Integrative Analysis of N-Linked Human Glycoproteomic Data Sets Reveals PTPRF Ectodomain as a Novel Plasma Biomarker Candidate for Prostate Cancer

2012 ◽  
Vol 11 (5) ◽  
pp. 2653-2665 ◽  
Author(s):  
Theodore E. Whitmore ◽  
Amelia Peterson ◽  
Ted Holzman ◽  
Ashley Eastham ◽  
Lynn Amon ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Integrative Analysis ◽  
Data Sets ◽  
Plasma Biomarker

scholarly journals Carbonic Anhydrase I as a New Plasma Biomarker for Prostate Cancer

ISRN Oncology ◽  
2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Michiko Takakura ◽  
Akira Yokomizo ◽  
Yoshinori Tanaka ◽  
Michimoto Kobayashi ◽  
Giman Jung ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Carbonic Anhydrase ◽  
Cancer Patients ◽  
Plasma Proteins ◽  
Specific Antigen ◽  
Healthy Controls ◽  
Gray Zone ◽  
Plasma Biomarker ◽  
Carbonic Anhydrase I ◽  
Discrimination Rate

Serum prostate-specific antigen (PSA) levels ranging from 4 to 10 ng/mL is considered a diagnostic gray zone for detecting prostate cancer because biopsies reveal no evidence of cancer in 75% of these subjects. Our goal was to discover a new highly specific biomarker for prostate cancer by analyzing plasma proteins using a proteomic technique. Enriched plasma proteins from 25 prostate cancer patients and 15 healthy controls were analyzed using a label-free quantitative shotgun proteomics platform called 2DICAL (2-dimensional image converted analysis of liquid chromatography and mass spectrometry) and candidate biomarkers were searched. Among the 40,678 identified mass spectrum (MS) peaks, 117 peaks significantly differed between prostate cancer patients and healthy controls. Ten peaks matched carbonic anhydrase I (CAI) by tandem MS. Independent immunological assays revealed that plasma CAI levels in 54 prostate cancer patients were significantly higher than those in 60 healthy controls (, Mann-Whitney test). In the PSA gray-zone group, the discrimination rate of prostate cancer patients increased by considering plasma CAI levels. CAI can potentially serve as a valuable plasma biomarker and the combination of PSA and CAI may have great advantages for diagnosing prostate cancer in patients with gray-zone PSA level.

scholarly journals Artificial Intelligence Is a Promising Prospect for the Detection of Prostate Cancer Extracapsular Extension With Mp-mri: A Two-center Comparative Study

2021 ◽  
Author(s):  
Ying Hou ◽  
Yi-Hong Zhang ◽  
Jie Bao ◽  
Mei-Ling Bao ◽  
Guang Yang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Artificial Intelligence ◽  
Paired Comparison ◽  
Characteristic Curve ◽  
Multiparametric Mri ◽  
Training Data ◽  
Urinary Continence ◽  
Data Sets ◽  
Extracapsular Extension ◽  
Promising Alternative

Abstract Purpose: A balance between preserving urinary continence and achievement of negative margins is of clinical relevance while implementary difficulty. Preoperatively accurate detection of prostate cancer (PCa) extracapsular extension (ECE) is thus crucial for determining appropriate treatment options. We aimed to develop and clinically validate an artificial intelligence (AI)-assisted tool for the detection of ECE in patients with PCa using multiparametric MRI. Methods: 849 patients with localized PCa underwent multiparametric MRI before radical prostatectomy were retrospectively included from two medical centers. The AI tool was built on a ResNeXt network embedded with a spatial attention map of experts’ prior knowledges (PAGNet) from 596 training data sets. The tool was validated in 150 internal and 103 external data sets, respectively; and its clinical applicability was compared with expert-based interpretation and AI-expert interaction.Results: An index PAGNet model using a single-slice image yielded the highest areas under the receiver operating characteristic curve (AUC) of 0.857 (95% confidence interval [CI], 0.827-0.884), 0.807 (95% CI, 0.735-0.867) and 0.728 (95% CI, 0.631-0.811) in the training, internal test and external test cohorts, compared to the conventional ResNeXt networks. For experts, the inter-reader agreement was observed in only 437/849 (51.5%) patients with a Kappa value 0.343. And the performance of two experts (AUC, 0.632 to 0.741 vs 0.715 to 0.857) was lower (paired comparison, all p values < 0.05) than that of AI assessment. When expert’ interpretations were adjusted by the AI assessments, the performance of both two experts was improved.Conclusion: Our AI tool, showing improved accuracy, offers a promising alternative to human experts for imaging staging of PCa ECE using multiparametric MRI.

scholarly journals Classification of Clinically Significant Prostate Cancer on Multi-Parametric MRI: A Validation Study Comparing Deep Learning and Radiomics

