Table 7.

A logistic regression model based on informative fusion types [Fx (III, IV, ETS)] and PSAD to predict risk of aggressive cancer on biopsy

			Univariable logistic regression models			Multivariable logistic regression model1
Biopsy cohort (n)	Dependent variable	Diagnostic variable	OR (95% CI)	P	Overall accuracy (%)	OR (95% CI)	P	Overall accuracy (%)2
39	Gleason score (≥7 or 6)	Fx (III, IV, ETS)3	2.06 (1.11–3.81)	0.022	69.2	2.23 (1.09–4.59)	0.029	79.54
		PSAD5	21942.60 (7.80–6.18 × 107)	0.014	71.8	51723.691 (11.54–2.31 × 108)	0.011

PSAD, prostate-specific antigen density.

¹Hosmer–Lemeshow Goodness-of-Fit of logistic regression model: P = 0.910.

²Defined as (true positives + true negatives)/all.

³TMP:ERG subtype III, IV, or TMP:ETS (ETV 1, 4, or 5) (log-transformed continuous variable).

⁴70.6% sensitivity and 86.4% specificity at 50% cut-off value.

⁵PSAD continuous variable.