Detection Of Extracapsular Extension Research Articles

Role of transrectal ultrasound and MRI in the diagnosis and localisation of carcinoma prostate - A comparison of diagnostic efficacy of trus versus MRI

In this study, we wanted to correlate the findings of TRUS and MRI with regard to the diagnosis and localization of carcinoma prostate and local staging of carcinoma prostate. Our study included 43 men, with age ranging from 49 to 76 years. They underwent TRUS, MRI and TRUS guided twelve core biopsies after being suspected with prostate cancer based on high PSA values (greater than 4.0 ng /ml) or abnormal DRE findings. This study was conducted from April 2018 -June 2019. Imaging findings were confirmed with histopathology. TRUS used for the detection of malignancy had sensitivity, specificity, PPV and NPV as 69.70%, 80 %, 92% and 44.44 % respectively. The values were 63.16%, 83.33%, 75.00%, and 74.07% for the sensitivity, specificity, PPV and NPV of TRUS respectively for the detection of ECE. rnFor the detection of malignancy, the sensitivity, specificity, PPV and NPV of MRI was 87.88%, 70%, 90.63% and 63.64% respectively and 85.71%, 89.66%, 80.00% and 92.86% for the sensitivity, specificity, PPV and NPV of MRI respectively for detection of SVI. For detection of extracapsular extension (ECE), MRI had sensitivity, specificity, PPV and NPV of 78.95%, 83.33%, 78.95% and 83.33% respectively. When compared to TRUS, MRI is more useful in the diagnosis and accurate staging of prostate cancer. MRI can improve the false-negative biopsies resulting due to the inability of TRUS in detection of abnormal areas.

A comparison study of 68gallium-prostate-specific membrane antigen positron emission tomography-computed tomography and multiparametric magnetic resonance imaging for locoregional staging of prostate cancer.

Prostate cancer (PCa) is the most common malignancy in men aged 50 years and older and the second cause of cancer death among men. Accurate staging of PCa preoperatively is of high importance for treatment decisions and patient management. Conventional imaging modalities (ultrasound, computed tomography [CT], and magnetic resonance imaging) are inaccurate for the staging of PCa. Newer modality multiparametric magnetic resonance imaging (mpMRI) and prostate-specific membrane antigen (PSMA) positron emission tomography (PET) scan show promising results for the staging of PCa. Only fewer studies are available for comparison of these modalities with histopathology as reference. The objective of our study is to evaluate the diagnostic accuracy of independent 68gallium PSMA (68Ga-PSMA) PET-CT compared with mpMRI for preoperative staging of PCa, using histopathology as the reference standard. From August 2021 to December 2022, 30 patients of biopsy-proven PCa were prospectively enrolled as per eligibility criteria. Preoperatively, 68Ga-PSMA PET scan and mpMRI were done in all the patients. Extracapsular extension (ECE), seminal vesicle invasion (SVI), and lymph node metastasis (LNM) were investigated separately. Subsequently, the patients underwent robotic-assisted radical prostatectomy with bilateral pelvic lymph node dissection. mpMRI prostate was more sensitive (66.66%) but less specific than PSMA PET-CT (55.55%) for ECE. mpMRI and PSMA PET-CT both had similar sensitivity (83.3%) and specificity (87.5%) for SVI. PSMA PET-CT was more sensitive (85.71%) and specific (95.6%) than mpMRI prostate (62.5% and 91.30%, respectively) for LNM. PSMA PET-CT is more specific for the detection of ECE and more sensitive and specific for the detection of LNM than mpMRI, and similar for the detection of SVI. mpMRI provides only local staging, while PSMA PET-CT provides information about local, regional, and distal staging. Overall, PSMA PET-CT is superior to mpMRI for locoregional staging of PCa.

Head-to-head comparison of prostate-specific membrane antigen PET and multiparametric MRI in the diagnosis of pretreatment patients with prostate cancer: a meta-analysis.

