scholarly journals Prediction of Prostate Cancer Disease Aggressiveness Using Bi-Parametric Mri Radiomics

Cancers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 6065
Author(s):  
Ana Rodrigues ◽  
João Santinha ◽  
Bernardo Galvão ◽  
Celso Matos ◽  
Francisco M. Couto ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Gleason Score ◽  
Feature Selection Method ◽  
Supervised Machine Learning ◽  
Lesion Volume ◽  
Clinical Images ◽  
Gland Volume ◽  
Volume Of Interest ◽  
Cancer Aggressiveness

Prostate cancer is one of the most prevalent cancers in the male population. Its diagnosis and classification rely on unspecific measures such as PSA levels and DRE, followed by biopsy, where an aggressiveness level is assigned in the form of Gleason Score. Efforts have been made in the past to use radiomics coupled with machine learning to predict prostate cancer aggressiveness from clinical images, showing promising results. Thus, the main goal of this work was to develop supervised machine learning models exploiting radiomic features extracted from bpMRI examinations, to predict biological aggressiveness; 288 classifiers were developed, corresponding to different combinations of pipeline aspects, namely, type of input data, sampling strategy, feature selection method and machine learning algorithm. On a cohort of 281 lesions from 183 patients, it was found that (1) radiomic features extracted from the lesion volume of interest were less stable to segmentation than the equivalent extraction from the whole gland volume of interest; and (2) radiomic features extracted from the whole gland volume of interest produced higher performance and less overfitted classifiers than radiomic features extracted from the lesions volumes of interest. This result suggests that the areas surrounding the tumour lesions offer relevant information regarding the Gleason Score that is ultimately attributed to that lesion.

418: Association between the Dihydrotestosterone Level in the Prostate and Prostate Cancer Aggressiveness Using the Gleason Score

2006 ◽  
Vol 175 (4S) ◽  
pp. 136-136
Author(s):  
Tsutomu Nishiyama ◽  
Toshihiko Ikarashi ◽  
Yutaka Hashimoto ◽  
Kazuya Suzuki ◽  
Kota Takahashi
Keyword(s):  
Prostate Cancer ◽  
Gleason Score ◽  
Cancer Aggressiveness

Machine learning-based radiomic models to predict intensity-modulated radiation therapy response, Gleason score and stage in prostate cancer

2019 ◽  
Vol 124 (6) ◽  
pp. 555-567 ◽  
Author(s):  
Hamid Abdollahi ◽  
Bahram Mofid ◽  
Isaac Shiri ◽  
Abolfazl Razzaghdoust ◽  
Afshin Saadipoor ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Radiation Therapy ◽  
Gleason Score ◽  
Intensity Modulated Radiation Therapy ◽  
Therapy Response ◽  
Intensity Modulated

CCP score and risk stratification for prostate cancer patients at biopsy.

2014 ◽  
Vol 32 (4_suppl) ◽  
pp. 47-47 ◽  
Author(s):  
E. David Crawford ◽  
Neal Shore ◽  
Peter T. Scardino ◽  
John W. Davis ◽  
Jonathan D. Tward ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cell Cycle ◽  
Risk Stratification ◽  
Gleason Score ◽  
Cell Cycle Progression ◽  
Specific Antigen ◽  
Risk Category ◽  
Cycle Progression ◽  
Aggressive Cancer ◽  
Cancer Aggressiveness

47 Background: New prognostic markers for prostate cancer play an important role in addressing the controversies of over diagnosis and treatment. The cell cycle progression score (CCP) (Prolaris, Myriad Genetic Laboratories, Inc.) is a new RNA-based marker, which improved the prediction of prostate cancer aggressiveness in eight separate cohorts. Each one-unit increase in CCP score corresponds with approximately a doubling of the risk of the studied event (recurrence or death from prostate cancer). In this analysis, we characterized the CCP score distribution from our initial CCP signature commercial testing. Methods: Our current laboratory database was evaluated for patients whose biopsy was analyzed with the CCP test and whose clinicopathologic data was collected by the ordering physician. Formalin fixed, prostate biopsy tissue from 1,648 patients diagnosed with adenocarcinoma ordered by more than 300 physicians were analyzed. The CCP score was calculated by measuring the RNA expression of 31 cell cycle progression genes normalized to 15 housekeeping genes. Results: Of the 1,648 samples that contained sufficient carcinoma (more than 0.5mm linear extent), 1,604 (97.3%) provided quality RNA for analysis. This retrospective analysis showed a normal distribution for the CCP score ranging from −2.9 to 3.1. Correlation with Gleason score was r=0.35. A relative classification of cancer aggressiveness based on CCP of approximately1,200 patients from multiple cohorts was developed to interpret how the patient’s CCP score compared to that of patients within the same AUA risk category. The thresholds between each of the five intervals are one unit of CCP score apart, with the "consistent" interval centered at the median CCP score. Based on the CCP score, 27.9% of men had a less aggressive cancer compared to the clinicopathologic prediction and were assigned to a lower risk group while 27.6% of patients had a more aggressive cancer. Conclusions: The CCP signature test is a novel assay that can improve risk stratification for men with prostate adenocarcinoma independent of the Gleason score and prostate-specific antigen level. Over 50% of men initially tested in the commercial assay were assigned to a different risk category than predicted by clinicopathologic features alone.

