scholarly journals Pre-treatment risk stratification of prostate cancer patients:A critical review

2012 ◽  
Vol 6 (2) ◽  
Author(s):  
George Rodrigues ◽  
Padraig Warde ◽  
Tom Pickles ◽  
Juanita Crook ◽  
Michael Brundage ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prognostic Factors ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Gleason Score ◽  
Critical Review ◽  
Specific Antigen ◽  
Prostate Cancer Risk ◽  
Original Research ◽  
Pre Treatment

Introduction:  The use of accepted prostate cancer risk stratification groups based on prostate-specific antigen, T stage and Gleason score assists in therapeutic treatment decision-making, clinical trial design and outcome reporting. The utility of integrating novel prognostic factors into an updated risk stratification schema is an area of current debate. The purpose of this work is to critically review the available literature on novel pre-treatment prognostic factors and alternative prostate cancer risk stratification schema to assess the feasibility and need for changes to existing risk stratification systems. Methods:  A systematic literature search was conducted to identify original research publications and review articles on prognostic factors and risk stratification in prostate cancer. Search terms included risk stratification, risk assessment, prostate cancer or neoplasms, and prognostic factors. Abstracted information was assessed to draw conclusions regarding the potential utility of changes to existing risk stratification schema. Results:  The critical review identified three specific clinically relevant potential changes to the most commonly used three-group risk stratification system: (1) the creation of a very-low risk category; (2) the splitting of intermediate-risk into a low- and highintermediate risk groups; and (3) the clarification of the interface between intermediate- and high-risk disease. Novel pathological factors regarding high-grade cancer, subtypes of Gleason score 7 and percentage biopsy cores positive were also identified as potentially important risk-stratification factors. Conclusions:  Multiple studies of prognostic factors have been performed to create currently utilized prostate cancer risk stratification systems. We propose potential changes to existing systems.

scholarly journals Identification of a novel non-invasive biological marker to overcome the shortcomings of PSA in diagnosis and risk stratification for prostate cancer: Initial prospective study of developmental endothelial locus-1 protein

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250254
Author(s):  
Jae-Wook Chung ◽  
Hyun Tae Kim ◽  
Yun-Sok Ha ◽  
Eun Hye Lee ◽  
So Young Chun ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prognostic Factors ◽  
Risk Stratification ◽  
Prospective Study ◽  
Gleason Score ◽  
Specific Antigen ◽  
Transrectal Ultrasound ◽  
Biological Marker ◽  
Clinicopathological Characteristics ◽  
Non Invasive

Objective This prospective study sought to clarify the developmental endothelial locus-1 (Del-1) protein as values of diagnosis and risk stratification of prostate cancer (PCa). Design From February 2017 to December 2019, a total 458 patients who underwent transrectal ultrasound guided prostate biopsy or surgery of benign prostatic hyperplasia agreed to research of Del-1 protein. We prospectively compared and analyzed the Del-1 protein and prostate specific antigen (PSA) in relation to the patients’ demographic and clinicopathological characteristics. Results Mean age was 68.86±8.55 years. Mean PSA and Del-1 protein was 21.72±89.37, 0.099±0.145, respectively. Two hundred seventy-six (60.3%) patients were diagnosed as PCa. Among them, 181 patients underwent radical prostatectomy (RP). There were significant differences in Del-1 protein between benign and PCa group (0.066±0.131 vs 0.121±0.149, respectively, p<0.001). When we set the cut-off value of del-1 protein as 0.120, in patients with 3≤PSA≤8, positive predictive value and specificity of Del-1 protein (≥0.120) for predicting PCa were 88.9% (56/63) and 93.5% (101/108), respectively. Among 181 patients who underwent RP, there were significant differences in Del-1 protein according to stage (pT2 vs pT3a vs ≥pT3b) (0.113±0.078, 0.171±0.121, 0.227±0.161, respectively, p<0.001) and to Gleason score (6 (3+3) or 7 (3+4) vs 7 (4+3) or 8 (4+4) vs 9 or 10) (0.134±0.103, 0.150±0.109, 0.212±0.178, respectively, P = 0.044). Multivariate analysis showed that PSA, Del-1 protein and high Gleason score (≥9) were the independent prognostic factors for predicting higher pT stage (≥3b). Furthermore, age, PSA and Del-1 protein were independent prognostic factors for predicting significant PCa. Conclusion Patients with PCa showed higher expression of Del-1 protein than benign patients. Del-1 protein increased with the stage and Gleason score of PCa. Collaboration with PSA, Del-1 protein can be a non-invasive useful marker for diagnosis and risk stratification of PCa.

scholarly journals Automating the Determination of Prostate Cancer Risk Strata From Electronic Medical Records

2017 ◽  
pp. 1-8 ◽  
Author(s):  
Justin R. Gregg ◽  
Maximilian Lang ◽  
Lucy L. Wang ◽  
Matthew J. Resnick ◽  
Sandeep K. Jain ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Gleason Score ◽  
Cancer Care ◽  
Clinical Stage ◽  
Prostate Cancer Risk ◽  
Risk Stratum