To compare prostate-specific membrane antigen (PSMA) PET with multiparametric MRI (mpMRI) in the diagnosis of pretreatment prostate cancer (PCa). Pubmed, Embase, Medline, Web of Science, and Cochrane Library were searched for eligible studies published before June 22, 2022. We assessed risk of bias and applicability by using QUADAS-2 tool. Data synthesis was performed with Stata 17.0 software, using the "midas" and "meqrlogit" packages. We included 29 articles focusing on primary cancer detection, 18 articles about primary staging, and two articles containing them both. For PSMA PET versus mpMRI in primary PCa detection, sensitivities and specificities in the per-patient analysis were 0.90 and 0.84 (p<0.0001), and 0.66 and 0.60 (p <0.0001), and in the per-lesion analysis they were 0.79 and 0.78 (p <0.0001), and 0.84 and 0.82 (p <0.0001). For the per-patient analysis of PSMA PET versus mpMRI in primary staging, sensitivities and specificities in extracapsular extension detection were 0.59 and 0.66 (p =0.005), and 0.79 and 0.76 (p =0.0074), and in seminal vesicle infiltration (SVI) detection they were 0.51 and 0.60 (p =0.0008), and 0.93 and 0.96 (p =0.0092). For PSMA PET versus mpMRI in lymph node metastasis (LNM) detection, sensitivities and specificities in the per-patient analysis were 0.68 and 0.46 (p <0.0001), and 0.91 and 0.90 (p =0.81), and in the per-lesion analysis they were 0.67 and 0.36 (p <0.0001), and 0.99 and 0.99 (p =0.18). PSMA PET has higher diagnostic value than mpMRI in the detection of primary PCa. Regarding the primary staging, mpMRI has potential advantages in SVI detection, while PSMA PET has relative advantages in LNM detection. The integration of prostate-specific membrane antigen (PSMA) PET into the diagnostic pathway may be helpful for improving the accuracy of prostate cancer detection. However, further studies are needed to address the cost implications and evaluate its utility in specific patient populations or clinical scenarios. Moreover, we recommend the combination of PSMA PET and mpMRI for cancer staging. • Prostate-specific membrane antigen PET has higher sensitivity and specificity for primary tumor detection in prostate cancer compared to multiparametric MRI. • Prostate-specific membrane antigen PET also has significantly better sensitivity and specificity for lymph node metastases of prostate cancer compared to multiparametric MRI. • Multiparametric MRI has better accuracy for extracapsular extension and seminal vesicle infiltration compared to ate-specific membrane antigen PET.

A gradient mapping guided explainable deep neural network for extracapsular extension identification in 3D head and neck cancer computed tomography images.

Diagnosis and treatment management for head and neck squamous cell carcinoma (HNSCC) is guided by routine diagnostic head and neck computed tomography (CT) scans to identify tumor and lymph node features. The extracapsular extension (ECE) is a strong predictor of patients' survival outcomes with HNSCC. It is essential to detect the occurrence of ECE as it changes staging and treatment planning for patients. Current clinical ECE detection relies on visual identification and pathologic confirmation conducted by clinicians. However, manual annotation of the lymph node region is a required data preprocessing step in most of the current machine learning-based ECE diagnosisstudies. In this paper, we propose a Gradient Mapping Guided Explainable Network (GMGENet) framework to perform ECE identification automatically without requiring annotated lymph node regioninformation. The gradient-weighted class activation mapping (Grad-CAM) technique is applied to guide the deep learning algorithm to focus on the regions that are highly related to ECE. The proposed framework includes an extractor and a classifier. In a joint training process, informative volumes of interest (VOIs) are extracted by the extractor without labeled lymph node region information, and the classifier learns the pattern to classify the extracted VOIs into ECE positive andnegative. In evaluation, the proposed methods are well-trained and tested using cross-validation. GMGENet achieved test accuracy and area under the curve (AUC) of 92.2% and 89.3%, respectively. GMGENetV2 achieved 90.3% accuracy and 91.7% AUC in the test. The results were compared with different existing models and further confirmed and explained by generating ECE probability heatmaps via a Grad-CAM technique. The presence or absence of ECE has been analyzed and correlated with ground truth histopathologicalfindings. The proposed deep network can learn meaningful patterns to identify ECE without providing lymph node contours. The introduced ECE heatmaps will contribute to the clinical implementations of the proposed model and reveal unknown features to radiologists. The outcome of this study is expected to promote the implementation of explainable artificial intelligence-assiste ECEdetection.

Machine learning in predicting extracapsular extension (ECE) of prostate cancer with MRI: a protocol for a systematic literature review

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...

Value of the capsular enhancement sign on dynamic contrast-enhanced prostate multiparametric MRI for the detection of extracapsular extension

PurposeTo retrospectively determine the prevalence and diagnostic performance of the capsular enhancement sign (CES) on multiparametric (mp) MRI for the detection of prostate cancer (PCa) extracapsular extension (ECE). MethodsThis retrospective study included patients who underwent mpMRI prior to radical prostatectomy. CES was defined as an area of asymmetrical early hyperenhancement on DCE-MRI adjacent to a peripheral zone tumour, matched or exceeded the tumour circumferential diameter, and with persistent enhancement. Two uro-radiologists evaluated the presence of CES on mpMRI, independently and in consensus, with interobserver agreement calculated using bias and prevalence-adjusted kappa (PABAK). CES performance for predicting ECE was assessed using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). ResultsThe study included 146 patients, with 91/146 (62%) having ECE on surgical pathology. Following initial review, Reader 1 identified 12/146 (8%) CES-positive cases, while Reader 2 reported 14/146 (10%) CES-positive cases, with 15/146 (10%) lesions determined as demonstrating the CES sign on consensus reading. PABAK for CES between the two readers was high at 0.90. All consensus determined CES-positive lesions represented pathological stage ≥ T3a disease, with the overall prevalence of CES among tumours with confirmed ECE being 15/91 (17%). The sign showed high specificity (100%) and PPV (100%) for ECE detection, but with low sensitivity, NPV, and accuracy at 16.5%, 41.3%, and 47.4%, respectively. ConclusionsCES was demonstrated to be a rare but highly specific ECE predictor on mpMRI that may improve local staging in the patients in whom it is demonstrated.

