The Role of Tumor Volume Ratio in Predicting Clinically Significant Prostate Cancer on Transperineal Biopsy

Abstract

Objectives:

Multiparametric magnetic resonance imaging (mpMRI) has made dramatic inroads into the management of localized prostate cancer (PCa); however, not all suspicious lesions represent clinically significant (cs) PCa. We aimed to analyze the hypothetical effect of incorporating tumor volume ratio (TVR) into prostate biopsy (PBx) decision-making.

Materials and Methods:

Two hundred and fifty-two patients with suspicious lesions at mpMRI undergoing transperineal PBx under local anesthesia between 2019 and 2022 were retrospectively evaluated. TVR was calculated by dividing the tumor volume by the prostate volume. A regression model was used to assess predictors of csPCa. Descriptive statistics were applied to evaluate the effect of including TVR in PBx decision-making.

Results:

Overall, 119 patients (47%) were found to have csPCa. Age (p < 0.001), prior negative PBx (p = 0.011), and TVR (p < 0.001) were found to be independent predictors of csPCa. Applying the TVR cutoff of 0.23, a total of 117/252 (46%) PBx would have been avoided at the cost of missing csPCa in 26 (10%) men.

Conclusions:

Age, previous biopsy status, and TVR were found to be independent predictors of csPCa in men with suspicious lesions at mpMRI. Implementation of TVR into PBx decision-making improves the accuracy of mpMRI. Future studies are required to validate our findings and evaluate the role of TVR in avoiding unnecessary PBx.

Introduction

Prostate cancer (PCa) is the most common solid tumor among men in Western countries and the second leading cause of cancer death.^1,2 The likelihood of adverse clinical outcomes is strongly correlated with the anatomopathological results of prostate biopsy (PBx), leading to the definition of clinically significant PCa (csPCa), which should be managed with active treatment, as opposed to clinically insignificant PCa (ciPCa), which is eligible for active surveillance (AS).³ Research is now focusing on imaging and molecular strategies capable of identifying patients who are more likely to present csPCa at PBx. Of these, multiparametric magnetic resonance imaging (mpMRI) has dramatically changed the diagnostic pathway for csPCa, emerging as the most accurate imaging test for detecting csPCa.^4

–7 Consequently, international guidelines recommend the use of mpMRI followed by mpMRI-guided targeted biopsy (MTBx) for the diagnosis of csPCa in biopsy-naive patients, as well as in patients enrolled in the AS protocol.^1,8 However, current data show that several patients with a suspicious lesion on mpMRI who undergo PBx are not found to have csPCa, regardless of Prostate Imaging Reporting and Data System (PI-RADS) score categories.⁹ Thus, in addition to PI-RADS score category, other clinical and imaging risk factors have been identified as predictors of csPCa, including age,¹⁰ previous PBx status,¹¹ suspicious digital rectal examination (DRE), lesion localization in the peripheral zone,¹² and prostate-specific antigen (PSA) density (PSAD), derived from pre-PBx PSA and glands volume.¹³ However, data on the role of tumor volume and tumor volume ratio (TVR), which is calculated by dividing tumor volume by prostate volume, in predicting csPCa are still controversial.¹⁴ Therefore, in a large cohort of mpMRI-positive patients undergoing transperineal (TP) PBx under local anesthesia in an outpatient setting, we aimed to evaluate csPCa detection rates and to assess the role of TVR in increasing mpMRI detection rate of csPCa.

Materials and Methods

Patients and data collection

This retrospective, single-center study was conducted between May 2019 and July 2022 in accordance with the principles of Good Clinical Practice and the Declaration of Helsinki. Patients were identified retrospectively from a prospectively maintained database that records all TP PBx from our tertiary center. There were three indications to undergo mpMRI: (1) based on high clinical suspicion despite previous negative PBx, (2) follow-up PBx in AS program, and (3) high clinical suspicion of PCa and no previous PBx underwent direct mpMRI-US fusion PBx. Out of an initial cohort of 325 patients with suspicious PCa, those with nonsuspicious mpMRIs PI-RADS 1 and 2 (n = 18), missing data on PI-RADS score category (n = 20), unrecorded mpMRI lesion size (n = 7) or a history of prior PCa treatment (n = 28) were excluded. Clinical data were collected from medical patient records and included: age, past medical history, pre-PBx PSA level, mpMRI report, PBx procedure report, and pathology report.

