Modified Predictive Model and Nomogram by Incorporating Prebiopsy Biparametric Magnetic Resonance Imaging With Clinical Indicators for Prostate Biopsy Decision Making

PurposeThe purpose of this study is to explore the value of combining bpMRI and clinical indicators in the diagnosis of clinically significant prostate cancer (csPCa), and developing a prediction model and Nomogram to guide clinical decision-making.MethodsWe retrospectively analyzed 530 patients who underwent prostate biopsy due to elevated serum prostate specific antigen (PSA) levels and/or suspicious digital rectal examination (DRE). Enrolled patients were randomly assigned to the training group (n = 371, 70%) and validation group (n = 159, 30%). All patients underwent prostate bpMRI examination, and T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences were collected before biopsy and were scored, which were respectively named T2WI score and DWI score according to Prostate Imaging Reporting and Data System version 2 (PI-RADS v.2) scoring protocol, and then PI-RADS scoring was performed. We defined a new bpMRI-based parameter named Total score (Total score = T2WI score + DWI score). PI-RADS score and Total score were separately included in the multivariate analysis of the training group to determine independent predictors for csPCa and establish prediction models. Then, prediction models and clinical indicators were compared by analyzing the area under the curve (AUC) and decision curves. A Nomogram for predicting csPCa was established using data from the training group.ResultsIn the training group, 160 (43.1%) patients had prostate cancer (PCa), including 128 (34.5%) with csPCa. Multivariate regression analysis showed that the PI-RADS score, Total score, f/tPSA, and PSA density (PSAD) were independent predictors of csPCa. The prediction model that was defined by Total score, f/tPSA, and PSAD had the highest discriminatory power of csPCa (AUC = 0.931), and the diagnostic sensitivity and specificity were 85.1% and 87.5%, respectively. Decision curve analysis (DCA) showed that the prediction model achieved an optimal overall net benefit in both the training group and the validation group. In addition, the Nomogram predicted csPCa revealed good estimation when compared with clinical indicators.ConclusionThe prediction model and Nomogram based on bpMRI and clinical indicators exhibit a satisfactory predictive value and improved risk stratification for csPCa, which could be used for clinical biopsy decision-making.

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Development and internal validation of a prediction model of prostate cancer on initial transperineal template-guided prostate biopsy

BMC Urology ◽

10.1186/s12894-021-00840-5 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Yuliang Chen ◽

Zhien Zhou ◽

Yi Zhou ◽

Xingcheng Wu ◽

Yu Xiao ◽

...

Keyword(s):

Prostate Cancer ◽

Decision Making ◽

Prediction Model ◽

Prostate Biopsy ◽

Clinical Decision Making ◽

Clinical Decision ◽

Good Discrimination ◽

Internal Validation ◽

Discrimination Ability ◽

Guided Biopsy

Abstract Background Due to the invasiveness of prostate biopsy, a prediction model of the individual risk of a positive biopsy result could be helpful to guide clinical decision-making. Most existing models are based on transrectal ultrasonography (TRUS)-guided biopsy. On the other hand, transperineal template-guided prostate biopsy (TTPB) has been reported to be more accurate in evaluating prostate cancer. The objective of this study is to develop a prediction model of the detection of high-grade prostate cancer (HGPC) on initial TTPB. Result A total of 1352 out of 3794 (35.6%) patients were diagnosed with prostate cancer, 848 of whom had tumour with Grade Group 2–5. Age, PSA, PV, DRE and f/t PSA are independent predictors of HGPC with p < 0.001. The model showed good discrimination ability (c-index 0.886) and calibration during internal validation and good clinical performance was observed through decision curve analysis. The external validation of CPCC-RC, an existing model, demonstrated that models based on TRUS-guided biopsy may underestimate the risk of HGPC in patients who underwent TTPB. Conclusion We established a prediction model which showed good discrimination ability and calibration in predicting the detection of HGPC by initial TTPB. This model can be used to aid clinical decision making for Chinese patients and other Asian populations with similar genomic backgrounds, after external validations are conducted to further confirm its clinical applicability.

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The Next Generation of Clinical Decision Making Tools: Development of a Real-Time Prediction Tool for Outcome of Prostate Biopsy in Response to a Continuously Evolving Prostate Cancer Landscape

The Journal of Urology ◽

10.1016/j.juro.2015.01.092 ◽

2015 ◽

Vol 194 (1) ◽

pp. 58-64 ◽

Cited By ~ 7

Author(s):

Andreas N. Strobl ◽

Ian M. Thompson ◽

Andrew J. Vickers ◽

Donna P. Ankerst

Keyword(s):

Prostate Cancer ◽

Decision Making ◽

Real Time ◽

Prostate Biopsy ◽

Clinical Decision Making ◽

Clinical Decision ◽

Prediction Tool ◽

Next Generation ◽

Time Prediction

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P05.82 Clinical decision making. The route from an ontology to a prediction model for stereotactic radiotherapy: PRE.M.I.S.E. (PREdiction Models in Stereotactic External Radiotherapy)

Neuro-Oncology ◽

10.1093/neuonc/noy139.408 ◽

2018 ◽

Vol 20 (suppl_3) ◽

pp. iii322-iii322

Author(s):

F Bianciardi ◽

S Chiesa ◽

B Tolu ◽

S Longo ◽

G Minniti ◽

...