Identification of Extracapsular Extension in Head and Neck Cancer Patients Using Artificial Intelligence.

<h3>Purpose/Objective(s)</h3> Routine diagnostic CT scan guides the management of head and neck squamous cell carcinoma (HNSCC)) by identifying tumor and lymph node features. Nevertheless, lymph node extracapsular extension (ECE) remains challenging to detect by clinicians even using modern imaging techniques. It is essential to detect the occurrence of ECE as it changes staging and management for the patients. Current clinical ECE detection relies on clinical examination, radiological identification, and histopathological confirmation. Therefore, we aim to perform ECE identification automatically using a gradient-based class activation mapping (Grad-CAM) technique to guide the deep learning model to focus on the regions highly suspicious of ECE. The presence or absence of ECE has been analyzed and correlated with gold standard histopathological findings. <h3>Materials/Methods</h3> This research proposes a novel deep learning framework to detect ECE from 3D head and neck CT scans. The research problem is treated as a classification task, i.e., to label patients' CT scans with ECE positive or negative categories. Grad-CAM can provide visual explanations for deep neural networks by indicating the critical regions in the image for prediction. The proposed network has two convolutional neural networks (CNNs) involved. The first CNN model acts as a preprocessing step. The Grad-CAM method is applied to the trained CNN to highlight and extract the volumes of interest (VOIs) related to the class ECE positive. The second CNN model takes the extracted VOIs as model input for ECE positive/negative classification. The data used for the second CNN training does not involve that in the first step training process. The studied dataset includes 130 patients' CT scans. Cross-validation has been applied in our experimental tests. <h3>Results</h3> The experimental results have demonstrated that our model can identify ECE positive and negative patients based on ten-fold cross-validation. We have achieved 95.03% test accuracy and 90.90% test AUC in ECE classification. Furthermore, ECE probability heatmaps are generated by Grad-CAM. <h3>Conclusion</h3> This study demonstrates that a deep learning model guided by the Grad-CAM approach can be used for ECE detection. The proposed two-step neural networks can help capture sufficient features and achieve high classification accuracy. The heatmaps can be used as a tool for radiologists to assist in the identification of ECE. The outcome of this research is expected to promote the implementation of artificial intelligence for head and neck cancer ECE identification diagnosis in the radiology computer vision field.

Value of Contrast-Enhanced Ultrasound in the Preoperative Evaluation of Papillary Thyroid Carcinoma Invasiveness.

ObjectiveTo evaluate the diagnostic performance of preoperative contrast-enhanced ultrasound (CEUS) in the detection of extracapsular extension (ECE) and cervical lymph node metastasis (LNM) of papillary thyroid carcinoma (PTC) and the added value of CEUS in the evaluation of PTC invasiveness to conventional ultrasound (US).Materials and MethodsA total of 62 patients were enrolled retrospectively, including 30 patients with invasive PTCs (Group A, ECE or LNM present) and 32 patients with non-invasive PTCs (Group B). All patients underwent US and CEUS examinations before surgery. US and CEUS features of PTCs and lymph nodes were compared between groups. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of US, CEUS, and the combination of the two in the detection of ECE and LNM of PTCs were calculated. Logistic regression was used to analyze relationships between variables.ResultsThe PTC size was larger in group A on both US and CEUS (P = 0.001, P = 0.003). More PTCs showed hyper-enhancement in group A (P = 0.013) than in group B. More PTCs had >25% contact between PTC and the thyroid capsule and discontinued capsule on US and CEUS (all P < 0.05) in group A than in group B. More absent hilum and calcification of lymph nodes were observed in group A (both P < 0.05) than in group B on US. More centripetal perfusion and enlarged lymph nodes were observed in group A (both P < 0.05) than in group B on CEUS. CEUS alone and US combined with CEUS manifested higher diagnostic accuracy (79.0%) than US alone (72.6%) in the detection of ECE. The combination of US and CEUS manifested the highest diagnostic accuracy (95.2%) than CEUS alone (90.3%) and US alone (82.2%) in the detection of LNM. Diagnoses of ECE and LNM by the combination of US and CEUS were independent risk factors for PTC invasiveness [odds ratio (OR) = 29.49 and 97.20, respectively; both P = 0.001].ConclusionCEUS or US combined with CEUS is recommended for the detection of PTC ECE, while the combination of US and CEUS is most recommended for LNM detection. CEUS plays an essential role in the preoperative evaluation of PTC invasiveness.