Purpose Risk stratification underlies system-wide efforts to promote the delivery of appropriate prostate cancer care. Although the elements of risk stratum are available in the electronic medical record, manual data collection is resource intensive. Therefore, we investigated the feasibility and accuracy of an automated data extraction method using natural language processing (NLP) to determine prostate cancer risk stratum. Methods Manually collected clinical stage, biopsy Gleason score, and preoperative prostate-specific antigen (PSA) values from our prospective prostatectomy database were used to categorize patients as low, intermediate, or high risk by D’Amico risk classification. NLP algorithms were developed to automate the extraction of the same data points from the electronic medical record, and risk strata were recalculated. The ability of NLP to identify elements sufficient to calculate risk (recall) was calculated, and the accuracy of NLP was compared with that of manually collected data using the weighted Cohen’s κ statistic. Results Of the 2,352 patients with available data who underwent prostatectomy from 2010 to 2014, NLP identified sufficient elements to calculate risk for 1,833 (recall, 78%). NLP had a 91% raw agreement with manual risk stratification (κ = 0.92; 95% CI, 0.90 to 0.93). The κ statistics for PSA, Gleason score, and clinical stage extraction by NLP were 0.86, 0.91, and 0.89, respectively; 91.9% of extracted PSA values were within ± 1.0 ng/mL of the manually collected PSA levels. Conclusion NLP can achieve more than 90% accuracy on D’Amico risk stratification of localized prostate cancer, with adequate recall. This figure is comparable to other NLP tasks and illustrates the known tradeoff between recall and accuracy. Automating the collection of risk characteristics could be used to power real-time decision support tools and scale up quality measurement in cancer care.

Cumulative Prostate Cancer Risk Assessment with the Aid of the Free-to-Total Prostate Specific Antigen Ratio

2004 ◽  
Vol 45 (2) ◽  
pp. 160-165 ◽  
Author(s):  
Gunnar Aus ◽  
Charlotte Becker ◽  
Stefan Franzén ◽  
Hans Lilja ◽  
Pär Lodding ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Risk Assessment ◽  
Cancer Risk ◽  
Prostate Specific Antigen ◽  
Specific Antigen ◽  
Prostate Cancer Risk ◽  
Cancer Risk Assessment ◽  
Total Prostate Specific Antigen

scholarly journals Entering an era of radiogenomics in prostate cancer risk stratification

2018 ◽  
Vol 7 (S4) ◽  
pp. S443-S452 ◽  
Author(s):  
Nachiketh Soodana-Prakash ◽  
Radka Stoyanova ◽  
Abhishek Bhat ◽  
Maria C. Velasquez ◽  
Omer E. Kineish ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cancer Risk ◽  
Risk Stratification ◽  
Prostate Cancer Risk

Polymorphisms in the androgen receptor and the prostate-specific antigen genes and prostate cancer risk

The Prostate ◽  
2005 ◽  
Vol 65 (1) ◽  
pp. 58-65 ◽  
Author(s):  
Claudia A. Salinas ◽  
Melissa A. Austin ◽  
Elaine O. Ostrander ◽  
Janet L. Stanford
Keyword(s):  
Prostate Cancer ◽  
Androgen Receptor ◽  
Cancer Risk ◽  
Prostate Specific Antigen ◽  
Specific Antigen ◽  
Prostate Cancer Risk

scholarly journals IGF-I, insulin and prostate cancer

2009 ◽  
Vol 53 (8) ◽  
pp. 969-975 ◽  
Author(s):  
Giovanna A. Balarini Lima ◽  
Lívia L. Corrêa ◽  
Rafael Gabrich ◽  
Luiz Carlos D. de Miranda ◽  
Mônica R. Gadelha
Keyword(s):  
Prostate Cancer ◽  
Growth Factors ◽  
Cancer Risk ◽  
Cancer Cells ◽  
Growth Regulation ◽  
Specific Antigen ◽  
Prostate Cancer Risk ◽  
Adult Men ◽  
Igf I ◽  
High Insulin

Prostate cancer is the second most frequent malignancy diagnosed in adult men. Androgens are considered the primary growth factors for prostate normal and cancer cells. However, other non-androgenic growth factors are involved in the growth regulation of prostate cancer cells. The association between IGF-I and prostate cancer risk is well established. However, there is no evidence that the measurement of IGF-I enhances the specificity of prostate cancer detection beyond that achievable by serum prostate-specific antigen (PSA) levels. Until now, there is no consensus on the possible association between IGFBP-3 and prostate cancer risk. Although not well established, it seems that high insulin levels are particularly associated with risk of aggressive prostatic tumours. This review describes the physiopathological basis, epidemiological evidence, and animal models that support the association of the IGFs family and insulin with prostate cancer. It also describes the potential therapies targeting these growth factors that, in the future, can be used to treat patients with prostate cancer.

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