Extraprostatic extension in prostate cancer: primer for radiologists

Quantitative differentiation of non-invasive bladder urothelial carcinoma and inverted papilloma based on CT urography

PURPOSE

To investigate the value of CT urography (CTU) indicators in the quantitative differential diagnosis of bladder urothelial carcinoma (BUC) and inverted papilloma of the bladder (IPB).

MATERIAL AND METHODS

The clinical and preoperative CTU imaging data of continuous 103 patients with histologically confirmed BUC or IPB were retrospectively analyzed. The imaging data included 6 qualitative indicators and 7 quantitative measures. The recorded clinical information and imaging features were subjected to univariate and multivariate logistic regression analysis to find independent risk factors for BUC, and a combined multi-indicator prediction model was constructed, and the prediction model was visualized using nomogram. ROC curve analysis was used to calculate and compare the predictive efficacy of independent risk factors and nomogram.

RESULTS

Junction smoothness, maximum longitudinal diameter, tumor-wall interface and arterial reinforcement rate were independent risk factors for distinguishing BUC from IPB. The AUC of the combined model was 0.934 (sensitivity = 0.808, specificity = 0.920, accuracy = 0.835), and its diagnostic efficiency was higher than that of junction smoothness (AUC=0.667, sensitivity = 0.654, specificity = 0.680, accuracy = 0.660), maximum longitudinal diameter (AUC=0.757, sensitivity = 0.833, specificity = 0.604, accuracy = 0.786), tumor-wall interface (AUC=0.888, sensitivity = 0.755, specificity = 0.808, accuracy = 0.816) and Arterial reinforcement rate (AUC=0.786, sensitivity = 0.936, specificity = 0.640, accuracy = 0.864).

CONCLUSION

Above qualitative and quantitative indicators based on CTU and the combination of them may be helpful to the differential diagnosis of BUC and IPB, thus better assisting in clinical decision-making.

KEY POINTS

1. Bladder urothelial carcinoma (BUC) and inverted papilloma of the bladder (IPB) exhibit similar clinical symptoms and imaging presentations. 2. The diagnostic value of CT urography (CTU) in distinguishing between BUC and IPB has not been documented. 3. BUC and IPB differ in lesion size, growth pattern and blood supply. 4. The diagnostic efficiency is optimized by integrating multiple independent risk factors into the prediction model.

Amide proton transfer imaging has added value for predicting extraprostatic extension in prostate cancer patients

Background

Prostate cancer invades the capsule is a key factor in selecting appropriate treatment methods. Accurate preoperative prediction of extraprostatic extension (EPE) can help achieve precise selection of treatment plans.

Purpose

The aim of this study is to verify the diagnostic efficacy of tumor size, length of capsular contact (LCC), apparent diffusion coefficient (ADC), and Amide proton transfer (APT) value in predicting EPE. Additionally, the study aims to investigate the potential additional value of APT for predicting EPE.

Method

This study include 47 tumor organ confined patients (age, 64.16 ± 9.18) and 50 EPE patients (age, 61.51 ± 8.82). The difference of tumor size, LCC, ADC and APT value between groups were compared. Binary logistic regression was used to screen the EPE predictors. The receiver operator characteristic curve analysis was performed to assess the diagnostic performance of variables for predicting EPE. The diagnostic efficacy of combined models (model I: ADC+LCC+tumor size; model II: APT+LCC+tumor size; and model III: APT +ADC+LCC+tumor size) were also analyzed.

Results

APT, ADC, tumor size and the LCC were independent predictors of EPE. The area under the curve (AUC) of APT, ADC, tumor size and the LCC were 0.752, 0.665, 0.700 and 0.756, respectively. The AUC of model I, model II, and model III were 0.803, 0.845 and 0.869, respectively. The cutoff value of APT, ADC, tumor size and the LCC were 3.65%, 0.97×10-3mm2/s, 17.30mm and 10.78mm, respectively. The sensitivity/specificity of APT, ADC, tumor size and the LCC were 76%/89.4.0%, 80%/59.6%, 54%/78.9%, 72%/66%, respectively. The sensitivity/specificity of model I, Model II and Model III were 74%/72.3%, 82%/72.5% and 84%/80.9%, respectively.

Data conclusion

Amide proton transfer imaging has added value for predicting EPE. The combination model of APT balanced the sensitivity and specificity.