Automatic Extracapsular Extension Identification in Head and Neck Cancer Using Deep Neural Network with Local-Global Information

<h3>Purpose/Objective(s)</h3> The extracapsular extension (ECE) is a strong predictor of patients' survival outcomes with head and neck squamous cell carcinoma (HNSCC). ECE occurs when metastatic tumor cells within the lymph node break through the nodal capsule into surrounding tissues. It is crucial to identify the occurrence of ECE as it changes staging and management for the patients. Current clinical ECE detection relying on radiologists' visual identification is extremely labor-intensive, time-consuming, and error-prone, and consequently, pathologic confirmation is required. Therefore, we aim to perform ECE identification automatically using a deep learning-based technique to analyze the presence or absence of ECE and correlate that with gold standard histopathological findings. <h3>Materials/Methods</h3> This research proposes a novel deep learning method to detect ECE from 3D computed tomography (CT) scans. The proposed network has multi-scale input that captures both local and global information. The two types of inputs are fitted into two pathways with deep convolutional neural networks (DCNNs), 3D CNN baseline and 3D DenseNet. A sliding-cube approach is applied to extract small paired 3D patches from patient data with different scales at data preparation. The network will classify the patient based on patch-level classification results. Different training scenarios are designed for the experimental test. <h3>Results</h3> Based on five-fold cross-validation, the experimental results have demonstrated that our model can identify ECE and non-ECE patients, specifically in the patch-level. We have achieved the ECE detection with 96.92% accuracy 98.84% AUC. Detailed results are shown in the Table 1. <h3>Conclusion</h3> The study demonstrates the ability to use a deep learning-based method for ECE direct detection. The proposed local-global network can help capture sufficient features and achieve high classification accuracy. The outcome of this research is expected to promote the implementation of artificial intelligence for ECE identification for head and neck cancer diagnosis in the radiology computer vision field.

Comparison of biparametric versus multiparametric prostate MRI for the detection of extracapsular extension and seminal vesicle invasion in biopsy naïve patients

PurposeTo compare biparametric MRI (bpMRI) with multiparametric MRI (mpMRI) staging accuracy in assessing extracapsular extension (ECE) and seminal vesicle invasion (SVI). MethodBiopsy-naïve patients undergoing 3 T-MRI before radical prostatectomy for clinically significant prostate cancer were included in this single-centre retrospective study. Two uroradiologists separately evaluated bpMRI and mpMRI for presence of ECE and SVI using a 5-point Likert scale (1: ECE/SVI highly unlikely, 5: ECE/SVI highly likely). Results110 men of median age 63 years and PSA 8.5 ng/mL were included. ECE and SVI was confirmed histologically in 71/110 (64.5 %) and 18/110 (16.4 %) patients, respectively. Sensitivity and specificity of bpMRI versus mpMRI for predicting ECE was 59.1 % and 87.2 % versus 66.2 % and 84.6 %, respectively. For SVI detection, the sensitivity and specificity for bpMRI versus mpMRI was 66.7 % and 92.4 % versus 83.3 % and 97.8 %, respectively. At an optimal cut-off Likert score ≥3 for ECE prediction, mpMRI area under the receiver operating curve (AUC) was 0.80 (95 % confidence interval (CI) 0.72–0.87) versus 0.78 (95 % CI 0.69–0.86) for bpMRI (p = 0.52) and for SVI, mpMRI AUC was 0.91 (95 % CI 0.84–0.96) versus 0.86 (95 % CI 0.78–0.92) for bpMRI (p = 0.02), respectively. Inter-reader agreement for both ECE and SVI prediction was substantial, with a marginally higher k-value for mpMRI (k range, 0.67–0.75) than bpMRI (k range, 0.65–0.69). ConclusionsDiagnostic performance of bpMRI and mpMRI was comparable for detection of ECE, however, mpMRI with contrast was superior for SVI detection and improved the inter-reader agreement.

Feasibility of multi-parametric magnetic resonance imaging in detection and local staging of prostatic carcinoma

BackgroundThe objective of this study was to assess the feasibility of multi-parametric magnetic resonance imaging (mpMRI) in detection, localization and local staging of prostate cancer (Pca).MethodsThe study included 58 patients with Pca who underwent mpMRI before radical prostatectomy (RP) at two university hospitals, during the period June 2014 to April 2018. All prostatectomies were performed on the basis of preoperative transrectal ultrasound-guided prostatic biopsies. For tumor localization, the prostate in each patient was divided into six segmental regions. Biopsy specimens, for each segmental region, were evaluated for the presence of cancer. The diagnostic performance of mpMRI in tumor localization as well as extracapsular extension (ECE) and seminal vesicle (SV) invasion of the tumor was evaluated, by using the histopathological findings of RP specimens as reference standard.ResultsThe mean age of patients was 63.45 ± 7.45 years. Of the total number of 348 segmental regions, tumor was detected in 143. From them, cancer was detected in 142 regions by mpMRI. The sensitivity and specificity of mpMRI for cancer localization were 99.30% and 97.56%. On RP specimen, nine cases had ECE and five had SV invasion. All of them were detected preoperatively by mpMRI. The sensitivity and specificity of mpMRI for detection of ECE were 100% and 97.96%. For detection of SV invasion, the sensitivity and specificity were 100% and 98.11%.ConclusionsmpMRI enables localization and staging of cancer prostate with reasonable accuracy. Its combination with ultrasound should be counted on for improvement in efficacy of the prostatic biopsy procedure.

