Criteria for the diagnosis of extranodal extension detected on radiological imaging in head and neck cancer: Head and Neck Cancer International Group consensus recommendations.

Extranodal extension of tumour on histopathology is known to be a negative prognostic factor in head and neck cancer. Compelling evidence suggests that extranodal extension detected on radiological imaging is also a negative prognostic factor. Furthermore, if imaging detected extranodal extension could be identified reliably before the start of treatment, it could be used to guide treatment selection, as patients might be better managed with non-surgical approaches to avoid the toxicity and cost of trimodality therapy (surgery, chemotherapy, and radiotherapy together). There are many aspects of imaging detected extranodal extension that remain unresolved or are without consensus, such as the criteria to best diagnose them and the associated terminology. The Head and Neck Cancer International Group conducted a five-round modified Delphi process with a group of 18 international radiology experts, representing 14 national clinical research groups. We generated consensus recommendations on the terminology and diagnostic criteria for imaging detected extranodal extension to harmonise clinical practice and research. These recommendations have been endorsed by 19 national and international organisations, representing 34 countries. We propose a new classification system to aid diagnosis, which was supported by most of the participating experts over existing systems, and which will require validation in the future. Additionally, we have created an online educational resource for grading imaging detected extranodal extensions.

Utility of positive core number on MRI-ultrasound fusion targeted biopsy in combination with PI-RADS scores for predicting unexpected extracapsular extension of clinically localized prostate cancer.

OBJECTIVES: To evaluate the utility of magnetic resonance imaging (MRI) and MRI-ultrasound fusion targeted biopsy (TB) for predicting unexpected extracapsular extension (ECE) in clinically localized prostate cancer (CLPC). METHODS: This study enrolled 89 prostate cancer patients with one or more lesions showing a Prostate Imaging-Reporting and Data System (PI-RADS) score ≥3 but without morphological abnormality in the prostatic capsule on pre-biopsy MRI. All patients underwent TB and systematic biopsy followed by radical prostatectomy (RP). Each lesion was examined by 3-core TB, taking cores from each third of the lesion. The preoperative variables predictive of ECE were explored by referring to RP specimens in the lesion-based analysis. RESULTS: Overall, 186 lesions, including 81 (43.5%), 73 (39.2%), and 32 (17.2%) with PI-RADS 3, 4, and 5, respectively, were analyzed. One hundred and twenty-two lesions (65.6%) were diagnosed as cancer on TB, and ECE was identified in 33 (17.7%) on the RP specimens. The positive TB core number was ≤2 in 129 lesions (69.4%) and three in 57 lesions (30.6%). On the multivariate analysis, PI-RADS ≥4 (p = 0.049, odds ratio [OR] = 2.39) and three positive cores on TB (p = 0.005, OR = 3.07) were independent predictors of ECE. Lesions with PI-RADS ≥4 and a positive TB core number of 3 had a significantly higher rate of ECE than those with PI-RADS 3 and a positive TB core number ≤2 (37.5% vs. 7.8%, p < 0.001). CONCLUSIONS: Positive TB core number in combination with PI-RADS scores is helpful to predict unexpected ECE in CLPC.

Defining the role of multiparametric MRI in predicting prostate cancer extracapsular extension.

OBJECTIVES: To identify the predictive factors of prostate cancer extracapsular extension (ECE) in an institutional cohort of patients who underwent multiparametric MRI of the prostate prior to radical prostatectomy (RP). PATIENTS AND METHODS: Overall, 126 patients met the selection criteria, and their medical records were retrospectively collected and analysed; 2 experienced radiologists reviewed the imaging studies. Logistic regression analysis was conducted to identify the variables associated to ECE at whole-mount histology of RP specimens; according to the statistically significant variables associated, a predictive model was developed and calibrated with the Hosmer-Lomeshow test. RESULTS: The predictive ability to detect ECE with the generated model was 81.4% by including the length of capsular involvement (LCI) and intraprostatic perineural invasion (IPNI). The predictive accuracy of the model at the ROC curve analysis showed an area under the curve (AUC) of 0.83 [95% CI (0.76-0.90)], p < 0.001. Concordance between radiologists was substantial in all parameters examined (p < 0.001). Limitations include the retrospective design, limited number of cases, and MRI images reassessment according to PI-RADS v2.0. CONCLUSION: The LCI is the most robust MRI factor associated to ECE; in our series, we found a strong predictive accuracy when combined in a model with the IPNI presence. This outcome may prompt a change in the definition of PI-RADS score 5.

