Whole prostate volume and shape changes with the use of an inflatable and flexible endorectal coil

Development and clinical utility analysis of a prostate zonal segmentation model on T2-weighted imaging: a multicenter study

OBJECTIVES

To automatically segment prostate central gland (CG) and peripheral zone (PZ) on T2-weighted imaging using deep learning and assess the model's clinical utility by comparing it with a radiologist annotation and analyzing relevant influencing factors, especially the prostate zonal volume.

METHODS

A 3D U-Net-based model was trained with 223 patients from one institution and tested using one internal testing group (n = 93) and two external testing datasets, including one public dataset (ETD_pub, n = 141) and one private dataset from two centers (ETD_pri, n = 59). The Dice similarity coefficients (DSCs), 95th Hausdorff distance (95HD), and average boundary distance (ABD) were calculated to evaluate the model's performance and further compared with a junior radiologist's performance in ETD_pub. To investigate factors influencing the model performance, patients' clinical characteristics, prostate morphology, and image parameters in ETD_pri were collected and analyzed using beta regression.

RESULTS

The DSCs in the internal testing group, ETD_pub, and ETD_pri were 0.909, 0.889, and 0.869 for CG, and 0.844, 0.755, and 0.764 for PZ, respectively. The mean 95HD and ABD were less than 7.0 and 1.3 for both zones. The U-Net model outperformed the junior radiologist, having a higher DSC (0.769 vs. 0.706) and higher intraclass correlation coefficient for volume estimation in PZ (0.836 vs. 0.668). CG volume and Magnetic Resonance (MR) vendor were significant influencing factors for CG and PZ segmentation.

CONCLUSIONS

The 3D U-Net model showed good performance for CG and PZ auto-segmentation in all the testing groups and outperformed the junior radiologist for PZ segmentation. The model performance was susceptible to prostate morphology and MR scanner parameters.

The Reproducibility of Deep Learning-Based Segmentation of the Prostate Gland and Zones on T2-Weighted MR Images

Volume of interest segmentation is an essential step in computer-aided detection and diagnosis (CAD) systems. Deep learning (DL)-based methods provide good performance for prostate segmentation, but little is known about the reproducibility of these methods. In this work, an in-house collected dataset from 244 patients was used to investigate the intra-patient reproducibility of 14 shape features for DL-based segmentation methods of the whole prostate gland (WP), peripheral zone (PZ), and the remaining prostate zones (non-PZ) on T2-weighted (T2W) magnetic resonance (MR) images compared to manual segmentations. The DL-based segmentation was performed using three different convolutional neural networks (CNNs): V-Net, nnU-Net-2D, and nnU-Net-3D. The two-way random, single score intra-class correlation coefficient (ICC) was used to measure the inter-scan reproducibility of each feature for each CNN and the manual segmentation. We found that the reproducibility of the investigated methods is comparable to manual for all CNNs (14/14 features), except for V-Net in PZ (7/14 features). The ICC score for segmentation volume was found to be 0.888, 0.607, 0.819, and 0.903 in PZ; 0.988, 0.967, 0.986, and 0.983 in non-PZ; 0.982, 0.975, 0.973, and 0.984 in WP for manual, V-Net, nnU-Net-2D, and nnU-Net-3D, respectively. The results of this work show the feasibility of embedding DL-based segmentation in CAD systems, based on multiple T2W MR scans of the prostate, which is an important step towards the clinical implementation.

Spatial and dosimetric evaluation of residual distortions of prostate and seminal vesicle bed after image-guided definitive and postoperative radiotherapy of prostate cancer with endorectal balloon

Purpose

To quantify daily residual deviations from the planned geometry after image‐guided prostate radiotherapy with endorectal balloon and to evaluate their effect on the delivered dose distribution.

Methods

Daily kV‐CBCT imaging was used for online setup‐correction in six degrees of freedom (6‐dof) for 24 patients receiving definitive (12 RT_def patients) or postoperative (12 RT_postop patients) radiotherapy with endorectal balloon (overall 739 CBCTs). Residual deviations were evaluated using several spatial and dosimetric variables, including: (a) posterior Hausdorff distance HD^post (=maximum distance between planned and daily CTV contour), (b) point P_worst with largest HD^post over all fractions, (c) equivalent uniform dose using a cell survival model (EUD_SF) and the generalized EUD concept (gEUD_a with parameter a = −7 and a = −20). EUD values were determined for planned (EUDSFplan), daily (EUDSFind), and delivered dose distributions (EUDSFaccum) for plans with 6 mm (=clinical plans) and 2 mm CTV‐to‐PTV margin. Time series analyses of interfractional spatial and dosimetric deviations were conducted.

