Accuracy of MRI in detecting seminal vesicle invasion in prostate cancer: a systematic review and meta-analysis

OBJECTIVE

To determine the diagnostic test accuracy of multiparametric magnetic resonance imaging (mpMRI) in detecting seminal vesicle invasion (SVI).

METHODS

The Medical Literature Analysis and Retrieval System Online (MEDLINE), PubMed, the Excerpta Medica dataBASE (EMBASE) and Cochrane databases were search up to May 2023. We included studies that investigated the accuracy of mpMRI in detecting SVI when compared to radical prostatectomy specimens as the reference standard. Data extraction was performed by two independent reviewers to construct 2 × 2 tables, as well as patient and study characteristics. The methodological quality of the included studies was assessed with the Quality of Assessment of Diagnostic Accuracy Studies-2 tool. Sensitivity and specificity were pooled and presented graphically with summary receiver operator characteristic (SROC) plots.

RESULTS

A total of 27 articles with 4862 patients were included for analysis. The summary sensitivity and specificity were 0.57 (95% confidence interval [CI] 0.45-0.68) and 0.95 (95% CI 0.92-0.99), respectively. Meta-regression indicated that there was no evidence that coil strength (P = 0.079), coil type (P = 0.589), year of publication (P = 0.503) or use of the Prostate Imaging-Reporting and Data System (P = 0.873) significantly influenced these results. The summary diagnostic odds ratio was 28.3 (95% CI 15.0-48.8) and the area under the curve for the SROC curve was 0.87. The I2 statistic was a modest 11.9%. In general, methodological quality was good.

CONCLUSION

The use of mpMRI in detecting SVI has excellent specificity but poor sensitivity. Both endorectal coils and magnetic field strength do not significantly impact the accuracy of MRI. These findings suggest that mpMRI cannot reliably rule out SVI in patients with prostate cancer.

Automatic removal of large blood vasculature for objective assessment of brain tumors using quantitative dynamic contrast-enhanced magnetic resonance imaging

The presence of a normal large blood vessel (LBV) in a tumor region can impact the evaluation of quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters and tumor classification. Hence, there is a need for automatic removal of LBVs from brain tissues including intratumoral regions for achieving an objective assessment of tumors. This retrospective study included 103 histopathologically confirmed brain tumor patients who underwent MRI, including DCE-MRI data acquisition. Quantitative DCE-MRI analysis was performed for computing various parameters such as wash-out slope (Slope-2), relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), blood plasma volume fraction (Vp), and volume transfer constant (Ktrans). An approach based on data-clustering algorithm, morphological operations, and quantitative DCE-MRI maps was proposed for the segmentation of normal LBVs in brain tissues, including the tumor region. Here, three widely used data-clustering algorithms were evaluated on two types of quantitative maps: (a) Slope-2, and (b) a new proposed combination of rCBV and Slope-2 maps. Fluid-attenuated inversion recovery-MRI hyperintense lesions were also automatically segmented using deep learning-based architecture. The accuracy of LBV segmentation was qualitatively assessed blindly by two experienced observers, and Likert scoring was also obtained from each individual and compared using Cohen's Kappa test, and multiple statistical features from quantitative DCE-MRI parameters were obtained in the segmented tumor. t-test and receiver operating characteristic (ROC) curve analysis were performed for comparing the effect of removal of LBVs on parameters as well as on tumor grading. k-means clustering exhibited better accuracy and computational efficiency. Tumors, in particular high-grade gliomas (HGGs), showed a high contrast compared with normal tissues (relative % difference = 18.5%) on quantitative maps after the removal of LBVs. Statistical features (95th percentile values) of all parameters in the tumor region showed a statistically significant difference (p < 0.05) between with and without LBV maps. Similar results were obtained for the ROC curve analysis for differentiation between low-grade gliomas and HGGs. Moreover, after the removal of LBVs, the rCBV, rCBF, and Vp maps show better visualization of tumor regions.

