Dataset Information

Development and comparison of a Chinese nomogram adding multi-parametric MRI information for predicting extracapsular extension of prostate cancer.

ABSTRACT:

Purpose

To improve the performation of a nomogram for predicting side-specific extracapsular extension (SS-ECE).

Results

One hundred and ninety-six patients (55.5%) had ECE on final pathology. Bilateral and unilateral ECE rate was 13.9% (49/353) and 41.6% (147/353), respectively. The mean age was 65.9 years and the mean serum prostate specific antigen (PSA) was 15.0 ng/ml. Based on multivariate logistic regression analysis, clinical stage (cStage), PSA, Gleason sum, percentage of positive cores, and ECE risk score were significant predictors of ECE. The current nomogram had higher predictive accuracy (0.851) and superior calibration. According to the decision curve analysis (DCA) results, the updated nomogram demonstrated a high net benefit across a wide range of threshold probabilities.

Materials and methods

We studied 353 patients with cStage T1c-T3 prostate cancer underwent radical prostatectomy. The candidate predictors associated with ECE were cStage, PSA, Gleason sum, percentage of positive cores, maximum cancer percentage and ECE risk score from multi-parametric magnetic resonance imaging (MP-MRI). The receiver operating characteristic (ROC) analysis was performed and an updated nomogram was constructed. The DCA was performed to test the predictive ability of the nomogram. In addition, the validation and calibration of the Memorial Sloan-Kettering cancer center (MSKCC) nomograms were performed in the current subjects.

Conclusions

Predictors, including cStage, PSA, Gleason sum, percentage of positive cores, maximum cancer percentage, and ECE risk score, were combined to construct a SS-ECE prediction nomogram. And the current nomogram might help urologists in decision-making process of preserving or resecting neurovascular bundles preoperatively.

SUBMITTER: Chen Y

PROVIDER: S-EPMC5400649 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Publications

Development and comparison of a Chinese nomogram adding multi-parametric MRI information for predicting extracapsular extension of prostate cancer.

Chen Yuke Y Yu Wei W Fan Yu Y Zhou Liqun L Yang Yang Y Wang Huihui H Jiang Yuan Y Wang Xiaoying X Wu Shiliang S Jin Jie J

Oncotarget 20170301 13

<h4>Purpose</h4>To improve the performation of a nomogram for predicting side-specific extracapsular extension (SS-ECE).<h4>Results</h4>One hundred and ninety-six patients (55.5%) had ECE on final pathology. Bilateral and unilateral ECE rate was 13.9% (49/353) and 41.6% (147/353), respectively. The mean age was 65.9 years and the mean serum prostate specific antigen (PSA) was 15.0 ng/ml. Based on multivariate logistic regression analysis, clinical stage (cStage), PSA, Gleason sum, percentage of ...[more]

PMID: 27564265

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Project description:BackgroundPrediction of extracapsular extension (ECE) is essential to achieve a balance between oncologic resection and neural tissue preservation. Microultrasound (MUS) is an attractive alternative to multiparametric magnetic resonance imaging (mpMRI) in the staging scenario.ObjectiveTo create a side-specific nomogram integrating clinicopathologic parameters and MUS findings to predict ipsilateral ECE and guide nerve sparing.Design setting and participantsProspective data were collected from consecutive patients who underwent robotic-assisted radical prostatectomy from June 2021 to May 2022 and had preoperative MUS and mpMRI. A total of 391 patients and 612 lobes were included in the analysis.Outcome measurements and statistical analysisECE on surgical pathology was the primary outcome. Multivariate regression analyses were carried out to identify predictors for ECE. The resultant multivariable model's performance was visualized using the receiver-operating characteristic curve. A nomogram was developed based on the coefficients of the logit function for the MUS-based model. A decision curve analysis (DCA) was performed to assess clinical utility.Results and limitationsThe areas under the receiver-operating characteristic curve (AUCs) of the MUS-based model were 81.4% and 80.9% (95% confidence interval [CI] 75.6, 84.6) after internal validation. The AUC of the mpMRI-model was also 80.9% (95% CI 77.2, 85.7). The DCA demonstrated the net clinical benefit of the MUS-based nomogram and its superiority compared with MUS and MRI alone for detecting ECE. Limitations of our study included its sample size and moderate inter-reader agreement.ConclusionsWe developed a side-specific nomogram to predict ECE based on clinicopathologic variables and MUS findings. Its performance was comparable with that of a mpMRI-based model. External validation and prospective trials are required to corroborate our results.Patient summaryThe integration of clinical parameters and microultrasound can predict extracapsular extension with similar results to models based on magnetic resonance imaging findings. This can be useful for tailoring the preservation of nerves during surgery.

