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

Deep learning-based image quality assessment: impact on detection accuracy of prostate cancer extraprostatic extension on MRI

OBJECTIVE

To assess impact of image quality on prostate cancer extraprostatic extension (EPE) detection on MRI using a deep learning-based AI algorithm.

MATERIALS AND METHODS

This retrospective, single institution study included patients who were imaged with mpMRI and subsequently underwent radical prostatectomy from June 2007 to August 2022. One genitourinary radiologist prospectively evaluated each patient using the NCI EPE grading system. Each T2WI was classified as low- or high-quality by a previously developed AI algorithm. Fisher's exact tests were performed to compare EPE detection metrics between low- and high-quality images. Univariable and multivariable analyses were conducted to assess the predictive value of image quality for pathological EPE.

RESULTS

A total of 773 consecutive patients (median age 61 [IQR 56-67] years) were evaluated. At radical prostatectomy, 23% (180/773) of patients had EPE at pathology, and 41% (131/318) of positive EPE calls on mpMRI were confirmed to have EPE. The AI algorithm classified 36% (280/773) of T2WIs as low-quality and 64% (493/773) as high-quality. For EPE grade ≥ 1, high-quality T2WI significantly improved specificity for EPE detection (72% [95% CI 67-76%] vs. 63% [95% CI 56-69%], P = 0.03), but did not significantly affect sensitivity (72% [95% CI 62-80%] vs. 75% [95% CI 63-85%]), positive predictive value (44% [95% CI 39-49%] vs. 38% [95% CI 32-43%]), or negative predictive value (89% [95% CI 86-92%] vs. 89% [95% CI 85-93%]). Sensitivity, specificity, PPV, and NPV for EPE grades ≥ 2 and ≥ 3 did not show significant differences attributable to imaging quality. For NCI EPE grade 1, high-quality images (OR 3.05, 95% CI 1.54-5.86; P < 0.001) demonstrated a stronger association with pathologic EPE than low-quality images (OR 1.76, 95% CI 0.63-4.24; P = 0.24).

CONCLUSION

Our study successfully employed a deep learning-based AI algorithm to classify image quality of prostate MRI and demonstrated that better quality T2WI was associated with more accurate prediction of EPE at final pathology.

Radiomics for the identification of extraprostatic extension with prostate MRI: a systematic review and meta-analysis

OBJECTIVES

Extraprostatic extension (EPE) of prostate cancer (PCa) is predicted using clinical nomograms. Incorporating MRI could represent a leap forward, although poor sensitivity and standardization represent unsolved issues. MRI radiomics has been proposed for EPE prediction. The aim of the study was to systematically review the literature and perform a meta-analysis of MRI-based radiomics approaches for EPE prediction.

MATERIALS AND METHODS

Multiple databases were systematically searched for radiomics studies on EPE detection up to June 2022. Methodological quality was appraised according to Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool and radiomics quality score (RQS). The area under the receiver operating characteristic curves (AUC) was pooled to estimate predictive accuracy. A random-effects model estimated overall effect size. Statistical heterogeneity was assessed with I2 value. Publication bias was evaluated with a funnel plot. Subgroup analyses were performed to explore heterogeneity.

RESULTS

Thirteen studies were included, showing limitations in study design and methodological quality (median RQS 10/36), with high statistical heterogeneity. Pooled AUC for EPE identification was 0.80. In subgroup analysis, test-set and cross-validation-based studies had pooled AUC of 0.85 and 0.89 respectively. Pooled AUC was 0.72 for deep learning (DL)-based and 0.82 for handcrafted radiomics studies and 0.79 and 0.83 for studies with multiple and single scanner data, respectively. Finally, models with the best predictive performance obtained using radiomics features showed pooled AUC of 0.82, while those including clinical data of 0.76.

CONCLUSION

MRI radiomics-powered models to identify EPE in PCa showed a promising predictive performance overall. However, methodologically robust, clinically driven research evaluating their diagnostic and therapeutic impact is still needed.

CLINICAL RELEVANCE STATEMENT

Radiomics might improve the management of prostate cancer patients increasing the value of MRI in the assessment of extraprostatic extension. However, it is imperative that forthcoming research prioritizes confirmation studies and a stronger clinical orientation to solidify these advancements.