Cancers ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 12
Author(s):  
Jose M. Castillo T. ◽  
Muhammad Arif ◽  
Martijn P. A. Starmans ◽  
Wiro J. Niessen ◽  
Chris H. Bangma ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Characteristic Curve ◽  
Model Development ◽  
Learning Model ◽  
Multiparametric Mri ◽  
Data Sets ◽  
Data Set ◽  
Test Sets ◽  
Deep Learning Model

The computer-aided analysis of prostate multiparametric MRI (mpMRI) could improve significant-prostate-cancer (PCa) detection. Various deep-learning- and radiomics-based methods for significant-PCa segmentation or classification have been reported in the literature. To be able to assess the generalizability of the performance of these methods, using various external data sets is crucial. While both deep-learning and radiomics approaches have been compared based on the same data set of one center, the comparison of the performances of both approaches on various data sets from different centers and different scanners is lacking. The goal of this study was to compare the performance of a deep-learning model with the performance of a radiomics model for the significant-PCa diagnosis of the cohorts of various patients. We included the data from two consecutive patient cohorts from our own center (n = 371 patients), and two external sets of which one was a publicly available patient cohort (n = 195 patients) and the other contained data from patients from two hospitals (n = 79 patients). Using multiparametric MRI (mpMRI), the radiologist tumor delineations and pathology reports were collected for all patients. During training, one of our patient cohorts (n = 271 patients) was used for both the deep-learning- and radiomics-model development, and the three remaining cohorts (n = 374 patients) were kept as unseen test sets. The performances of the models were assessed in terms of their area under the receiver-operating-characteristic curve (AUC). Whereas the internal cross-validation showed a higher AUC for the deep-learning approach, the radiomics model obtained AUCs of 0.88, 0.91 and 0.65 on the independent test sets compared to AUCs of 0.70, 0.73 and 0.44 for the deep-learning model. Our radiomics model that was based on delineated regions resulted in a more accurate tool for significant-PCa classification in the three unseen test sets when compared to a fully automated deep-learning model.

Abstract LB-297: Integrative analysis identifies driver lncRNAs in prostate cancer

2015 ◽  
Author(s):  
Haiyang Guo ◽  
Musaddeque Ahmed ◽  
Junjie Hua ◽  
Yi Liang ◽  
Jens Langstein ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Integrative Analysis

Use of crowdsourced research to develop a prognostic model for first-line metastatic castrate resistant prostate cancer (mCRPC).

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 180-180
Author(s):  
Fang Liz Zhou ◽  
Justin Guinney ◽  
Tao Wang ◽  
J. Christopher Bare ◽  
Thea C Norman ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Clinical Data ◽  
Prognostic Model ◽  
Trial Data ◽  
Machine Learning Techniques ◽  
Phase Iii ◽  
Prognostic Models ◽  
Data Sets ◽  
First Line ◽  
Validation Data

180 Background: Project Data Sphere, LLC (PDS) and Sage Bionetworks/DREAM have completed the “Prostate Cancer DREAM Challenge” (Challenge), a crowdsourced competition, using historical prostate cancer clinical trial data from PDS. The Challenge aimed to improve prognostic models for overall survival (OS) and to explore predictive models for treatment toxicity in mCRPC patients. Methods: Control arms of 4 randomized phase III trials (total 2,070 patients) were used as training and validation data sets for the Challenge: ASCENT2, MAINSAIL, VENICE and ENTHUSE33. All subjects were first line mCRPC patients receiving docetaxel treatment. Curated baseline clinical covariates (demographics, comorbidity, prior treatment, laboratory, lesion and vital signs) were modeled along with raw clinical data tables. The primary purpose of the Challenge was to develop a prognostic model for OS (SubChallenge 1). The models were scored using concordance index and integrated area under receiver operator curve (iAUC) from 6-30 months. The published mCRPC OS model of Halabi, et al., JCO, 2014, was used as the benchmark. Results: The Challenge attracted over 160 active participants who formed 50 teams that submitted final models for SubChallenge 1. Median iAUC was 0.76 (0.67-0.78) with a maximum score of 0.792. Over half (n = 35) of these models exceeded the published benchmark (0.743 iAUC). Teams explored new methodologies such as model-based imputation and machine learning techniques to develop the best performing models. Many leveraged raw clinical data sets to create their own covariates and expanded beyond existing prognostic models. Conclusions: The Challenge externally validated Halabi’s first line prognostic model. New prognostic models were proposed and validated with significant improvements over the benchmark. Further analyses are needed to examine the winning models for new prognostic factors and to validate them using additional trial data from PDS. The Challenge drove interest from cross-disciplinary teams of global experts to explore and enhance their technical abilities using real clinical data whilst serving as a vehicle to accelerate medical innovation.

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