mpMRI and biopsy procedure

All prebiopsy mpMRI scans, consisting of multiplanar T2-weighted images, diffusion-weighted imaging, dynamic contrast-enhanced mpMRI, and T1-weighted images with fat suppression,¹⁵ were performed either at our institutions or externally using a 1.5- or 3-T scanner and were reported according to the PI-RADS v.2 (until 2019) and PI-RADS v.2.1 (since 2020) by dedicated radiologists.^16,17 Prostate volume was estimated based on prebiopsy mpMRI (cm³). Index lesion volumes were assessed using the ellipsoidal formula on the diffusion-weighted imaging sequence with the highest b value and in sagittal T2 sequences (cm³). The TVR was determined by dividing the volume of the tumor by the volume of the prostate as measured by mpMRI. PSAD was calculated by dividing PSA by prostate volume. Board-certified urologists or residents under supervision performed mpMRI fusion PBx via TP route under local anesthesia in an outpatient setting according to the center’s clinical routine. Lesions with a PI-RADS score ≥3 on mpMRI were subjected to MTBx (with a minimum of two cores per lesion). MTBx was performed using MRI/US fusion software. Systematic biopsy (SBx) cores were also obtained in all biopsy-naive patients. In patients with a previous negative PBx or in the AS program, the addition of SBx cores was at the discretion of the urologist. SBx cores, when included, did not cover the regions targeted by MTBx. The number of SBx cores was at the discretion of the urologist, based primarily on lesion location and prostate volume. All the specimens were assessed by institutional pathologists. All the pathologists were specialists in urogenital cancer, and PBx results were reported separately for MTBx and SBx, and in accordance with the classification of the International Society of Urological Pathology (ISUP).¹⁸

Data synthesis and analysis

PCa with ISUP ≥2 was considered csPCa. Descriptive statistics were used to characterize our cohort based on the presence of csPCa at pathology reports. Comparisons between the two groups were performed with χ² and Fisher’s exact tests for categorical data and Student’s t-test or Wilcoxon rank-sum test for continuous data. The median and interquartile range were used to report results for continuous variables, and the frequency with percentage for categorical variables. Continuous variables were described as medians with interquartile ranges. Categorical variables were described with integers and percentages. To identify the predictors of detecting csPCa (primary endpoint), we performed a univariate binary logistic regression analysis including the following covariates: age, family history of PCa, DRE, previous PBx status, PSA value, prostate volume, PSAD cutoff ≥0.15, number of lesions, total number of cores taken, target localization, index lesion volume, and TVR. Significant predictors were tested in a multivariate regression analysis. Receiver-operating characteristic (ROC) curves were calculated for a further analysis of the significant predictors from a multivariate analysis.¹⁹ Sensitivity (SE), specificity (SP), positive predictive value (PPV), and negative predictive value (NPV) were calculated for TVR. A p-value of <0.05 was considered statistically significant. Statistical analysis was done with STATA^®16.1 (StataCorp, College Station, Texas).

Results

Baseline characteristics

A total of 252 patients were included in the analysis. Baseline characteristics stratified by the presence of csPCa versus no PCa/ciPCa are shown in Table 1. Men with csPCa were older (68 vs 64 years, p < 0.001), had a lower prevalence of previous negative PBx (10% vs 29%, p = 0.003), and had higher rates of PI-RADS 5 score at mpMRI (47% vs 19%, p < 0.001) compared with patients without csPCa. Additionally, men with csPCa had higher mean PSA values at PBx (9 vs 7 ng/mL, p = 0.008), smaller prostate volume (42 vs 52 cm³, p < 0.001), and higher lesion volume at mpMRI (14 vs 10 cm³, p < 0.001) resulting in a significantly higher PSAD (0.2 vs 0.1, p < 0.001) and higher TVR (0.3 vs 0.2, p < 0.001).

Table 1.

Baseline Characteristics Stratified by Clinically Significant Prostate Cancer

		No PCa/insignificant PCa	Clinically significant PCa
		n = 133	n = 119	p
Age, median (IQR)	Years	64 (57–68)	68 (62–74)	<0.001
Family history of PCa, n (%)	Yes	9 (7)	4 (3)	0.2
Suspicious DRE, n (%)	Yes	1 (1)	4 (3)	0.2
Prior negative biopsies, n (%)	Yes	29 (22)	10 (8)	0.003
Active surveillance, n (%)	Yes	34 (26)	37 (31)	0.3
Total PSA, median (IQR)	ng/mL	7 (5–10)	9 (6–12)	0.008
Prostate volume at mpMRI, median (IQR)	cm³	52 (38–72)	42 (31–57)	<0.001
Lesion volume at mpMRI, median (IQR)	cm³	10 (7–14)	14 (10–19)	<0.001
PSAD, median (IQR)	ng/mL/mL	0.1 (0.09–0.2)	0.2 (0.1–0.3)	<0.001
Tumor volume ratio, median (IQR)		0.2 (0.1–0.3)	0.3 (0.2–0.5)	<0.001
PI-RADS score, n (%)
	3	41 (31)	4 (3.4)	<0.001
	4	73 (55)	68 (57)
	5	19 (14)	47 (39)
Total cores, median (IQR)		10 (7–12)	10 (7–12)	0.4
Target lesion location, n (%)
	Transitional zone	64 (48)	50 (42)	0.2
	Peripheral zone	65 (49)	60 (50)
	Both	4 (3)	9 (8)

