Which measurement method should be used for prostate volume for PI-RADS? A comparison of ellipsoid and segmentation methods

Prostatic Urethral Length on MRI Potentially Predicts Late Genitourinary Toxicity After Prostate Cancer Radiation

RATIONALE AND OBJECTIVES

The purpose of our study was to evaluate pretreatment prostate quantitative magnetic resonance imaging (MRI) measurements and clinical characteristics in predicting genitourinary (GU) toxicity after radiotherapy (RT) for prostate cancer.

MATERIALS AND METHODS

In this single-institution retrospective cohort study, we evaluated patients with prostate adenocarcinoma who underwent MRI within 6 months before completing definitive RT and follow-up information in our GU toxicity database from June 2016 to February 2023. MRI measurements included quantitative urethra, prostate, and bladder measurements. GU toxicity was physician-scored using the Common Terminology Criteria for Adverse Events (CTCAE v4.0) with acute toxicity defined as ≤180 days and late defined as >180 days. Multivariable logistic regression model was constructed for grade ≥2 acute toxicity and Cox proportional hazards regression for late toxicity, adjusted for clinical factors and RT method.

RESULTS

A total of 361 men (median age 68 years, interquartile range [IQR] 62-73) were included; 14.4% (50/347) men experienced grade ≥2 acute toxicity. Brachytherapy (odds ratio [OR]: 2.9, 95% confidence interval [CI]: 1.5-5.8), P < 0.01) was associated with increased odds of acute GU toxicity, and longer MUL (OR: 0.41 [95%CI: 0.18-0.92], P = 0.03) with decreased odds. Median follow-up for late toxicity was 15.0 months (IQR: 9.0-28.0) with approximately 88.7% and 72.0% patients free of toxicity at 1 and 3 years, respectively. Only longer prostatic urethral length (hazard ratio [HR]: 1.6, 95%CI: 1.2-2.1, P < 0.01) was associated with increased risk of late GU toxicity, notably urinary frequency/urgency symptoms (HR: 1.7 [95%CI: 1.3-2.3], P < 0.01).

CONCLUSION

Longer prostatic urethral length measured on prostate MRI is independently associated with higher risk of developing late grade ≥2 GU toxicity after radiation therapy for prostate cancer. This pretreatment metric may be potentially valuable in risk-stratification models for quality of life following prostate RT.

Prostate zones and tumor morphological parameters on magnetic resonance imaging for predicting the tumor-stage diagnosis of prostate cancer

PURPOSE

To determine whether the morphological parameters of prostate zones and tumors on magnetic resonance imaging (MRI) can predict the tumor-stage (T-stage) of prostate cancer (PCa) and establish an optimal T-stage diagnosis protocol based on three-dimensional reconstruction and quantization after image segmentation.

METHODS

A dataset of the prostate MRI scans and clinical data of 175 patients who underwent biopsy and had pathologically proven PCa from January 2018 to November 2020 was retrospectively analyzed. The authors manually segmented and measured the volume, major axis, and cross-sectional area of the peripheral zone (PZ), transition zone, central zone (CZ), anterior fibromuscular stroma, and tumor. The differences were evaluated by the One-Way analysis of variance, Pearson's chi-squared test, or independent samples t-test. Spearman's correlation coefficient and receiver operating characteristic curve analyses were also performed. The cut-off values of the T-stage diagnosis were generated using Youden's J index.

RESULTS

The prostate volume (PV), PZ volume (PZV), CZ volume, tumor's major axis (TA), tumor volume (TV), and volume ratio of the TV and PV were significantly different among stages T1 to T4. The cut-off values of the PV, PZV, CZV, TA, TV, and the ratio of TV/PV for the discrimination of the T1 and T2 stages were 53.63 cm3, 11.60 cm3, 1.97 cm3, 2.30 mm, 0.90 cm3, and 0.03 [area under the curves (AUCs): 0.628, 0.658, 0.610, 0.689, 0.724, and 0.764], respectively. The cut-off values of the TA, TV, and the ratio of TV/PV for the discrimination of the T2 and T3 stages were 2.80 mm, 8.29 cm3, and 0.12 (AUCs: 0.769, 0.702, and 0.688), respectively. The cut-off values of the TA, TV, and the ratio of TV/PV for the discrimination of the T3 and T4 stages were 4.17 mm, 18.71 cm3, and 0.22 (AUCs: 0.674, 0.709, and 0.729), respectively.

CONCLUSION

The morphological parameters of the prostate zones and tumors on the MRIs are simple and valuable diagnostic factors for predicting the T-stage of patients with PCa, which can help make accurate diagnoses and lateral treatment decisions.

