Extracapsular extension on MRI indicates a more aggressive cell cycle progression genotype of prostate cancer.
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SUBMITTER: Wibmer AG
PROVIDER: S-EPMC7446140 | biostudies-literature | 2019 Aug
REPOSITORIES: biostudies-literature
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Project description:PurposeTo investigate the performance of magnetic resonance imaging (MRI)-based radiomics models for benign and malignant prostate lesion discrimination and extracapsular extension (ECE) and positive surgical margins (PSM) prediction.Methods and materialsIn total, 459 patients who underwent multiparametric MRI (mpMRI) before prostate biopsy were included. Radiomic features were extracted from both T2-weighted imaging (T2WI) and the apparent diffusion coefficient (ADC). Patients were divided into different training sets and testing sets for different targets according to a ratio of 7:3. Radiomics signatures were built using radiomic features on the training set, and integrated models were built by adding clinical characteristics. The areas under the receiver operating characteristic curves (AUCs) were calculated to assess the classification performance on the testing sets.ResultsThe radiomics signatures for benign and malignant lesion discrimination achieved AUCs of 0.775 (T2WI), 0.863 (ADC) and 0.855 (ADC + T2WI). The corresponding integrated models improved the AUC to 0.851/0.912/0.905, respectively. The radiomics signatures for ECE achieved the highest AUC of 0.625 (ADC), and the corresponding integrated model achieved the highest AUC (0.728). The radiomics signatures for PSM prediction achieved AUCs of 0.614 (T2WI) and 0.733 (ADC). The corresponding integrated models reached AUCs of 0.680 and 0.766, respectively.ConclusionsThe MRI-based radiomics models, which took advantage of radiomic features on ADC and T2WI scans, showed good performance in discriminating benign and malignant prostate lesions and predicting ECE and PSM. Combining radiomics signatures and clinical factors enhanced the performance of the models, which may contribute to clinical diagnosis and treatment.
| S-EPMC8259026 | biostudies-literature
Project description:The objective of this review is to survey radiomics signatures for detecting pathological extracapsular extension (pECE) on magnetic resonance imaging (MRI) in patients with prostate cancer (PCa) who underwent prostatectomy. Scientific Literature databases were used to search studies published from January 2007 to October 2023. All studies related to PCa MRI staging and using radiomics signatures to detect pECE after prostatectomy were included. Systematic review was performed according to Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA). The risk of bias and certainty of the evidence was assessed using QUADAS-2 and the radiomics quality score. From 1247 article titles screened, 16 reports were assessed for eligibility, and 11 studies were included in this systematic review. All used a retrospective study design and most of them used 3 T MRI. Only two studies were performed in more than one institution. The highest AUC of a model using only radiomics features was 0.85, for the test validation. The AUC for best model performance (radiomics associated with clinical/semantic features) varied from 0.72-0.92 and 0.69-0.89 for the training and validation group, respectively. Combined models performed better than radiomics signatures alone for detecting ECE. Most of the studies showed a low to medium risk of bias. After thorough analysis, we found no strong evidence supporting the clinical use of radiomics signatures for identifying extracapsular extension (ECE) in pre-surgery PCa patients. Future studies should adopt prospective multicentre approaches using large public datasets and combined models for detecting ECE.Critical relevant statementThe use of radiomics algorithms, with clinical and AI integration, in predicting extracapsular extension, could lead to the development of more accurate predictive models, which could help improve surgical planning and lead to better outcomes for prostate cancer patients.Protocol of systematic review registrationPROSPERO CRD42021272088. Published: https://doi.org/10.1136/bmjopen-2021-052342 .Key pointsRadiomics can extract diagnostic features from MRI to enhance prostate cancer diagnosis performance. The combined models performed better than radiomics signatures alone for detecting extracapsular extension. Radiomics are not yet reliable for extracapsular detection in PCa patients.
