Modified Predictive Model and Nomogram by Incorporating Prebiopsy Biparametric Magnetic Resonance Imaging With Clinical Indicators for Prostate Biopsy Decision Making

A nomogram with coagulation markers for prostate cancer prediction in patients with PSA levels of 4-20 ng/mL

BACKGROUND

The global incidence of prostate cancer (PCa) is rising, necessitating improved diagnostic strategies. This study explores coagulation parameters' predictive value for clinically significant PCa (csPCa) and develops a nomogram.

RESEARCH DESIGN AND METHODS

This study retrospectively analyzed data from 702 patients who underwent prostate biopsy at Shandong Provincial Hospital (SDPH) and 142 patients at Shandong Cancer Hospital and Institute (SDCHI). SDPH patients were randomly assigned at a 7:3 ratio for internal validation, while SDCHI data served as external validation. LASSO and logistic regression identified the best predictive factors for csPCa, which were used to construct a model. The model's efficacy was tested using AUC, calibration curves, and decision curve analysis.

RESULTS

TPSA, age, D-dimer, prostate volume (PV), and digital rectal examination (DRE) were identified as independent risk factors for csPCa. A predictive model was constructed using a nomogram. The AUC for the training set was 0.841, for internal validation 0.809, and for external validation 0.814. Calibration and decision curves confirmed the model's clinical utility.

CONCLUSIONS

The nomogram incorporating D-dimer, TPSA, age, PV, and DRE provides a highly accurate tool for assessing csPCa risk in individuals with PSA levels of 4-20 ng/mL, supporting personalized diagnostics and clinical decision-making.

Comparisons of three scoring systems based on biparametric magnetic resonance imaging for prediction of clinically significant prostate cancer

Purpose

In this study, we aimed to validate and compare three scoring systems based on biparametric magnetic resonance imaging (bpMRI) for the detection of clinically significant prostate cancer (csPCa) in biopsy-naïve patients.

Method

In this study, we included patients who underwent MRI examinations between January 2018 and December 2022, with MRI-targeted fusion biopsy (MRGB) as the reference standard. The MRI findings were categorized using three bpMRI-based scorings, in all of them the diffusion-weighted imaging (DWI) was the dominant sequence for peripheral zone (PZ) and T2-weighed imaging (T2WI) was the dominant sequence for transition zone (TZ). We also used the Prostate Imaging Reporting and Data System version (PI-RADS) v2.1 to evaluate each lesion. For each scoring, we calculated the sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and area under the receiver operating characteristic (ROC) curves (AUC).

Results

The calculated AUC for three bpMRI-based scorings were 83.2% (95% CI 78.8%-87.6%), 85.0% (95% CI 80.8%-89.3%), 82.9% (95% CI 78.4%-87.5%), and 86.0% (95% CI 81.8%-90.1%), respectively. Scoring 2 exhibited significantly superior performance than scoring 1 (P = 0.01) and scoring 3 (P < 0.001). Moreover, the accuracy of scoring 2 was not decreased significantly as compared to PI-RADS v2.1 (P = 0.05). There was no significant difference between 3 bpMRI-based scorings and with PI-RADS in TZ. However, although scoring 2 yielded the highest AUC, it was still notably inferior to PI-RADS (P = 0.02).

Conclusion

All three bpMRI-based scorings demonstrated favorite diagnostic accuracy, and scoring 2 performed significantly better than the other two bpMRI-based scorings. Notably, scoring 2 was not significantly inferior to the full-sequence PI-RADS v2.1 in terms of sensitivity and specificity.

Diagnostic Performance of Prostate-specific Antigen Density for Detecting Clinically Significant Prostate Cancer in the Era of Magnetic Resonance Imaging: A Systematic Review and Meta-analysis

CONTEXT

There has been a dramatic increase in the use of prostate magnetic resonance imaging (MRI) in the diagnostic workup. With prostate volume calculated from MRI, prostate-specific antigen density (PSAD) now is a ready-to-use parameter for prostate cancer (PCa) risk stratification before prostate biopsy, especially among patients with negative MRI or equivocal lesions.

OBJECTIVE

In this review, we aimed to evaluate the diagnostic performance of PSAD for clinically significant prostate cancer (CSPCa) among patients who received MRI before prostate biopsy.

EVIDENCE ACQUISITION

Two investigators performed a systematic review according of the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) criteria. Studies (published between January 1, 2012, and December 31, 2021) reporting the diagnostic performance (outcomes) of PSAD (intervention) for CSPCa among men who received prebiopsy prostate MRI and subsequent prostate biopsy (patients), using biopsy pathology as the gold standard (comparison), were eligible for inclusion.