Using longitudinal PSA values and machine learning for predicting progression of early stage prostate cancer in veterans.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e17554-e17554
Author(s):  
Ioana Danciu ◽  
Samantha Erwin ◽  
Greeshma Agasthya ◽  
Tate Janet ◽  
Benjamin McMahon ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Disease Progression ◽  
Gleason Score ◽  
Prediction Models ◽  
Early Stage ◽  
Treatment Plan ◽  
Specific Antigen ◽  
Machine Learning Algorithms ◽  
Age At Diagnosis

e17554 Background: The ability to understand and predict at the time of diagnosis the trajectories of prostate cancer patients is critical for deciding the appropriate treatment plan. Evidence-based approaches for outcome prediction include predictive machine learning algorithms that harness health record data. Methods: All our analyses used the Veterans Affairs Clinical Data Warehouse (CDW). We included all individuals with a non-metastatic (early stage) prostate cancer diagnosis between 2002 and 2017 as documented in the CDW cancer registry (N = 111351). Our predictors were demographics (age at diagnosis, race), disease staging parameters abstracted at diagnosis ( Stage grouping AJCC, Gleason score, SEER summary stage) and prostate specific antigen (PSA) laboratory values in the last 5 years prior to diagnosis (last value, the value before last, average, minimum, maximum, rate of the change of the last 2 PSAs and density). The predicted outcome was disease progression at 2 years (N = 3469) and 5 years (N = 6325) defined as metastasis - taking either Abiraterone, Sipuleucel-T, Enzalutamide or Radium 223, registry cancer related death or PSA > 50. We used 4 different machine learning classifiers to train prediction models: random forest, k-nearest neighbor, decision trees, and xgboost all with hyper parameter optimization. For testing, we used two approaches: (1) 20% sample held out at the beginning of the study, and (2) stratified test/train split on the remaining data. Results: The table below shows the performance of the best classifier, xgboost. The top five predictors of disease progression were the last PSA, Gleason Score, maximum PSA, age at diagnosis, and SEER summary stage. The last PSA had a significantly higher contribution than the other predictors. More than one PSA value is important for prediction, emphasizing the need for investigating the PSA trajectory in the period before diagnosis. The models are overall very robust going from outcome at 2 years compared to 5 years. Conclusions: A machine learning based xgboost classifier can be integrated in clinical decision support at diagnosis, to robustly predict disease progression at 2 and 5 years. [Table: see text]

scholarly journals Preoperative Prostate Health Index predicts adverse pathology and Gleason score upgrading after radical prostatectomy for prostate cancer

2020 ◽  
Author(s):  
Vojtěch Novák ◽  
Štěpán Veselý ◽  
Hana Lukšanová ◽  
Richard Průša ◽  
Otakar Čapoun ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Radical Prostatectomy ◽  
Gleason Score ◽  
Specific Antigen ◽  
Surgical Margin ◽  
Positive Surgical Margin ◽  
Health Index ◽  
Cancer Aggressiveness ◽  
Prostate Health Index ◽  
Prostate Health

Abstract Background: We aimed to explore the utility of prostate specific antigen (PSA) isoform [-2]proPSA and its derivatives for prediction of pathological outcome after radical prostatectomy (RP).Methods: Preoperative blood samples were prospectively and consecutively analyzed from 472 patients treated with RP for clinically localized prostate cancer at four medical centers. Measured parameters were PSA, free PSA (fPSA), fPSA/PSA ratio, [-2]proPSA (p2PSA), p2PSA/fPSA ratio and Prostate Health Index (PHI) (p2PSA/fPSA)*√PSA]. Logistic regression models were fitted to determine the accuracy of markers for prediction of pathological Gleason score (GS) ≥7, Gleason score upgrading, extracapsular extension of the tumor (pT3) and the presence of positive surgical margin (PSM). Results: Of 472 patients undergoing RP, 339 (72%) were found to have pathologic GS ≥ 7, out of them 178 (53%) experienced an upgrade from their preoperative GS=6. The findings of pT3 and PSM were present in 132 (28%) and 133 (28%) cases, respectively. At univariable analysis of all the preoperative parameters, PHI was the most accurate predictor of pathological GS ≥7, GS upgrading, pT3 disease and the presence of PSM. Adding of PHI into the base multivariable model increased significantly the accuracy for prediction of pathological GS and GS upgrading by 4.4% (p=0.015) and 5.0% (p=0.025), respectively. Conclusion: We found that PHI provides the highest accuracy in predicting prostate cancer aggressiveness and expansion of the tumor detected at final pathology. The ability of PHI to predict the risk of Gleason score upgrade may help to identify potentially high-risk patients among men with biopsy proven insignificant prostate cancer.

scholarly journals Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga]Ga-PSMA-11 PET/MRI