KEY POINTS

• MRI radiomics deserves attention as a tool to overcome the limitations of MRI in prostate cancer local staging. • Pooled AUC was 0.80 for the 13 included studies, with high heterogeneity (84.7%, p < .001), methodological issues, and poor clinical orientation. • Methodologically robust radiomics research needs to focus on increasing MRI sensitivity and bringing added value to clinical nomograms at patient level.

Extra-prostatic extension grading system: correlation with MRI features and integration of capsular enhancement sign for "enhanced" detection of T3a lesions

PURPOSE

This study aims to confirm the diagnostic accuracy of extra-prostatic extension (EPE) grading system and to explore the predictive capabilities of the prostate MRI while considering various MRI features such as lesion location, apparent diffusion coefficient (ADC) values and capsular enhancement sign (CES).

METHODS

Our monocentric study is based on a retrospective analysis of 99 patients who underwent radical prostatectomy from January 2021 to January 2023. The observers reviewed for each lesion, including location (transitional or peripheral zone, anterior or posterior location), capsular contact length, irregular bulging of the capsule, asymmetry of the neurovascular bundle, obliteration of the recto-prostatic angle, macroscopic EPE, ADC value, and CES.

RESULTS

Among 99 patients, 31 patients had EPE. Lesions with EPE have broadercapsule contact (24 mm vs 12 mm) with contact ≥14 mm being the optimal cut-off for EPE discrimination. Among the morphological MRI criteria used to determine the EPE, the one with major sensitivity was shown to be bulging (sen 81%), while macroscopic extension had highest specificity (100%). Univariate analysis showed as significative risk factors for EPE: capsular contact ≥14 mm (P < .001), International Society of Urological Pathology score ≥3 (P = .005), CES (P < .001), bulging (P = .001), neurovascular bundle asymmetry (P < .001) and EPE score ≥2 (P < .001), and in multivariate analysis CES (P = .001) and EPE score ≥2 (P = .004) were significant. The AUC of the EPE score was 0.76, raised to 0.83 when combining it with CES (P = .11).

CONCLUSION

CES in the setting of multiparametric MRI can increase diagnostic accuracy for the prediction of extracapsular disease.

ADVANCES IN KNOWLEDGE

This study highlights the potential of contrast media in prostate cancer local staging.

MRI-based radiomics for prediction of extraprostatic extension of prostate cancer: a systematic review and meta-analysis

PURPOSE

We to systematically evaluate the diagnostic performance of MRI radiomics in detecting extracapsular extension (EPE) of prostate cancer (PCa).

METHODS

A literature search of online databases of PubMed, EMBASE, Cochrane Library, Web of Science, and Google Scholar online scientific publication databases was performed to identify studies published up to July 2023. The summary estimates were pooled with the hierarchical summary receiver-operating characteristic (HSROC) model. This study was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement, the quality of included studies was assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool (QUADAS-2) and the radiomics quality score (RQS). Meta-regression and subgroup analyses were performed to explore the impact of varying clinical settings.

RESULTS

A total of ten studies met the inclusion criteria. The pooled sensitivity and specificity were 0.77 (95% CI 0.68-0.84, I2 = 83.5%) and 0.75 (95% CI 0.67-0.82, I2 = 83.5%), respectively, with an area under the HSROC curve of 0.88 (95% CI 0.85-0.91). Study quality was not high while assessing with the RQS. Substantial heterogeneity was observed between studies; however, meta-regression analysis did not reveal any significant contributing factors.

CONCLUSIONS

MRI radiomics demonstrated moderate sensitivity and specificity, offering similar diagnostic performance with previous risk stratifications and models that primarily based on radiologists' subjective experience. However, all studies included were retrospective, thus the performance of radiomics needs to validate in prospective, multicenter studies.

Prostate MRI for the detection of clinically significant prostate cancer: Update and future directions

PURPOSE OF REVIEW

In recent decades, there has been an increasing role for magnetic resonance imaging (MRI) in the detection of clinically significant prostate cancer (csPC). The purpose of this review is to provide an update and outline future directions for the role of MRI in the detection of csPC.