MP62-10 UTILITY OF A MULTIVARIATE LOGISTIC REGRESSION MODEL FOR THE PREDICTION OF PROSTATE CANCER EXTRACAPSULAR EXTENSION BASED ON 3TMPMRI, CLINICAL, AND BIOPSY

You have accessJournal of UrologyProstate Cancer: Staging I (MP62)1 Apr 2020MP62-10 UTILITY OF A MULTIVARIATE LOGISTIC REGRESSION MODEL FOR THE PREDICTION OF PROSTATE CANCER EXTRACAPSULAR EXTENSION BASED ON 3TMPMRI, CLINICAL, AND BIOPSY Sohrab Afshari Mirak*, Robert Reiter, and Steven Raman Sohrab Afshari Mirak*Sohrab Afshari Mirak* More articles by this author , Robert ReiterRobert Reiter More articles by this author , and Steven RamanSteven Raman More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000937.010AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Extracapsular extension (ECE) of prostate cancer (PCa) is a poor prognostic factor associated with progression, recurrence after treatment, and increased prostate cancer- related mortality. Accurate staging prior to radical prostatectomy is crucial in avoidance of positive margins and when planning nerve-sparing procedures. In this study, we investigated the predictive value of the clinical, biopsy & 3T multiparametric MRI (3TmpMRI) parameters using a multivariate logistic model for per-lesion detection of PCa extracapsular extension (ECE) with wholemount histopathology (WMHP) as reference. METHODS: This IRB approved, HIPAA compliant study included 575 patients with 774 true positive PCa lesions, who underwent radical prostatectomy between 7/2010-2/2019. The relationship between pathologic ECE & parameters including clinical; age, prostate specific antigen (PSA) & PSA density (PSAD), biopsy; percentage of positive systematic cores & Gleason score (GS) & 3TmpMRI; prostate volume, number of lesions per patient, size, location, level, PIRADSv2 score, laterality, apparent diffusion coefficient (ADC) value & risk of ECE on MRI was evaluated using bivariate and multivariate analysis. The accuracy of the final model was evaluated using ROC analysis. RESULTS: 27.8% (215/774), 42.9% (332/774) & 29.3% (227/774) of the lesions were PIRADSv2 score 3, 4 & 5 & 59.9% (464/774), 24.7% (191/774) & 17.7% (137/774) were low, intermediate & high risk for ECE, respectively. 23.6% (183/774) of the lesions had ECE on WMHP. On bivariate analysis higher PSA, PSAD, percentage of positive biopsy cores, biopsy GS, size, PIRADSv2 score, ADC value, risk of ECE on MRI, location (posterior), level (midgland & base), bilaterality & lower number of lesions per patient were significant for ECE prediction. The multivariate logistic model included age, PSAD, number of lesions per patient, size, location, level, PIRADSv2 score & risk of ECE on MRI. The AUC for the prediction of ECE for this model was 0.85. CONCLUSIONS: This multivariate regression model based on clinical, biopsy & 3TmpMRI parameters have a high predictive value for pathology ECE detection. Source of Funding: none © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e949-e949 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Sohrab Afshari Mirak* More articles by this author Robert Reiter More articles by this author Steven Raman More articles by this author Expand All Advertisement PDF downloadLoading ...

A multivariate logistic regression model based on 3T mpMRI, clinical, and biopsy parameters for the prediction of prostate cancer extracapsular extension.

