Dataset Information

IVIM Parameters on MRI Could Predict ISUP Risk Groups of Prostate Cancers on Radical Prostatectomy.

ABSTRACT:

Purpose

To elucidate the usefulness of intravoxel incoherent motion (IVIM)/apparent diffusion coefficient (ADC) parameters in preoperative risk stratification using International Society of Urological Pathology (ISUP) grades.

Materials and methods

Forty-five prostate cancer (PCa) patients undergoing radical prostatectomy (RP) after prostate multiparametric magnetic resonance imaging (mpMRI) were included. The ISUP grades were categorized into low-risk (I-II) and high-risk (III-V) groups, and the concordance between the preoperative and postoperative grades was analyzed. The largest region of interest (ROI) of the dominant tumor on each IVIM/ADC image was delineated to obtain its histogram values (i.e., minimum, mean, and kurtosis) of diffusivity (D), pseudodiffusivity (D*), perfusion fraction (PF), and ADC. Multivariable logistic regression analysis of the IVIM/ADC parameters without and with preoperative ISUP grades were performed to identify predictors for the postoperative high-risk group.

Results

Thirty-two (71.1%) of 45 patients had concordant preoperative and postoperative ISUP grades. D_mean, D*_kurtosis, PF_kurtosis, ADC_min, and ADC_mean were significantly associated with the postoperative ISUP risk group (all p < 0.05). D_mean and D*_kurtosis (model I, both p < 0.05) could predict the postoperative ISUP high-risk group with an area under the curve (AUC) of 0.842 and a 95% confidence interval (CI) of 0.726-0.958. The addition of D*_kurtosis to the preoperative ISUP grade (model II) may enhance prediction performance, with an AUC of 0.907 (95% CI 0.822-0.992).

Conclusions

The postoperative ISUP risk group could be predicted by D_mean and D*_kurtosis from mpMRI, especially D*_kurtosis. Obtaining the biexponential IVIM parameters is important for better risk stratification for PCa.

SUBMITTER: Chang CB

PROVIDER: S-EPMC8282053 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

IVIM Parameters on MRI Could Predict ISUP Risk Groups of Prostate Cancers on Radical Prostatectomy.

Chang Chun-Bi CB Lin Yu-Chun YC Wong Yon-Cheong YC Lin Shin-Nan SN Lin Chien-Yuan CY Lin Yu-Han YH Sheng Ting-Wen TW Huang Chen-Chih CC Yang Lan-Yan LY Wang Li-Jen LJ

Frontiers in oncology 20210701

<h4>Purpose</h4>To elucidate the usefulness of intravoxel incoherent motion (IVIM)/apparent diffusion coefficient (ADC) parameters in preoperative risk stratification using International Society of Urological Pathology (ISUP) grades.<h4>Materials and methods</h4>Forty-five prostate cancer (PCa) patients undergoing radical prostatectomy (RP) after prostate multiparametric magnetic resonance imaging (mpMRI) were included. The ISUP grades were categorized into low-risk (I-II) and high-risk (III-V) ...[more]

PMID: 34277409

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Project description:BackgroundIn order to improve postoperative functional outcome, including urinary continence and erectile function, nerve sparing surgery is recommended for patients with clinically localized prostate cancer (PCa). However, due to poor diagnosis accuracy at the preoperative stage, upstaging occurs in a considerable proportion of patients. Multiparametric magnetic resonance imaging (mpMRI) and the Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) have recently shown excellent performance in diagnosis and staging of PCa. The aim of this study was to develop a predictive model based on PI-RADS v2 for postoperative upstaging in patients with low-intermediate risk PCa.MethodsThe medical records of 314 patients with low-intermediate risk PCa [prostate-specific antigen (PSA) level ≤20 ng/mL, Gleason score (GS) <8, and clinical stage < T3] who underwent preoperative mpMRI and radical prostatectomy in the Department of Urology, Peking University First Hospital between January 2012 and July 2019 were reviewed retrospectively. Clinicopathological characteristics were collected. All MRI reports were done at our institution as part of routine clinical practice before prostate biopsy and there was no re-reporting occurred. Using PI-RADS v2, the mpMRI results were assigned to three groups: "negative", "suspicious", and "positive". Multivariate logistic regression analysis was used to assess factors associated with postoperative pathological upstaging, defined as the presence of pT3 at final pathology. A regression coefﬁcient based model for predicting postoperative upstaging was constructed and internally validated using 1,000 bootstrap resamples. The performance of the model was assessed using the area under the receiver operating characteristic curve (AUC). With the optimal cutoff point the performance of the model was assessed through analysis of sensitivity, specificity, positive predictive value, and negative predictive value.ResultsUpstaging was observed in 119 (37.9%) patients. The univariate and multivariate analyses revealed that PSA density, biopsy Gleason grade group (GGG), and mpMRI findings were significantly independent predictors for postoperative upstaging (all P<0.05). A predictive model showing very favorable calibration characteristics and higher accuracy than the single variables was constructed (AUC =0.74; P<0.001). At the optimal cutoff point, the model demonstrated a sensitivity and negative predictive value of 87.4% and 87.0%, respectively.ConclusionsPI-RADS v2 assessment proved to be one of the most valuable predictors for postoperative upstaging in patients with low-intermediate risk PCa. The predictive model, based on PI-RADS v2 assessment, PSA density, and biopsy GGG, may help to select suitable candidates for nerve sparing radical prostatectomy among patients with low-intermediate risk PCa.

