scholarly journals Prognostic Index for Predicting Prostate Cancer Survival in a Randomized Screening Trial: Development and Validation

Cancers ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 435
Author(s):  
Subas Neupane ◽  
Jaakko Nevalainen ◽  
Jani Raitanen ◽  
Kirsi Talala ◽  
Paula Kujala ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Score ◽  
Cancer Survival ◽  
Area Under The Curve ◽  
Prognostic Index ◽  
Risk Groups ◽  
Risk Scores ◽  
Development And Validation ◽  
Screening Trial

We developed and validated a prognostic index to predict survival from prostate cancer (PCa) based on the Finnish randomized screening trial (FinRSPC). Men diagnosed with localized PCa (N = 7042) were included. European Association of Urology risk groups were defined. The follow-up was divided into three periods (0–3, 3–9 and 9–20 years) for development and two corresponding validation periods (3–6 and 9–15 years). A multivariable complementary log–log regression model was used to calculate the full prognostic index. Predicted cause-specific survival at 10 years from diagnosis was calculated for the control arm using a simplified risk score at diagnosis. The full prognostic index discriminates well men with PCa with different survival. The area under the curve (AUC) was 0.83 for both the 3–6 year and 9–15 year validation periods. In the simplified risk score, patients with a low risk score at diagnosis had the most favorable survival, while the outcome was poorest for the patients with high risk scores. The prognostic index was able to distinguish well between men with higher and lower survival, and the simplified risk score can be used as a basis for decision making.

scholarly journals Predicting prostate cancer specific-mortality with artificial intelligence-based Gleason grading

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Ellery Wulczyn ◽  
Kunal Nagpal ◽  
Matthew Symonds ◽  
Melissa Moran ◽  
Markus Plass ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Artificial Intelligence ◽  
Risk Groups ◽  
Risk Scores ◽  
Pathology Report ◽  
Gleason Grading ◽  
Specific Mortality ◽  
Cancer Specific Mortality ◽  
Open Question

Abstract Background Gleason grading of prostate cancer is an important prognostic factor, but suffers from poor reproducibility, particularly among non-subspecialist pathologists. Although artificial intelligence (A.I.) tools have demonstrated Gleason grading on-par with expert pathologists, it remains an open question whether and to what extent A.I. grading translates to better prognostication. Methods In this study, we developed a system to predict prostate cancer-specific mortality via A.I.-based Gleason grading and subsequently evaluated its ability to risk-stratify patients on an independent retrospective cohort of 2807 prostatectomy cases from a single European center with 5–25 years of follow-up (median: 13, interquartile range 9–17). Results Here, we show that the A.I.’s risk scores produced a C-index of 0.84 (95% CI 0.80–0.87) for prostate cancer-specific mortality. Upon discretizing these risk scores into risk groups analogous to pathologist Grade Groups (GG), the A.I. has a C-index of 0.82 (95% CI 0.78–0.85). On the subset of cases with a GG provided in the original pathology report (n = 1517), the A.I.’s C-indices are 0.87 and 0.85 for continuous and discrete grading, respectively, compared to 0.79 (95% CI 0.71–0.86) for GG obtained from the reports. These represent improvements of 0.08 (95% CI 0.01–0.15) and 0.07 (95% CI 0.00–0.14), respectively. Conclusions Our results suggest that A.I.-based Gleason grading can lead to effective risk stratification, and warrants further evaluation for improving disease management.

Elevated HOXC6/DLX1 mRNA biomarker levels in urine to help select patients at increased risk for high-grade prostate cancer detection upon prostate biopsy.

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 31-31
Author(s):  
Rianne Hendriks ◽  
Siebren Dijkstra ◽  
Erik Bastiaan Cornel ◽  
Sander Jannink ◽  
Hans de Jong ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Score ◽  
Prostate Biopsy ◽  
Area Under The Curve ◽  
Risk Scores ◽  
Grey Zone ◽  
High Grade ◽  
Multicenter Studies ◽  
Biopsy Procedure ◽  
Increased Risk