27 Background: Extracapsular extension (ECE) of prostate cancer (PCa) is a poor prognostic factor associated with progression, recurrence after treatment and increased PCa- related mortality. Accurate staging prior to radical prostatectomy is crucial in avoidance of positive margins and when planning nerve-sparing procedures. This study investigated the predictive value of clinical, biopsy &amp; 3TmpMRI parameters using a multivariate logistic model for per-lesion detection of PCa ECE with wholemount histopathology (WMHP) as reference. Methods: This IRB approved, HIPAA compliant study included 575 patients with 774 true positive PCa lesions, who underwent radical prostatectomy between 7/2010-2/2019. The relationship between pathologic ECE &amp; parameters including clinical; age, prostate specific antigen (PSA) &amp; PSA density (PSAD), biopsy; percentage of positive systematic cores &amp; Gleason score (GS) &amp; 3TmpMRI; prostate volume, number of lesions per patient, size, location, level, PIRADSv2 score, laterality, apparent diffusion coefficient (ADC) value &amp; risk of ECE on MRI was evaluated using bivariate and multivariate analysis. The accuracy of the final model was evaluated using ROC analysis. Results: 27.8% (215/774), 42.9% (332/774) &amp; 29.3% (227/774) of the lesions were PIRADSv2 score 3, 4 &amp; 5 &amp; 59.9% (464/774), 24.7% (191/774) &amp; 17.7% (137/774) were low, intermediate &amp; high risk for ECE, respectively. 23.6% (183/774) of the lesions had ECE on WMHP. On bivariate analysis higher PSA, PSAD, percentage of positive biopsy cores, biopsy GS, size, PIRADSv2 score, ADC value, risk of ECE on MRI, location (posterior), level(midgland &amp; base), bilaterality &amp; lower number of lesions per patient were significant for ECE prediction. The multivariate logistic model included age, PSAD, number of lesions per patient, size, location, level, PIRADSv2 score &amp; risk of ECE on MRI. The AUC for the prediction of ECE for this model was 0.85. Conclusions: This multivariate regression model based on clinical, biopsy &amp; 3TmpMRI parameters have a high predictive value for pathology ECE detection.

Radiological Wheeler staging system: a retrospective cohort analysis to improve the local staging of prostate cancer with multiparametric MRI.

The knowledge of tumor location and extension can allow a modulated radical prostatectomy in order to minimize positive surgical margins and reduce functional morbidity after surgery in patients with prostate cancer (PCa). Multiparametric (mp) magnetic resonance imaging (MRI) could allow the assessment of tumor extension and of its relationship with external structures. Aim of this study is to propose a new radiological Wheeler (rW) staging system applied to mp-MRI, based on the pathologic staging system (pW) for the local assessment of PCa. This retrospective single-center multi-reader study included consecutive patients with PCa and preoperative mp-MRI, who underwent non-nerve sparing radical prostatectomy. Three radiologists reported on all examinations and classified each selected lesion according to imaging criteria following rW. Whole-mount histological sections were used as the reference standard. An experienced pathologist classified the extent of prostatic capsular invasion of each PCa according to the pW. Each histological section was scanned for comparison with mp-MRI findings. The rate of PCa correctly classified by radiologists using the pW was assessed. To evaluate the accuracy of mp-MRI in the discrimination between T2 and T3 PCa, the AUC was computed. One-hundred and five patients with a total of 195 PCa foci were included in the study. 130/195 tumors with a clear overlap between mp-MRI and surgical specimens were selected. The sensitivity of the most experienced reader was lower than that of the other two readers (48.6% vs. 68.6% and 62.9%, P>0.09) while specificity and PPV were higher (95.8% vs. 79.0% and 57.9%, P<0.001; 81.0% vs. 54.6% and 35.5%, P<0.041; respectively). The AUC values for the most and the intermediate experienced readers in the detection of extracapsular extension were in the range 0.72-0.74. The rW staging system has low accuracy in predicting each single pW class, while accuracy was over 80% for experienced readers in the identification of organ-confined (T2 stage class) tumors and non-organ confined cases (T3 stage class).

The role of magnetic resonance image guided prostate biopsy in stratifying men for risk of extracapsular extension at radical prostatectomy. Raskolnikov D, George AK, Rais-Bahrami S, Turkbey B, Siddiqui MM, Shakir NA, Okoro C, Rothwax JT, Walton-Diaz A, Sankineni S, Su D, Stamatakis L, Merino MJ, Choyke PL, Wood BJ, Pinto PA. Urologic Oncology Branch, National Cancer Institute, National Institutes

PurposeMagnetic resonance imaging detects extracapsular extension by prostate cancer with excellent specificity but low sensitivity. This limits surgical planning, which could be modified to account for focal extracapsular extension with image directed guidance for wider excision. In this study, we evaluate the performance of multiparametric magnetic resonance imaging in extracapsular extension detection and determine which preoperative variables predict extracapsular extension on final pathology when multiparametric magnetic resonance imaging predicts organ confined disease. Materials and methodsFrom May 2007 to March 2014, 169 patients underwent pre-biopsy multiparametric magnetic resonance imaging, magnetic resonance imaging/transrectal ultrasound fusion guided biopsy, extended sextant 12-core biopsy, and radical prostatectomy at our institution. A subset of 116 men had multiparametric magnetic resonance imaging negative for extracapsular extension and were included in the final analysis. ResultsThe 116 men with multiparametric magnetic resonance imaging negative for extracapsular extension had a median age of 61 years (IQR: 57–66) and a median prostate specific antigen of 5.51 ng/ml (IQR: 3.91–9.07). The prevalence of extracapsular extension was 23.1% in the overall population. Sensitivity, specificity, and positive and negative predictive values of multiparametric magnetic resonance imaging for extracapsular extension were 48.7%, 73.9%, and 35.9% and 82.8%, respectively. On multivariate regression analysis, only patient age (P = 0.002) and magnetic resonance imaging/transrectal ultrasound fusion guided biopsy Gleason score (P = 0.032) were independent predictors of extracapsular extension on final radical prostatectomy pathology. ConclusionsBecause of the low sensitivity of multiparametric magnetic resonance imaging for extracapsular extension, further tools are necessary to stratify men at risk for occult extracapsular extension that would otherwise only become apparent on final pathology. Magnetic resonance imaging/transrectal ultrasound fusion guided biopsy Gleason score can help identify which men with prostate cancer have extracapsular extension that may not be detectable by imaging.