Application of transumbilical single-incision laparoscopy in the treatment of complicated appendicitis in overweight/obese adolescents

OBJECTIVE

To investigate the clinical efficacy of transumbilical single-incision laparoscopic surgery in the treatment of complicated appendicitis in overweight/obese adolescents.

METHODS

A retrospective analysis was conducted on the clinical data of 226 adolescent patients with complicated appendicitis who were admitted to our hospital from January 2014 to June 2022. Among them, 102 cases underwent transumbilical single-incision laparoscopic appendectomy as the observation group, and another 124 cases underwent conventional three-port laparoscopic appendectomy as the control group. The surgical time, intraoperative blood loss, duration of incisional pain, postoperative flatus time, length of hospital stay, surgical site infection (SSI), satisfaction with cosmetic result, and occurrence of postoperative complications were compared between the two groups.

RESULTS

Both groups completed the surgery smoothly, and there were no statistically significant differences in gender, age, BMI, duration of illness, white blood cell count, and preoperative CRP value between the two groups (P > 0.05). There were no statistically significant differences in surgical time and intraoperative blood loss between the two groups (P > 0.05). However, the observation group had shorter hospital stays, shorter duration of incisional pain, shorter postoperative time to flatus, and lower overall postoperative complication rates compared to the control group, with statistically significant differences (P < 0.05). The observation group had higher satisfaction with cosmetic result compared to the control group, with statistically significant differences (P < 0.05). Both groups were followed up for one year postoperatively, and there were no occurrences of residual appendicitis or severe adhesive intestinal obstruction.

CONCLUSION

When proficiently mastered, the application of transumbilical single-incision laparoscopy in the treatment of complicated appendicitis in overweight/obese adolescents offers advantages such as minimal trauma, rapid recovery, fewer complications, and improved aesthetic outcomes.

Advantage of whole-mount histopathology in prostate cancer: current applications and future prospects

BACKGROUND

Whole-mount histopathology (WMH) has been a powerful tool to investigate the characteristics of prostate cancer. However, the latest advancement of WMH was yet under summarization. In this review, we offer a comprehensive exposition of current research utilizing WMH in diagnosing and treating prostate cancer (PCa), and summarize the clinical advantages of WMH and outlines potential on future prospects.

METHODS

An extensive PubMed search was conducted until February 26, 2023, with the search term "prostate", "whole-mount", "large format histology", which was limited to the last 4 years. Publications included were restricted to those in English. Other papers were also cited to contribute a better understanding.

RESULTS

WMH exhibits an enhanced legibility for pathologists, which improved the efficacy of pathologic examination and provide educational value. It simplifies the histopathological registration with medical images, which serves as a convincing reference standard for imaging indicator investigation and medical image-based artificial intelligence (AI). Additionally, WMH provides comprehensive histopathological information for tumor volume estimation, post-treatment evaluation, and provides direct pathological data for AI readers. It also offers complete spatial context for the location estimation of both intraprostatic and extraprostatic cancerous region.

CONCLUSIONS

WMH provides unique benefits in several aspects of clinical diagnosis and treatment of PCa. The utilization of WMH technique facilitates the development and refinement of various clinical technologies. We believe that WMH will play an important role in future clinical applications.

Prediction of extracapsular extension in prostate cancer using the Likert scale combined with clinical and pathological parameters

Abstract

This study aimed to investigate the independent clinical, pathological, and radiological factors associated with extracapsular extension in radical prostatectomy specimens and to improve the accuracy of predicting extracapsular extension of prostate cancer before surgery.

Methods

From August 2018 to June 2023, the clinical and pathological data of 229 patients with confirmed prostate cancer underwent radical prostatectomy from The Second Hospital of Yinzhou. The patients' multiparametric magnetic resonance imaging data were graded using the Likert scale. The chi-square or independent-sample T-test was used to analyze the related factors for an extracapsular extension. Multivariate analysis was used to identify independent factors associated with extracapsular extension in prostate cancer. Additionally, receiver operating characteristic curve analysis was used to calculate the area under the curve and assess the diagnostic performance of our model. The clinical decision curve was used to analyze the clinical net income of Likert scale, biopsy positive rate, biopsy GG, and combined mode.