31 Background: The major challenge in prostate cancer (PCa) diagnostics is to improve the early detection of clinically significant or high-grade PCa, especially in the sPSA "grey-zone" (4-10 ng/ml). Ideally, PCa-specific biomarkers would be obtained non-invasively, for example derived from urine. The aim of this study was to evaluate the expression levels and clinical utility of the recently identified urinary HOXC6-DLX1 mRNA biomarker combination in men at risk of having high-grade PCa. Methods: From two prospective, multicenter studies, a total of 863 post-DRE urine samples were collected from men with elevated sPSA levels before undergoing a prostate biopsy procedure. The HOXC6-DLX1 mRNA biomarkers were measured in urine using RT-qPCR and results were quantified using the Delta DeltaCt method (ΔΔCT), normalized and expressed in a score from 1 to 1421. Results: The HOXC6-DLX1 risk score was significantly higher in urine from patients with high-grade PCa upon prostate biopsy compared to no PCa and PCa Gleason score ≤6. In the sPSA "grey-zone", the HOXC6-DLX1 combination had the highest area-under-the-curve (AUC) of 0.67 (95% confidence interval (CI): 0.58-0.75) for prediction of high-grade PCa upon prostate biopsy in cohort A and 0.68 (95% CI: 0.59-0.76) in cohort B; as compared to sPSA with an AUC of 0.60 (95% CI: 0.51-0.70) and 0.62 (95% CI: 0.52-0.73) respectively. Overall, elevated HOXC6-DLX1 risk scores correlated with an increased risk of high-grade PCa detected on biopsy; 47% of men with a score >108 had significant cancer as compared to 6% with a risk score <17. Using a HOXC6-DLX1 risk score cut-off of 27.5 in the sPSA "grey-zone", 165 biopsies (31%) could have been avoided, and only 4% of patients with high-grade PCa would have been missed. Conclusions: The urine-based HOXC6-DLX1 assay provides a non-invasive solution to improve the selection of patients at increased risk for high-grade PCa who would benefit most from a prostate biopsy procedure, while reducing the number of unnecessary biopsies, particularly in the sPSA "grey-zone".

scholarly journals Association Between the Multidimensional Prognostic Index and Mortality During 15 Years of Follow-up in the InCHIANTI Study

2020 ◽  
Author(s):  
Alberto Pilotto ◽  
Nicola Veronese ◽  
Giacomo Siri ◽  
Stefania Bandinelli ◽  
Toshiko Tanaka ◽  
...  
Keyword(s):  
High Risk ◽  
Age Groups ◽  
Area Under The Curve ◽  
Prognostic Index ◽  
Risk Groups ◽  
Community Dwelling ◽  
Low Risk ◽  
Using Data ◽  
Dose Response Effect

Abstract Background Multidimensional Prognostic Index (MPI) is recognized as a prognostic tool in hospitalized patients, but data on the value of MPI in community-dwelling older persons are limited. Using data from a representative cohort of community-dwelling persons, we tested the hypothesis that MPI explains mortality during 15 years of follow-up. Methods A standardized comprehensive geriatric assessment was used to calculate the MPI and to categorize participants in low-, moderate-, and high-risk classes. The results were reported as hazard ratios (HRs) and the accuracy was evaluated with the area under the curve (AUC), with 95% confidence intervals (CIs) and the C-index. We also reported the median survival time by standard age groups. Results All 1453 participants (mean age 68.9 years, women = 55.8%) enrolled in the InCHIANTI study at baseline were included. Compared to low-risk group, participants in moderate (HR = 2.10; 95% CI: 1.73–2.55) and high-risk MPI group (HR = 4.94; 95% CI: 3.91–6.24) had significantly higher mortality risk. The C-index of the model containing age, sex, and MPI was 82.1, indicating a very good accuracy of this model in explaining mortality. Additionally, the time-dependent AUC indicated that the accuracy of the model incorporating MPI to age and sex was excellent (&gt;85.0) during the whole follow-up period. Compared to participants in the low-risk MPI group across different age groups, those in moderate- and high-risk groups survived 2.9–7.0 years less and 4.3–8.9 years less, respectively. Conclusions In community-dwelling individuals, higher MPI values are associated with higher risk of all-cause mortality with a dose–response effect.

scholarly journals External assessment of the EUROMACS right-sided heart failure risk score