RECENT FINDINGS

In diagnosing clinically significant prostate cancer pre-biopsy, advances include our understanding of MRI-targeted biopsy, the role of biparametric MRI (non-contrast) and changing indications, for example the role of MRI in screening for prostate cancer. Furthermore, the role of MRI in identifying csPC is maturing, with emphasis on standardization of MRI reporting in active surveillance (PRECISE), clinical staging (EPE grading, MET-RADS-P) and recurrent disease (PI-RR, PI-FAB). Future directions of prostate MRI in detecting csPC include quality improvement, artificial intelligence and radiomics, positron emission tomography (PET)/MRI and MRI-directed therapy.

SUMMARY

The utility of MRI in detecting csPC has been demonstrated in many clinical scenarios, initially from simply diagnosing csPC pre-biopsy, now to screening, active surveillance, clinical staging, and detection of recurrent disease. Continued efforts should be undertaken not only to emphasize the reporting of prostate MRI quality, but to standardize reporting according to the appropriate clinical setting.

MR image quality in local staging of prostate cancer: Role of PI-QUAL in the detection of extraprostatic extension

PURPOSE

To assess the impact of prostate MRI image quality by means of the Prostate Imaging Quality (PI-QUAL) score, on the identification of extraprostatic extension of disease (EPE), predicted using the EPE Grade Score, Likert Scale Score (LSS) and a clinical nomogram (MSKCCn).

METHODS

We retrospectively included 105 patients with multiparametric prostate MRI prior to prostatectomy. Two radiologists evaluated image quality using PI-QUAL (≥4 was considered high quality) in consensus. All cases were also scored using the EPE Grade, the LSS, and the MSKCCn (dichotomized). Inter-rater reproducibility for each score was also assessed. Accuracy was calculated for the entire population and by image quality, considering two thresholds for EPE Grade (≥2 and = 3) and LSS (≥3 and ≥ 4) and using McNemar's test for comparison.

RESULTS

Overall, 66 scans achieved high quality. The accuracy of EPE Grade ranged from 0.695 to 0.743, while LSS achieved values between 0.705 and 0.733. Overall sensitivity for the radiological scores (range = 0.235-0.529) was low irrespective of the PI-QUAL score, while specificity was higher (0.775-0.986). The MSKCCn achieved an AUC of 0.76, outperforming EPE Grade (=3 threshold) in studies with suboptimal image quality (0.821 vs 0.564, p = 0.016). EPE Grade (=3 threshold) accuracy was also better in high image quality studies (0.849 vs 0.564, p = 0.001). Reproducibility was good to excellent overall (95 % Confidence Interval range = 0.782-0.924).

CONCLUSION

Assessing image quality by means of PI-QUAL is helpful in the evaluation of EPE, as a scan of low quality makes its performance drop compared to clinical staging tools.

Value of a combined magnetic resonance imaging-based radiomics-clinical model for predicting extracapsular extension in prostate cancer: a preliminary study

Background

Extracapsular extension (ECE) of prostate cancer (PCa) is closely related to the treatment and prognosis of patients, and radiomics has been widely used in the study of PCa. This study aimed to evaluate the value of a combined model considering magnetic resonance imaging (MRI)-based radiomics and clinical parameters for predicting ECE in PCa.

Methods

A total of 392 PCa patients enrolled in this retrospective study were randomly divided into the training and validation sets at a ratio of 7:3. Radiologists assessed all lesions by Mehralivand grade. Radiomics features were extracted and selected to build a radiomics model, while clinical parameters were noted to construct the clinical model. The combined model was constructed by the integration of the radiomics model and clinical model. Meanwhile, the nomogram for predicting ECE was constructed based on the combined model. Then, the area under the receiver operating characteristic (ROC) curve (AUC), Delong test and the decision curve analysis (DCA) were used to compare the performance among the combined model, radiomics model, clinical model and Mehralivand grade.