Results

Of the 229 patients, 52 had an extracapsular extension, and 177 did not. Multivariate analysis showed that the Likert scale score, biopsy grade group and biopsy positive rate were independent risk factors for extracapsular extension in prostate cancer. The area under the curves for the Likert scale score, biopsy grade group, and biopsy positive rate were 0.802, 0.762, and 0.796, respectively. Furthermore, there was no significant difference in the diagnostic efficiency for extracapsular extension (P>0.05). However, when these three factors were combined, the diagnostic efficiency was significantly improved, and the area under the curve increased to 0.905 (P<0.05). In the analysis of the decision curve, The clinical net income of the combined model is obviously higher than that of Likert scale, biopsy positive rate, and biopsy GG.

Conclusion

The Likert scale, biopsy grade group and biopsy positive rate are independent risk factors for extracapsular extension in prostate cancer, and their combination can significantly improve the diagnostic efficiency for an extracapsular extension.

Preoperative Multiparametric Prostate Magnetic Resonance Imaging Structured Report Informs Risk for Positive Apical Surgical Margins During Radical Prostatectomy

BACKGROUND

The prostatic apex is the most frequent location of positive surgical margin (PSM) after surgery. Data regarding the ability of multiparametric magnetic resonance imaging (mpMRI) to prospectively identify men at risk for apical PSMs (aPSMs) using a structured report are lacking.

OBJECTIVES

The aims of the study are to determine and to compare the rate of aPSM in men with versus without prospectively flagged at-risk prostate lesions during clinical mpMRI interpretation using whole-mount histopathology as the reference standard.

METHODS

This single-center, retrospective study of prospectively collected data included treatment-naive men with abnormal 3T mpMRI (PI-RADS v2 score ≥3) between January 2016 and December 2018 followed by surgery. During routine clinical interpretation, radiologists flagged prostate lesions abutting the apical most gland and/or encircling the distal most prostatic urethra using standardized language available as a "pick list" option in the structured report. Logistic regression was used to compare the rate of PSM in 2 groups (flagged vs nonflagged men). Propensity score covariate adjustment corrected for potential selection bias according to age, prostate-specific antigen (PSA), PSA density, grade group, and pT stage. The estimate was further adjusted by including surgeon as a covariate.

RESULTS

A total of 428 men were included. A statistically significant higher proportion of aPSMs was noted in flagged (56% [51/91]) compared with nonflagged apical lesions (31% [105/337]; adjusted odds ratio, 2.5; 95% confidence interval, 1.6-4.1; P < 0.01). The difference in aPSM between both groups also varied according to the surgeon performing the RP. Prostate-specific antigen, PSA density, lesion size, apical location, Prostate Imaging Reporting & Data System score, grade group, pT stage, and surgeon's experience were associated with higher PSM rate. Biochemical recurrence, defined as PSA greater than 0.2 ng/mL on 2 measurements after RP, was significantly associated with PSM status (propensity score adjusted odds ratio, 3.1; 95% confidence interval, 1.8-5.3; P < 0.0001); however, patients flagged by radiologists did not have a significant difference in biochemical recurrence rates as compared with nonflagged patients ( P = 0.11).

CONCLUSIONS

Standard language built into structured reports for mpMRI of the prostate helps identify preoperatively patients at risk for aPSM.

CLINICAL IMPACT

Multiparametric MRI is able to identify patients at increased risk for aPSM, and this information can be conveyed in a structured report to urologists, facilitating patient counseling and treatment decisions.