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hirak Shah ◽  
Thomas Murray ◽  
Jessica Schultz ◽  
Ranjit John ◽  
Cindy M. Martin ◽  
...  
Keyword(s):  
Heart Failure ◽  
Right Ventricular ◽  
Risk Score ◽  
Pressure Ratio ◽  
Area Under The Curve ◽  
Left Ventricular ◽  
Right Atrial ◽  
Risk Scores ◽  
External Assessment ◽  
Failure Risk

AbstractThe EUROMACS Right-Sided Heart Failure Risk Score was developed to predict right ventricular failure (RVF) after left ventricular assist device (LVAD) placement. The predictive ability of the EUROMACS score has not been tested in other cohorts. We performed a single center analysis of a continuous-flow (CF) LVAD cohort (n = 254) where we calculated EUROMACS risk scores and assessed for right ventricular heart failure after LVAD implantation. Thirty-nine percent of patients (100/254) had post-operative RVF, of which 9% (23/254) required prolonged inotropic support and 5% (12/254) required RVAD placement. For patients who developed RVF after LVAD implantation, there was a 45% increase in the hazards of death on LVAD support (HR 1.45, 95% CI 0.98–2.2, p = 0.066). Two variables in the EUROMACS score (Hemoglobin and Right Atrial Pressure to Pulmonary Capillary Wedge Pressure ratio) were not predictive of RVF in our cohort. Overall, the EUROMACS score had poor external discrimination in our cohort with area under the curve of 58% (95% CI 52–66%). Further work is necessary to enhance our ability to predict RVF after LVAD implantation.

scholarly journals The development and validation of prognostic models for overall survival in the presence of missing data in the training dataset: a strategy with a detailed example

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Kara-Louise Royle ◽  
David A. Cairns
Keyword(s):  
Overall Survival ◽  
Missing Data ◽  
Prognostic Model ◽  
Prognostic Index ◽  
Risk Groups ◽  
Prognostic Models ◽  
Training Dataset ◽  
Validation Process ◽  
Test Dataset ◽  
Development And Validation

Abstract Background The United Kingdom Myeloma Research Alliance (UK-MRA) Myeloma Risk Profile is a prognostic model for overall survival. It was trained and tested on clinical trial data, aiming to improve the stratification of transplant ineligible (TNE) patients with newly diagnosed multiple myeloma. Missing data is a common problem which affects the development and validation of prognostic models, where decisions on how to address missingness have implications on the choice of methodology. Methods Model building The training and test datasets were the TNE pathways from two large randomised multicentre, phase III clinical trials. Potential prognostic factors were identified by expert opinion. Missing data in the training dataset was imputed using multiple imputation by chained equations. Univariate analysis fitted Cox proportional hazards models in each imputed dataset with the estimates combined by Rubin’s rules. Multivariable analysis applied penalised Cox regression models, with a fixed penalty term across the imputed datasets. The estimates from each imputed dataset and bootstrap standard errors were combined by Rubin’s rules to define the prognostic model. Model assessment Calibration was assessed by visualising the observed and predicted probabilities across the imputed datasets. Discrimination was assessed by combining the prognostic separation D-statistic from each imputed dataset by Rubin’s rules. Model validation The D-statistic was applied in a bootstrap internal validation process in the training dataset and an external validation process in the test dataset, where acceptable performance was pre-specified. Development of risk groups Risk groups were defined using the tertiles of the combined prognostic index, obtained by combining the prognostic index from each imputed dataset by Rubin’s rules. Results The training dataset included 1852 patients, 1268 (68.47%) with complete case data. Ten imputed datasets were generated. Five hundred twenty patients were included in the test dataset. The D-statistic for the prognostic model was 0.840 (95% CI 0.716–0.964) in the training dataset and 0.654 (95% CI 0.497–0.811) in the test dataset and the corrected D-Statistic was 0.801. Conclusion The decision to impute missing covariate data in the training dataset influenced the methods implemented to train and test the model. To extend current literature and aid future researchers, we have presented a detailed example of one approach. Whilst our example is not without limitations, a benefit is that all of the patient information available in the training dataset was utilised to develop the model. Trial registration Both trials were registered; Myeloma IX-ISRCTN68454111, registered 21 September 2000. Myeloma XI-ISRCTN49407852, registered 24 June 2009.

