3D Registration of mpMRI for Assessment of Prostate Cancer Focal Therapy

Role of MRI for the detection of prostate cancer

The use of multiparametric MRI has been hastened under expanding, novel indications for its use in the diagnostic and management pathway of men with prostate cancer. This has helped drive a large body of the literature describing its evolving role over the last decade. Despite this, prostate cancer remains the only solid organ malignancy routinely diagnosed with random sampling. Herein, we summarize the components of multiparametric MRI and interpretation, and present a critical review of the current literature supporting is use in prostate cancer detection, risk stratification, and management.

Segmentation of prostate zones using probabilistic atlas-based method with diffusion-weighted MR images

BACKGROUND AND OBJECTIVE

Accurate segmentation of prostate and its zones constitute an essential preprocessing step for computer-aided diagnosis and detection system for prostate cancer (PCa) using diffusion-weighted imaging (DWI). However, low signal-to-noise ratio and high variability of prostate anatomic structures are challenging for its segmentation using DWI. We propose a semi-automated framework that segments the prostate gland and its zones simultaneously using DWI.

METHODS

In this paper, the Chan-Vese active contour model along with morphological opening operation was used for segmentation of prostate gland. Then segmentation of prostate zones into peripheral zone (PZ) and transition zone (TZ) was carried out using in-house developed probabilistic atlas with partial volume (PV) correction algorithm. The study cohort included MRI dataset of 18 patients (n = 18) as our dataset and methodology were also independently evaluated using 15 MRI scans (n = 15) of QIN-PROSTATE-Repeatability dataset. The atlas for zones of prostate gland was constructed using dataset of twelve patients of our patient cohort. Three-fold cross-validation was performed with 10 repetitions, thus total 30 instances of training and testing were performed on our dataset followed by independent testing on the QIN-PROSTATE-Repeatability dataset. Dice similarity coefficient (DSC), Jaccard coefficient (JC), and accuracy were used for quantitative assessment of the segmentation results with respect to boundaries delineated manually by an expert radiologist. A paired t-test was performed to evaluate the improvement in zonal segmentation performance with the proposed PV correction algorithm.

RESULTS

For our dataset, the proposed segmentation methodology produced improved segmentation with DSC of 90.76 ± 3.68%, JC of 83.00 ± 5.78%, and accuracy of 99.42 ± 0.36% for the prostate gland, DSC of 77.73 ± 2.76%, JC of 64.46 ± 3.43%, and accuracy of 82.47 ± 2.22% for the PZ, and DSC of 86.05 ± 1.50%, JC of 75.80 ± 2.10%, and accuracy of 91.67 ± 1.56% for the TZ. The segmentation performance for QIN-PROSTATE-Repeatability dataset was, DSC of 85.50 ± 4.43%, JC of 75.00 ± 6.34%, and accuracy of 81.52 ± 5.55% for prostate gland, DSC of 74.40 ± 1.79%, JC of 59.53 ± 8.70%, and accuracy of 80.91 ± 5.16% for PZ, and DSC of 85.80 ± 5.55%, JC of 74.87 ± 7.90%, and accuracy of 90.59 ± 3.74% for TZ. With the implementation of the PV correction algorithm, statistically significant (p<0.05) improvements were observed in all the metrics (DSC, JC, and accuracy) for both prostate zones, PZ and TZ segmentation.

CONCLUSIONS

The proposed segmentation methodology is stable, accurate, and easy to implement for segmentation of prostate gland and its zones (PZ and TZ). The atlas-based segmentation framework with PV correction algorithm can be incorporated into a computer-aided diagnostic system for PCa localization and treatment planning.

Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods

Prostate cancer represents today the most typical example of a pathology whose diagnosis requires multiparametric imaging, a strategy where multiple imaging techniques are combined to reach an acceptable diagnostic performance. However, the reviewing, weighing and coupling of multiple images not only places additional burden on the radiologist, it also complicates the reviewing process. Prostate cancer imaging has therefore been an important target for the development of computer-aided diagnostic (CAD) tools. In this survey, we discuss the advances in CAD for prostate cancer over the last decades with special attention to the deep-learning techniques that have been designed in the last few years. Moreover, we elaborate and compare the methods employed to deliver the CAD output to the operator for further medical decision making.

Two-year Outcomes Following Focal Laser Ablation of Localized Prostate Cancer

BACKGROUND

There is no consensus regarding how to assess oncological control following focal ablation of prostate cancer.

OBJECTIVE

To assess quality of life and in-field recurrence following focal laser ablation (FLA).

DESIGN, SETTING, AND PARTICIPANTS

Of 34 men participating in a prospective outcomes study following FLA, 32 underwent prostate-specific antigen (PSA) testing and magnetic resonance imaging (MRI) at 6 mo and 2 yr. All underwent assessment of urinary and sexual function at 1 yr.

INTERVENTION

FLA and MRI-targeted biopsy of the ablation zone.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The American Urological Association Symptom Score and the Sexual Health Inventory for Men at baseline and 12 mo were compared using a two-sided Wilcoxon signed-rank test with a significance level of p=0.05. The percentage of positive and negative in-field biopsies was calculated for suspicious and nonsuspicious post-ablation MRI scans.

RESULTS AND LIMITATIONS

FLA was associated with no adverse impact on urinary or sexual function. For men with suspicious MRI (MRI+) findings, in-field disease recurrence of intermediate and low risk disease was detected in 75% and 25% of cases, respectively. For men with nonsuspicious MRI (MRI-) findings, in-field disease recurrence of intermediate- and low-risk disease was detected in 22.4% and 50% of cases, respectively. The change in PSA from baseline did not discriminate cases with MRI- findings with and without cancer at 2 yr.

CONCLUSIONS

MRI reliably identifies in-field recurrence of only intermediate-risk prostate cancer at 2 yr after FLA. A biopsy of the ablation zone must be performed for MRI+ findings. The decision to perform an ablation-zone biopsy for men with MRI- scans should be influenced by whether detection of low-risk disease would influence management.

PATIENT SUMMARY

Our study provides compelling evidence that men should undergo interval magnetic resonance imaging to assess the probability of intermediate-risk disease in the ablation zone following focal laser ablation of localized prostate cancer.