Diagnosis of Extracapsular Extension of Prostate Cancer on Prostate MRI: Impact of Second-Opinion Readings by Subspecialized Genitourinary Oncologic Radiologists

The purpose of this article is to investigate the added value of second-opinion evaluation of prostate MRI by subspecialized genitourinary oncologic radiologists for the assessment of extracapsular extension (ECE) of prostate cancer. We performed a retrospective evaluation of initial and second-opinion radiology reports of 76 patients who underwent MRI of the prostate before prostatectomy for histologically proven prostate cancer. Initial outside reports and second-opinion reports were unpaired and reviewed in random order by a urologist who was blinded to patients' clinical details and histopathologic data. Histopathologic analysis of the prostatectomy specimen served as the reference standard. Among cases with diagnostic-quality images available (71/76; 93%), disagreement between the initial report and the second-opinion report was observed in 30% of cases (21/71; κ = 0.35); in 18 of these 21 cases (86%), histopathologic analysis proved that the second-opinion report was correct. The second-opinion interpretations had statistically significantly higher sensitivity (66% vs 24%; p < 0.0001) than did the initial reports, whereas there was no statistically significant difference in specificity (87% vs 93%; p = 0.317). On ROC curve analysis, the second-opinion reports yielded a statistically significantly higher AUC for the detection of ECE (0.80 vs 0.65; p = 0.004). The reinterpretation of prostate MRI examinations by subspecialized genitourinary oncologic radiologists improved the detection of ECE of prostate cancer.

The Role of Magnetic Resonance Image Guided Prostate Biopsy in Stratifying Men for Risk of Extracapsular Extension at Radical Prostatectomy

Magnetic resonance imaging detects extracapsular extension by prostate cancer with excellent specificity but low sensitivity. This limits surgical planning, which could be modified to account for focal extracapsular extension with image directed guidance for wider excision. In this study we evaluate the performance of multiparametric magnetic resonance imaging in extracapsular extension detection and determine which preoperative variables predict extracapsular extension on final pathology when multiparametric magnetic resonance imaging predicts organ confined disease. From May 2007 to March 2014, 169 patients underwent pre-biopsy multiparametric magnetic resonance imaging, magnetic resonance imaging/transrectal ultrasound fusion guided biopsy, extended sextant 12-core biopsy and radical prostatectomy at our institution. A subset of 116 men had multiparametric magnetic resonance imaging negative for extracapsular extension and were included in the final analysis. The 116 men with multiparametric magnetic resonance imaging negative for extracapsular extension had a median age of 61 years (IQR 57-66) and a median prostate specific antigen of 5.51 ng/ml (IQR 3.91-9.07). The prevalence of extracapsular extension was 23.1% in the overall population. Sensitivity, specificity, and positive and negative predictive values of multiparametric magnetic resonance imaging for extracapsular extension were 48.7%, 73.9%, 35.9% and 82.8%, respectively. On multivariate regression analysis only patient age (p=0.002) and magnetic resonance imaging/transrectal ultrasound fusion guided biopsy Gleason score (p=0.032) were independent predictors of extracapsular extension on final radical prostatectomy pathology. Because of the low sensitivity of multiparametric magnetic resonance imaging for extracapsular extension, further tools are necessary to stratify men at risk for occult extracapsular extension that would otherwise only become apparent on final pathology. Magnetic resonance imaging/transrectal ultrasound fusion guided biopsy Gleason score can help identify which men with prostate cancer have extracapsular extension that may not be detectable by imaging.

