Abstract
Objectives: To compare prostate cancer rates in magnetic resonance imaging (MRI)-detected lesions for patients who are chronically taking beta-blockers, nonsteroidal anti-inflammatory drugs (NSAIDs), or immunosuppressors. Methods: This cohort consisted of 897 Prostate Imaging Reporting & Data System (PI-RADS)v2 3-5 lesions from 590 MRI-targeted fusion prostate biopsies (UroNav). Baseline characteristics and clinicopathological data were collected. A matching cohort was analyzed, and multivariate analysis was completed for each medication group. Matching analysis accounted for age, prostate-specific antigen (PSA), and PI-RADS score. Multivariate analysis additionally considered lesion size. Results: Of the 897 lesions, 261/897 (29%) of lesions were identified as PI-RADS 3, 373/897 (42%) were PI-RADS 4, and 263/897 (29%) were PI-RADS 5. In the patient cohort, 16% were taking a beta-blocker, 3.9% were taking an NSAID, and 5.4% were taking an immunosuppressant. An equal number of lesions in controls were matched to 148 lesions in males taking beta-blockers, 37 lesions in males taking NSAIDs, and 46 lesions in males taking immunosuppressants. Matching was based on age, PSA, and PI-RADS score. In the matched cohort, neither beta-blockers, NSAIDs, nor immunosuppressants altered clinically significant prostate cancer (csPCa) identification on MRI (OR 1.11, CI 95% 0.6, 1.9; OR 0.70, CI 95% 0.32, 1.66; OR 1.73, CI 95% 0.59, 5.35, respectively). Conclusion: This pilot study shows no difference in csPCa detection rates in patients using anti-inflammatories or drugs that alter prostate blood flow.
Keywords: Prostate cancer, multiparametric magnetic resonance imaging (mpMRI), magnetic resonance imaging (MRI) targeted fusion biopsy
Introduction
The widespread use of multiparametric magnetic resonance imaging (mpMRI) and mpMRI-transrectal ultrasound (TRUS) fusion-targeted prostate biopsies has changed the diagnostic pathway of prostate cancer (PCa). Targeted biopsies can improve the detection of clinically significant PCa (Gleason score ≥ 7, Grade Group ≥ 2) while reducing the detection of clinically insignificant PCa [1-3].
Men undergoing magnetic resonance imaging (MRI) for suspicion of PCa often present with other associated comorbidities for which different classes of medications are prescribed, both for urological and nonurological conditions, such as benign prostate hyperplasia, hypertension, and diabetes [4,5]. Some medications have been proposed to potentially alter prostate vasculature (5-alpha reductase inhibitors, beta-blockers), change prostate smooth muscle tone (α-antagonists, phosphodiesterase inhibitors), reduce inflammation (nonsteroidal anti-inflammatory drugs (NSAIDs)), or change the prostate immune microenvironment (systemic immunosuppressors) [6-8]. However, the precise ways in which these medications may affect prostate cancer detection via MRI have not been thoroughly investigated.
No study to date has evaluated the effect of chronic use of such medicines within a cohort of MRI-targeted fusion biopsies. Therefore, the association between potential changes in prostate inflammation or blood flow and the detection of clinically significant prostate cancer remains unclear. In this study, we aim to compare prostate cancer detection rates stratified by different Prostate Imaging Reporting & Data System (PI-RADS) scores with MRI-TRUS fusion biopsy in patients using chronic beta-blockers, NSAIDS, and immunosuppressors.
Materials and methods
Patient population
Clinical, imaging, and pathologic data were collected for patients who underwent an MRI-targeted fusion prostate biopsy from 2016 through 2022 at the University of Wisconsin, a single tertiary medical center. Biopsies that solely included Prostate Imaging Reporting & Data System Version 2.1 (PI-RADSv2.1) lesions < 3 were excluded from the cohort. Biopsies that contained at least one lesion of at least PI-RADS 3 on index MRI were included in the study.
Clinical data collected included age, race, index prostate-specific antigen (PSA), and prior biopsy history (Figure 1). Prostate volumes were calculated and recorded from index MRI. Pathology results were used to elucidate the grade group for each biopsied lesion.