Clinical application of machine learning models in patients with prostate cancer before prostatectomy

BACKGROUND

To build machine learning predictive models for surgical risk assessment of extracapsular extension (ECE) in patients with prostate cancer (PCa) before radical prostatectomy; and to compare the use of decision curve analysis (DCA) and receiver operating characteristic (ROC) metrics for selecting input feature combinations in models.

METHODS

This retrospective observational study included two independent data sets: 139 participants from a single institution (training), and 55 from 15 other institutions (external validation), both treated with Robotic Assisted Radical Prostatectomy (RARP). Five ML models, based on different combinations of clinical, semantic (interpreted by a radiologist) and radiomics features computed from T2W-MRI images, were built to predict extracapsular extension in the prostatectomy specimen (pECE+). DCA plots were used to rank the models' net benefit when assigning patients to prostatectomy with non-nerve-sparing surgery (NNSS) or nerve-sparing surgery (NSS), depending on the predicted ECE status. DCA model rankings were compared with those drived from ROC area under the curve (AUC).

RESULTS

In the training data, the model using clinical, semantic, and radiomics features gave the highest net benefit values across relevant threshold probabilities, and similar decision curve was observed in the external validation data. The model ranking using the AUC was different in the discovery group and favoured the model using clinical + semantic features only.

CONCLUSIONS

The combined model based on clinical, semantic and radiomic features may be used to predict pECE + in patients with PCa and results in a positive net benefit when used to choose between prostatectomy with NNS or NNSS.

Comprehensive analysis of predictive factors for upstaging in intraprostatic cancer after radical prostatectomy: Different patterns of spread exist in lesions at different locations

BACKGROUND

Accurate assessment of the clinical staging is crucial for determining the need for radical prostatectomy (RP) in prostate cancer (PCa). However, the current methods for PCa staging may yield incorrect results. This study aimed to comprehensively analyze independent predictors of postoperative upstaging of intraprostatic cancer.

METHODS

We conducted a retrospective analysis of data from intraprostatic cancer patients who underwent radical surgery between March 2019 and December 2022. Intraprostatic cancer was defined as a lesion confined to the prostate, excluding cases where multiparameter magnetic resonance imaging (mpMRI) showed the lesion in contact with the prostatic capsule. We assessed independent predictors of extraprostatic extension (EPE) and analyzed their association with positive surgical margin (PSM) status. In addition, based on the distance of the lesion from the capsule on mpMRI, we divided the patients into non-transition zone and transition zone groups for further analysis.

RESULTS

A total of 500 patients were included in our study. Logistic regression analysis revealed that biopsy Gleason grade group (GG) (odds ratio, OR: 1.370, 95% confidence interval, CI: 1.093-1.718) and perineural invasion (PNI) (OR: 2.746, 95% CI: 1.420-5.309) were predictive factors for postoperative EPE. Both biopsy GG and PNI were associated with lateral (GG: OR: 1.270, 95% CI: 1.074-1.501; PNI: OR: 2.733, 95% CI: 1.521-4.911) and basal (GG: OR: 1.491, 95% CI: 1.194-1.862; PNI: OR: 3.730, 95% CI: 1.929-7.214) PSM but not with apex PSM (GG: OR: 1.176, 95% CI: 0.989-1.399; PNI: OR: 1.204, 95% CI: 0.609-2.381) after RP. Finally, PNI was an independent predictor of EPE in the transition zone (OR: 11.235, 95% CI: 2.779-45.428) but not in the non-transition zone (OR: 1.942, 95% CI: 0.920-4.098).

CONCLUSION

PNI and higher GG may indicate upstaging of tumors in patients with intraprostatic carcinoma. These two factors are associated with PSM in locations other than the apex of the prostate. Importantly, cancer in the transition zone of the prostate is more likely to spread externally through nerve invasion than cancer in the non-transition zone.

Local Staging of Prostate Cancer at MRI: What the Urologist and Radiation Oncologist Want to Know

This slide presentation provides an overview of prostate cancer risk stratification and management, reviews key anatomic and imaging principles pertaining to local staging of prostate cancer, and highlights inflection points where MRI findings regarding local staging may modify clinical management.

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.

Nomogram Models for Distinguishing Intraductal Carcinoma of the Prostate From Prostatic Acinar Adenocarcinoma Based on Multiparametric Magnetic Resonance Imaging

OBJECTIVE

To compare multiparametric magnetic resonance imaging (MRI) features of intraductal carcinoma of the prostate (IDC-P) with those of prostatic acinar adenocarcinoma (PAC) and develop prediction models to distinguish IDC-P from PAC and IDC-P with a high proportion (IDC ≥ 10%, hpIDC-P) from IDC-P with a low proportion (IDC < 10%, lpIDC-P) and PAC.

