Value of preoperative 3T multiparametric MRI for surgical margin status in patients with prostate cancer

Tamada, Tsutomu; Sone, Toshio; Kanomata, Naoki; Miyaji, Yoshiyuki; Kido, Ayumu; Jo, Yoshimasa; Yamamoto, Akira; Ito, Katsuyoshi

doi:10.1002/jmri.25185

Cited by 14 publications

(13 citation statements)

References 42 publications

(59 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Schieda et al proposed a modified criteria for assessing ECE, which could reduce differences among readers compared with the nonstandardized reporting method, yielding a superior performance with significantly improved sensitivity and comparable specificity. In our study, the radiologists' diagnosis sensitivity of ECE was relatively low (46.88–50.00%, in the validation cohort), which was concordant with previously reported observations . The varying results of subjective assessment might be explained by the anatomical constraints (ie, an absence of a distinct capsule, the intimate relationships between the prostate and surrounding structures like the vessels, nerves, and the urethral sphincter).…”

Section: Discussionsupporting

confidence: 90%

“…In our study, the radiologists' diagnosis sensitivity of ECE was relatively low (46.88-50.00%, in the validation cohort), which was concordant with previously reported observations. 3,36 The varying results of subjective assessment might be explained by the anatomical constraints (ie, an absence of a distinct capsule, the intimate relationships between the prostate and surrounding structures like the vessels, nerves, and the urethral sphincter). This may lead to a lack of diagnostic confidence during the MR reporting.…”

Section: Discussionmentioning

confidence: 99%

“…FOR PATIENTS WITH PROSTATE CANCER (PCa), surgical resection, targeted therapy, and active surveillance have been regarded as curative treatment strategies, and among these radical prostatectomy (RP) is considered the preferred approach for individuals with localized PCa . Imaging techniques such as multiparametric magnetic resonance imaging (mp‐MRI) may help to predict positive surgical margins (PSM) by the detection and localization of extracapsular extension (ECE) . For patients with PCa, the presence of ECE may affect surgical decisions such as whether to perform a nerve‐sparing approach.…”

mentioning

confidence: 99%

“…help to predict positive surgical margins (PSM) by the detection and localization of extracapsular extension (ECE). 2,3 For patients with PCa, the presence of ECE may affect surgical decisions such as whether to perform a nerve-sparing approach. Once ECE is suspected, structures (cavernous nerves, for instance, which are responsible for erectile function) are often resected to avoid the risk of potential PSM.…”

mentioning

confidence: 99%

See 3 more Smart Citations

MRI‐Based Radiomics Signature for the Preoperative Prediction of Extracapsular Extension of Prostate Cancer

Xie

Wang

et al. 2019

Magnetic Resonance Imaging

View full text Add to dashboard Cite

Background Radiomics approaches based on multiparametric MRI (mp‐MRI) have shown high accuracy in prostate cancer (PCa) management. However, there is a need to apply radiomics to the preoperative prediction of extracapsular extension (ECE). Purpose To develop and validate a radiomics signature to preoperatively predict the probability of ECE for patients with PCa, compared with the radiologists' interpretations. Study Type Retrospective. Population In total, 210 patients with pathology‐confirmed ECE status (101 positive, 109 negative) were enrolled. Field Strength/Sequence T2‐weighted imaging (T2WI), diffusion‐weighted imaging, and dynamic contrast‐enhanced imaging were performed on two 3.0T MR scanners. Assessment A radiomics signature was constructed to predict the probability of ECE prior to radical prostatectomy (RP). In all, 17 stable radiomics features of 1619 extracted features based on T2WI were selected. The same images were also evaluated by three radiologists. The predictive performance of the radiomics signature was validated and compared with radiologists' interpretations. Statistical Tests A radiomics signature was developed by a least absolute shrinkage and selection operator (LASSO) regression algorithm. Samples enrolled were randomly divided into two groups (143 for training and 67 for validation). Discrimination, calibration, and clinical usefulness were validated by analysis of the receiver operating characteristic (ROC) curve, calibration curve, and the decision curve, respectively. The predictive performance was then compared with visual assessments of three radiologists. Results The radiomics signature yielded an AUC of 0.902 and 0.883 in the training and validation cohort, respectively, and outperformed the visual assessment (AUC: 0.600–0.697) in the validation cohort. Pairwise comparisons demonstrated that the radiomics signature was more sensitive than the radiologists (75.00% vs. 46.88%–50.00%, all P < 0.05), but obtained comparable specificities (91.43% vs. (88.57%–94.29%); P ranged from 0.64–1.00). Data Conclusion A radiomics signature was developed and validated that outperformed the radiologists' visual assessments in predicting ECE status. Level of Evidence: 4 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:1914–1925.

show abstract

Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

MRI‐Based Radiomics Signature for the Preoperative Prediction of Extracapsular Extension of Prostate Cancer

Xie

Wang

et al. 2019

Magnetic Resonance Imaging

View full text Add to dashboard Cite

show abstract

“…Tamada et al. [36] retrospectively analyzed a group of 56 men with prostate cancer who underwent 3-T mpMRI prior to radical prostatectomy and found similar results. Positive surgical margins, found in 27% of patients, were linked with MRI findings suggestive of ECE, as well as lower tumor ADC and extension into the proximal or apical prostate.…”