Clinical Impact of Extranodal Metabolic Tumor Volume in 240 Diffuse Large B cell Lymphoma Patients with Extranodal Involvement.

The present study is to investigate whether extranodal (EN) metabolic tumor volume (MTV) would have a specific clinical meaning for survival in EN diffuse large B cell lymphoma (DLBCL) patients. Two hundred forty DLBCL patients with EN involvement received 18F-fluorodeoxygenase (FDG) positron emission tomography/computed tomography (PET/CT) were enrolled. Survival analysis revealed that low EN MTV (PFS [progression-free survival], HR = 0.278, 95% CI = 0.127-0.807, p = 0.001; OS [overall survival], HR = 0.320, 95% CI = 0.145-0.703, p = 0.003), low total MTV (PFS, HR = 0.194, 95% CI = 0.085-0.445, p < 0.001; OS, HR = 0.213, 95% CI = 0.092-0.491, p < 0.007), and high National Cancer Center Network-International Prognostic Index score (PFS, HR = 3.152, 95% CI = 1.732-5.734, p < 0.001; OS, HR = 2.457, 95% CI = 1.363-4.430, p = 0.003) were independently associated with survivals in the patients. Our data showed that EN MTV is a useful and novel prognostic parameter for predicting survival in DLBCL patients with EN involvement.

A novel nomogram predicting lymph node invasion among patients with prostate cancer: The importance of extracapsular extension at multiparametric magnetic resonance imaging.

PURPOSE: To develop a novel risk tool that allows the prediction of lymph node invasion (LNI) among patients with prostate cancer (PCa) treated with robot-assisted radical prostatectomy (RARP) and extended pelvic lymph node dissection (ePLND). METHODS: We retrospectively identified 742 patients treated with RARP + ePLND at a single center between 2012 and 2018. All patients underwent multiparametric magnetic resonance imaging (mpMRI) and were diagnosed with targeted biopsies. First, the nomogram published by Briganti et al. was validated in our cohort. Second, three novel multivariable logistic regression models predicting LNI were developed: (1) a complete model fitted with PSA, ISUP grade groups, percentage of positive cores (PCP), extracapsular extension (ECE), and Prostate Imaging Reporting and Data System (PI-RADS) score; (2) a simplified model where ECE score was not included (model 1); and (3) a simplified model where PI-RADS score was not included (model 2). The predictive accuracy of the models was assessed with the receiver operating characteristic-derived area under the curve (AUC). Calibration plots and decision curve analyses were used. RESULTS: Overall, 149 patients (20%) had LNI. In multivariable logistic regression models, PSA (OR: 1.03; P= 0.001), ISUP grade groups (OR: 1.33; P= 0.001), PCP (OR: 1.01; P= 0.01), and ECE score (ECE 4 vs. 3 OR: 2.99; ECE 5 vs. 3 OR: 6.97; P< 0.001) were associated with higher rates of LNI. The AUC of the Briganti et al. model was 74%. Conversely, the AUC of model 1 vs. model 2 vs. complete model was, respectively, 78% vs. 81% vs. 81%. Simplified model 1 (ECE score only) was then chosen as the best performing model. A nomogram to calculate the individual probability of LNI, based on model 1 was created. Setting our cut-off at 5% we missed only 2.6% of LNI patients. CONCLUSIONS: We developed a novel nomogram that combines PSA, ISUP grade groups, PCP, and mpMRI-derived ECE score to predict the probability of LNI at final pathology in RARP candidates. The application of a nomogram derived cut-off of 5% allows to avoid a consistent number of ePLND procedures, missing only 2.6% of LNI patients. External validation of our model is needed.