Figure 1.
Data Collection Flowchart. Patients with a magnetic resonance imaging (MRI)-targeted prostate biopsy and at least one PI-RADS lesion ≥ 3 were included in the study. Multiple variables, including age, race, index PSA, prostate volume, and grade group, were collected for each patient. Patients were then screened for the use of a beta-blocker, NSAID, or immunosuppressant for at least 3 months prior to UroNav. MRI = magnetic resonance imaging, PI-RADS = Prostate Imaging Reporting & Data System, PSA = prostate specific antigen.
Patients in each medication cohort (beta-blocker, NSAID, immunosuppressant) were actively taking the respective medication at least three months prior to the index MRI. Inhaled and oral immunosuppressants of any dose were included; topical and nasal immunosuppressants were excluded. This was based on the immunosuppressant’s systemic absorption. Patients who were on 81 mg daily of aspirin were not included in the NSAID cohort.
Analysis was performed on a per-lesion basis. Comparative analysis was performed using RStudio (Boston, MA). Baseline variables between the groups were compared using either a Student’s T-test, Mann-Whitney Test, or Chi-Square Test. Multivariable logistic regression analyses adjusting for age, PSA, and PI-RADS lesion score were performed, with final pathology identified on targeted biopsy as the dependent variable.
Biopsy protocol and histopathology
All patients underwent targeted biopsies. Fellowship-trained abdominal radiologists manually contoured the prostate and PI-RADSv2.1 score 3, 4, and 5 lesions (DynaCAD, Philips Healthcare, Massachusetts, USA) and imported them to ultrasound (bk3000, BK Medical, Massachusetts, USA) with image-fusion software (UroNav, Philips Healthcare), fellowship-trained Urologists performed the biopsies. Tissue cores were fixed in a 10% formalin solution and re-reviewed by a subspecialty urologic pathologist. Tissue cores obtained from the targeted and template biopsies were labeled and fixed separately. Biopsies with no associated Gleason Score due to non-malignant designation are referred to as “negative biopsies”.
Prostate volume calculation
Prostate volume was measured using T2-weighted MRI images acquired on a 3T scanner with axial, sagittal, and coronal orientations. A radiologist blinded to outcomes manually contoured the prostate on axial slices. Volume was calculated using the ellipsoid formula, where length was the cranio-caudal dimension on sagittal view, width the maximal transverse diameter on axial view, and height the anteroposterior dimension on sagittal view. In cases where segmentation software with exponential signal modeling was used, volume was computed by summing cross-sectional areas across slices, adjusting for signal decay to enhance boundary detection. Measurements were recorded in mL.
Statistical analysis
Given the numerical imbalance between all three medication cohorts and the non-medicated cohort, lesions in patients on each medication with lesions in non-medicated patients were subsequently matched. All matching analysis was performed using the MatchIt package in R, which calls functions from the Matching package [9,10]. Several matching methods were tested, including coarsened exact matching (CEM), nearest neighbor matching with and without replacement, and genetic matching. The balance of each matching method was assessed by evaluating standardized mean differences (SMD) and variance ratios. Pre-matching and post-matching SMD are shown in the Love Plot’s. Lesions were matched based on age, corrected PSA, and PI-RADS score. The genetic matching algorithm ultimately provided the most balanced results, with all SMDs less than the threshold of 0.1. Logistic regression was performed to estimate the marginal treatment effect on the pathologic outcome.
Results
Characteristics of cohort
590 MRI-targeted fusion patient prostate biopsies (UroNav) were reviewed, and 897 of the biopsied lesions (36%) met inclusion criteria and were identified as clinically significant prostate cancer (csPCa) (defined as PI-RADSv2.1 3-5). Of these 590 UroNavs, 94 (15.9%) of patients were taking a beta-blocker, 23 (3.9%) were taking an NSAID, and 32 (5.4%) were taking an immunosuppressant. Patients had an average of 1.52 lesions found on UroNav. The average age was 66.4 years, and the average PSA was 10.4 ng/mL. 55.4% of patients were found to have clinically significant prostate cancer (Supplementary Table 1). Analyzing the 897 individual lesions, 29% of lesions were identified as PI-RADS 3, 42% were PI-RADS 4, and 29% were PI-RADS 5. 16% of PIRADS lesions were found in patients on a beta-blocker, 4.1% in patients on an NSAID, and 5.1% in patients on an immunosuppressant. As indicated by the pathology report, 45.5% of lesions were negative, 32.9% were Grade Group (GG)1-2, and 21.6% were GG3-5 (Table 1).