PI-RADS and Likert scales for structured reporting in multiparametric MR imaging of the prostate

Multiparametric MRI (mpMRI) plays a critical role in the detection, staging and risk stratification of prostate cancer (PCa). There are two widely accepted structured reporting systems used for interpretation of mpMRI of the prostate - PI-RADS v2.1 and Likert. Both these systems demonstrate good diagnostic performance with high cancer detection rates however have key conceptual differences. In this commentary, the authors highlight the individual strengths and areas of potential improvement as well as emphasize the need for continued clinical validation for these interpreting and reporting systems.

Diagnostic Performance of Extraprostatic Extension Grading System for Detection of Extraprostatic Extension in Prostate Cancer: A Diagnostic Systematic Review and Meta-Analysis

PURPOSE

To evaluate the diagnostic performance of the extraprostatic extension (EPE) grading system for detection of EPE in patients with prostate cancer (PCa).

MATERIALS AND METHODS

We performed a literature search of Web of Science, MEDLINE (Ovid and PubMed), Cochrane Library, EMBASE, and Google Scholar to identify eligible articles published before August 31, 2021, with no language restrictions applied. We included studies using the EPE grading system for the prediction of EPE, with histopathological results as the reference standard. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), and diagnostic odds ratio (DOR) were calculated with the bivariate model. Quality assessment of included studies was performed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool.

RESULTS

A total of 4 studies with 1,294 patients were included in the current systematic review. The pooled sensitivity and specificity were 0.82 (95% CI 0.76-0.87) and 0.63 (95% CI 0.51-0.73), with the area under the hierarchical summary receiver operating characteristic (HSROC) curve of 0.82 (95% CI 0.79-0.85). The pooled LR+, LR-, and DOR were 2.20 (95% CI 1.70-2.86), 0.28 (95% CI 0.22-0.36), and 7.77 (95% CI 5.27-11.44), respectively. Quality assessment for included studies was high, and Deeks's funnel plot indicated that the possibility of publication bias was low (p = 0.64).

CONCLUSION

The EPE grading system demonstrated high sensitivity and moderate specificity, with a good inter-reader agreement. However, this scoring system needs more studies to be validated in clinical practice.

Repeat MRI during active surveillance: natural history of prostatic lesions and upgrading rates

OBJECTIVES

To assess upgrading rates in patients on active surveillance (AS) for prostate cancer (PCa) after serial multiparametric magnetic resonance imaging (mpMRI).

METHODS

We conducted a retrospective analysis of 558 patients. Five different criteria for mpMRI progression were used: 1) a Prostate Imaging Reporting and Data System (PI-RADS) score increase; 2) a lesion size increase; 3) an extraprostatic extension score increase; 4) overall mpMRI progression; and 5) the number of criteria met for mpMRI progression (0 vs 1 vs 2-3). In addition, two definitions of PCa upgrading were evaluated: 1) International Society of Urological Pathology Grade Group (ISUP GG) ≥2 with >10% of pattern 4 and 2) ISUP GG ≥ 3. Estimated annual percent changes methodology was used to show the temporal trends of mpMRI progression criteria. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of mpMRI progression criteria were also analysed. Multivariable logistic regression models tested PCa upgrading rates.

RESULTS

Lower rates over time for all mpMRI progression criteria were observed. The NPV of serial mpMRI scans ranged from 90.5% to 93.5% (ISUP GG≥2 with >10% of pattern 4 PCa upgrading) and from 98% to 99% (ISUP GG≥3 PCa upgrading), depending on the criteria used for mpMRI progression. A prostate-specific antigen density (PSAD) threshold of 0.15 ng/mL/mL was used to substratify those patients who would be able to skip a prostate biopsy. In multivariable logistic regression models assessing PCa upgrading rates, all five mpMRI progression criteria achieved independent predictor status.

CONCLUSION

During AS, approximately 27% of patients experience mpMRI progression at first repeat MRI. However, the rates of mpMRI progression decrease over time at subsequent mpMRI scans. Patients with stable mpMRI findings and with PSAD < 0.15 ng/mL/mL could safely skip surveillance biopsies. Conversely, patients who experience mpMRI progression should undergo a prostate biopsy.