Keyword(s):

Decision Making ◽

Prediction Model ◽

Stereotactic Radiotherapy ◽

Clinical Decision Making ◽

Prediction Models ◽

Clinical Decision ◽

External Radiotherapy

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Quantitative and Qualitative Analysis of Blood-based Liquid Biopsies to Inform Clinical Decision-making in Prostate Cancer

European Urology ◽

10.1016/j.eururo.2020.12.037 ◽

2021 ◽

Author(s):

Irene Casanova-Salas ◽

Alejandro Athie ◽

Paul C. Boutros ◽

Marzia Del Re ◽

David T. Miyamoto ◽

...

Keyword(s):

Prostate Cancer ◽

Decision Making ◽

Qualitative Analysis ◽

Clinical Decision Making ◽

Clinical Decision ◽

Liquid Biopsies

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P10.03 Establishment and validation of clinical prediction model in WHO grade II glioma

Neuro-Oncology ◽

10.1093/neuonc/noab180.096 ◽

2021 ◽

Vol 23 (Supplement_2) ◽

pp. ii28-ii28

Author(s):

X Xue ◽

Q Gao

Keyword(s):

Prediction Model ◽

Training Group ◽

Clinical Prediction ◽

Validation Group ◽

Who Grade ◽

Clinical Prediction Model ◽

Cox Regression Analysis ◽

Who Grade Ii Glioma ◽

Grade Ii ◽

Grade Ii Glioma

Abstract OBJECTIVE WHO grade II glioma has the characteristics of heterogeneity, and this disease progresses rapidly in some patients, in whom the malignant degree is equivalent to that of high-grade glioma. In order to accurately predict the prognosis of patients, an effective clinical prediction model based on relevant risk factors is needed which could provide a theoretical basis for optimization of clinical individualized treatment. METHODS According to the inclusion and exclusion criteria, eligible patients from January 2010 to December 2018 in our hospital were selected, and those who met the criteria were randomly assigned 4:1 to the training group and the validation group, respectively. The predictors were screened by univariate and multivariate Cox regression analysis, the prediction model was established, and the model was verified and evaluated. RESULTS A total of 258 patients with WHO grade II glioma were recruited, including 208 patients as the training group and 50 patients as the validation group. Six independent risk factors, including patient age, preoperative Karnofsky performance status (KPS) score, preoperative seizure symptoms, surgical resection range, tumor size and IDH status, were selected and included into the prediction model by univariate and multivariate Cox regression analysis, and were visualized in the form of Nomogram. The concordance index (C index) was used to evaluate the predictive ability of the model. Results showed that the C-index was 0.832 in the training group and 0.853 in the validation group, respectively, indicating good performance for the prediction model. The calibration charts were drawn in both groups respectively, which showed that the calibration lines were in good agreement with the standard lines, indicating good consistency between the two groups. CONCLUSIONS In this study, a clinical prediction model for WHO grade II glioma was established, and it was verified that the model has good predictive ability, which may be beneficial for clinical work.

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Prognostic Genomic Tissue-Based Biomarkers in the Treatment of Localized Prostate Cancer

Journal of Personalized Medicine ◽

10.3390/jpm12010065 ◽

2022 ◽

Vol 12 (1) ◽

pp. 65

Author(s):

Gianluca Ingrosso ◽

Emanuele Alì ◽

Simona Marani ◽

Simonetta Saldi ◽

Rita Bellavita ◽

...

Keyword(s):

Prostate Cancer ◽

Decision Making ◽

Clinical Decision Making ◽

Positive Impact ◽

Wide Spectrum ◽

Locally Advanced ◽

Treatment Strategies ◽

Quality Criteria ◽

Localized Prostate Cancer ◽

Decision Making Process

In localized prostate cancer clinicopathologic variables have been used to develop prognostic nomograms quantifying the probability of locally advanced disease, of pelvic lymph node and distant metastasis at diagnosis or the probability of recurrence after radical treatment of the primary tumor. These tools although essential in daily clinical practice for the management of such a heterogeneous disease, which can be cured with a wide spectrum of treatment strategies (i.e., active surveillance, RP and radiation therapy), do not allow the precise distinction of an indolent instead of an aggressive disease. In recent years, several prognostic biomarkers have been tested, combined with the currently available clinicopathologic prognostic tools, in order to improve the decision-making process. In the following article, we reviewed the literature of the last 10 years and gave an overview report on commercially available tissue-based biomarkers and more specifically on mRNA-based gene expression classifiers. To date, these genomic tests have been widely investigated, demonstrating rigorous quality criteria including reproducibility, linearity, analytical accuracy, precision, and a positive impact in the clinical decision-making process. Albeit data published in literature, the systematic use of these tests in prostate cancer is currently not recommended due to insufficient evidence.

