy during 63 months' follow-up. We generated the nomogram for leprosy in the validation cohort (C-index 0·773 [95%CI 0·658-0·888], sensitivity75·0%, specificity 66·8%). Interpretation The nomogram achieved an effective prediction of leprosy in HHCs. Using the model, the risk of an individual contact developing leprosy can be determined, which can lead to a rational preventive choice for tracing higher-risk leprosy contacts. FUNDING: The ministry of health of China, ministry of science and technology of China, Chinese academy of medical sciences, Jiangsu provincial department of science and technology, Nanjing municipal science and technology bureau.

Optimizing the clinical utility of PCA3 to diagnose prostate cancer in initial prostate biopsy.

BACKGROUND: PCA3 has been included in a nomogram outperforming previous clinical models for the prediction of any prostate cancer (PCa) and high grade PCa (HGPCa) at the initial prostate biopsy (IBx). Our objective is to validate such IBx-specific PCA3-based nomogram. We also aim to optimize the use of this nomogram in clinical practice through the definition of risk groups. METHODS: Independent external validation. Clinical and biopsy data from a contemporary cohort of 401 men with the same inclusion criteria to those used to build up the reference's nomogram in IBx. The predictive value of the nomogram was assessed by means of calibration curves and discrimination ability through the area under the curve (AUC). Clinical utility of the nomogram was analyzed by choosing thresholds points that minimize the overlapping between probability density functions (PDF) in PCa and no PCa and HGPCa and no HGPCa groups, and net benefit was assessed by decision curves. RESULTS: We detect 28% of PCa and 11 % of HGPCa in IBx, contrasting to the 46 and 20% at the reference series. Due to this, there is an overestimation of the nomogram probabilities shown in the calibration curve for PCa. The AUC values are 0.736 for PCa (C.I.95%:0.68-0.79) and 0.786 for HGPCa (C.I.95%:0.71-0.87) showing an adequate discrimination ability. PDF show differences in the distributions of nomogram probabilities in PCa and not PCa patient groups. A minimization of the overlapping between these curves confirms the threshold probability of harboring PCa >30 % proposed by Hansen is useful to indicate a IBx, but a cut-off > 40% could be better in series of opportunistic screening like ours. Similar results appear in HGPCa analysis. The decision curve also shows a net benefit of 6.31% for the threshold probability of 40%. CONCLUSIONS: PCA3 is an useful tool to select patients for IBx. Patients with a calculated probability of having PCa over 40% should be counseled to undergo an IBx if opportunistic screening is required.

Prostate cancer antigen-3 (PCA3) and PCA3-based nomograms in the diagnosis of prostate cancer: an external validation of Hansen's nomogram on a Norwegian cohort.

OBJECTIVE: The aim of this study was to test the ability of prostate cancer antigen-3 (PCA3) and Hansen's PCA3-based nomogram to predict prostate cancer (PCa) probability in a Norwegian cohort, with the goal of reducing unnecessary biopsies. MATERIAL AND METHODS: Altogether, 127 consecutive patients were recruited to this study at Haukeland University Hospital, Norway. Prostate-specific antigen (PSA), PCA3 score, digital rectal examination (DRE), prostate volume (Pvol) and age were determined. All patients had an extended 10-core biopsy. The performance of PCA3 score and Hansen's nomogram was tested. RESULTS: There were 124 evaluable patients. Among these, 59 patients had PCa on the initial biopsies. Mean PSA, PCA3 score and age were significantly higher and Pvol was significantly lower in patients with PCa. PCA3 scores of 35 and 21 led to a sensitivity of 71% and 81% and specificity of 72% and 55%, respectively. Hansen's nomogram gave an area under the curve (AUC) of 0.806. The intraclass correlation was 0.959 (Cronbach's alpha). Applied to this material, PCa would be missed in 15.2% of patients when applying the suggested threshold probability of 30%, among whom 66.7% had high-grade PCa. With a threshold probability of 20% only one patient had PCa and this was low grade. CONCLUSIONS: Hansen's PCA3-based nomogram is valid for this cohort. A threshold probability of 20% seems more adequate than 30% for this less screened cohort. PCA3 score only affects the biopsy indication in some patients and is recommended only for this subset. The results need to be confirmed in a larger study.