Section: Discussionmentioning

confidence: 75%

Multi-parametric MRI of the prostate: Factors predicting extracapsular extension at the time of radical prostatectomy

Gaunay

Patel

Shah

et al. 2017

Asian Journal of Urology

View full text Add to dashboard Cite

ObjectiveExtracapsular extension (ECE) of prostate cancer 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. Multi-parametric magnetic resonance imaging (mpMRI) of the prostate has shown promise in this regard, but is hampered by poor sensitivity. We sought to identify additional clinical variables associated with pathologic ECE and determine our institutional accuracy in the detection of ECE amongst patients who went on to radical prostatectomy.MethodsmpMRI studies performed between the years 2012 and 2014 were cross-referenced with radical prostatectomy specimens. Predictive properties of ECE as well as additional clinical and biochemical variables to identify pathology-proven prostate cancer ECE were analyzed.ResultsThe prevalence of ECE was 32.4%, and the overall accuracy of mpMRI for ECE was 84.1%. Overall mpMRI sensitivity, specificity, positive predictive value, and negative predictive value for detection of ECE were 58.3%, 97.8%, 93.3%, and 81.5%, respectively. Specific mpMRI characteristics predictive of pathologic ECE included primary lesion size ((20.73 ± 9.09) mm, mean ± SD, p < 0.001), T2 PIRADS score (p = 0.009), overall primary lesion score (p < 0.001), overall study suspicion score (p = 0.003), and MRI evidence of seminal vesicle invasion (SVI) (p = 0.001).ConclusionWhile mpMRI is an accurate preoperative assessment tool for the detection of ECE, its overall sensitivity is poor, likely related to the low detection rate of standard protocol MRI for microscopic extraprostatic disease. The additional mpMRI findings described may also be considered in surgical margin planning prior to radical prostatectomy.

show abstract

Detecting Adverse Pathology of Prostate Cancer With a Deep Learning Approach Based on a 3D Swin‐Transformer Model and Biparametric MRI: A Multicenter Retrospective Study

Zhao,

Bao,

Wang

et al. 2023

Magnetic Resonance Imaging

View full text Add to dashboard Cite

BackgroundAccurately detecting adverse pathology (AP) presence in prostate cancer patients is important for personalized clinical decision‐making. Radiologists' assessment based on clinical characteristics showed poor performance for detecting AP presence.PurposeTo develop deep learning models for detecting AP presence, and to compare the performance of these models with those of a clinical model (CM) and radiologists' interpretation (RI).Study TypeRetrospective.PopulationTotally, 616 men from six institutions who underwent radical prostatectomy, were divided into a training cohort (508 patients from five institutions) and an external validation cohort (108 patients from one institution).Field Strength/SequencesT2‐weighted imaging with a turbo spin echo sequence and diffusion‐weighted imaging with a single‐shot echo plane‐imaging sequence at 3.0 T.AssessmentThe reference standard for AP was histopathological extracapsular extension, seminal vesicle invasion, or positive surgical margins. A deep learning model based on the Swin‐Transformer network (TransNet) was developed for detecting AP. An integrated model was also developed, which combined TransNet signature with clinical characteristics (TransCL). The clinical characteristics included biopsy Gleason grade group, Prostate Imaging Reporting and Data System scores, prostate‐specific antigen, ADC value, and the lesion maximum cross‐sectional diameter.Statistical TestsModel and radiologists' performance were assessed using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The Delong test was used to evaluate difference in AUC. P < 0.05 was considered significant.ResultsThe AUC of TransCL for detecting AP presence was 0.813 (95% CI, 0.726–0.882), which was higher than that of TransNet (0.791 [95% CI, 0.702–0.863], P = 0.429), and significantly higher than those of CM (0.749 [95% CI, 0.656–0.827]) and RI (0.664 [95% CI, 0.566–0.752]).Data ConclusionTransNet and TransCL have potential to aid in detecting the presence of AP and some single adverse pathologic features.Level of Evidence4Technical EfficacyStage 4

show abstract

Value of preoperative 3T multiparametric MRI for surgical margin status in patients with prostate cancer

Abstract: PC with positive SM was characterized by tumor extension in the apical and proximal regions, lower tumor ADC, and tumors with positive MRI findings of ECE, compared to tumors with negative SM. J. Magn. Reson. Imaging 2016;44:584-593.

Cited by 14 publications

References 42 publications

MRI‐Based Radiomics Signature for the Preoperative Prediction of Extracapsular Extension of Prostate Cancer

MRI‐Based Radiomics Signature for the Preoperative Prediction of Extracapsular Extension of Prostate Cancer

Multi-parametric MRI of the prostate: Factors predicting extracapsular extension at the time of radical prostatectomy

Detecting Adverse Pathology of Prostate Cancer With a Deep Learning Approach Based on a 3D Swin‐Transformer Model and Biparametric MRI: A Multicenter Retrospective Study

Contact Info

Product

Resources

About