PET/CT Poorly Predicts AJCC 8th Edition Pathologic Staging in HPV-Related Oropharyngeal Cancer.

OBJECTIVE: The American Joint Committee on Cancer (AJCC) 8th edition introduced distinct clinical and pathological staging paradigms for human papilloma virus positive (HPV+) oropharyngeal squamous cell carcinoma (OPSCC). Treatment planning for OPSCC often utilizes positron emission tomography/computed tomography (PET/CT) to assess clinical stage. We hypothesize that PET/CT will accurately predict final pathologic AJCC 8th edition staging in patients with HPV+ OPSCC. METHODS: All patients with primary HPV+ OPSCC with preoperative PET/CT who underwent transoral robotic surgery and neck dissection between 2011 and 2017 were identified. Data were collected via chart review. Two neuroradiologists performed blinded re-evaluation of all scans. Primary tumor size and cervical nodal disease characteristics were recorded and TNM staging was extrapolated. Cohen's kappa statistic was used to assess interrater reliability. Test for symmetry was performed to analyze discordance between radiologic and pathologic staging. RESULTS: Forty-nine patients met inclusion criteria. Interrater reliability was substantial between radiologists for nodal (N) and overall staging (OS) (κ = 0.715 and 0.715). Radiologist A review resulted in identical OS for 67% of patients, overstaging for 31%, and understaging for 2%. Radiologist B review resulted in 61% identical OS, 39% overstaging, and 0% understaging. In misclassified cases, the test of symmetry shows strong bias toward overstaging N stage and OS (P < .001). Radiologic interpretation of extracapsular extension showed poor interrater reliability (κ = 0.403) and poor accuracy. CONCLUSION: PET/CT predicts a higher nodal and overall stage than pathologic staging. PET/CT should not be relied upon for initial tumor staging, as increased FDG uptake is not specific for nodal metastases. PET/CT is shown to be a poor predictor of ECE. LEVEL OF EVIDENCE: 4 Laryngoscope, 131:1535-1541, 2021.

Multimodal Imaging With Positron Emission Tomography/Computed Tomography and Magnetic Resonance Imaging to Detect Extracapsular Extension in Head and Neck Cancer.

OBJECTIVES/HYPOTHESIS: To assess the ability of specific positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) features to detect extracapsular extension (ECE) in head and neck squamous cell carcinoma (HNSCC) patients. STUDY DESIGN: Retrospective study in a tertiary certified university cancer institute. METHODS: We performed a review of patients with advanced HNSCC at Bern University Hospital between 2014 and 2018. Patients with pretherapeutic PET/CT and/or MRI who underwent neck dissection were included, with 212 patients fulfilling inclusion criteria. Blinded evaluation of specific PET/CT and MRI features with respect to presence of ECE was performed. Histopathological examination of neck dissection specimens was used as the gold standard to determine ECE status. RESULTS: Out of the 212 included patients, 184 had PET/CT, 186 MRI, and 158 both modalities. Overall clinical stage IV (odds ratio [OR]: 2.26, 95% confidence interval [CI]: 2.25-11.74), ill-defined margins in both PET/CT and MRI (OR: 3.48, 95% CI: 1.21-9.98 and OR: 2.14, 95% CI: 0.94-4.89, respectively), and a maximum standardized uptake value ≥ 10 (OR: 5.44, 95% CI: 1.21-9.98) were all significant independent predictors of ECE. When combined, these four features led to a cumulative score able to predict ECE status with an accuracy of 91.43%. CONCLUSIONS: The current findings indicate specific features in PET/CT and MRI are potential predictors of ECE status and may help in pretherapeutic stratification in HNSCC. LEVEL OF EVIDENCE: 4 Laryngoscope, 131:E163-E169, 2021.

Diagnostic Value of FDG-PET/CT for the Identification of Extranodal Extension in Patients With Head and Neck Squamous Cell Carcinoma.