The influence of physical exercise on college students' mental health and social adaptability from the cognitive perspective

BACKGROUND

the relationship between physical exercise (PE) and mental health (MH) had been an important research topic in exercise psychology. With the development of society, the increasingly fierce social competition had put forward higher and higher requirements for college students' social adaptability (SA). As members of the new era, college students were expected to not only have innovative knowledge concept, solid knowledge foundation, and healthy psychology, but also have the ability to adapt to the changes in the environment, know how to get along with others, and deal with problems alone.

OBJECTIVE

this study aimed to evaluate the PE, MH, and SA of college students, and to analyze the internal relationships among PE, MH, and SA.

METHODS

based on questionnaire survey, college students were randomly selected for investigation and the data were statistically processed.

RESULTS

there were significant differences in the MH of students of different genders, majors, grades, and origins. There was a significant difference between the amount of exercise and the MH of college students. The amount of exercise was positively correlated with the MH level of college students, and there was also a positive correlation between PE and MH. The SA of the physical exercisers was average, but the SA of the non-physical exercisers was poor. There was a significant difference between the SA of the physical exercisers and the non-physical exercisers. There were significant differences in the SA between physical exercisers and non-physical exercisers of different genders, majors, grades, and origins. Physical exercisers who participated in team sports were more socially adaptable. There was no significant difference in the SA of physical exercisers of different genders, majors, and origins, and the SA of senior students was stronger.

CONCLUSIONS

PE had a positive effect on college students' MH and social adaptation ability. Colleges and universities could improve their MH and social adaptation ability by arranging appropriate PE.

Preoperative Prediction of Extracapsular Extension: Radiomics Signature Based on Magnetic Resonance Imaging to Stage Prostate Cancer

PURPOSE

To investigate and validate the potential role of a radiomics signature in predicting the side-specific probability of extracapsular extension (ECE) of prostate cancer (PCa).

PROCEDURES

The preoperative magnetic resonance imaging data of 238 prostatic samples from 119 enrolled PCa patients were retrospectively assessed. The samples with were randomized in a two-to-one ratio into training (n = 74) and validation (n = 45) datasets. The radiomics features were derived from T2-weighted images (T2WIs). The optimal radiomics features were identified from the least absolute shrinkage and selection operator (LASSO) logistic regression model and were used to construct a predictive radiomics signature via dimension reduction and selection approaches. The association between the radiomics signatures and pathological ECE status was explored. Receiver operating characteristic (ROC) analysis was used to assess the discriminatory ability of the signature. The calibration performance and clinical usefulness of the radiomics signature were subsequently assessed by calibration curve and decision curve analyses.

RESULTS

The proposed radiomics signature that incorporated 17 selected radiomics features was significantly associated with pathological ECE outcomes (P < 0.001) in both the training and validation datasets. The constructed model displayed good discrimination, with areas under the curve (AUC) of 0.906 (95 % confidence interval (CI), 0.847, 0.948) and 0.821 (95 % CI, 0.726, 0.894) for the training and validation datasets, respectively, and had a good calibration performance. The clinical utility of this model was confirmed through decision curve analysis.

CONCLUSIONS

The radiomics signature based on T2WIs showed the potential to predict the side-specific probability of pathological ECE status and can facilitate the preoperative individualized predictions for PCa patients.

MRI index lesion radiomics and machine learning for detection of extraprostatic extension of disease: a multicenter study

Objectives

To build a machine learning (ML) model to detect extraprostatic extension (EPE) of prostate cancer (PCa), based on radiomics features extracted from prostate MRI index lesions.

Methods

Consecutive MRI exams of patients undergoing radical prostatectomy for PCa were retrospectively collected from three institutions. Axial T2-weighted and apparent diffusion coefficient map images were annotated to obtain index lesion volumes of interest for radiomics feature extraction. Data from one institution was used for training, feature selection (using reproducibility, variance and pairwise correlation analyses, and a correlation-based subset evaluator), and tuning of a support vector machine (SVM) algorithm, with stratified 10-fold cross-validation. The model was tested on the two remaining institutions’ data and compared with a baseline reference and expert radiologist assessment of EPE.