MATERIALS AND METHODS

One hundred and six patients with hpIDC-P, 105 with lpIDC-P and 168 with PAC, who underwent pretreatment multiparametric MRI between January 2015 and December 2020 were included in this study. Imaging parameters, including invasiveness and metastasis, were evaluated and compared between the PAC and IDC-P groups as well as between the hpIDC-P and lpIDC-P subgroups. Nomograms for distinguishing IDC-P from PAC, and hpIDC-P from lpIDC-P and PAC, were made using multivariable logistic regression analysis. The discrimination performance of the models was assessed using the receiver operating characteristic area under the curve (ROC-AUC) in the sample, where the models were derived from without an independent validation sample.

RESULTS

The tumor diameter was larger and invasive and metastatic features were more common in the IDC-P than in the PAC group (P < 0.001). The distribution of extraprostatic extension (EPE) and pelvic lymphadenopathy was even greater, and the apparent diffusion coefficient (ADC) ratio was lower in the hpIDC-P than in the lpIDC-P group (P < 0.05). The ROC-AUCs of the stepwise models based solely on imaging features for distinguishing IDC-P from PAC and hpIDC-P from lpIDC-P and PAC were 0.797 (95% confidence interval, 0.750-0.843) and 0.777 (0.727-0.827), respectively.

CONCLUSION

IDC-P was more likely to be larger, more invasive, and more metastatic, with obviously restricted diffusion. EPE, pelvic lymphadenopathy, and a lower ADC ratio were more likely to occur in hpIDC-P, and were also the most useful variables in both nomograms for predicting IDC-P and hpIDC-P.

Radiologic-pathologic correlation of prostatic cancer extracapsular extension (ECE)

Recent advancements on nerve-sparing robotic prostatectomy allow fewer side effects such as urinary incontinence and sexual dysfunction. To perform such techniques, it is essential for the surgeon to know if the neurovascular bundle is involved. Despite being the gold-standard imaging method for Prostate Cancer (PCa) staging, Magnetic Resonance Imaging (MRI) lacks high specificity for detecting extracapsular extension (ECE). Therefore, it is essential to understand the pathologic aspects of ECE to better evaluate the MRI findings of PCa. We reviewed the normal MRI appearance of the prostate gland and the periprostatic space and correlated them to prostatectomy specimens. The different findings of ECE and neurovascular bundle invasion are exemplified with images of both MRI and histologic specimens.

MR-based simplified extraprostatic extension evaluation: comparison of performances of different predictive models

OBJECTIVES

To test reproducibility and predictive value of a simplified score for assessment of extraprostatic tumor extension (sEPE grade).

METHODS

Sixty-five patients (mean age ± SD, 67 years ± 6.3) treated with radical prostatectomy for prostate cancer who underwent 1.5-Tesla multiparametric magnetic resonance imaging (mpMRI) 6 months before surgery were enrolled. sEPE grade was derived from mpMRI metrics: curvilinear contact length > 15 mm (CCL) and capsular bulging/irregularity. The diameter of the index lesion (dIL) was also measured. Evaluations were independently performed by seven radiologists, and inter-reader agreement was tested by weighted Cohen K coefficient. A nested (two levels) Monte Carlo cross-validation was used. The best cut-off value for dIL was selected by means of the Youden J index to classify values into a binary variable termed dIL*. Logistic regression models based on sEPE grade, dIL, and clinical scores were developed to predict pathologic EPE. Results on validation set were assessed by the main metrics of the receiver operating characteristics curve (ROC) and by decision curve analysis (DCA). Based on our findings, we defined and tested an alternative sEPE grade formulation.

RESULTS

Pathologic EPE was found in 31/65 (48%) patients. Average κ_w was 0.65 (95% CI 0.51-0.79), 0.66 (95% CI 0.48-0.84), 0.67 (95% CI 0.50-0.84), and 0.43 (95% CI 0.22-0.63) for sEPE grading, CLL ≥ 15 mm, dIL*, and capsular bulging/irregularity, respectively. The highest diagnostic yield in predicting EPE was obtained by combining both sEPE grade and dIL*(ROC-AUC 0.81).

