Role of apparent diffusion coefficient values in prostate diseases characterization on diffusion-weighted magnetic resonance imaging

Evaluation of the Efficiency of MRI-Based Radiomics Classifiers in the Diagnosis of Prostate Lesions

Objective

To compare the performance of different imaging classifiers in the prospective diagnosis of prostate diseases based on multiparameter MRI.

Methods

A total of 238 patients with pathological outcomes were enrolled from September 2019 to July 2021, including 142 in the training set and 96 in the test set. After the regions of interest were manually segmented, decision tree (DT), Gaussian naive Bayes (GNB), XGBoost, logistic regression, random forest (RF) and support vector machine classifier (SVC) models were established on the training set and tested on the independent test set. The prospective diagnostic performance of each classifier was compared by using the AUC, F1-score and Brier score.

Results

In the patient-based data set, the top three classifiers of combined sequences in terms of the AUC were logistic regression (0.865), RF (0.862), and DT (0.852); RF “was significantly different from the other two classifiers (P =0.022, P =0.005), while logistic regression and DT had no statistical significance (P =0.802). In the lesions-based data set, the top three classifiers of combined sequences in terms of the AUC were RF (0.931), logistic regression (0.922) and GNB (0.922). These three classifiers were significantly different from.

Conclusion

The results of this experiment show that radiomics has a high diagnostic efficiency for prostate lesions. The RF classifier generally performed better overall than the other classifiers in the experiment. The XGBoost and logistic regression models also had high classification value in the lesions-based data set.

How to read biparametric MRI in men with a clinical suspicious of prostate cancer: Pictorial review for beginners with public access to imaging, clinical and histopathological database

Prostate Magnetic Resonance Imaging (MRI) is increasingly being used in men with a clinical suspicion of prostate cancer (PCa). Performing prostate MRI without the use of an intravenous contrast (IV) agent in men with a clinical suspicion of PCa can lead to reduced MRI scan time. Enabling a large array of different medical providers (from mid-level to specialized radiologists) to evaluate and potentially report prostate MRI in men with a clinical suspicion of PCa with a high accuracy could be one way to enable wide adoption of prostate MRI in men with a clinical suspicion of PCa. The aim of this pictorial review is to provide an insight into acquisition, quality control and reporting of prostate MRI performed without IV contrast agent in men with a clinical suspicion of PCa, aimed specifically at radiologists starting reporting prostate MRI, urologists, urology/radiology residents and mid-level medical providers without experience in reporting prostate MRI. Free public access (http://petiv.utu.fi/improd/and http://petiv.utu.fi/multiimprod/) to complete datasets of 161 and 338 men is provided. The imaging datasets are accompanied by clinical, laboratory and histopathological findings. Several topics are simplified in order to provide a solid base for the development of skills needed for an unsupervised review and potential reporting of prostate MRI in men with a clinical suspicion of PCa. The current review represents the first step towards enabling a large array of different medical providers to review and report accurately prostate MRI performed without IV contrast agent in men with a clinical suspicion of PCa.

Atypical small acinar proliferation and its significance in pathological reports in modern urological times

Atypical small acinar proliferation is a histopathological diagnosis of unspecified importance in prostate needle-biopsy reports, suggestive but not definitive for cancer. The terminology corresponds to some uncertainty in the biopsy report, as the finding might represent an underlying non-cancerous pathology mimicking cancer or an under-sampled prostate cancer site. Therefore, traditional practice favors an immediate repeat biopsy. However, in modern urological times, the need of urgent repeat biopsy is being challenged by some authors as in the majority of cases, the grade of cancer found in subsequent biopsy is reported to be low or the disease to be non-significant. On the other hand, high risk disease cannot be excluded, whereas no clinical or pathological factors can predict the final outcome. In this review, we discuss the significance of the diagnosis of atypical small acinar proliferation in the biopsy report, commenting on its importance in modern urological practice.

Attitudes and perceptions towards multiparametric magnetic resonance imaging of the prostate: A national survey among Italian urologists

OBJECTIVE

We aimed to assess the attitudes and perceptions towards multiparametric magnetic resonance imaging (mpMRI) of the prostate among Italian urologists.

MATERIAL AND METHODS

A national, web-based survey was performed. A questionnaire composed of 18 multiple choice questions was e-mailed to 941 currently active urologists, members of the Italian Society of Urology. Preserving anonymity, respondents' demographics were collected (e.g. geographic region, type of workplace, prostate procedures performed) as well as data concerning their attitudes and perceptions towards mpMRI (e.g. indications deemed appropriate, degree of confidence in mpMRI results). Data were expressed as raw numbers and percentages of survey answers.