Results

In total, 193 patients were included. From an initial dataset of 2436 features, 2287 were excluded due to either poor stability, low variance, or high collinearity. Among the remaining, 14 features were used to train the ML model, which reached an overall accuracy of 83% in the training set. In the two external test sets, the SVM achieved an accuracy of 79% and 74% respectively, not statistically different from that of the radiologist (81–83%, p = 0.39–1) and outperforming the baseline reference (p = 0.001–0.02).

Conclusions

A ML model solely based on radiomics features demonstrated high accuracy for EPE detection and good generalizability in a multicenter setting. Paired to qualitative EPE assessment, this approach could aid radiologists in this challenging task.

Key Points

• Predicting the presence of EPE in prostate cancer patients is a challenging task for radiologists.

• A support vector machine algorithm achieved high diagnostic accuracy for EPE detection, with good generalizability when tested on multiple external datasets.

• The performance of the algorithm was not significantly different from that of an experienced radiologist.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-07856-3.

Extraprostatic extension in prostate cancer: primer for radiologists

The presence of extraprostatic extension (EPE) on multiparametric MRI (mpMRI) is an important factor in determining the management of prostate cancer. EPE is an established risk factor for biochemical recurrence of prostate cancer after radical prostatectomy (RP) and patients with EPE may be considered for wider resection margins, non-nerve-sparing surgery, adjuvant radiation therapy (RT), or androgen deprivation therapy (ADT). Several statistical nomograms and scoring systems have been developed to predict pathological stage at time of RP but with varying accuracies. Using the current PI-RADS v2 mpMRI staging guidelines results in high specificity but lacks in sensitivity. These findings reveal the need for more standardization and further refinement of existing MRI protocols and prostate cancer prediction tools. Current studies have looked into indirect additional imaging criteria such as index tumor volume, length of capsular contact, and apparent diffusion coefficient. Measuring for these features can improve the robustness of mpMRI in staging prostate cancer, as they have been shown to be independent predictors of EPE. MRI/ultrasound fusion-guided targeted biopsy can detect EPE not found on standard biopsy. Collectively, these measurements and imaging techniques can augment the detection of EPE and subsequent risk stratification.

External validation of the Martini nomogram for prediction of side-specific extraprostatic extension of prostate cancer in patients undergoing robot-assisted radical prostatectomy

INTRODUCTION

To establish oncological safe nerve-sparing robot-assisted radical prostatectomy, accurate assessment of extraprostatic extension (EPE) is critical. A recently developed nomogram including magnetic resonance imaging parameters accurately predicted side-specific EPE in the development cohort. The aim of this study is to assess this model's performance in an external patient population.

PATIENTS AND METHODS

Model fit was assessed in a cohort of 550 patients who underwent robot-assisted radical prostatectomy in 2014 to 2017 for prostate cancer. Model calibration was evaluated using calibration slopes. Discriminative ability was quantified using the area under the receiver operating characteristic curve. Model updating was done by adjusting the linear predictor to minimize differences in expected and observed risk for EPE.

RESULTS

A total of 792 prostate lobes were included for model validation. Discriminative ability expressed in terms of receiver operating characteristic curve was 0.78, 95%CI 0.75-0.82. Graphical evaluation of the calibration showed poor fit with a high disagreement between predicted probabilities and observed probabilities of EPE in the population. Model updating resulted in excellent agreement between mean predicted and observed probabilities. However, calibration plots showed substantial miscalibration; including both under- and overestimation.

CONCLUSION

External validation of the novel nomogram for the prediction of side specific EPE developed by Martini and co-workers showed good discriminative ability but poor calibration. After updating, substantial miscalibration was still present. Use of this nomogram for individualized risk predictions is therefore not recommended.