Results

The AUC of the combined model in the validation set was comparable to that of the radiomics model [AUC =0.894 (95% confidence interval (CI): 0.837-0.950) vs. 0.835 (95% CI: 0.763-0.908), P>0.05]. In addition, the sensitivity of the combined model and radiomics model was 90.7% and 77.8%, with an accuracy of 81.4% and 76.3%, respectively. On the other hand, the AUCs of the Mehralivand grade of radiologists and clinical model were 0.774 (95% CI: 0.691-0.857) and 0.749 (95% CI: 0.658-0.840), respectively, in the validation set, which were lower than those in the combined model (P<0.05). The DCA implied that the combined model could obtain the maximum net clinical benefits compared with the clinical model, the Mehralivand grade and radiomics model.

Conclusions

The combined model has a satisfactory predictive value for ECE in PCa patients compared with the clinical model, Mehralivand grade of radiologists, and the radiomics model.

Perineural Invasion and Spread in Common Abdominopelvic Diseases: Imaging Diagnosis and Clinical Significance

Malignancies and other diseases may spread by multiple pathways, including direct extension, hematogenous spread, or via lymphatic vessels. A less-well-understood route is the peripheral nervous system, which is known as perineural spread (PNS). In addition to accounting for pain and other neurologic symptoms, PNS affects both disease prognosis and management. Although PNS is commonly discussed in relation to head and neck tumors, there is emerging data regarding PNS in abdominopelvic malignancies and other conditions such as endometriosis. Due to improved contrast and spatial resolution, perineural invasion, a finding heretofore diagnosed only at pathologic examination, can be detected at CT, MRI, and PET/CT. PNS most commonly manifests as abnormal soft-tissue attenuation extending along neural structures, and diagnosis of it is aided by optimizing imaging parameters, understanding pertinent anatomy, and becoming familiar with the typical neural pathways of spread that largely depend on the disease type and location. In the abdomen, the celiac plexus is a central structure that innervates the major abdominal organs and is the principal route of PNS in patients with pancreatic and biliary carcinomas. In the pelvis, the lumbosacral plexus and inferior hypogastric plexus are the central structures and principal routes of PNS in patients with pelvic malignancies. Although the imaging findings of PNS may be subtle, a radiologic diagnosis can have a substantial effect on patient care. Knowledge of anatomy and known routes of PNS and optimizing imaging parameters is of utmost importance in providing key information for prognosis and treatment planning. © RSNA, 2023 Supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article. Quiz questions for this article are available through the Online Learning Center.

Imaging features of the PI-RADS for predicting extraprostatic extension of prostate cancer: systematic review and meta-analysis

OBJECTIVES

To systematically determine the diagnostic performance of each MRI feature of the PI-RADS for predicting extraprostatic extension (EPE) in prostate cancer.

METHODS

A literature search in the MEDLINE and EMBASE databases was conducted to identify original studies reporting the accuracy of each feature on MRI for the dichotomous diagnosis of EPE. The meta-analytic pooled diagnostic odds ratio (DOR), sensitivity, specificity, and their 95% confidence intervals (CIs) were obtained using a bivariate random-effects model.

RESULTS

After screening 1955 studies, 17 studies with a total of 3062 men were included. All six imaging features, i.e., bulging prostatic contour, irregular or spiculated margin, asymmetry or invasion of neurovascular bundle, obliteration of rectoprostatic angle, tumor-capsule interface > 10 mm, and breach of the capsule with evidence of direct tumor extension, were significantly associated with EPE. Breach of the capsule with direct tumor extension demonstrated the highest pooled DOR (15.6, 95% CI [7.7-31.5]) followed by tumor-capsule interface > 10 mm (10.5 [5.4-20.2]), asymmetry or invasion of neurovascular bundle (7.6 [3.8-15.2]), and obliteration of rectoprostatic angle (6.1 [3.8-9.8]). Irregular or spiculated margin showed the lowest pooled DOR (2.3 [1.3-4.2]). Breach of the capsule with direct tumor extension and tumor-capsule interface > 10 mm showed the highest pooled specificity (98.0% [96.2-99.0]) and sensitivity (86.3% [70.0-94.4]), respectively.

CONCLUSIONS

Among the six MRI features of prostate cancer, breach of the capsule with direct tumor extension and tumor-capsule interface > 10 mm were the most predictive of EPE with the highest specificity and sensitivity, respectively.