Table 1.
Baseline characteristics of the overall cohort and medication subgroups on a per-lesion basis
| Characteristic | Total Cohort | Beta-Blocker | NSAID | Immunosuppressant |
|---|---|---|---|---|
| Number of Lesions | 897 | 148 (16%) | 37 (4.1%) | 46 (5.1%) |
| PI-RADS Score | ||||
| 3 | 261 (29%) | 27 (18%) | 10 (27%) | 18 (39%) |
| 4 | 373 (42%) | 69 (47%) | 17 (46%) | 18 (39%) |
| 5 | 263 (29%) | 52 (35%) | 10 (27%) | 10 (22%) |
| (p-value) | --- | (0.006) | (0.9) | (0.3) |
| csPCa on Targeted Biopsy | 324 (36%) | 65 (44%) | 13 (35%) | 18 (39%) |
| (p-value) | --- | (0.03) | (0.9) | (0.7) |
| Pathology of Targeted Biopsy | ||||
| No cancer | 408 (45.5%) | 60 (40.5%) | 14 (37.8%) | 19 (41.3%) |
| GG1-2 | 295 (32.9%) | 47 (31.8%) | 13 (35.1%) | 19 (41.3%) |
| GG3-5 | 194 (21.6%) | 41 (27.7%) | 10 (27.0%) | 8 (17.4%) |
| (p-value) | --- | (0.13) | (0.58) | (0.44) |
Each medication group was compared to the remainder of the total cohort to avoid duplicative comparison. PI-RADS = Prostate Imaging Reporting & Data System, csPCa = clinically significant prostate cancer, GG = grade group.
Characteristics of medication sub-groups
The baseline characteristics of the NSAID and immunosuppressant subgroups were not significantly different from their respective non-NSAID and non-immunosuppressant groups when considering age, PI-RADS score, PSA, number of csPCa lesions identified on targeted biopsy, and pathological Grade Group (Table 1).
In the beta-blocker sub-group, PI-RADS scores were significantly different between the two groups. 31% of lesions were PI-RADS 3 in the non-beta-blocker group compared to 18% in the beta-blocker group. Conversely, 28% of lesions were PI-RADS 5 in the non-beta-blocker group compared to 35% in the beta-blocker group. Significantly more lesions were found to be clinically significant on targeted prostate biopsy in the beta-blocker group (44%) compared to the non-beta-blocker group (35%). Grade Groups were not significantly different between the two groups (Table 1).
There were no significant differences found in age, PI-RADS score, PSA, presence of csPCa on targeted biopsy, or pathology of targeted biopsy between the beta-blocker, NSAID, nor immunosuppressant groups and their associated matched groups (Table 2).
Table 2.