Project description:ObjectiveThis study aims to analyse multiparametric MRI (mpMRI) characteristics of patients diagnosed with ISUP grade group (GG) 1 prostate cancer (PC) on initial target plus systematic MRI/TRUS fusion-guided biopsy and investigate histopathological progression during follow-up.MethodsA retrospective single-centre cohort analysis was conducted on consecutive patients with mpMRI visible lesions (PI-RADS ≥ 3) and detection of ISUP-1-PC at the time of initial biopsy. The study assessed clinical, mpMRI, and histopathological parameters. Subcohorts were analysed with (1) patients who had confirmed ISUP-1-PC and (2) patients who experienced histopathological upgrading to ISUP ≥ 2 PC during follow-up either at re-biopsy or radical prostatectomy (RP).ResultsA total of 156 patients (median age 65 years) between March 2014 and August 2021 were included. Histopathological upgrading to ISUP ≥ 2 was detected in 55% of patients during a median follow-up of 9.5 months (IQR 2.2-16.4). When comparing subgroups with an ISUP upgrade and sustained ISUP 1 PC, they differed significantly in contact length of the index lesion to the pseudocapsule, ADC value, PI-RADS category, and the MRI grading group (mGG) (p < 0.05). In the ISUP GG ≥ 2 subgroup, 91% of men had PI-RADS category 4 or 5 and 82% exhibited the highest mGG (mGG3). In multivariate analysis, mGG was the only independent parameter for predicting ISUP ≥ 2-PC in these patients.ConclusionsMRI reveals important information about PC aggressiveness and should be incorporated into clinical decision-making when ISUP-1-PC is diagnosed. In cases of specific MRI characteristics adverse to the histopathology, early re-biopsy might be considered.Clinical relevance statementIn cases with clear MRI characteristics for clinically significant prostate cancer (e.g., mGG 3 and/or PI-RADS 5, cT3, or clear focal PI-RADS 4 lesions on MRI) and ISUP GG 1 PC diagnosed on initial prostate biopsy, MRI findings should be incorporated into clinical decision-making and early re-biopsy (e.g., within 6 months) might be considered.Key pointsMRI reveals important information about prostate cancer (PC) aggressiveness. MRI should be incorporated into clinical decision-making when ISUP GG 1 PC is diagnosed on initial prostate biopsy. In cases of specific MRI characteristics adverse to the histopathology, early re-biopsy might be considered.

Project description:BackgroundPelvic morphological parameters on magnetic resonance imaging (MRI), such as the membranous urethral length (MUL), can predict urinary incontinence after radical prostatectomy but are prone to interobserver disagreement. Our objective was to improve interobserver agreement among radiologists in measuring pelvic parameters using deep learning (DL)-based segmentation of pelvic structures on MRI scans.MethodsPreoperative MRI was collected from 167 prostate cancer patients undergoing radical prostatectomy within our regional multicentric cohort. Two DL networks (nnU-Net) were trained on coronal and sagittal scans and evaluated on a test cohort using an 80/20% train-test split. Pelvic parameters were manually measured by three abdominal radiologists on raw MRI images and with the use of DL-generated segmentations. Automated measurements were also performed for the pelvic parameters. Interobserver agreement was evaluated using the intraclass correlation coefficient (ICC) and the Bland-Altman plot.ResultsThe DL models achieved median Dice similarity coefficient (DSC) values of 0.85-0.97 for coronal structures and 0.87-0.98 for sagittal structures. When radiologists used DL-generated segmentations of pelvic structures, the interobserver agreement for sagittal MUL improved from 0.64 (95% confidence interval 0.28-0.83) to 0.91 (95% CI 0.84-0.95). Furthermore, there was an increase in ICC values for the obturator internus muscle from 0.74 (95% CI 0.42-0.87) to 0.86 (95% CI 0.75-0.92) and for the levator ani muscle from 0.40 (95% CI 0.05-0.66) to 0.61 (95% CI 0.31-0.78).ConclusionsDL-based automated segmentation of pelvic structures improved interobserver agreement in measuring pelvic parameters on preoperative MRI scans.Relevance statementThe implementation of deep learning segmentations allows for more consistent measurements of pelvic parameters by radiologists. Standardized measurements are crucial for incorporating these parameters into urinary continence prediction models.Key points• DL-generated segmentations improve interobserver agreement for pelvic measurements among radiologists. • Membranous urethral length measurement improved from substantial to almost perfect agreement. • Artificial intelligence enhances objective pelvic parameter assessment for continence prediction models.

Dataset Information

IVIM Parameters on MRI Could Predict ISUP Risk Groups of Prostate Cancers on Radical Prostatectomy.

Purpose

Materials and methods

Results

Conclusions

Publications

IVIM Parameters on MRI Could Predict ISUP Risk Groups of Prostate Cancers on Radical Prostatectomy.

Similar Datasets

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