EVIDENCE SYNTHESIS

A total of 1536 papers were identified in PubMed, Scopus, and Embase. Of these, 248 studies were reviewed in detail and 39 were qualified. The pooled sensitivity (SENS) and specificity (SPEC) for diagnosing CSPCa among patients with positive MRI were, respectively, 0.87 and 0.35 for PSAD of 0.1 ng/ml/ml, 0.74 and 0.61 for PSAD of 0.15 ng/ml/ml, and 0.51 and 0.81 for PSAD of 0.2 ng/ml/ml. The pooled SENS and SPEC for diagnosing CSPCa among patients with negative MRI were, respectively, 0.85 and 0.36 for PSAD of 0.1 ng/ml/ml, 0.60 and 0.66 for PSAD of 0.15 ng/ml/ml, and 0.33 and 0.84 for PSAD of 0.2 ng/ml/ml. The pooled SENS and SPEC among patients with Prostate Imaging Reporting and Data System (PI-RADS) 3 or Likert 3 lesions were, respectively, 0.87 and 0.39 for PSAD of 0.1 ng/ml/ml, 0.61 and 0.69 for PSAD of 0.15 ng/ml/ml, and 0.42 and 0.82 for PSAD of 0.2 ng/ml/ml. The post-test probability for CSPCa among patients with negative MRI was 6% if PSAD was <0.15 ng/ml/ml and dropped to 4% if PSAD was <0.10 ng/ml/ml.

CONCLUSIONS

In this systematic review, we quantitatively evaluated the diagnosis performance of PSAD for CSPCa in combination with prostate MRI. It demonstrated a complementary performance and predictive value, especially among patients with negative MRI and PI-RADS 3 or Likert 3 lesions. Integration of PSAD into decision-making for prostate biopsy may facilitate improved risk-adjusted care.

PATIENT SUMMARY

Prostate-specific antigen density is a ready-to-use parameter in the era of increased magnetic resonance imaging (MRI) use in clinically significant prostate cancer (CSPCa) diagnosis. Findings suggest that the chance of having CSPCa was very low (4% or 6% for those with negative prebiopsy MRI or Prostate Imaging Reporting and Data System (Likert) score 3 lesion, respectively, if the PSAD was <0.10 ng/ml/ml), which may lower the need for biopsy in these patients.

Magnetic Resonance Imaging, Clinical, and Biopsy Findings in Suspected Prostate Cancer: A Systematic Review and Meta-Analysis

Importance

Multiple strategies integrating magnetic resonance imaging (MRI) and clinical data have been proposed to determine the need for a prostate biopsy in men with suspected clinically significant prostate cancer (csPCa) (Gleason score ≥3 + 4). However, inconsistencies across different strategies create challenges for drawing a definitive conclusion.

Objective

To determine the optimal prostate biopsy decision-making strategy for avoiding unnecessary biopsies and minimizing the risk of missing csPCa by combining MRI Prostate Imaging Reporting & Data System (PI-RADS) and clinical data.

Data Sources

PubMed, Ovid MEDLINE, Embase, Web of Science, and Cochrane Library from inception to July 1, 2022.

Study Selection

English-language studies that evaluated men with suspected but not confirmed csPCa who underwent MRI PI-RADS followed by prostate biopsy were included. Each study had proposed a biopsy plan by combining PI-RADS and clinical data.

Data Extraction and Synthesis

Studies were independently assessed for eligibility for inclusion. Quality of studies was appraised using the Quality Assessment of Diagnostic Accuracy Studies 2 tool and the Newcastle-Ottawa Scale. Mixed-effects meta-analyses and meta-regression models with multimodel inference were performed. Reporting of this study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline.

Main Outcomes and Measures

Independent risk factors of csPCa were determined by performing meta-regression between the rate of csPCa and PI-RADS and clinical parameters. Yields of different biopsy strategies were assessed by performing diagnostic meta-analysis.

Results

The analyses included 72 studies comprising 36 366 patients. Univariable meta-regression showed that PI-RADS 4 (β-coefficient [SE], 7.82 [3.85]; P = .045) and PI-RADS 5 (β-coefficient [SE], 23.18 [4.46]; P < .001) lesions, but not PI-RADS 3 lesions (β-coefficient [SE], -4.08 [3.06]; P = .19), were significantly associated with a higher risk of csPCa. When considered jointly in a multivariable model, prostate-specific antigen density (PSAD) was the only clinical variable significantly associated with csPCa (β-coefficient [SE], 15.50 [5.14]; P < .001) besides PI-RADS 5 (β-coefficient [SE], 9.19 [3.33]; P < .001). Avoiding biopsy in patients with lesions with PI-RADS category of 3 or less and PSAD less than 0.10 (vs <0.15) ng/mL2 resulted in reducing 30% (vs 48%) of unnecessary biopsies (compared with performing biopsy in all suspected patients), with an estimated sensitivity of 97% (vs 95%) and number needed to harm of 17 (vs 15).