scholarly journals A novel predictor of clinical progression in patients on active surveillance for prostate cancer

2019 ◽  
Vol 13 (8) ◽  
Author(s):  
Guan Hee Tan ◽  
Antonio Finelli ◽  
Ardalan Ahmad ◽  
Marian Wettstein ◽  
Alexandre Zlotta ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Active Surveillance ◽  
Cox Regression ◽  
Area Under The Curve ◽  
Progression Free Survival ◽  
Roc Curves ◽  
Standard Of Care ◽  
Low Risk ◽  
Clinical Progression

Introduction: Active surveillance (AS) is standard of care in low-risk prostate cancer (PC). This study describes a novel total cancer location (TCLo) density metric and aims to determine its performance in predicting clinical progression (CP) and grade progression (GP).     Methods: This was a retrospective study of patients on AS after confirmatory biopsy (CBx). We excluded patients with Gleason ≥7 at CBx and <2 years follow-up. TCLo was the number of locations with positive cores at diagnosis (DBx) and CBx. TCLo density was TCLo / prostate volume (PV). CP was progression to any active treatment while GP occurred if Gleason ≥7 was identified on repeat biopsy or surgical pathology. Independent predictors of time to CP or GP were estimated with Cox regression. Kaplan-Meier analysis compared progression-free survival curves between TCLo density groups. Test characteristics of TCLo were explored with receiver operating characteristic (ROC) curves.     Results: We included 181 patients who had CBx between 2012-2015, and met inclusion criteria. The mean age of patients was 62.58 years (SD=7.13) and median follow-up was 60.9 months (IQR=23.4). A high TCLo density score (>0.05) was independently associated with time to CP (HR 4.70, 95% CI: 2.62-8.42, p<0.001), and GP (HR 3.85, 95% CI: 1.91-7.73, p<0.001). ROC curves showed TCLo density has greater area under the curve than number of positive cores at CBx in predicting progression.     Conclusion: TCLo density is able to stratify patients on AS for risk of CP and GP. With further validation, it could be added to the decision-making algorithm in AS for low-risk localized PC.

scholarly journals Association of the Lung Immune Prognostic Index with Immunotherapy Outcomes in Mismatch Repair Deficient Tumors

Cancers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 3776
Author(s):  
Edouard Auclin ◽  
Perrine Vuagnat ◽  
Cristina Smolenschi ◽  
Julien Taieb ◽  
Jorge Adeva ◽  
...  
Keyword(s):  
Checkpoint Inhibitors ◽  
Performance Status ◽  
Prognostic Index ◽  
Predictive Marker ◽  
Risk Groups ◽  
Two Factors ◽  
One Year ◽  
Metastatic Sites ◽  
Tumor Types

Background: MSI-H/dMMR is considered the first predictive marker of efficacy for immune checkpoint inhibitors (ICIs). However, around 39% of cases are refractory and additional biomarkers are needed. We explored the prognostic value of pretreatment LIPI in MSI-H/dMMR patients treated with ICIs, including identification of fast-progressors. Methods: A multicenter retrospective study of patients with metastatic MSI-H/dMMR tumors treated with ICIs between April 2014 and May 2019 was performed. LIPI was calculated based on dNLR > 3 and LDH > upper limit of normal. LIPI groups were good (zero factors), intermediate (one factor) and poor (two factors). The primary endpoint was overall survival (OS), including the fast-progressor rate (OS < 3 months). Results: A total of 151 patients were analyzed, mainly female (59%), with median age 64 years, performance status (PS) 0 (42%), and sporadic dMMR status (68%). ICIs were administered as first or second-line for 59%. The most frequent tumor types were gastrointestinal (66%) and gynecologic (22%). LIPI groups were good (47%), intermediate (43%), and poor (10%). The median follow-up was 32 months. One-year OS rates were 81.0%, 67.1%, and 21.4% for good, intermediate, and poor-risk groups (p <0.0001). After adjustment for tumor site, metastatic sites and PS, LIPI remained independently associated with OS (HR, poor-LIPI: 3.50, 95%CI: 1.46–8.40, p = 0.02. Overall, the fast-progressor rate was 16.0%, and 35.7% with poor-LIPI vs. 7.5% in the good-LIPI group (p = 0.02). Conclusions: LIPI identifies dMMR patients who do not benefit from ICI treatment, particularly fast-progressors. LIPI should be included as a stratification factor for future trials.