Baseline characteristics of the matched medication subgroups
| Characteristic | Beta-Blocker | No Beta-Blocker | NSAID | No NSAID | Immuno-suppressant | No Immuno-suppressant |
|---|---|---|---|---|---|---|
| Match Size (N) | 148 | 148 | 37 | 37 | 46 | 46 |
| Age (years)* | 67 | 67 | 66 | 66 | 67 | 68 |
| (p-value) | > 0.9 | > 0.9 | > 0.9 | |||
| PSA (ng/mL)* | 8.6 | 8.7 | 8.3 | 8.2 | 7.5 | 7.7 |
| (p-value) | > 0.9 | > 0.9 | > 0.9 | |||
| PI-RADS Score* | ||||||
| 3 | 27 (18%) | 27 (18%) | 10 (27%) | 10 (27%) | 18 (39%) | 18 (39%) |
| 4 | 69 (47%) | 69 (47%) | 17 (46%) | 17 (46%) | 18 (39%) | 18 (39%) |
| 5 | 52 (35%) | 52 (35%) | 10 (27%) | 10 (27%) | 10 (22%) | 10 (22%) |
| (p-value) | > 0.9 | > 0.9 | > 0.9 | |||
| csPCa on Targeted Biopsy | 65 (44%) | 62 (42%) | 13 (35%) | 15 (41%) | 18 (39%) | 14 (30%) |
| (p-value) | 0.7 | 0.6 | 0.4 | |||
| Pathology of Targeted Biopsy | ||||||
| No cancer | 73 (49%) | 76 (51%) | 19 (51%) | 21 (57%) | 24 (52%) | 29 (63%) |
| GG1 | 10 (6.8%) | 10 (6.8%) | 5 (14%) | 1 (2.7%) | 4 (8.7%) | 3 (6.5%) |
| GG2 | 31 (21%) | 42 (28%) | 6 (16%) | 7 (19%) | 12 (26%) | 5 (11%) |
| GG3 | 12 (8.1%) | 9 (6.1%) | 2 (5.4%) | 5 (14%) | 3 (6.5%) | 3 (6.5%) |
| GG4 | 9 (6.1%) | 6 (4.1%) | 4 (11%) | 1 (2.7%) | 2 (4.3%) | 3 (6.5%) |
| GG5 | 13 (8.8%) | 5 (3.4%) | 1 (2.7%) | 2 (5.4%) | 1 (2.2%) | 3 (6.5%) |
| (p-value) | 0.3 | 0.3 | 0.5 | |||
Indicates matching criteria.
NSAIDs = Nonsteroidal anti-inflammatory drugs, PSA = prostate-specific antigen, PI-RADS = Prostate Imaging Reporting & Data System, csPCa = clinically significant prostate cancer, GG = grade group.
Analysis of medication sub-groups
Multivariate analysis of the medication subgroups, adjusting for age, PSA, lesion size, and PI-RADS lesion score, showed that medication use was not significantly associated with the detection of csPCa on targeted biopsy: beta-blocker use (P = 0.93, Table 3), NSAID use (P = 0.34, Table 3), and immunosuppressant use (P = 0.33, Table 3).
Table 3.
Multivariate analysis of medication sub-groups (OR = Odds Ratio, CI = Confidence Interval)
| Medication | Characteristic | OR | 95% CI | p-value |
|---|---|---|---|---|
| Beta-blocker | Age (years) | 1.05 | 1.03, 1.08 | -- |
| PSA (ng/mL) | 1.03 | 1.01, 1.06 | -- | |
| Lesion size (cm) | 1.05 | 0.80, 1.38 | -- | |
| PI-RADS score | ||||
| 3 | -- | -- | -- | |
| 4 | 2.41 | 1.71, 3.39 | -- | |
| 5 | 4.32 | 2.74, 6.82 | -- | |
| Beta-blocker use | 1.02 | 0.69, 1.50 | 0.93 | |
| NSAID | Age (years) | 1.05 | 1.03, 1.08 | -- |
| PSA (ng/mL) | 1.03 | 1.01, 1.06 | -- | |
| Lesion size (cm) | 1.05 | 0.80, 1.38 | -- | |
| PI-RADS score | ||||
| 3 | -- | -- | -- | |
| 4 | 2.41 | 1.71, 3.38 | -- | |
| 5 | 4.34 | 2.75, 6.84 | -- | |
| NSAID use | 1.42 | 0.69, 2.91 | 0.34 | |
| Immunosuppressant | Age (years) | 1.05 | 1.03, 1.08 | -- |
| PSA (ng/mL) | 1.03 | 1.01, 1.06 | -- | |
| Lesion size (cm) | 1.05 | 0.80, 1.38 | -- | |
| PI-RADS score | ||||
| 3 | -- | -- | -- | |
| 4 | 2.43 | 1.73, 3.42 | -- | |
| 5 | 4.38 | 2.78, 6.92 | -- | |
| Immunosuppressant use | 1.38 | 0.72, 2.66 | 0.33 |
No drug was shown to have an impact on the detection rate of clinically significant prostate cancer (p-value 0.93, 0.34, and 0.33 for beta-blocker, NSAID, and immunosuppressant use, respectively). NSAIDs = Nonsteroidal anti-inflammatory drugs, PSA = prostate-specific antigen, PI-RADS = Prostate Imaging Reporting & Data System.