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Risk Analysis for Pulmonary Metastasis of Chondrosarcoma and Establishment and Validation of Novel Clinical Prediction Models: a Clinical Study Based on the SEER Database

10.21203/rs.3.rs-120575/v1 ◽

2020 ◽

Author(s):

Wenle LI ◽

WANG Hao-sheng ◽

NING Li-Jun ◽

GAO Sen ◽

ZHANG Wen-shi ◽

...

Keyword(s):

Lung Metastasis ◽

Prediction Models ◽

Lung Metastases ◽

Training Group ◽

Clinical Impact ◽

Individual Risk ◽

Seer Database ◽

Validation Group ◽

Decision Curve Analysis ◽

The Individual

Abstract Objective: Lung metastasis of chondrosarcoma is associated with poor prognosis. The purpose of this study was to develop and validate the nomogram to predict the risk of lung metastasis in patients with chondrosarcoma, thus contributing to clinical diagnosis and treatment.Methods: Data on chondrosarcoma patients from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2016 were then screened by univariate and multivariate Logistic regression to construct a Nomogram predicting lung metastasis risk. Nomogram model discrimination was assessed by calibration charts, while prediction accuracy and clinical values were measured by decision curve analysis (DCA) and clinical impact charts. In addition, the predicted Nomogram were validated in the internal test set. Results: A total of 944 patients were enrolled and randomly divided into the training group (n=664) and the validation group (n=280) in a ratio of 7 to 3.After logistics regression analysis, significant variables were gender, age, marital status, tumor volume and lymphatic metastasis. Calibration curves show consistency between Nomogram predictions and actual observations, while DCA and clinical impact diagrams show the clinical utility of Nomogram. In addition, ROC also showed good discrimination and calibration in the training group (AUC = 0.789, 95%CI 0.789 -- 0.808) and the validation group (AUC = 0.796, 95%CI 0.744 -- 0.841).Conclusions: Nomogram for lung metastases in chondrosarcoma can effectively predict the individual risk of lung metastases and provide clinicians with enlightening information to optimize treatment.

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Beyond the Development of Health-Related Quality-of-Life (HRQOL) Measures: A Checklist for Evaluating HRQOL Outcomes in Cancer Clinical Trials—Does HRQOL Evaluation in Prostate Cancer Research Inform Clinical Decision Making?

Journal of Clinical Oncology ◽

10.1200/jco.2003.12.121 ◽

2003 ◽

Vol 21 (18) ◽

pp. 3502-3511 ◽

Cited By ~ 182

Author(s):

Fabio Efficace ◽

Andrew Bottomley ◽

David Osoba ◽

Carolyn Gotay ◽

Henning Flechtner ◽

...

Keyword(s):

Prostate Cancer ◽

Decision Making ◽

Clinical Trials ◽

Clinical Decision Making ◽

Clinical Decision ◽

Cancer Clinical Trials ◽

Health Related Quality ◽

Related Quality ◽

Health Related

Purpose: The aim of this study was to evaluate whether the inclusion of health-related quality of life (HRQOL), as a part of the trial design in a randomized controlled trial (RCT) setting, has supported clinical decision making for the planning of future medical treatments in prostate cancer. Materials and Methods: A minimum standard checklist for evaluating HRQOL outcomes in cancer clinical trials was devised to assess the quality of the HRQOL reporting and to classify the studies on the grounds of their robustness. It comprises 11 key HRQOL issues grouped into four broader sections: conceptual, measurement, methodology, and interpretation. Relevant studies were identified in a number of databases, including MEDLINE and the Cochrane Controlled Trials Register. Both their HRQOL and traditional clinical reported outcomes were systematically analyzed to evaluate their consistency and their relevance for supporting clinical decision making. Results: Although 54% of the identified studies did not show any differences in traditional clinical end points between treatment arms and 17% showed a difference in overall survival, 74% of the studies showed some difference in terms of HRQOL outcomes. One third of the RCTs provided a comprehensive picture of the whole treatment including HRQOL outcomes to support their conclusions. Conclusion: A minimum set of criteria for assessing the reported outcomes in cancer clinical trials is necessary to make informed decisions in clinical practice. Using a checklist developed for this study, it was found that HRQOL is a valuable source of information in RCTs of treatment in metastatic prostate cancer.

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