Conclusions and Relevance

These findings suggest that in patients with suspected csPCa, patient-tailored prostate biopsy decisions based on PI-RADS and PSAD could prevent unnecessary procedures while maintaining high sensitivity.

Nomograms for predicting clinically significant prostate cancer in men with PI-RADS-3 biparametric magnetic resonance imaging

This study aimed to construct nomograms for predicting the likelihood of clinically significant prostate cancer (csPCa) in patients with lesions rated as Prostate Imaging Reporting and Data System (PI-RADS) 3 on biparametric magnetic resonance imaging (bpMRI). We retrospectively analyzed a cohort of 457 patients from the Peking Union Medical College Hospital (January 2017-July 2021) to develop the model and externally validated it with a cohort of 238 patients from the Second Hospital of Tianjin Medical University (September 2017-September 2021). Univariate and multivariate logistic regression analyses identified significant predictors of csPCa, defined by tumor volumes ≥ 0.5 cm3, Gleason score ≥ 7, or presence of extracapsular extension. Diagnostic performance for the peripheral zone (PZ) and transitional zone (TZ) was compared using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Through univariate and multivariate logistic regression analyses, we identified age, prostate-specific antigen (PSA), and prostate volume (PV) as predictors of csPCa for the PZ, and age, serum-free to total PSA ratio (f/t PSA), and PSA density (PSAD) for the TZ. The nomograms demonstrated robust discriminative ability, with an area under the ROC curve (AUC) of 0.819 for PZ and 0.804 for TZ. The external validation corroborated the model's high predictive accuracy (AUC of 0.831 for PZ and 0.773 for TZ). Calibration curves indicated excellent agreement between predicted and observed outcomes, and DCA underscored the nomogram's clinical utility for both PZ and TZ. Overall, the nomograms offer high predictive accuracy for csPCa at initial biopsy, potentially reducing unnecessary biopsies in clinical settings.

A nomogram based on biparametric magnetic resonance imaging for detection of clinically significant prostate cancer in biopsy-naïve patients

PURPOSE

This study aimed to develop and validate a model based on biparametric magnetic resonance imaging (bpMRI) for the detection of clinically significant prostate cancer (csPCa) in biopsy-naïve patients.

METHOD

This retrospective study included 324 patients who underwent bpMRI and MRI targeted fusion biopsy (MRGB) and/or systematic biopsy, of them 217 were randomly assigned to the training group and 107 were assigned to the validation group. We assessed the diagnostic performance of three bpMRI-based scorings in terms of sensitivity and specificity. Subsequently, 3 models (Model 1, Model 2, and Model 3) combining bpMRI scorings with clinical variables were constructed and compared with each other using the area under the receiver operating characteristic (ROC) curves (AUC). The statistical significance of differences among these models was evaluated using DeLong's test.

RESULTS

In the training group, 68 of 217 patients had pathologically proven csPCa. The sensitivity and specificity for Scoring 1 were 64.7% (95% CI 52.2%-75.9%) and 80.5% (95% CI 73.3%-86.6%); for Scoring 2 were 86.8% (95% CI 76.4%-93.8%) and 73.2% (95% CI 65.3%-80.1%); and for Scoring 3 were 61.8% (95% CI 49.2%-73.3%) and 80.5% (95% CI 73.3%-86.6%), respectively. Multivariable regression analysis revealed that scorings based on bpMRI, age, and prostate-specific antigen density (PSAD) were independent predictors of csPCa. The AUCs for the 3 models were 0.88 (95% CI 0.83-0.93), 0.90 (95% CI 0.85-0.94), and 0.88 (95% CI 0.83-0.93), respectively. Model 2 showed significantly higher performance than Model 1 (P = 0.03) and Model 3 (P < 0.01).

CONCLUSION

All three scorings had favorite diagnostic accuracy. While in conjunction with age and PSAD the prediction power was significantly improved, and the Model 2 that based on Scoring 2 yielded the highest performance.

Development and validation of a nomogram based on biparametric MRI PI-RADS v2.1 and clinical parameters to avoid unnecessary prostate biopsies

BACKGROUND

Biparametric MRI (bpMRI) is a faster, contrast-free, and less expensive MRI protocol that facilitates the detection of prostate cancer. The aim of this study is to determine whether a biparametric MRI PI-RADS v2.1 score-based model could reduce unnecessary biopsies in patients with suspected prostate cancer (PCa).

METHODS

The patients who underwent MRI-guided biopsies and systematic biopsies between January 2020 and January 2022 were retrospectively analyzed. The development cohort used to derive the prediction model consisted of 275 patients. Two validation cohorts included 201 patients and 181 patients from 2 independent institutions. Predictive models based on the bpMRI PI-RADS v2.1 score (bpMRI score) and clinical parameters were used to detect clinically significant prostate cancer (csPCa) and compared by analyzing the area under the curve (AUC) and decision curves. Spearman correlation analysis was utilized to determine the relationship between International Society of Urological Pathology (ISUP) grade and clinical parameters/bpMRI score.