Results

Large HD^post values ≥ 12.5 mm (≥15 mm) were observed in 20/739 (5/739) fractions distributed across 7 (3) patients. Points P_worst were predominantly located at the posterior CTV boundary in the seminal vesicle region (16/24 patients, 6/7 patients with HD^post ≥ 12.5 mm). Time series analyses revealed a stationary white noise characteristic of HD^post and relative dose at P_worst. The EUD_SF difference between planned and accumulated dose distributions was < 5.4% for all 6‐mm plans. Evaluating 2‐mm plans, EUD_SF deteriorated by < 10% (<5%) in 75% (58.5%) of the patients. EUDSFaccum was well described by the median value of the EUDSFind distribution. PTV margin calculation at P_worst yielded 8.8 mm.

Conclusions

Accumulated dose distributions in prostate radiotherapy with endorectal balloon are forgiving of considerable residual distortions after 6‐dof patient setup if they are observed in a minority of fractions and the median value of EUDSFind determined per fraction stays within 95% of prescribed dose. Common PTV margin calculations are overly conservative because after online correction of translational and rotational errors only residual deformations need to be included. These results provide guidelines regarding online navigation, margin optimization, and treatment adaptation strategies.

How accurately can multiparametric magnetic resonance imaging measure the tumour volume of a prostate cancer? Results of a systematic review

The tumour volume of a cancer within the prostate gland is commonly measured with multiparametric MRI. The measurement has a role in many clinical scenarios including focal therapy, but the accuracy of it has never been systematically reviewed. We included articles if they compared tumour volume measurements obtained by mpMRI with a reference volume measurement obtained after radical prostatectomy. Correlation and concordance statistics were summarised. A simple accuracy score was derived by dividing the given mean or median mpMRI volume by the histopathological reference volume. Factors affecting the accuracy were noted. Scores for potential bias and quality were calculated for each article. A total of 18 articles describing 1438 patients were identified. Nine articles gave Pearson's correlation scores, with a median value of 0.75 but the range was wide (0.42-0.97). A total of 11 articles reported mean values for volume while 9 reported median values. For all 18 articles, the mean or median values for MRI volumes were lower than the corresponding reference values suggesting consistent underestimation. For articles reporting mean and median values for volume, the median accuracy scores were 0.83 and 0.80, respectively. The accuracy was higher for tumours of greater volume, higher grade and when an endorectal coil was used. Accuracy did not seem to improve over time, with a 3 Tesla magnet or by applying a shrinkage factor to the reference measurement. Most studies showed evidence of at least moderate bias, and their quality was highly variable, but neither of these appeared to affect accuracy.

How Accurately Can Prostate Gland Imaging Measure the Prostate Gland Volume? Results of a Systematic Review

AIM

The measurement of the volume of the prostate gland can have an influence on many clinical decisions. Various imaging methods have been used to measure it. Our aim was to conduct the first systematic review of their accuracy.

METHODS

The literature describing the accuracy of imaging methods for measuring the prostate gland volume was systematically reviewed. Articles were included if they compared volume measurements obtained by medical imaging with a reference volume measurement obtained after removal of the gland by radical prostatectomy. Correlation and concordance statistics were summarised.

RESULTS

28 articles describing 7768 patients were identified. The imaging methods were ultrasound, computed tomography, and magnetic resonance imaging (US, CT, and MRI). Wide variations were noted but most articles about US and CT provided correlation coefficients that lay between 0.70 and 0.90, while those describing MRI seemed slightly more accurate at 0.80-0.96. When concordance was reported, it was similar; over- and underestimation of the prostate were variably reported. Most studies showed evidence of at least moderate bias and the quality of the studies was highly variable.

DISCUSSION

The reported correlations were moderate to high in strength indicating that imaging is sufficiently accurate when quantitative measurements of prostate gland volume are required. MRI was slightly more accurate than the other methods.