In the matched cohorts, beta-blockers, NSAIDs, nor immunosuppressants were associated with enhanced or detracted csPCa identification on MRI (OR 1.11, CI 95% 0.6, 1.9; OR 0.70, CI 95% 0.32, 1.66; OR 1.73, CI 95% 0.59, 5.35, respectively).
Discussion
The use of multiparametric magnetic resonance imaging (mpMRI) combined with MRI-transrectal ultrasound (TRUS) fusion-targeted prostate biopsies has enhanced the detection of clinically significant prostate cancer (PCa) while reducing the identification of clinically insignificant cancer. In our research, we reviewed 590 patients undergoing targeted biopsies, correlating the radiological findings with histopathology in patients using drugs with potential effects on prostate blood flow and inflammation. Despite variations in lesion detection rates and characteristics between medication sub-groups, multivariate analysis, and matching analysis revealed that chronic use of beta-blockers, NSAIDs, and immunosuppressants was not significantly associated with changes in the detection rate of clinically significant PCa on MRI-targeted biopsies. This provides additional evidence that imaging protocols should not be interpreted differently when these medications are present.
Beta-blockers bind to beta-1 and beta-2 adrenergic receptors, thereby counteracting the actions of catecholamines like epinephrine and norepinephrine. These medications are most commonly prescribed for their effects on slowing the heart rate and reducing blood pressure by decreasing renin and cardiac output [11]. Studies have shown that the prostate has a high density of adrenergic nerves, which helps the prostate smooth muscle contract to expel prostatic fluid [12]. Murine models have revealed that adrenergic nerve density increases in early prostate cancer, and human studies have shown higher adrenergic nerve density correlates with more aggressive pathology and poorer prognosis [13,14]. Because beta-blockers reduce blood flow and there is a high adrenergic nerve density in early prostate cancer, this blood flow reduction could be particularly pronounced in lesions where prostatic cancer is present. Since mpMRI relies on blood flow changes to detect lesions, this reduction of blood flow in prostate cancer lesions could potentially alter imaging properties associated with lesion identification and grading. Several research articles have explored how beta-blockers can affect prostate cancer mortality and their effect on the incidence of prostate cancer. Still, none have addressed how beta-blockers could alter the detection of clinically significant prostate cancer on imaging [14-17]. We find that PI-RADS 3 lesions are found less commonly in patients on beta-blockers (Table 1), potentially reflecting alterations in vascular flow. However, our results suggest that despite the physiological potential for beta-blockers to alter properties within the prostate, these changes are not consequential enough to change the ability to detect clinically significant prostate cancer using mpMRI on a per-lesion basis. It is not clear if there is a dose-dependent effect, or whether a time-dependent effect would show any differences.
NSAIDs primarily reduce inflammation by inhibiting cyclooxygenase-2, which is inducibly expressed during an inflammatory response [18]. In contrast, immunosuppressants can reduce inflammation by inhibiting the release of immune mediators and reducing cytokine production [19]. Prior studies have shown that prostatic inflammation can confound the ability to accurately detect prostate cancer on MRI [20,21]. However, no studies have explored how NSAID or immunosuppressant use may alter the detection of clinically significant prostate cancer on imaging. By reducing prostatic inflammation through either NSAIDs or systemic immunosuppressants, cancerous prostatic lesions may have higher visibility on imaging and decrease the rate of false positives. However, our results did not show that using systemic NSAIDs or immunosuppressants reduced inflammation in the prostate enough to enhance the detection of clinically significant prostate cancer using mpMRI, which leads us to believe the cumulative effect is not substantial enough to change radiological findings, altering accuracy for prostate cancer detection.