| S-EPMC11347513 | biostudies-literature
Project description:PurposeTo improve the performation of a nomogram for predicting side-specific extracapsular extension (SS-ECE).ResultsOne hundred and ninety-six patients (55.5%) had ECE on final pathology. Bilateral and unilateral ECE rate was 13.9% (49/353) and 41.6% (147/353), respectively. The mean age was 65.9 years and the mean serum prostate specific antigen (PSA) was 15.0 ng/ml. Based on multivariate logistic regression analysis, clinical stage (cStage), PSA, Gleason sum, percentage of positive cores, and ECE risk score were significant predictors of ECE. The current nomogram had higher predictive accuracy (0.851) and superior calibration. According to the decision curve analysis (DCA) results, the updated nomogram demonstrated a high net benefit across a wide range of threshold probabilities.Materials and methodsWe studied 353 patients with cStage T1c-T3 prostate cancer underwent radical prostatectomy. The candidate predictors associated with ECE were cStage, PSA, Gleason sum, percentage of positive cores, maximum cancer percentage and ECE risk score from multi-parametric magnetic resonance imaging (MP-MRI). The receiver operating characteristic (ROC) analysis was performed and an updated nomogram was constructed. The DCA was performed to test the predictive ability of the nomogram. In addition, the validation and calibration of the Memorial Sloan-Kettering cancer center (MSKCC) nomograms were performed in the current subjects.ConclusionsPredictors, including cStage, PSA, Gleason sum, percentage of positive cores, maximum cancer percentage, and ECE risk score, were combined to construct a SS-ECE prediction nomogram. And the current nomogram might help urologists in decision-making process of preserving or resecting neurovascular bundles preoperatively.
| S-EPMC5400649 | biostudies-literature
Project description:IntroductionIn patients with prostate cancer (PCa), the detection of extracapsular extension (ECE) and seminal vesicle invasion is not only important for selecting the appropriate therapy but also for preoperative planning and patient prognosis. It is of paramount importance to stage PCa correctly before surgery, in order to achieve better surgical and outcome results. Over the last years, MRI has been incorporated in the classical prostate staging nomograms with clinical improvement accuracy in detecting ECE, but with variability between studies and radiologist's experience.Methods and analysisThe research question, based on patient, index test, comparator, outcome and study design criteria, was the following: what is the diagnostic performance of artificial intelligence algorithms for predicting ECE in PCa patients, when compared with that of histopathological results after radical prostatectomy. To answer this question, we will use databases (EMBASE, PUBMED, Web of Science and CENTRAL) to search for the different studies published in the literature and we use the QUADA tool to evaluate the quality of the research selection.Ethics and disseminationThis systematic review does not require ethical approval. The results will be disseminated through publication in a peer-review journal, as a chapter of a doctoral thesis and through presentations at national and international conferences.Prospero registration numberCRD42020215671.
| S-EPMC9083401 | biostudies-literature
Project description:BackgroundPrediction of extracapsular extension (ECE) is essential to achieve a balance between oncologic resection and neural tissue preservation. Microultrasound (MUS) is an attractive alternative to multiparametric magnetic resonance imaging (mpMRI) in the staging scenario.ObjectiveTo create a side-specific nomogram integrating clinicopathologic parameters and MUS findings to predict ipsilateral ECE and guide nerve sparing.Design setting and participantsProspective data were collected from consecutive patients who underwent robotic-assisted radical prostatectomy from June 2021 to May 2022 and had preoperative MUS and mpMRI. A total of 391 patients and 612 lobes were included in the analysis.Outcome measurements and statistical analysisECE on surgical pathology was the primary outcome. Multivariate regression analyses were carried out to identify predictors for ECE. The resultant multivariable model's performance was visualized using the receiver-operating characteristic curve. A nomogram was developed based on the coefficients of the logit function for the MUS-based model. A decision curve analysis (DCA) was performed to assess clinical utility.Results and limitationsThe areas under the receiver-operating characteristic curve (AUCs) of the MUS-based model were 81.4% and 80.9% (95% confidence interval [CI] 75.6, 84.6) after internal validation. The AUC of the mpMRI-model was also 80.9% (95% CI 77.2, 85.7). The DCA demonstrated the net clinical benefit of the MUS-based nomogram and its superiority compared with MUS and MRI alone for detecting ECE. Limitations of our study included its sample size and moderate inter-reader agreement.ConclusionsWe developed a side-specific nomogram to predict ECE based on clinicopathologic variables and MUS findings. Its performance was comparable with that of a mpMRI-based model. External validation and prospective trials are required to corroborate our results.Patient summaryThe integration of clinical parameters and microultrasound can predict extracapsular extension with similar results to models based on magnetic resonance imaging findings. This can be useful for tailoring the preservation of nerves during surgery.