The ANOVA test was used to compare categorical variables, while the Mann–Whitney U test was used to compare continuous variables. All values are reported as median and interquartile range.

The bold data in the table highlights statistically significant values (p < 0.05).

ANOVA = analysis of variance; DRE = digital rectal examination; IQR = interquartile range; mpMRI = multiparametric magnetic resonance imaging; PCa = prostate cancer; PI-RADS = Prostate Imaging Reporting and Data System; PSA = prostate-specific antigen; PSAD = PSA density.

Identification of predictors of csPCa

Predictors of csPCa at univariate and multivariate regression models are shown in Table 2. At univariate binary logistic regression analysis, older age (odds ratio [OR] 1.09 [95% CI: 1.05–1.12], p < 0.001) and history of prior negative PBx (OR 0.33 [95% CI: 0.15–0.71], p = 0.005) were significantly associated with the detection of csPCa. Moreover, pre-PBx PSA levels (OR 1.03 [95% CI: 1.00–1.07], p = 0.066), lower prostate volume (OR 0.99 [95% CI: 0.98–0.99], p < 0.010), increased lesion volume on mpMRI (OR 1.08 [95% CI: 1.04–1.13], p < 0.010) and consequently PSAD cutoff ≥0.15 (OR 2.78 [95% CI: 1.67–4.65], p < 0.010), and higher TVR (p < 0.001) were associated with the presence of csPCa. A multivariate logistic regression analysis confirmed age (OR 1.11 [95% CI: 1.06–1.16], p < 0.001), prior negative biopsy (OR 0.30 [0.11–0.76], p = 0.011), and TVR (p < 0.001) as independent predictors of csPCa.

Table 2.

Binary Logistic Regression Models for the Prediction of Clinically Significant Prostate Cancer

Predictor	Univariate regression			Multivariate regression
Predictor	OR	(95% CI)	p	OR	(95% CI)	p
Age	1.09	(1.05–1.12)	<0.001	1.11	(1.06–1.16)	<0.001
Family history of PCa	0.48	(0.14–1.60)	0.2
Suspicious DRE	0.27	(0.03–2.48)	0.2
Prior negative biopsies	0.33	(0.15–0.71)	0.005	0.30	(0.11–0.76)	0.011
Active surveillance	1.31	(0.76–2.28)	0.3
Total PSA	1.03	(1.00–1.07)	0.066	1.04	(0.99–1.09)	0.1
Prostate volume at mpMRI	0.99	(0.98–0.99)	0.010	0.98	(0.97–1.00)	0.1
Lesion volume at mpMRI	1.08	(1.04–1.13)	<0.001	1.00	(0.95–1.05)	0.9
PSAD cutoff
<0.15	Ref.			Ref.
≥0.15	2.78	(1.67–4.65)	<0.001	1.14	(0.51–2.52)	0.7
TVR cutoff
<0.17	Ref.
0.18 ≤ TVR > 0.23	4.97	(2.65–9.33)	<0.001	2.38	(0.95–5.93)	0.063
0.23 ≤ TVR > 0.28	7.75	(4.39–13.7)	<0.001	7.22	(3.02–17.3)	<0.001
≥0.28	4.83	(2.81–8.31)	<0.001	3.90	(1.67–9.11)	0.002
Number of lesion	1.16	(0.85–1.59)	0.3
Total cores	1.07	(0.99–1.17)	0.09
Target lesion location
Transitional zone	Ref.
Peripheral zone	1.37	(0.89–2.09)	0.2
Both	1.01	(0.96–2.56)	0.1

CI = confidence interval; DRE = digital rectal examination; mpMRI = multiparametric magnetic resonance imaging; OR = odds ratio; PCa = prostate cancer; PSA = prostate-specific antigen; PSAD = PSA density; TVR = tumor volume ratio. Significant p values are bold. For the multivariate analysis, we considered only significant parameters in the univariate analysis.

