Performance of biopsy factors in predicting unfavorable disease in patients eligible for active surveillance according to the PRIAS criteria

Adverse pathology after radical prostatectomy: the prognostic role of cumulative cancer length >6-mm threshold in prostate cancer-positive biopsies

Background

To investigate the role of Cumulative Cancer Length (CCL) and PCa positive core number (PCapcn) in random prostate biopsies as predictors of Adverse Pathology (AP) at definitive pathology.

Methods

We prospectively enrolled patients submitted to random ultrasound guided prostate biopsies for suspect PCa in our center since 2016. Inclusion criteria were PSA <20 ng/ml or >3 ng/ml and age<71 years. Data on CCL and Grade Group (GG) at biopsy and pathology after Radical Prostatectomy (RP) were collected. AP was defined as pT3 or higher TNM, Positive Surgical Margin (>2mm) or PCa Positive Lymph Node. ROC curve was used to establish an appropriate CCL and PCapcn thresholds that were then investigated as predictors of AP at definitive pathology.

Results

Among 882 eligible biopsies, 344 had PCa and underwent RP. Mean age was 64 years (SD 5). Mean PSA was 7.75 (SD: 3.66). At definitive pathology there were AP features in 196 (56.9%) RP. PCapcn and CCL were statistically significantly associated with AP (p<0.0001). At multivariate age-adjusted logistic regression only PCapcn had an OR of 1.513 (CI95% 1.140-2.007) p=0.004. Through ROC curve a CCL>6mm and PCapcn >3 thresholds for AP were established (Area: 0.769; p<0.0001 CI 95% 0.698-0.840 and Area: 0.767; p<0.0001 CI 95% 0.696-0.837). When considering CCL>6mm AP had OR 5.462 (CI 95% 2.717-10.978) p<0.0001 and PCapcn >3 had OR 7.127 (CI 95% 3.366-15.090) p<0.0001. In particular, for GG 1 and 2, CCL>6mm had OR 3.989 (CI 95% 1.839-8.652) p<0.0001, while PCapcn >3 had OR 5.541 (CI 95% 2.390-12.849) p<0.0001.

Conclusions

At present time, random prostate biopsies might carry useful information regarding tumor extension and aggressiveness. A CCL>6mm or PCapcn >3 might be associated with AP features, in particular for low and favorable intermediate risk PCa.

Measurement error and timing of predictor values for multivariable risk prediction models are poorly reported

OBJECTIVE

Measurement error in predictor variables may threaten the validity of clinical prediction models. We sought to evaluate the possible extent of the problem. A secondary objective was to examine whether predictors are measured at the intended moment of model use.

METHODS

A systematic search of Medline was used to identify a sample of articles reporting the development of a clinical prediction model published in 2015. After screening according to a predefined inclusion criteria, information on predictors, strategies to control for measurement error, and intended moment of model use were extracted. Susceptibility to measurement error for each predictor was classified into low and high risks.

RESULTS

Thirty-three studies were reviewed, including 151 different predictors in the final prediction models. Fifty-one (33.7%) predictors were categorized as high risk of error; however, this was not accounted for in the model development. Only 8 (24.2%) studies explicitly stated the intended moment of model use and when the predictors were measured.

CONCLUSION

Reporting of measurement error and intended moment of model use is poor in prediction model studies. There is a need to identify circumstances where ignoring measurement error in prediction models is consequential and whether accounting for the error will improve the predictions.

Predictors of pathological upgrading in low-risk prostate cancer patients without hypointense lesions on an apparent diffusion coefficient map of multiparametric magnetic resonance imaging

PURPOSE

To examine the clinicopathological features and identify the predictors of pathological upgrading in low-risk prostate cancer (PCa) patients without hypointense lesions on the apparent diffusion coefficient (ADC) map calculated from multiparametric magnetic resonance imaging.

METHODS

We reviewed the medical records of 1905 PCa patients who underwent radical prostatectomy between 2007 and 2015. All ADC images were graded using the five-grade Likert scale; the positive hypointense lesions were graded 4-5. We analyzed 256 patients with low-risk classifications according to D'Amico criteria. Patients were classified into two groups according to the pathologic upgrading in the surgical specimens. The predictive factors for pathologic upgrading were evaluated using a multivariate logistic regression analysis.

RESULTS

In 256 patients with low-risk PCa, the percentage of positive cores [odds ratio (OR) 1.09; 95 % confidence interval (CI) 1.02-1.16], the percentage of cancer in the positive cores (OR 1.07, 95 % CI 1.03-1.12), and the presence of hypointensity on an ADC map (OR 2.28; 95 % CI 1.23-4.22) were independent predictors of pathologic upgrading. Notably, 138 of low-risk patients (53.9 %) had no hypointense lesions on an ADC map. Of these 138 patients, the percentage of positive cores (OR 1.09; 95 % CI 1.01-1.18) and the percentage of cancer in the positive cores (OR 1.06; 95 % CI 1.01-1.12) remained independent predictors of pathologic upgrading.

CONCLUSIONS

In low-risk PCa patients without hypointense lesions on an ADC map, biopsy-related parameters such as the percentage of positive cores and the percentage of cancer in the positive cores were independent predictors of pathological upgrading following radical prostatectomy.