Although the retrospective acquisition of the data is a limitation to the interpretation of the findings, the database analyzed has few missing data points, as the prostate MRIs were reread by fellowship-trained radiologists, which also reduces the interobserver variability. Our per-lesion analysis method guided by MRI has both strengths and limitations. This approach provides insight into our primary aim of understanding how an individual MRI lesion may be affected by medications. We did not focus this analysis on accounting for multiple lesions in a patient and did not assess the overall clinical impact of medication use on cancer detection given that this analysis would be confounded by nontargeted biopsies done concurrently.
Conclusion
Based on multivariate analysis and analysis of a matched MRI-targeted fusion biopsy cohort, no differences in lesion detection rates of clinically significant prostate cancer were seen across different medication subgroups. These results suggest that the long-term use of beta-blockers, NSAIDs, and immunosuppressants do not significantly alter the detection rates of clinically significant prostate cancer on MRI-targeted biopsies, and such findings support no changes in the current reporting protocols in case of the presence of these medications.
Disclosure of conflict of interest
None.
Supporting Information
References
- 1.Drost FH, Osses DF, Nieboer D, Steyerberg EW, Bangma CH, Roobol MJ, Schoots IG. Prostate MRI, with or without MRI-targeted biopsy, and systematic biopsy for detecting prostate cancer. Cochrane Database Syst Rev. 2019;4:CD012663. doi: 10.1002/14651858.CD012663.pub2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Siddiqui MM, Rais-Bahrami S, Turkbey B, George AK, Rothwax J, Shakir N, Okoro C, Raskolnikov D, Parnes HL, Linehan WM, Merino MJ, Simon RM, Choyke PL, Wood BJ, Pinto PA. Comparison of MR/ultrasound fusion-guided biopsy with ultrasound-guided biopsy for the diagnosis of prostate cancer. JAMA. 2015;313:390–397. doi: 10.1001/jama.2014.17942. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Kasivisvanathan V, Rannikko AS, Borghi M, Panebianco V, Mynderse LA, Vaarala MH, Briganti A, Budäus L, Hellawell G, Hindley RG, Roobol MJ, Eggener S, Ghei M, Villers A, Bladou F, Villeirs GM, Virdi J, Boxler S, Robert G, Singh PB, Venderink W, Hadaschik BA, Ruffion A, Hu JC, Margolis D, Crouzet S, Klotz L, Taneja SS, Pinto P, Gill I, Allen C, Giganti F, Freeman A, Morris S, Punwani S, Williams NR, Brew-Graves C, Deeks J, Takwoingi Y, Emberton M, Moore CM PRECISION Study Group Collaborators. MRI-targeted or standard biopsy for prostate-cancer diagnosis. N Engl J Med. 2018;378:1767–1777. doi: 10.1056/NEJMoa1801993. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Benzo RM, Moreno PI, Fox RS, Silvera CA, Walsh EA, Yanez B, Balise RR, Oswald LB, Penedo FJ. Comorbidity burden and health-related quality of life in men with advanced prostate cancer. Support Care Cancer. 2023;31:496. doi: 10.1007/s00520-023-07962-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Boakye D, Günther K, Niedermaier T, Haug U, Ahrens W, Nagrani R. Associations between comorbidities and advanced stage diagnosis of lung, breast, colorectal, and prostate cancer: a systematic review and meta-analysis. Cancer Epidemiol. 2021;75:102054. doi: 10.1016/j.canep.2021.102054. [DOI] [PubMed] [Google Scholar]