BACKGROUND/AIM: We evaluated the diagnostic value of functional imaging with [18F]-fluoro-2-deoxy-D-glucose (FDG)-positron emission tomography/computed tomography (PET/CT) for the identification of extranodal extension (ENE) in patients with head and neck squamous cell carcinoma (HNSCC). PATIENTS AND METHODS: In this study, 94 patients with HNSCC who underwent FDG-PET/CT were enrolled. We recorded the maximum standardized uptake value (SUVmax), compared the results with pathologic findings, and evaluated the diagnostic performance of using a SUVmax cut-off value for ENE. RESULTS: Of the 566 dissected levels examined, 53 (9.4%) exhibited ENE. The mean SUVmax of LN with and without ENE were 6.67 and 1.64, respectively (p<0.001). A receiver operating characteristics (ROC) curve analysis for SUVmax showed an area under the ROC curve of 0.913. A SUVmax cut-off of 3.0 achieved diagnostic performance for identifying ENE with sensitivity, specificity, and accuracy of 81.1%, 94.3% and 93.1%, respectively. CONCLUSION: FDG-PET/CT findings using a SUVmax cut-off of 3.0 provides appropriate diagnostic value in identifying ENE.

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

PURPOSE: To investigate and validate the potential role of a radiomics signature in predicting the side-specific probability of extracapsular extension (ECE) of prostate cancer (PCa). PROCEDURES: The preoperative magnetic resonance imaging data of 238 prostatic samples from 119 enrolled PCa patients were retrospectively assessed. The samples with were randomized in a two-to-one ratio into training (n = 74) and validation (n = 45) datasets. The radiomics features were derived from T2-weighted images (T2WIs). The optimal radiomics features were identified from the least absolute shrinkage and selection operator (LASSO) logistic regression model and were used to construct a predictive radiomics signature via dimension reduction and selection approaches. The association between the radiomics signatures and pathological ECE status was explored. Receiver operating characteristic (ROC) analysis was used to assess the discriminatory ability of the signature. The calibration performance and clinical usefulness of the radiomics signature were subsequently assessed by calibration curve and decision curve analyses. RESULTS: The proposed radiomics signature that incorporated 17 selected radiomics features was significantly associated with pathological ECE outcomes (P < 0.001) in both the training and validation datasets. The constructed model displayed good discrimination, with areas under the curve (AUC) of 0.906 (95 % confidence interval (CI), 0.847, 0.948) and 0.821 (95 % CI, 0.726, 0.894) for the training and validation datasets, respectively, and had a good calibration performance. The clinical utility of this model was confirmed through decision curve analysis. CONCLUSIONS: The radiomics signature based on T2WIs showed the potential to predict the side-specific probability of pathological ECE status and can facilitate the preoperative individualized predictions for PCa patients.

Multi-Institutional Validation of Deep Learning for Pretreatment Identification of Extranodal Extension in Head and Neck Squamous Cell Carcinoma.

PURPOSE: Extranodal extension (ENE) is a well-established poor prognosticator and an indication for adjuvant treatment escalation in patients with head and neck squamous cell carcinoma (HNSCC). Identification of ENE on pretreatment imaging represents a diagnostic challenge that limits its clinical utility. We previously developed a deep learning algorithm that identifies ENE on pretreatment computed tomography (CT) imaging in patients with HNSCC. We sought to validate our algorithm performance for patients from a diverse set of institutions and compare its diagnostic ability to that of expert diagnosticians. METHODS: We obtained preoperative, contrast-enhanced CT scans and corresponding pathology results from two external data sets of patients with HNSCC: an external institution and The Cancer Genome Atlas (TCGA) HNSCC imaging data. Lymph nodes were segmented and annotated as ENE-positive or ENE-negative on the basis of pathologic confirmation. Deep learning algorithm performance was evaluated and compared directly to two board-certified neuroradiologists. RESULTS: A total of 200 lymph nodes were examined in the external validation data sets. For lymph nodes from the external institution, the algorithm achieved an area under the receiver operating characteristic curve (AUC) of 0.84 (83.1% accuracy), outperforming radiologists' AUCs of 0.70 and 0.71 (P = .02 and P = .01). Similarly, for lymph nodes from the TCGA, the algorithm achieved an AUC of 0.90 (88.6% accuracy), outperforming radiologist AUCs of 0.60 and 0.82 (P < .0001 and P = .16). Radiologist diagnostic accuracy improved when receiving deep learning assistance. CONCLUSION: Deep learning successfully identified ENE on pretreatment imaging across multiple institutions, exceeding the diagnostic ability of radiologists with specialized head and neck experience. Our findings suggest that deep learning has utility in the identification of ENE in patients with HNSCC and has the potential to be integrated into clinical decision making.