A New Sign for Detection of Extracapsular Extension of Prostatic Adenocarcinoma: Sum of Positive Sextants per Lobe Combining Interpretation of Magnetic Resonance Imaging and Prostate Biopsy Results

BackgroundThe objective of this study was to assess the possibility of improving the reliability of preoperative detection of extracapsular extension (ECE) in each prostate lobe by using a new sign called sum of positive sextants per lobe (SPS-L), combining interpretation of MRI and prostate biopsy results. Patients and MethodsWe reviewed the charts of 590 patients undergoing radical prostatectomy between 2002 and 2007. All patients were assessed by preoperative 1.5 Tesla MRI using an integrated endorectal and pelvic phased array coil. A sextant was defined as “positive” when tumor infiltration was observed on a biopsy sample or a pathologic image was observed on MRI (0 = absent, 1 = present). A score, called sum of positive sextants per lobe (SPS-L), was defined as the sum of positive sextants on biopsy samples and positive sextants on MRI (MRI-L) for each lobe. A second score taking into account the presence or absence of ECE visualized on MRI (T3 MRI-L + SPS-L) was also tested for the detection of ECE per lobe. ResultsOn multivariate analysis, the SPS-L and T3 MRI-L + SPS-L scores were significantly higher in the presence of ECE and extensive ECE (P < .0001). The areas under receiver operating characteristic (ROC) curves were significantly greater for the T3 MRI-L + SPS-L score than for the positive biopsy result per lobe (PB+/L) rate (P < .0001). ConclusionThe use of indirect signs (SPS-L) associated with direct signs (T3 MRI) allows the preoperative detection of ECE per lobe by endorectal 1.5 Tesla MRI with high sensitivity.

Prostate cancer: Utility of diffusion‐weighted imaging as a marker of side‐specific risk of extracapsular extension

To assess the utility of diffusion-weighted imaging (DWI) findings as an indirect marker of side-specific risk of extracapsular extension (ECE) of prostate cancer. Fifty-one patients underwent 3T magnetic resonance imaging (MRI) before prostatectomy. Radiologists 1 and 2 (4 and 1 years experience) assessed each side for ECE using T2-weighted imaging (T2WI) and evaluated apparent diffusion coefficient (ADC) maps for the presence of apparent tumor in each lobe and to measure peripheral zone ADC. A uropathologist measured the extent of any ECE. In all, 28/102 lobes had ECE, of which 12 measured ≤1 mm, 11 measured >1 mm and ≤2 mm, and five measured >2 mm. Side-specific accuracies for detection of ECE for readers 1 and 2 were respectively: T2WI 68.6% and 74.5%; presence of apparent tumor on ADC map 66.7% and 60.8%; ADC value 75.5% and 69.6%. For ECE >2 mm, both readers achieved 100% sensitivity based on apparent tumor on ADC map or ADC values and 80% sensitivity using T2WI. For detection of ECE ≤2 mm, sensitivity for all combinations of the three methods and two readers ranged from 58.3%-81.8%, aside from assessment for ECE using T2WI by the less experienced reader, which exhibited sensitivity of 17.4%. Interreader agreement for the presence of ECE was 0.18 using T2WI, 0.37 using apparent tumor on ADC map, and 0.60 using ADC values. Compared with T2WI, DWI had comparable accuracy for side-specific assessment of ECE, greater sensitivity for ECE <2 mm for the less-experienced radiologist, and greater interreader agreement.

Prophylactic Neck Dissection in Early Oral Tongue Squamous Cell Carcinoma 2.1 to 4.0 mm Depth

Regional recurrence is common following surgery for T1/T2 oral tongue squamous cell carcinoma (SCC). Tumor depth >4.0 mm is commonly assigned as an indication for prophylactic neck dissection to improve regional control. Prophylactic neck dissection may detect extracapsular extension, a poor prognostic sign where adjuvant chemotherapy is indicated. The hypothesis in this study is that regional recurrence is a significant problem in 2.1- to 4.0-mm-depth tumors, and detection of extracapsular extension may be important in this group. Retrospective chart review. Australian tertiary referral center. Review of all patients with T1/T2 oral tongue SCC treated surgically between January 1991 and January 2009 (n = 81). Twenty-nine prophylactic and 5 therapeutic neck dissections followed for a median 34 months (range, 4-132 months). Tumor depths were 0 to 2.0 mm (n = 15), 2.1 to 4.0 mm (n = 18), 4.1 to 7.0 mm (n = 26), and >7.0 mm (n = 22). Tumors 2.1 to 4.0 mm depth had similar rates of occult nodes as 4.1 to 7.0 mm depth (25% vs 20%). Regional recurrence occurred in 31% overall, 44% in tumors 2.1 to 4.0 mm, and 27% in tumors 4.1 to 7.0 mm depth. Prophylactic neck dissection reduced regional recurrence (17% vs 43%, P = .02). Patients with pathologically negative necks had lower rates of regional recurrence than those with occult nodes (9% vs 50%, P < .01). Extracapsular extension increased regional recurrence (43% vs 7%, P = .02), including 25% of dissected necks with tumor depth 2.1 to 4.0 mm. Regional recurrence is a significant problem in 2.1- to 4.0-mm-depth T1/T2 tongue tumors. Prophylactic neck dissection may improve regional control in patients with adequate primary resection margins and determine need for adjuvant therapies in 2.1- to 4.0-mm-depth tumors.