- 6.Starobinets O, Kurhanewicz J, Noworolski SM. Improved multiparametric MRI discrimination between low-risk prostate cancer and benign tissues in a small cohort of 5α-reductase inhibitor treated individuals as compared with an untreated cohort. NMR Biomed. 2017:30. doi: 10.1002/nbm.3696. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Kim JK, Lee HJ, Hwang SI, Choe G, Kim HJ, Hong SK. The effect of 5 alpha-reductase inhibitor therapy on prostate cancer detection in the era of multi-parametric magnetic resonance imaging. Sci Rep. 2019;9:17862. doi: 10.1038/s41598-019-54464-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Krughoff K, Buller DM, Wu SC, Rodriguez R, Kilchevsky A, Santis WF, Sverrisson EF, Seigne JD, Wagner JR, Dagrosa LM. Do commonly used prostate medications alter prostate MRI and fusion biopsy performance? Clin Nephrol. 2022;97:339–345. doi: 10.5414/CN110665. [DOI] [PubMed] [Google Scholar]
- 9.Diamond A, Sekhon JS. Genetic matching for estimating causal effects: a general multivariate matching method for achieving balance in observational studies. Rev Econ Stat. 2013;95:932–945. [Google Scholar]
- 10.Sekhon JS. Multivariate and propensity score matching software with automated balance optimization: the matching package for R. J Stat Softw. 2011;42:1–52. [Google Scholar]
- 11.Farzam K, Jan A. Beta Blockers. StatPearls. Florida: StatPearls Publishing; 2023. [PubMed] [Google Scholar]
- 12.Björnebo L, Razdan S, Discacciati A, Palsdottir T, Aly M, Nordström T, Eklund M, Lundon D, Grönberg H, Tewari A, Wiklund P, Kyprianou N, Lantz A. Prostate cancer incidence and mortality in men exposed to α1-adrenergic receptor antagonists. J Natl Cancer Inst. 2024;116:1459–1465. doi: 10.1093/jnci/djae108. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Li X, Peng X, Yang S, Wei S, Fan Q, Liu J, Yang L, Li H. Targeting tumor innervation: premises, promises, and challenges. Cell Death Discov. 2022;8:131. doi: 10.1038/s41420-022-00930-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Zahalka AH, Fram E, Lin W, Mohn L, Frenette PS, Agalliu I, Watts KL. Use of beta-blocker types and risk of incident prostate cancer in a multiethnic population. Urol Oncol. 2020;38:794.e11–794.e16. doi: 10.1016/j.urolonc.2020.03.024. [DOI] [PubMed] [Google Scholar]
- 15.Grytli HH, Fagerland MW, Fosså SD, Taskén KA, Håheim LL. Use of β-blockers is associated with prostate cancer-specific survival in prostate cancer patients on androgen deprivation therapy. Prostate. 2013;73:250–260. doi: 10.1002/pros.22564. [DOI] [PubMed] [Google Scholar]
- 16.Lu H, Liu X, Guo F, Tan S, Wang G, Liu H, Wang J, He X, Mo Y, Shi B. Impact of beta-blockers on prostate cancer mortality: a meta-analysis of 16,825 patients. Onco Targets Ther. 2015;8:985–990. doi: 10.2147/OTT.S78836. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Cardwell CR, Coleman HG, Murray LJ, O’Sullivan JM, Powe DG. Beta-blocker usage and prostate cancer survival: a nested case-control study in the UK clinical practice research datalink cohort. Cancer Epidemiol. 2014;38:279–285. doi: 10.1016/j.canep.2014.03.011. [DOI] [PubMed] [Google Scholar]
- 18.Ghlichloo I, Gerriets V. Nonsteroidal Anti-Inflammatory Drugs (NSAIDs). StatPearls. Florida: StatPearls Publishing; 2023. [PubMed] [Google Scholar]
- 19.Hussain Y, Khan H. Immunosuppressive drugs. Encyclopedia of Infection and Immunity. 2022:726–740. [Google Scholar]
- 20.Rourke E, Sunnapwar A, Mais D, Kukkar V, DiGiovanni J, Kaushik D, Liss MA. Inflammation appears as high prostate imaging-reporting and data system scores on prostate magnetic resonance imaging (MRI) leading to false positive MRI fusion biopsy. Investig Clin Urol. 2019;60:388–395. doi: 10.4111/icu.2019.60.5.388. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Falagario UG, Recchia M, Silecchia G, Milillo P, Francavilla A, Bruno SM, Selvaggio O, Busetto GM, Sanguedolce F, Macarini L, Carrieri G, Cormio L. Bioptic prostatic inflammation correlates with false positive rates of multiparametric magnetic resonance imaging in detecting clinically significant prostate cancer. Cent European J Urol. 2021;74:308–314. doi: 10.5173/ceju.2021.3.074.R1. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.