Radiologic versus Segmentation Measurements to Quantify Wilms Tumor Volume on MRI in Pediatric Patients

Wilms tumor is a common pediatric solid tumor. To evaluate tumor response to chemotherapy and decide whether nephron-sparing surgery is possible, tumor volume measurements based on magnetic resonance imaging (MRI) are important. Currently, radiological volume measurements are based on measuring tumor dimensions in three directions. Manual segmentation-based volume measurements might be more accurate, but this process is time-consuming and user-dependent. The aim of this study was to investigate whether manual segmentation-based volume measurements are more accurate and to explore whether these segmentations can be automated using deep learning. We included the MRI images of 45 Wilms tumor patients (age 0–18 years). First, we compared radiological tumor volumes with manual segmentation-based tumor volume measurements. Next, we created an automated segmentation method by training a nnU-Net in a five-fold cross-validation. Segmentation quality was validated by comparing the automated segmentation with the manually created ground truth segmentations, using Dice scores and the 95th percentile of the Hausdorff distances (HD95). On average, manual tumor segmentations result in larger tumor volumes. For automated segmentation, the median dice was 0.90. The median HD95 was 7.2 mm. We showed that radiological volume measurements underestimated tumor volume by about 10% when compared to manual segmentation-based volume measurements. Deep learning can potentially be used to replace manual segmentation to benefit from accurate volume measurements without time and observer constraints.

Impact of the bladder detrusor muscular ring on lower urinary tract symptoms due to benign prostatic hyperplasia: A quantitative MRI analysis

BACKGROUND

The etiology of lower urinary tract symptoms secondary to benign prostatic hyperplasia (LUTS/BPH) remains uncertain.

OBJECTIVE

The purpose of our study was to quantitatively analyze anatomic characteristics on magnetic resonance imaging (MRI) to assess novel independent factors for symptoms.

METHODS

This retrospective single-institution study evaluated treatment-naïve men who underwent prostate MRI within 3 months of international prostate symptom score (IPSS) scoring from June 2021 to February 2022. Factors measured on MRI included: size of the detrusor muscular ring (DMR) surrounding the bladder outlet, central gland (CG) mean apparent diffusion coefficient (ADC), levator hiatus (LH) volume, intrapelvic volume, intravesicular prostate protrusion (IPP) volume, CG volume, peripheral zone (PZ) volume, prostate urethra angle (PUA), and PZ background ordinal score. Multivariable logistic regression and receiver operating characteristic analysis were used to analyze factors for moderate/severe (IPSS ≥ 8) and severe LUTS/BPH (IPSS ≥ 20).

RESULTS

A total of 303 men (mean age: 66.1 [SD: 8.1]) were included: 154 demonstrated moderate or severe symptoms with 28 severe and 149 with asymptomatic/mild symptoms. Increasing age [p = 0.02; odds ratio (OR): 1.05 (1.01-1.08)], PUA [p = 0.02; OR: 1.05 (1.01-1.09)], LH volume [p = 0.04; OR: 1.02 (1.00-1.05)], and DMR size measured as diameter [p < 0.001; OR: 5.0 (3.01-8.38)] or area [p < 0.001; OR: 1.92 (1.47-2.49)] were significantly independently associated with moderate/severe symptoms, with BMI [p = 0.02; OR: 0.93 (0.88-0.99)] inversely related. For every one cm increase in DMR diameter, patients had approximately five times the odds for moderate/severe symptoms. Increasing DMR size [diameter p < 0.001; OR: 2.74 (1.76-4.27) or area p < 0.001; OR: 1.37 (1.18-1.58)] was independently associated with severe symptoms. Optimal criterion cutoff of DMR diameter for moderate/severe symptoms was 1.2 cm [sensitivity: 77.3; specificity: 71.8; AUC: 0.80 (0.75-0.84)]. Inter-reader reliability was excellent for DMR diameter [ICC = 0.92 (0.90-0.94)].

CONCLUSION

Expansion of the DMR surrounding the bladder outlet is a novel anatomic factor independently associated with moderate and severe LUTS/BPH, taking into account prostate volumes, including quantified IPP volume, which were unrelated. Detrusor ring diameter, easily and reliably measured on routine prostate MRI, may relate to detrusor dysfunction from chronic stretching of this histologically distinct smooth muscle around the bladder neck.