CONCLUSIONS

sEPE grade is reproducible and when combined with the dIL* accurately predicts extraprostatic tumor extension.

KEY POINTS

• Simple and reproducible mpMRI semi-quantitative scoring system for extraprostatic tumor extension. • sEPE grade accurately predicts extraprostatic tumor extension regardless of reader expertise. • Accurate pre-operative staging and risk stratification for optimized patient management.

A Faster Prostate MRI: Comparing a Novel Denoised, Single-Average T2 Sequence to the Conventional Multiaverage T2 Sequence Regarding Lesion Detection and PI-RADS Score Assessment

BACKGROUND

The T₂ w sequence is a standard component of a prostate MRI examination; however, it is time-consuming, requiring multiple signal averages to achieve acceptable image quality.

PURPOSE/HYPOTHESIS

To determine whether a denoised, single-average T₂ sequence (T₂ -R) is noninferior to the standard multiaverage T₂ sequence (T₂ -S) in terms of lesion detection and PI-RADS score assessment.

STUDY TYPE

Retrospective.

POPULATION

A total of 45 males (age range 60-75 years) who underwent clinically indicated prostate MRI examinations, 21 of whom had pathologically proven prostate cancer.

FIELD STRENGTH/SEQUENCE

A 3 T; T₂ w FSE, DWI with ADC maps, and dynamic contrast-enhanced images with color-coded perfusion maps. T₂ -R images were created from the raw data utilizing a single "average" with iterative denoising.

ASSESSMENT

Nine readers randomly assessed complete exams including T₂ -R and T₂ -S images in separate sessions. PI-RADS version 2.1 was used. All readers then compared the T₂ -R and T₂ -S images side by side to evaluate subjective preference. An additional detailed image quality assessment was performed by three senior level readers.

STATISTICAL TESTS

Generalized linear mixed effects models for differences in lesion detection, image quality features, and overall preference between T₂ -R and T₂ -S sequences. Intraclass correlation coefficients (ICC) were used to assess reader agreement for all comparisons. A significance threshold of P = 0.05 was used for all statistical tests.

RESULTS

There was no significant difference between sequences regarding identification of lesions with PI-RADS ≥3 (P = 0.10) or PI-RADS score (P = 0.77). Reader agreement was excellent for lesion identification (ICC = 0.84). There was no significant overall preference between the two sequences regarding image quality (P = 0.07, 95% CI: [-0.23, 0.01]). Reader agreement was good regarding sequence preference (ICC = 0.62).

DATA CONCLUSION

Use of single-average, denoised T₂ -weighted images was noninferior in prostate lesion detection or PI-RADS scoring when compared to standard multiaverage T₂ -weighted images.

EVIDENCE LEVEL

3.

TECHNICAL EFFICACY

Stage 3.

Early biomarkers of extracapsular extension of prostate cancer using MRI-derived semantic features

BACKGROUND

To construct a model based on magnetic resonance imaging (MRI) features and histological and clinical variables for the prediction of pathology-detected extracapsular extension (pECE) in patients with prostate cancer (PCa).

METHODS

We performed a prospective 3 T MRI study comparing the clinical and MRI data on pECE obtained from patients treated using robotic-assisted radical prostatectomy (RARP) at our institution. The covariates under consideration were prostate-specific antigen (PSA) levels, the patient's age, prostate volume, and MRI interpretative features for predicting pECE based on the Prostate Imaging-Reporting and Data System (PI-RADS) version 2.0 (v2), as well as tumor capsular contact length (TCCL), length of the index lesion, and prostate biopsy Gleason score (GS). Univariable and multivariable logistic regression models were applied to explore the statistical associations and construct the model. We also recruited an additional set of participants-which included 59 patients from external institutions-to validate the model.

RESULTS

The study participants included 184 patients who had undergone RARP at our institution, 26% of whom were pECE+ (i.e., pECE positive). Significant predictors of pECE+ were TCCL, capsular disruption, measurable ECE on MRI, and a GS of ≥7(4 + 3) on a prostate biopsy. The strongest predictor of pECE+ is measurable ECE on MRI, and in its absence, a combination of TCCL and prostate biopsy GS was significantly effective for detecting the patient's risk of being pECE+. Our predictive model showed a satisfactory performance at distinguishing between patients with pECE+ and patients with pECE-, with an area under the ROC curve (AUC) of 0.90 (86.0-95.8%), high sensitivity (86%), and moderate specificity (70%).