ROC curve analysis

The ROC curves of the significant predictors of csPCa at multivariate analysis show an area under the curve (AUC) of 0.67 for age, 0.43 for prior negative PBx, and 0.73 for TVR (as a continuous variable). The combined model including all three parameters showed the best predictive performance with an AUC of 0.84 (Fig. 1).

FIG. 1.

Diagnostic accuracy of the multivariate logistic regression model represented as area under ROC. ROC = receiver-operating characteristic

Evaluation of different TVR cutoffs

After identifying TVR as an independent predictor of csPCa, we analyzed the hypothetical effect of using different TVR cutoffs to guide PBx decision-making. Table 3 shows the number of missed PCa cases stratified by different ISUP grade groups and the number of avoided PBx for each TVR cutoff. Applying the TVR cutoff of 0.23, which achieved the highest rates of SE, SP, PPV, and NPV (78%, 68%, 69%, and 78%, respectively), a total of 117/252 (46%) PBx would have been avoided at the cost of missing csPCa in 26 (10%) men.

Table 3.

Hypothetical Risk and Benefit of Biopsy Decisions Based on Different TVR Cutoffs

TVR cutoff for rejecting biopsy	Number of missed PCa (n/N, %)		Number of avoided biopsies (n/N, %)
TVR cutoff for rejecting biopsy	ISUP 1	ISUP 2–5	Number of avoided biopsies (n/N, %)
≥0.18	8/22 (36)	19/119 (16)	84/252 (33)
≥0.20	8/22 (36)	21/119 (18)	94/252 (37)
≥0.23	10/22 (45)	26/119 (21)	117/252 (46)
≥0.25	11/22 (50)	33/119 (28)	130/252 (52)
≥0.28	14/22 (64)	47/119 (39)	148/252 (59)

ISUP = International Society of Urological Pathology; PCa = prostate cancer; TVR = tumor volume ratio.

Subgroup analysis

In a subgroup analysis of patients stratified by PI-RADS category, a TVR cutoff ≥0.23 predicted 100% (4/4) and 97% (46/47) of csPCa in PI-RADS 3 and PI-RADS 5 categories, respectively (Supplementary Table S1). Furthermore, in a subgroup of patients with a PSAD ≤0.15, patients with an elevated TVR had higher rates of csPCa compared with patients with a low TVR (57% [28/49] vs 18% [13/71], respectively; Supplementary Fig. S1). Applying the TVR cutoff of 0.23 and the PSA density cutoff of 0.15, a total of 58/252 (23%) PBx would have been avoided at the cost of missing csPCa in 11 (9%) men.

Discussion

In this large series of mpMRI-positive patients undergoing outpatient TP PBx under local anesthesia, higher TVR was significantly associated with the detection of csPCa. Not only did patients with csPCa have a significantly higher median TVR (0.2 vs 0.3, p < 0.001) compared with patients without csPCa but TVR was also identified as the independent predictor of csPCa, showing good performance in predicting csPCa with an AUC of 0.73 on ROC curve analysis. We identified 0.23 as the optimal cutoff for TVR, able to avoid the highest number of PBx (46%) while limiting missed csPCa (10%).

MpMRI has become a cornerstone in the diagnostic pathway for PCa.²⁰ However, many additional strategies have been proposed to further improve its accuracy. Although biomarkers and predictive models have been shown to improve the mpMRI detection rate of csPCa,²¹ they also increase procedural costs without localizing PCa foci.²² The use of imaging-based predictors of csPCa, such as TVR, appears to be a valuable option for improving the performance of mpMRI.

Indeed, our findings are consistent with previous studies reporting TVR as an independent predictor of csPCa.^23