Project description:The objective of this review is to survey radiomics signatures for detecting pathological extracapsular extension (pECE) on magnetic resonance imaging (MRI) in patients with prostate cancer (PCa) who underwent prostatectomy. Scientific Literature databases were used to search studies published from January 2007 to October 2023. All studies related to PCa MRI staging and using radiomics signatures to detect pECE after prostatectomy were included. Systematic review was performed according to Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA). The risk of bias and certainty of the evidence was assessed using QUADAS-2 and the radiomics quality score. From 1247 article titles screened, 16 reports were assessed for eligibility, and 11 studies were included in this systematic review. All used a retrospective study design and most of them used 3 T MRI. Only two studies were performed in more than one institution. The highest AUC of a model using only radiomics features was 0.85, for the test validation. The AUC for best model performance (radiomics associated with clinical/semantic features) varied from 0.72-0.92 and 0.69-0.89 for the training and validation group, respectively. Combined models performed better than radiomics signatures alone for detecting ECE. Most of the studies showed a low to medium risk of bias. After thorough analysis, we found no strong evidence supporting the clinical use of radiomics signatures for identifying extracapsular extension (ECE) in pre-surgery PCa patients. Future studies should adopt prospective multicentre approaches using large public datasets and combined models for detecting ECE.Critical relevant statementThe use of radiomics algorithms, with clinical and AI integration, in predicting extracapsular extension, could lead to the development of more accurate predictive models, which could help improve surgical planning and lead to better outcomes for prostate cancer patients.Protocol of systematic review registrationPROSPERO CRD42021272088. Published: https://doi.org/10.1136/bmjopen-2021-052342 .Key pointsRadiomics can extract diagnostic features from MRI to enhance prostate cancer diagnosis performance. The combined models performed better than radiomics signatures alone for detecting extracapsular extension. Radiomics are not yet reliable for extracapsular detection in PCa patients.

Project description:INTRODUCTION:Extracapsular extension (ECE) in regional lymph nodes and positive surgical margins (PSM) are considered high-risk adverse pathologic features in patients with oropharyngeal squamous cell carcinoma (OPSCC) that each constitute an indication for postoperative adjuvant chemoradiation. We identify pre-operative clinical factors that can predict post-operative ECE and/or PSM and create a nomogram to help clinical decision making. METHODS:Adult patients with non-metastatic OPSCC with initial surgical treatment and confirmed HPV status diagnosed between 2010 and 2014 were selected from the National Cancer Database. Clinical staging was modified to American Joint Committee on Cancer 8th edition parameters. Logistic regression was used for multivariate analysis to identify predictors of pathologic ECE and/or PSM. RESULTS:5065 patients were included. 47.5% of the 3336 HPV-positive (HPV+) patients had ECE/PSM. 40.4% of the 1729 HPV-negative (HPV-) patients with had ECE/PSM. A model was built that included age, clinical ECE, tumor grade, and clinical T and N staging for HPV+ patients. Increasing N-classification was highly predictive of pathologic ECE and/or PSM (N1 OR?=?3.6, N2 OR?=?7.0, N3 OR?=?11.2, p?<?0.01). Clinical ECE (OR?=?4.1, p?<?0.01), tumor grade (ORs 2.2-4.4 with p?<?0.05), and increasing clinical T-classification (ORs 1.2-1.8, p?<?0.05) were also associated with ECE and/or PSM. A similar model was built for HPV- with similar predictive capability. Two internally validated nomograms were designed that demonstrated good discrimination (HPV+ AUC?=?0.66, 95% CI: 0.64-0.68, and HPV- AUC?=?0.70, 95% CI: 0.67-0.72) and good calibration (goodness-of-fit statistic of HPV+ 6.32, p?=?0.61 and HPV- 11.66, p?=?0.17). CONCLUSIONS:These are the first nomograms designed to help predict ECE or PSM for both HPV+ and HPV- OPSCC. The nomograms can facilitate shared decision-making between clinicians and patients as they consider upfront treatment selection for OPSCC.

Project description:Objectives: To develop and validate a predictive model for discriminating clinically significant prostate cancer (csPCa) from clinically insignificant prostate cancer (ciPCa). Methods: This retrospective study was performed with 159 consecutively enrolled pathologically confirmed PCa patients from two medical centers. The dataset was allocated to a training group (n = 54) and an internal validation group (n = 22) from one center along with an external independent validation group (n = 83) from another center. A total of 1,188 radiomic features were extracted from T2WI, diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) images derived from DWI for each patient. Multivariable logistic regression analysis was performed to develop the model, incorporating the radiomic signature, ADC value, and independent clinical risk factors. This was presented using a radiomic nomogram. The receiver operating characteristic (ROC) curve was utilized to assess the predictive efficacy of the radiomic nomogram in both the training and validation groups. The decision curve analysis was used to evaluate which model achieved the most net benefit. Results: The radiomic signature, which was made up of 10 selected features, was significantly associated with csPCa (P < 0.001 for both training and internal validation groups). The area under the curve (AUC) values of discriminating csPCa for the radiomics signature were 0.95 (training group), 0.86 (internal validation group), and 0.81 (external validation group). Multivariate logistic analysis identified the radiomic signature and ADC value as independent parameters of predicting csPCa. Then, the combination nomogram incorporating the radiomic signature and ADC value demonstrated a favorable classification capability with the AUC of 0.95 (training group), 0.93 (internal validation group), and 0.84 (external validation group). Appreciable clinical utility of this model was illustrated using the decision curve analysis for the nomogram. Conclusions: The nomogram, incorporating radiomic signature and ADC value, provided an individualized, potential approach for discriminating csPCa from ciPCa.

Dataset Information

Development and comparison of a Chinese nomogram adding multi-parametric MRI information for predicting extracapsular extension of prostate cancer.

Purpose

Results

Materials and methods

Conclusions

Publications

Development and comparison of a Chinese nomogram adding multi-parametric MRI information for predicting extracapsular extension of prostate cancer.

Similar Datasets

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