| S-EPMC9895764 | biostudies-literature
Project description:ObjectivesTo assess the added value of 3D T2-weighted imaging (T2WI) over conventional 2D T2WI in diagnosing extracapsular extension (ECE).MethodsSeventy-five patients undergoing 3-T MRI before radical prostatectomy were included. PI-RADS ??4 lesions were assessed for ECE on 2D T2W images using a 5-point Likert scale (1?=?no ECE, 5?=?definite ECE) and the length of tumour prostatic capsular contact. A second read using 3D T2W images and reformats evaluated ECE and the maximal 3D capsular contact length and surface.ResultsOne hundred six lesions were identified at MRI. ECE was confirmed by histology in 54% (57/106) of lesions and 64% (48/75) of patients. Sensitivity and specificity for 3D T2 reads were 75.4% versus 64.9% (p?=?0.058), respectively, and 83.7% versus 85.7% (p?=?0.705) for 2D T2 reads, respectively. 3D T2W reads showed significantly higher mean subjective Likert scores of 3.7?±?1.4 versus 3.3?±?1.4 (p?=?0.001) in ECE-positive lesions and lower mean Likert score of 1.5?±?1 versus 1.6?±?0.9 (p?=?0.27) in ECE-negative lesions compared with 2D T2W reads. 3D contact significantly increased sensitivity from 59.6 to 73.7% (p?=?0.03), whilst maintaining the same specificity of 87.8% (p?=?1). High-grade group tumours (? Gleason 4?+?3) showed significantly higher ECE prevalence than low-grade tumours (88% versus 44%, p?<?0.001) and a positive predictive value (PPV) for ECE of 90.9% with ??5 mm of contact versus PPV of 90.4% at ??12.5 mm for lower grade tumours.Conclusions3D T2WI significantly increases sensitivity and confidence in calling ECE. The capsular contact length threshold differed between low- and high-grade cancers.Key points• 3D capsular contact length and 3D surface contact significantly increased sensitivity in diagnosing ECE. • 3D T2W reads significantly increased reader confidence in calling ECE. • Thresholds for capsular contact length differed between low-grade and high-grade cancers.
| S-EPMC6719333 | biostudies-literature
Project description:Kaiso, a p120 catenin-binding protein, is expressed in the cytoplasmic and nuclear compartments of cells; however, the biological consequences and clinical implications of a shift between these compartments have yet to be established. Herein, we report an enrichment of nuclear Kaiso expression in cells of primary and metastatic prostate tumors relative to the normal prostate epithelium. Nuclear expression of Kaiso correlates with Gleason score (P < 0.001) and tumor grade (P < 0.001). There is higher nuclear expression of Kaiso in primary tumor/normal matched samples and in primary tumors from African American men (P < 0.0001). We further found that epidermal growth factor (EGF) receptor up-regulates Kaiso at the RNA and protein levels in prostate cancer cell lines, but more interestingly causes a shift of cytoplasmic Kaiso to the nucleus that is reversed by the EGF receptor-specific kinase inhibitor, PD153035. In both DU-145 and PC-3 prostate cancer cell lines, Kaiso inhibition (short hairpin RNA-Kaiso) decreased cell migration and invasion even in the presence of EGF. Further, Kaiso directly binds to the E-cadherin promoter, and inhibition of Kaiso in PC-3 cells results in increased E-cadherin expression, as well as re-establishment of cell-cell contacts. In addition, Kaiso-depleted cells show more epithelial morphology and a reversal of the mesenchymal markers N-cadherin and fibronectin. Our findings establish a defined oncogenic role of Kaiso in promoting the progression of prostate cancer.