MRI-measured adipose features as predictive factors for detection of prostate cancer in males undergoing systematic prostate biopsy: a retrospective study based on a Chinese population

In this study, we retrospectively evaluated the data of 901 men undergoing ultrasonography-guided systematic prostate biopsy between March 2013 and May 2022. Adipose features, including periprostatic adipose tissue (PPAT) thickness and subcutaneous fat thickness, were measured using MRI before biopsy. Prediction models of all PCa and clinically significant PCa (csPCa) (Gleason score higher than 6) were established based on variables selected by multivariate logistic regression and prediction nomograms were constructed. Patients with PCa had higher PPAT thickness (4.64 [3.65-5.86] vs. 3.54 [2.49-4.51] mm, p < 0.001) and subcutaneous fat thickness (29.19 [23.05-35.95] vs. 27.90 [21.43-33.93] mm, p = 0.013) than those without PCa. Patients with csPCa had higher PPAT thickness (4.78 [3.80-5.88] vs. 4.52 [3.80-5.63] mm, p = 0.041) than those with non-csPCa. Adding adipose features to the prediction models significantly increased the area under the receiver operating characteristics curve for the prediction of all PCa (0.850 vs. 0.819, p < 0.001) and csPCa (0.827 vs. 0.798, p < 0.001). Based on MRI-measured adipose features and clinical parameters, we established two nomograms that were simple to use and could improve patient selection for prostate biopsy in Chinese population.

New Diagnostic Model for Clinically Significant Prostate Cancer in Biopsy-Naïve Men With PIRADS 3

Purpose

The aim of this study was to explore a new model of clinical decision-making to predict the occurrence of clinically significant prostate cancer (csPCa).

Patients and Methods

The demographic and clinical characteristics of 152 patients were recorded. Prostate-specific antigen (PSA), PSA density (PSAD), adjusted PSAD of peripheral zone (aPSADPZ), and peripheral zone volume ratio (PZ ratio) were calculated and subjected to receiver operating characteristic (ROC) curve analysis. The calibration and discrimination abilities of new nomograms were verified with calibration curve and area under the ROC curve (AUC). The clinical benefits of these models were evaluated by decision curve analysis and clinical impact curves.

Results

The AUCs of PSA, PSAD, aPSADPZ, and PZ ratio were 0.521, 0.645, 0.745, and 0.717 for prostate cancer (PCa) diagnosis, while the corresponding values were 0.590, 0.678, 0.780, and 0.731 for csPCa diagnosis, respectively. All nomograms displayed higher net benefit and better overall calibration than the scenarios for predicting the occurrence of csPCa. The new model significantly improved the diagnostic accuracy of csPCa (0.865 vs. 0.741, p = 0.0284) compared with the base model. In addition, the new model was better than the base model for predicting csPCa in the low or medium probability while the number of patients with csPCa predicted by the new model was in good agreement with the actual number of patients with csPCa in the high-risk threshold.

Conclusions

This study demonstrates that aPSADPZ has a higher predictive accuracy for csPCa diagnosis than the conventional indicators. Including aPSADPZ, PZ ratio, and age can improve csPCa diagnosis and avoid unnecessary biopsies.

Comparison of different thresholds of PSA density for risk stratification of PI-RADSv2.1 categories on prostate MRI

OBJECTIVES

To compare the effect of different PSA density (PSAD) thresholds on the accuracy for clinically significant prostate cancer (csPCa) of the Prostate Imaging Reporting And Data System v.2.1 (PI-RADSv2.1).

METHODS

We retrospectively included 123 biopsy-naïve men who underwent multiparametric magnetic resonance imaging (mpMRI) and transperineal mpMRI-targeted and systematic prostate biopsy between April 2019 and October 2020. mpMRI, obtained on a 3.0T magnet with a PI-RADSv2.1-compliant protocol, was read by two radiologists (>1500/>500 mpMRI examinations). csPCa was defined as International Society of Urogenital Pathology grading group ≥2. Receiver operating characteristic analysis was used to calculate per-index lesion sensitivity, specificity, and area under the curve (AUC) of PI-RADSv.2.1 categories after adjusting for PSAD ≥0.10,≥0.15, and ≥0.20 ng/mL ml^-1. Per-adjusted category cancer detection rate (CDR) was calculated, and decision analysis performed to compare PSAD-adjusted PI-RADSv.2.1 categories as a biopsy trigger.

RESULTS

csPCa prevalence was 43.9%. PSAD-adjustment increased the CDR of PI-RADSv2.1 category 4. Sensitivity/specificity/AUC were 92.6%/53.6%/0.82 for unadjusted PI-RADS, and 85.2%/72.4%/0.84, 62.9%/85.5%/0.83, and 92.4%/53.6%/0.82 when adjusting PI-RADS categories for a 0.10, 0.15, and 0.20 ng/ml ml^-1 PSAD threshold, respectively. Triggering biopsy for PI-RADS four lesions and PSAD ≥0.10 ng/mL ml^-1 was the strategy with greatest net benefit at 30 and 40% risk probability (0.307 and 0.271, respectively).

CONCLUSIONS

PI-RADSv2.1 category four with PSAD ≥0.10 ng/mL ml^-1 was the biopsy-triggering cut-off with the highest net benefit in the range of expected prevalence for csPCa.

ADVANCES IN KNOWLEDGE

0.10 ng/mL ml^-1 is the PSAD threshold with higher clinical utility in stratifying the risk for prostate cancer of PI-RADSv.2.1 categories.