–26 However, because of the absence of validated cutoffs in large multicenter cohorts, the search for a cutoff capable of categorizing patients into TVR groups at increased risk of harboring csPCa is still helpful to guide clinical decision-making. We found a linear association between increasing TVR and csPCa detection rate. Hence, there was no optimal cutoff for TVR, and we decided to test the hypothetical effect of different cutoffs in terms of benefit (avoiding unnecessary biopsy) and harm (missing csPCa) to assess the utility of incorporating TVR into biopsy decision-making. When applying a cutoff value of 0.23, optimal values of SE, SP, PPV, and NPV were achieved (78%, 68%, 69%, and 78%, respectively). In a recent retrospective study by Erlich G. and colleagues, the authors used a TVR cutoff of 0.06 to achieve a 90% SE.²³ The authors reported that TVR was an independent predictor of csPCa in univariate and multivariate analyses (OR 38, 95% CI: 13–134, p < 0.001; OR 11.3, 95% CI: 3.92–39.1, p < 0.001, respectively) only for lesions located in the peripheral zone.²³ Interestingly, in our study, TVR was significantly associated with csPCa regardless of tumor location. Furthermore, in the study by Erlich G. and colleagues, subgroup analyses of PI-RADS 3 patients did not show an association between peripheral TVR and csPCa. Looking at our results, applying a TVR cutoff of 0.23 was able to predict all csPCa in PI-RADS three patients. Martorana and colleagues, in a retrospective cohort of patients undergoing TP PBx, reported that within each PI-RADS lesion group, the probability of detecting csPCa was 3.5-fold higher when the lesion volume was between 0.5 and 1 mL, and 4.3-fold higher when it was ≥1 mL.²⁶ Additionally, consistent with our findings, the authors did not detect csPCa in patients with PI-RADS three lesions and tumor volume <0.5 mL, concluding that this subgroup of patients should be informed of the low risk of PCa when undergoing PBx even if a TP approach is used. Salami et al. in a retrospective study of 312 men with lesions suspicious for cancer on mpMRI found a linear association between tumor volume and csPCa detection rate. Specifically, the authors report that 48% of lesions ≥1.0 cm³ were ISUP 2 compared with 24% of lesions <0.2 cm³.²⁷ Taken together, these results suggest that tumor volume and TVR may help to guide biopsy decision-making by optimizing the trade-off between avoiding unnecessary biopsies and missing csPCa.

Furthermore, in our study, PSAD cutoff of 0.15 was confirmed as a predictor of csPCa in univariate analysis, but not in multivariate analysis (OR 2.78, 95% CI: 1.67–4.65, p < 0.001; OR 1.14, 95% CI: 0.51–2.52, p = 0.751, respectively). Similarly, Nordströmet et al. in a multicenter retrospective study of 5291 men concluded that a cutoff of 0.15 may lead to an underdetection of csPCa and that a lower threshold (≤0.07) should be considered.²⁸ However, such a low cutoff value is associated with higher rates of negative findings and ciPCa on biopsy. Furthermore, Nativ et al. found that the SE for detecting csPCa decreases significantly with increasing prostate size, with values of 73%, 42%, and 3.0% in small, medium, and large prostates, respectively.²⁹ In addition, high-grade, undifferentiated tumors may secrete lower levels of PSA per unit volume and therefore PSAD may no longer be predictive in this population³⁰; thus, additional predictors of outcome are warranted. For instance, in this study, the combination of the TVR and the PSAD cutoff achieved a higher prediction of csPCa than the PSAD alone.

There are several limitations of our study that need to be mentioned. First, the evidence is limited due to the retrospective design of the study. Moreover, the study cohort is heterogeneous as patients in different biopsy settings were included. However, the data were prospectively collected, and this represents one of the largest series evaluating tumor volume and TVR in patients with suspicious lesions on mpMRI undergoing TP PBx under local anesthesia. Second, mpMRI results did not routinely undergo internal reevaluation by the institutions’ radiologists and were performed with both 1.5- and 3.0-T scanners. While this reflects common urological practice, it may lower the diagnostic performance of mpMRI in the study population. Third, the number of SBx and MTBx cores that were taken per patient was not standardized. Therefore, some cases of csPCa may have been missed in patients with fewer cores taken from both MTBx and SBx. However, an ideal reference standard such as TP template mapping biopsies was used for all patients in the current study, leading to improve overall csPCa detection rate. Lastly, we could not involve the whole mount specimen as a reference standard for the definition of csPCa in our work.

Conclusion

Age, previous biopsy status, and TVR were found to be independent predictors of csPCa in men with PI-RADS ≥3 lesions at mpMRI. Implementation of TVR into PBx decision-making can improve the accuracy of mpMRI. Future studies are required to validate our findings and evaluate the role of TVR in avoiding unnecessary PBx.

Footnotes

Authors’ Contributions

All the authors have made substantial contributions to all of the following: (1) the conception and design of the study, acquisition of data, or analysis and interpretation of data; (2) drafting the article or revising it critically for important intellectual content; and (3) final approval of the version to be submitted.

Author Disclosure Statement

P.P.A. certifies that all conflicts of interest, including specific financial interests and relationships and affiliations relevant to the subject matter or materials discussed in the article (e.g., employment/affiliation, grants or funding, consultancies, honoraria, stock ownership or options, expert testimony, royalties, or patent filed, received, or pending), are the following: Nothing to disclose.

Funding Information

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Supplementary Material

Supplementary Figure S1

Supplementary Table S1

Abbreviations Used

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