RESULTS

Logistic regression models were constructed using data from the development cohort to generate nomograms. By applying the models to the all cohorts, the AUC for csPCa was significantly higher for the bpMRI PI-RADS v2.1 score-based model than for the clinical model in both cohorts (p < 0.001). Considering the test trade-offs, urologists would agree to perform 10 fewer bpMRIs to avoid one unnecessary biopsy, with a risk threshold of 10-20% in practice. Correlation analysis showed a strong correlation between the bpMRI score and ISUP grade.

CONCLUSION

A predictive model based on the bpMRI score and clinical parameters significantly improved csPCa risk stratification, and the bpMRI score can be used to determine the aggressiveness of PCa prior to biopsy.

Prebiopsy bpMRI and hematological parameter-based risk scoring model for predicting outcomes in biopsy-naive men with PSA 4-20 ng/mL

Excessive prostate biopsy is a common problem for clinicians. Although some hematological and bi-parametric magnetic resonance imaging (bpMRI) parameters might help increase the rate of positive prostate biopsies, there is a lack of studies on whether their combination can further improve clinical detection efficiency. We retrospectively enrolled 394 patients with PSA levels of 4-20 ng/mL who underwent prebiopsy bpMRI during 2010-2021. Based on bpMRI and hematological indicators, six models and a nomogram were constructed to predict the outcomes of biopsy. Furthermore, we constructed and evaluated a risk scoring model based on the nomogram. Age, prostate-specific antigen (PSA) density (PSAD), systemic immune-inflammation index, cystatin C level, and the Prostate Imaging Reporting and Data System (PI-RADS) v2.1 score were significant predictors of prostate cancer (PCa) on multivariable logistic regression analyses (P < 0.05) and the five parameters were used to construct the XYFY nomogram. The area under the receiver operating characteristic (ROC) curve (AUC) of the nomogram was 0.916. Based on the nomogram, a risk scoring model (XYFY risk model) was constructed and then we divided the patients into low-(XYFY score: < 95), medium-(XYFY score: 95-150), and, high-risk (XYFY score: > 150) groups. The predictive values for diagnosis of PCa and clinically-significant PCa among the three risk groups were 3.0%(6/201), 41.8%(51/122), 91.5%(65/71); 0.5%(1/201), 19.7%(24/122), 60.6%(43/71), respectively. In conclusion, in this study, we used hematological and bpMRI parameters to establish and internally validate a XYFY risk scoring model for predicting the biopsy outcomes for patients with PSA levels of 4-20 ng/mL and this risk model would support clinical decision-making and reduce excessive biopsies.

The prognostic value of mean apparent diffusion coefficient measured with diffusion-weighted magnetic resonance image in patients with prostate cancer treated with definitive radiotherapy

PURPOSE

To assess the correlation between initial tumor apparent diffusion coefficient (ADC) values and clinicopathological parameters in prostate cancer (PCa) patients treated with definitive radiotherapy (RT). Additionally, the prognostic factors for freedom from biochemical failure (FFBF) and progression-free survival (PFS) in this patient cohort were analyzed.

MATERIALS AND METHODS

The clinical data of 503 patients with biopsy-confirmed PCa were evaluated retrospectively. All patients had clearly evident tumors on diffusion-weighted magnetic resonance imaging (DW-MRI) for ADC values. Univariable and multivariable analyses were used to determine prognostic factors for FFBF and PFS.

RESULTS

The median follow-up was 72.9 months. The 5-year FFBF and PFS rates were 93.2% and 86.2%, respectively. Significantly lower ADC values were found in patients with a high PSA level; advanced clinical stage; higher ISUP score, and higher risk group than their counterparts. Receiver operating characteristic (ROC) curve analysis revealed an ADC cut-off value of 0.737 × 10-3 mm2/sec for tumor recurrence. Patients who progressed had a lower mean ADC value than those who did not (0.712 ± 0.158 vs. 1.365 ± 0.227 × 10-3 mm2/sec; p < 0.001). There was a significant difference in 5-year FFBF (96.3% vs. 90%; p < 0.001) and PFSrates (83.8% vs. 73.5%; p = 0.002) between patients with higher and lower mean ADC values. The FFBF and PFS were found to be correlated with tumor ADC value and ISUP grades in multivariable analysis. Additionally, older age was found to be a significant predictor of worse PFS.

CONCLUSIONS

Lower ADC values were found in patients with high-risk characteristics such as a high serum PSA level, stage or grade of tumor, or high-risk disease, implying that ADC values could be used to predict prognosis. Lower ADC values and higher ISUP grades were associated with an increased risk of BF and progression, implying that treatment intensification may be required in these patients.