CONCLUSIONS

Our predictive model, based on consistent MRI features (i.e., measurable ECE and TCCL) and a prostate biopsy GS, has satisfactory performance and sufficiently high sensitivity for predicting pECE+. Hence, the model could be a valuable tool for surgeons planning preoperative nerve sparing, as it would reduce positive surgical margins.

Computational Detection of Extraprostatic Extension of Prostate Cancer on Multiparametric MRI Using Deep Learning

The localization of extraprostatic extension (EPE), i.e., local spread of prostate cancer beyond the prostate capsular boundary, is important for risk stratification and surgical planning. However, the sensitivity of EPE detection by radiologists on MRI is low (57% on average). In this paper, we propose a method for computational detection of EPE on multiparametric MRI using deep learning. Ground truth labels of cancers and EPE were obtained in 123 patients (38 with EPE) by registering pre-surgical MRI with whole-mount digital histopathology images from radical prostatectomy. Our approach has two stages. First, we trained deep learning models using the MRI as input to generate cancer probability maps both inside and outside the prostate. Second, we built an image post-processing pipeline that generates predictions for EPE location based on the cancer probability maps and clinical knowledge. We used five-fold cross-validation to train our approach using data from 74 patients and tested it using data from an independent set of 49 patients. We compared two deep learning models for cancer detection: (i) UNet and (ii) the Correlated Signature Network for Indolent and Aggressive prostate cancer detection (CorrSigNIA). The best end-to-end model for EPE detection, which we call EPENet, was based on the CorrSigNIA cancer detection model. EPENet was successful at detecting cancers with extraprostatic extension, achieving a mean area under the receiver operator characteristic curve of 0.72 at the patient-level. On the test set, EPENet had 80.0% sensitivity and 28.2% specificity at the patient-level compared to 50.0% sensitivity and 76.9% specificity for the radiologists. To account for spatial location of predictions during evaluation, we also computed results at the sextant-level, where the prostate was divided into sextants according to standard systematic 12-core biopsy procedure. At the sextant-level, EPENet achieved mean sensitivity 61.1% and mean specificity 58.3%. Our approach has the potential to provide the location of extraprostatic extension using MRI alone, thus serving as an independent diagnostic aid to radiologists and facilitating treatment planning.

Current Opinion on the Use of Magnetic Resonance Imaging in Staging Prostate Cancer: A Narrative Review

Accurate staging is critical for treatment planning and prognosis in men with prostate Cancer. Prostate magnetic imaging resonance (MRI) may aid in the staging evaluation by verifying organ-confined status, assessing the status of the pelvic lymph nodes, and establishing the local extent of the tumor in patients being considered for therapy. MRI has a high specificity for diagnosing extracapsular extension, and therefore may impact the decision to perform nerve sparing prostatectomy, along with seminal vesicle invasion and lymph node metastases; however, its sensitivity remains limited. Current guidelines vary significantly regarding endorsing the use of MRI for staging locoregional disease. For high-risk prostate cancer, most guidelines recommend cross sectional imaging, including MRI, to evaluate for more extensive disease that may merit change in radiation field, extended androgen deprivation therapy, or guiding surgical planning. Although MRI offers reasonable performance characteristics to evaluate bone metastases, guidelines continue to support the use of bone scintigraphy. Emerging imaging technologies, including coupling positron emission tomography (PET) with MRI, have the potential to improve the accuracy of prostate cancer staging with the use of novel radiotracers.

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.

The Quantitative Assessment of Using Multiparametric MRI for Prediction of Extraprostatic Extension in Patients Undergoing Radical Prostatectomy: A Systematic Review and Meta-Analysis

Purpose

To investigate the diagnostic performance of using quantitative assessment with multiparametric MRI (mpMRI) for prediction of extraprostatic extension (EPE) in patients with prostate cancer (PCa).

Methods

We performed a computerized search of MEDLINE, Embase, Cochrane Library, Web of Science, and Google Scholar from inception until July 31, 2021. Summary estimates of sensitivity and specificity were pooled with the bivariate model, and quality assessment of included studies was performed with the Quality Assessment of Diagnostic Accuracy Studies-2. We plotted forest plots to graphically present the results. Multiple subgroup analyses and meta-regression were performed to explore the variate clinical settings and heterogeneity.