Assessment of a Staging System for Sigmoid Colon Cancer Based on Tumor Deposits and Extramural Venous Invasion on Computed Tomography.

Importance: Preoperative TNM stratification of colon cancer on computed tomography (CT) does not identify patients who are at high risk of recurrence that could be selected for preoperative treatment. Objective: To evaluate the utility of CT findings for prognosis of sigmoid colon cancer. Design, Setting, and Participants: This prognostic study used retrospective data from patients who underwent bowel resection for sigmoid colon cancer between January 1, 2006, and January 1, 2015, at a tertiary care center receiving international and national referrals for colorectal cancer. Statistical analysis was performed in April 2019. Main Outcomes and Measures: Cox proportional hazards regression analysis was performed to investigate CT findings associated with disease recurrence. Kaplan-Meier survival plots were calculated for disease-free survival using CT staging systems. Results: Of the 414 patients who had sigmoid colon cancer (248 [60.0%] men; mean [SD] age, 66.1 [12.7] years), with median follow-up of 61 months (interquartile range, 40-87 months), 122 patients (29.5%) developed disease recurrence. On multivariate analysis, nodal disease was not associated with disease recurrence; only tumor deposits (hazard ratio [HR], 1.90; 95% CI, 1.21-2.98; P = .006) and extramural venous invasion (HR, 1.97; 95% CI, 1.26-3.06; P = .003) on CT were associated with disease recurrence. Significant differences in disease-free survival were found using CT-T3 substage classification (HR, 1.88; 95% CI, 1.32-2.68) but not CT-TNM (HR, 1.55; 95% CI, 0.94-2.55). The presence of tumor deposits or extramural venous invasion on CT (HR, 2.45; 95% CI, 1.68-3.56) had the strongest association with poor outcome. Conclusions and Relevance: In this study, T3 substaging and detection of tumor deposits or extramural venous invasion on preoperative CT scans of sigmoid colon cancer were prognostic factors for disease-free survival, whereas TNM and nodal staging on CT had no prognostic value. T3 substaging and detection of tumor deposits or extramural venous invasion of sigmoid colon cancer was superior to TNM on CT and could be used to preoperatively identify patients at high risk of recurrence.

Prognostic Value of Radiologic Extranodal Extension in Human Papillomavirus-Related Oropharyngeal Squamous Cell Carcinoma.

OBJECTIVE: To determine whether radiologic extranodal extension (ENE) appearing on pretreatment CT and MRI could predict the prognosis in patients with human papillomavirus (HPV)-related oropharyngeal squamous cell carcinoma (OPSCC). MATERIALS AND METHODS: The study population was obtained from a historical cohort diagnosed with HPV-related OPSCC. A total of 134 OPSCC patients who had a metastatic lymph node on pretreatment CT or MRI were included, and radiologic ENE was evaluated by two experienced head and neck radiologists. Kaplan-Meier and multivariate Cox regression analyses were performed to evaluate the impact of radiologic ENE on progression-free survival (PFS). The diagnostic performance of CT and MRI for the diagnosis of ENE was also evaluated in patients who underwent neck dissection. RESULTS: Seventy patients (52.2%) showed radiologic ENE-positive findings. Although patients showing radiologic ENE had a worse 3-year PFS (83.7% vs. 95.3%, p = 0.023), the association between radiologic ENE and PFS was not statistically significant on multivariate analysis (p = 0.141; hazard ratio, 2.68; 95% confidence interval, 0.72-9.97). CT or MRI had a sensitivity of 62%, specificity of 77.8%, and accuracy of 71.9% for predicting pathologic ENE. CONCLUSION: Radiologic ENE on CT or MRI did not predict poor PFS in patients with HPV-related OPSCC, although there was a trend towards worse PFS. Further studies are warranted to determine whether radiologic ENE is a useful imaging biomarker to risk-stratify patients with HPV-related OPSCC.