RESULTS

In total, 98 responses were received (participation rate = 10.4%). Respondents mostly worked in urban areas (96%) and primarily in hospital settings (89%), while 48% of them worked in southern Italy. 97% of respondents considered mpMRI useful to detect Prostate Cancer (PCa) in patients with prior negative biopsy, 64% in biopsy-naïve patients and 60% for PCa pre-operatory staging. About half (42%) of the participants declared that mpMRI results frequently lead them to change PCa management strategy. Standardization of mpMRI acquisition and reporting was partially unsatisfactory. Reported waiting time for mpMRI scans was longer than 4 weeks for 51% of respondents. The major limitation of this survey includes the small number of participants.

CONCLUSIONS

Prostate mpMRI is used by Italian urologists mainly for detection and for pre-operative staging of PCa. Further improvements in terms of mpMRI availability and report standardization are required.

Fusion US/MRI prostate biopsy using a computer aided diagnostic (CAD) system

BACKGROUND

The aim of this study was to investigate the impact of computer aided diagnostic (CAD) system on the detection rate of prostate cancer (PCa) in a series of fusion prostate biopsy (FPB).

METHODS

Two prospective transperineal FPB series (with or without CAD assistance) were analyzed and PCa detection rates compared with per-patient and per-target analyses. The χ² and Mann-Whitney test were used to compare categorical and continuous variables, respectively. Univariable and multivariable regression analyses were applied to identify predictors of any and clinically significant (cs) PCa detection. Subgroup analyses were performed after stratifying for PI-RADS Score and lesion location.

RESULTS

Out of 183 FPB, 89 were performed with CAD assistance. At per-patient analysis the detection rate of any PCa and of cs PCa were 56.3% and 30.6%, respectively; the aid of CAD was negligible for either any PCa or csPCa detection rates (P=0.45 and P=0.99, respectively). Conversely in a per-target analysis, CAD-assisted biopsy had significantly higher positive predictive value (PPV) for any PCa versus MRI-only group (58% vs. 37.8%, P=0.001). PI-RADS Score was the only independent predictor of any and csPCa, either in per-patient or per-target multivariable regression analysis (all P<0.029). In a subgroup per-patient analysis of anterior/transitional zone lesions, csPCa detection rate was significantly higher in the CAD cohort (54.5%vs.11.1%, respectively; P=0.028), and CAD assistance was the only predictor of csPCa detection (P=0.013).

CONCLUSIONS

CAD assistance for FPB seems to improve detection of csPCa located in anterior/transitional zone. Enhanced identification and improved contouring of lesions may justify higher diagnostic performance.

The role of metabolic syndrome in high grade prostate cancer: development of a clinical nomogram

BACKGROUND

The aim of our study is to develop a clinical nomogram including metabolic syndrome status for the prediction of high-grade prostate cancer (HG PCa).

METHODS

A series of men at increased risk of PCa undergoing prostate biopsies were enrolled in a single center. Demographic and clinical characteristics of the patients were recorded. Metabolic syndrome was defined according to the adult treatment panel III. A nomogram was generated based on the logistic regression model and used to predict high grade prostate cancer defined as grade group ≥3 (ISUP 2014). ROC curves, calibration plots and decision curve analysis were used to evaluate the performance of the nomogram.

RESULTS

Overall, 738 patients were enrolled. Greater than or equal to 294/738 (40%) of the patients presented PCa and of those patients, 84/294 (39%) presented high grade disease (Grade Group ≥3). On multivariate analysis, DRE (OR: 3.24, 95% CI: 1.80-5.84), PSA (OR: 1.10, 95% CI: 1.05-1.16), PV (OR: 0.98, 95% CI: 0.97-0.99) and MetS (OR: 2.02, 95% CI: 1.13-3.59) were predictors of HG PCa. The nomogram based on the model presented good discrimination (AUC: 0.76), good calibration (Hosmer-Lemeshow Test, P>0.05) and a net benefit in the range of probabilities between 10% and 70%.

CONCLUSIONS

Metabolic syndrome is highly prevalent in patients at risk of prostate cancer and is particularly associated with high-grade prostate cancer. Our nomogram offers the possibility to include metabolic status in the assessment of patients at risk of prostate cancer to identify men who may have a high-grade form of the disease. External validation is warranted before its clinical implementation.

AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics

INTRODUCTION

The quantitative imaging features (radiomics) that can be obtained from the different modalities of current-generation hybrid imaging can give complementary information with regard to the tumour environment, as they measure different morphologic and functional imaging properties. These multi-parametric image descriptors can be combined with artificial intelligence applications into predictive models. It is now the time for hybrid PET/CT and PET/MRI to take the advantage offered by radiomics to assess the added clinical benefit of using multi-parametric models for the personalized diagnosis and prognosis of different disease phenotypes.

OBJECTIVE

The aim of the paper is to provide an overview of current challenges and available solutions to translate radiomics into hybrid PET-CT and PET-MRI imaging for a smart and truly multi-parametric decision model.