Results

A total of 23 studies with 3,931 participants were included. The pooled sensitivity and specificity for length of capsular contact (LCC) were 0.79 (95% CI 0.75-0.83) and 0.77 (95% CI 0.73-0.80), for apparent diffusion coefficient (ADC) were 0.71 (95% CI 0.50-0.86) and 0.71 (95% CI 059-0.81), for tumor size were 0.62 (95% CI 0.57-0.67) and 0.75 (95% CI 0.67-0.82), and for tumor volume were 0.77 (95% CI 0.68-0.84) and 0.72 (95% CI 0.56-0.83), respectively. Substantial heterogeneity was presented among included studies, and meta-regression showed that publication year (≤2017 vs. >2017) was the significant factor in studies using LCC as the quantitative assessment (P=0.02).

Conclusion

Four quantitative assessments of LCC, ADC, tumor size, and tumor volume showed moderate to high diagnostic performance of predicting EPE. However, the optimal cutoff threshold varied widely among studies and needs further investigation to establish.

Clinicopathological Analysis of the ISUP Grade Group And Other Parameters in Prostate Cancer: Elucidation of Mutual Impact of the Various Parameters

Background

Prostate cancer has become increasingly common worldwide. Although Grade group (GG) is widely accepted as an indicator of prostate cancer grade, there are malignancies that cannot be defined by GG alone. Moreover, the relationship between GG and other parameters remains unclear. Herein, we aimed to explore the biological characteristics of prostate cancer.

Methods

This study included 299 radical prostatectomy cases. The Chi-square test and analysis of variance were used to analyze the association of GG with binary and continuous variables. We then conducted morphological analyses. Multivariate analyses were performed to extract the data on risk factors for biochemical recurrence (BCR) and lymph node metastasis.

Results

The lymphatic, venous, perineural, and seminal vesicle invasion rates were 37/299 (12.4%), 25/299 (8.4%), 280/299 (93.6%), and 23/299 (7.7%), respectively. The extraprostatic extension (EPE), positive surgical margin, tertiary Gleason pattern 5, intraductal carcinoma of the prostate gland, and lymph node metastasis rates were 89/299 (29.8%), 106/299 (35.5%), 33/260 (12.7%), 56/299 (18.7%), and 23/299 (7.7%), respectively. As GG increased, various parameters became easier to visualize; however, there were differences between the parameters. Postoperative BCR was observed in 31/242 (12.8%) cases without preoperative hormone therapy; GG2, GG3, GG4, and GG5 accounted for 4, 7, 7, and 13 cases, respectively. Multivariate analyses revealed that GG and tumor diameter were significant risk factors for early BCR, whereas lymphatic invasion, EPE, and seminal vesicle invasion were significant risk factors for lymph node metastasis. For BCR, the odds ratios (ORs) for GG and tumor diameter were 2.253 (95% confidence interval (CI]): 1.297–3.912; P=0.004) and 1.074 (95% CI: 1.011–1.142; P=0.022), respectively. For lymph node metastasis, ORs for the presence of lymphatic invasion, EPE, and seminal vesicle invasion were 7.425 (95% CI: 1.688–22.583; P=0.004), 4.391 (95% CI: 1.037–18.589; P=0.044), and 5.755 (95% CI: 1.308–25.316; P=0.021), respectively.

Conclusions

We summarized various parameters correlating with each GG. Through multivariate analyses, we established the independent risk factors for early BCR and lymph node metastasis. In addition to GG, other important indices of malignancy were determined and weighted to provide a basis for future investigations.

Cohesive cancer invasion of the biophysical barrier of smooth muscle

Smooth muscle is found around organs in the digestive, respiratory, and reproductive tracts. Cancers arising in the bladder, prostate, stomach, colon, and other sites progress from low-risk disease to high-risk, lethal metastatic disease characterized by tumor invasion into, within, and through the biophysical barrier of smooth muscle. We consider here the unique biophysical properties of smooth muscle and how cohesive clusters of tumor use mechanosensing cell-cell and cell-ECM (extracellular matrix) adhesion receptors to move through a structured muscle and withstand the biophysical forces to reach distant sites. Understanding integrated mechanosensing features within tumor cluster and smooth muscle and potential triggers within adjacent adipose tissue, such as the unique damage-associated molecular pattern protein (DAMP), eNAMPT (extracellular nicotinamide phosphoribosyltransferase), or visfatin, offers an opportunity to prevent the first steps of invasion and metastasis through the structured muscle.