| S-EPMC3483816 | biostudies-literature
Project description:PurposeTo validate the MRI grading system proposed by Mehralivand et al in 2019 (the "extraprostatic extension [EPE] grade") in an independent cohort and to compare the Mehralivand EPE grading system with EPE interpretation on the basis of a five-point Likert score ("EPE Likert").Materials and methodsA total of 310 consecutive patients underwent multiparametric MRI according to a standardized institutional protocol before radical prostatectomy was performed by using the same 1.5-T MRI unit at a single institution between 2010 and 2012. Two radiologists blinded to clinical information assessed EPE according to standardized criteria. On the basis of the readings performed until 2017, the diagnostic performance of EPE Likert and Mehralivand EPE score were compared using receiver operating characteristics (ROC) and decision curve methodology against histologic EPE as standard of reference. Prediction of biochemical recurrence-free survival (BRFS) was assessed by Kaplan-Meier analysis and log rank test.ResultsOf the 310 patients, 80 patients (26%) had EPE, including 33 with radial distance 1.1 mm or greater. Interrater reliability was fair (weighted κ 0.47 and 0.45) for both EPE grade and EPE Likert. Sensitivity for identifying EPE using EPE grade versus EPE Likert was 0.83 versus 0.86 and 0.86 versus 0.91 for radiologist 1 and 2, respectively. Specificity was 0.48 versus 0.58 and 0.39 versus 0.70 (P < .05 for radiologist 2). There were no significant differences in the ROC area under the curve or on decision curve analysis. Both EPE grade and EPE Likert were significant predictors of BRFS.ConclusionMehralivand EPE grade and EPE Likert have equivalent diagnostic performance for predicting EPE and BRFS with a similar degree of observer dependence.© RSNA, 2020Keywords: MR-Imaging, Neoplasms-Primary, Observer Performance, Outcomes Analysis, Prostate, StagingSupplemental material is available for this article.See also the commentary by Choyke in this issue.
| S-EPMC7983691 | biostudies-literature
Project description:To identify appropriate candidates for aggressive treatment such as radical prostatectomy or radiation therapy of localized prostate cancer (PCa), novel predictive biomarkers of PCa aggressiveness are essential. Core2 β-1,6-N-acetylglucosaminyltransferase-1 (GCNT1) is a key enzyme that forms core 2-branched O-glycans. Its expression is associated with the progression of several cancers. We established a mouse IgG monoclonal antibody (mAb) against GCNT1 and examined the relationship of GCNT1 expression to the clinicopathological status of PCa. Paraffin-embedded PCa specimens were analyzed by immunohistochemistry for GCNT1 expression using a newly established mouse anti-GCNT1 mAb by ourselves. GCNT1-positive tumor showed significantly higher Gleason score and larger tumor volume. The number of GCNT1-positive cases was significantly lower in cases of organ-confined disease than in cases of extracapsular extension. GCNT1-negative tumors were associated with significantly better prostate-specific antigen (PSA)-free survival compared with GCNT1-positive tumors. Multivariate analysis revealed that detection of GCNT1 expression was an independent risk factor for PSA recurrence. We established new methods for GCNT1 detection from PCa specimens. Immunoblotting was used to examine post-digital rectal examination (DRE) urine from PCa patients. Over 90% of GCNT1-positive PCa patients with high concentrations of PSA showed extracapsular extension. In conclusion, GCNT1 expression closely associates with the aggressive potential of PCa. Further research aims to develop GCNT1 detection in post-DRE urine as a marker for PCa aggressiveness.
| S-EPMC4577128 | biostudies-literature
Project description:MRI is used to image prostate cancer and target tumors for biopsy or therapeutic ablation. The objective was to understand the biology of tumors not visible on MRI that may go undiagnosed and untreated. Methods: Prostate cancers visible or invisible on multiparametric MRI were macrodissected and examined by RNAseq. Differentially expressed genes (DEGs) based on MRI visibility status were cross-referenced with publicly available gene expression databases to identify genes associated with disease progression. Genes with potential roles in determining MRI visibility and disease progression were knocked down in murine prostate cancer xenografts, and imaged by MRI. Results: RNAseq identified 1,654 DEGs based on MRI visibility status. Comparison of DEGs based on MRI visibility and tumor characteristics revealed that Gleason score (dissimilarity test, p<0.0001) and tumor size (dissimilarity test, p<0.039) did not completely determine MRI visibility. Genes in previously reported prognostic signatures significantly correlated with MRI visibility suggesting that MRI visibility was prognostic. Cross-referencing DEGs with external datasets identified four genes (PHYHD1, CENPF, ALDH2, GDF15) that predict MRI visibility, progression free survival and metastatic deposits. Genetic modification of a human prostate cancer cell line to induce miR-101 and suppress CENPF decreased cell migration and invasion. As prostate cancer xenografts in mice, these cells had decreased visibility on diffusion weighted MRI and decreased perfusion, which correlated with immunostaining showing decreased cell density and proliferation. Conclusions: Genes involved in prostate cancer prognosis and metastasis determine MRI visibility, indicating that MRI visibility has prognostic significance. MRI visibility was associated with genetic features linked to poor prognosis.
| S-EPMC5858498 | biostudies-literature