scholarly journals Development and Validation of an Interpretable Artificial Intelligence Model to Predict 10-Year Prostate Cancer Mortality

Cancers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 3064
Author(s):  
Jean-Emmanuel Bibault ◽  
Steven Hancock ◽  
Mark K. Buyyounouski ◽  
Hilary Bagshaw ◽  
John T. Leppert ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Treatment ◽  
Cancer Mortality ◽  
Treatment Strategies ◽  
Gradient Boosting ◽  
Prostate Cancer Treatment ◽  
Prospective Data ◽  
Prostate Cancer Mortality ◽  
Development And Validation

Prostate cancer treatment strategies are guided by risk-stratification. This stratification can be difficult in some patients with known comorbidities. New models are needed to guide strategies and determine which patients are at risk of prostate cancer mortality. This article presents a gradient-boosting model to predict the risk of prostate cancer mortality within 10 years after a cancer diagnosis, and to provide an interpretable prediction. This work uses prospective data from the PLCO Cancer Screening and selected patients who were diagnosed with prostate cancer. During follow-up, 8776 patients were diagnosed with prostate cancer. The dataset was randomly split into a training (n = 7021) and testing (n = 1755) dataset. Accuracy was 0.98 (±0.01), and the area under the receiver operating characteristic was 0.80 (±0.04). This model can be used to support informed decision-making in prostate cancer treatment. AI interpretability provides a novel understanding of the predictions to the users.

scholarly journals Development and validation of a novel risk score for the detection of insignificant prostate cancer in unscreened patient cohorts

2018 ◽  
Vol 119 (12) ◽  
pp. 1445-1450
Author(s):  
Lorenzo Dutto ◽  
Amar Ahmad ◽  
Katerina Urbanova ◽  
Christian Wagner ◽  
Andreas Schuette ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Score ◽  
Development And Validation ◽  
Insignificant Prostate Cancer

scholarly journals Limited Utility of Cardiovascular Risk Scores for People Living with HIV in Malawi

2020 ◽  
Author(s):  
Chia Goh ◽  
Henry Mwandumba ◽  
Alicja Rapala ◽  
Willard Tingao ◽  
Irene Sheha ◽  
...  
Keyword(s):  
Risk Score ◽  
Low Income ◽  
Intima Media Thickness ◽  
Risk Groups ◽  
Carotid Intima Media Thickness ◽  
Endothelial Damage ◽  
Risk Scores ◽  
People Living With Hiv ◽  
Cvd Risk ◽  
Living With Hiv

HIV is associated with increased cardiovascular disease (CVD) risk. Despite the high prevalence of HIV in low income subSaharan Africa, there are few data on the assessment of CVD risk in the region. In this study, we aimed to compare the utility of existing CVD risk scores in a cohort of Malawian adults, and assess to what extent they correlate with established markers of endothelial damage: carotid intima media thickness (IMT) and pulse wave velocity (PWV). WHO/ISH, SCORE, FRS, ASCVD, QRISK2 and D:A:D scores were calculated for 279 Malawian adults presenting with HIV and low CD4. Correlation of the calculated 10year CVD risk score with IMT and PWV was assessed using Spearmans rho. The median (IQR) age of patients was 37 (31 to 43) years and 122 (44%) were female. Median (IQR) blood pressure was 120/73mmHg (108/68 to 128/80) and 88 (32%) study participants had a new diagnosis of hypertension. The FRS and QRISK2 scores included the largest number of participants in this cohort (96% and 100% respectively). D:A:D, a risk score specific for people living with HIV, identified more patients in moderate and high risk groups. Although all scores correlated well with physiological markers of endothelial damage, FRS and QRISK2 correlated most closely with both IMT [r2 0.51, p<0.0001 and r2 0.47, p<0.0001 respectively] and PWV [r2 0.47, p<0.0001 and r2 0.5, p<0.0001 respectively]. Larger cohort studies are required to adapt and validate risk prediction scores in this region, so that limited healthcare resources can be effectively targeted.

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