Prognostic value of radiographically defined extranodal extension in human papillomavirus-associated locally advanced oropharyngeal carcinoma.

BACKGROUND: Pathologic extranodal extension (ENE) has traditionally guided the management of head and neck cancers. The prognostic value of radiographic ENE (rENE) in human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (HPV + OPX) is uncertain. METHODS: Patients with HPV + OPX with adequate pretreatment radiographic nodal evaluation from a single institution were analyzed. rENE status was determined by neuroradiologists' at time of diagnosis. Distant metastasis-free survival (DMFS), overall survival (OS), and locoregional recurrence-free survival (LRFS) were estimated using Kaplan-Meier methods. Cox proportional hazards models were fit to assess the impact of rENE on survival endpoints. RESULTS: Hundred sixty-eight patients with OPX + squamous cell carcinomas diagnosed between April 2008 and December 2014 were included for analysis with median follow-up of 3.3 years. Eighty-eight percent of patients received concurrent chemoradiotherapy. rENE was not prognostic; its presence in patients with HPV + OPX did not significantly impact OS, LRFS, or DMFS. CONCLUSIONS: In patients with HPV + OPX, rENE was not significantly associated with OS, LRFS, or DMFS.

Radiologic Extranodal Extension Portends Worse Outcome in cN+ TNM-8 Stage I Human Papillomavirus-Mediated Oropharyngeal Cancer.

PURPOSE: To identify adverse radiologic nodal features in cN+ TNM-8 stage I human papillomavirus-related (HPV+) oropharyngeal cancer (OPC). METHODS AND MATERIALS: All patients with HPV+ cT1-T2cN1 OPC treated with definitive intensity modulated radiation therapy from 2008 to 2015 were included. Radiologically involved lymph node number (LN), radiologic extranodal extension (rENE), retropharyngeal LN (RPLN), and lower neck (level 4 or 5b) LN involvement were assessed on pre-treatment computed tomography/magnetic resonance imaging by a specialized head and neck neuroradiologist. Disease-free survival (DFS), locoregional control, and distant control were compared between those with versus without rENE. Univariable and multivariable analysis with stepwise modal selection were applied to identify prognostic factors for DFS. RESULTS: A total of 45 rENE+ and 234 rENE- were identified. The rENE+ cohort had a higher number of LNs per patient (median: 6 vs 2, P < .001) and was more likely to have necrotic LNs (33 [73%] vs 132 [56%], P = .046). Median follow-up was 4.8 years. Although locoregional control was high in both cohorts (93% vs 97%, P = .34), the rENE+ group had inferior 5-year distant control (78% [59-88] vs 95% [91-97], P < .001) and DFS (58% [43-77] vs 90% [86-94], P < .001). In multivariable analysis, rENE+ (HR [hazard ratio] 4.3 [2.3-8.1], P < .001], T2 (vs T1) category (HR 2.1 [1.0-4.2], P = .039), smoking pack-years (HR 1.02 [1.0-1.03], P = .013), and the addition of systemic agents (HR 0.4 [0.2-0.8], P = .005) were prognostic for DFS. RPLN was prognostic for distant metastasis (HR 3.2, P = .013) but not for DFS after adjusting for rENE. CONCLUSIONS: Data from this contemporaneously treated cT1-T2N1 HPV+ OPC cohort suggest that the presence of rENE is an independent prognostic factor within stage I HPV+ OPC. RPLN is also associated with DM risk but not with DFS.

Defining the incremental value of 3D T2-weighted imaging in the assessment of prostate cancer extracapsular extension.

OBJECTIVES: To assess the added value of 3D T2-weighted imaging (T2WI) over conventional 2D T2WI in diagnosing extracapsular extension (ECE). METHODS: Seventy-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. RESULTS: One 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. CONCLUSIONS: 3D 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.