2020 ◽  
Author(s):  
L. Papp ◽  
C. P. Spielvogel ◽  
B. Grubmüller ◽  
M. Grahovac ◽  
D. Krajnc ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Cross Validation ◽  
Prospective Trial ◽  
Specific Antigen ◽  
Tnm Staging ◽  
Risk Classification ◽  
Supervised Machine Learning ◽  
In Vivo Models ◽  
Primary Prostate Cancer

Abstract Purpose Risk classification of primary prostate cancer in clinical routine is mainly based on prostate-specific antigen (PSA) levels, Gleason scores from biopsy samples, and tumor-nodes-metastasis (TNM) staging. This study aimed to investigate the diagnostic performance of positron emission tomography/magnetic resonance imaging (PET/MRI) in vivo models for predicting low-vs-high lesion risk (LH) as well as biochemical recurrence (BCR) and overall patient risk (OPR) with machine learning. Methods Fifty-two patients who underwent multi-parametric dual-tracer [18F]FMC and [68Ga]Ga-PSMA-11 PET/MRI as well as radical prostatectomy between 2014 and 2015 were included as part of a single-center pilot to a randomized prospective trial (NCT02659527). Radiomics in combination with ensemble machine learning was applied including the [68Ga]Ga-PSMA-11 PET, the apparent diffusion coefficient, and the transverse relaxation time-weighted MRI scans of each patient to establish a low-vs-high risk lesion prediction model (MLH). Furthermore, MBCR and MOPR predictive model schemes were built by combining MLH, PSA, and clinical stage values of patients. Performance evaluation of the established models was performed with 1000-fold Monte Carlo (MC) cross-validation. Results were additionally compared to conventional [68Ga]Ga-PSMA-11 standardized uptake value (SUV) analyses. Results The area under the receiver operator characteristic curve (AUC) of the MLH model (0.86) was higher than the AUC of the [68Ga]Ga-PSMA-11 SUVmax analysis (0.80). MC cross-validation revealed 89% and 91% accuracies with 0.90 and 0.94 AUCs for the MBCR and MOPR models respectively, while standard routine analysis based on PSA, biopsy Gleason score, and TNM staging resulted in 69% and 70% accuracies to predict BCR and OPR respectively. Conclusion Our results demonstrate the potential to enhance risk classification in primary prostate cancer patients built on PET/MRI radiomics and machine learning without biopsy sampling.

scholarly journals A Hierarchical Machine Learning Model to Discover Gleason Grade Group-specific Biomarkers in Prostate Cancer

2019 ◽  
Author(s):  
Osama Hamzeh ◽  
Abedalrhman Alkhateeb ◽  
Julia Zheng ◽  
Srinath Kandalam ◽  
Crystal Lueng ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Cancer Patients ◽  
Gleason Score ◽  
Cancer Progression ◽  
Class Imbalance ◽  
Gleason Grade ◽  
Gene Expressions ◽  
Grade Group ◽  
Potential Biomarkers

1) Background: One of the deadliest cancers that affect men worldwide and North American men is prostate cancer. This disease motivates parts of the cells in the prostate to lose control of their growth and division. 2) Methods: We are proposing a machine learning method used to analyze gene expressions of prostate tumors with different Gleason scores, and to identify potential genetic biomarkers for each group. A publicly-available RNA-Seq dataset of a cohort of 104 prostate cancer patients have been retrieved from the National Center for Biotechnology Information's (NCBI) Gene Expression Omnibus (GEO) repository. We categorize patients by their Gleason scores into different groups to create a hierarchy of disease progression. A hierarchical model with standard classifiers in different Gleason groups (hereinafter called nodes) to identify and predict nodes based on their mRNA or gene expressions. At each node, patient samples are analyzed via class imbalance and hybrid feature selection techniques to build the prediction model. The outcome of each node is a set of genes that can separate the Gleason group from the remaining groups. To validate the proposed method, the set of identified genes are used to classify a second dataset of 499 prostate cancer patients that have been collected from cBioportal.. 3) Results: Two genes have been found to be potential biomarkers of specific Gleason groups; PIAS3 has been identifed for Gleason score 4+3=7, while UBE2 could be a poteintial biomarker for Gleason score 6. Other proposed genes that were not found in the literature might be potential biomarkers. 4) Conclusion: The latest literature supports that the genes predicted by the proposed method are strongly correlated with prostate cancer progression and tumour development processes. Furthermore, pathway analysis shows that both PIAS3 and UBE2 share the same protein interaction pathway, the JAK/STAT signaling process.

Genomic alterations associated with the progression from castration-sensitive to castration-resistant metastatic prostate cancer based on machine learning analysis of cell-free DNA genomic profile.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e17596-e17596
Author(s):  
Edwin Lin ◽  
Andrew W. Hahn ◽  
Roberto Nussenzveig ◽  
Sergiusz Wesolowski ◽  
Benjamin Louis Maughan ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Supervised Machine Learning ◽  
Pi3k Signaling ◽  
Genomic Alterations ◽  
Cell Free Dna ◽  
Associated Gas ◽  
Castration Resistant ◽  
Free Dna ◽  
Pathway Level

e17596 Background: Metastatic castration-sensitive prostate cancer (mCSPC) eventually progresses to metastatic castration-resistant prostate cancer (mCRPC), which has few treatment options and carries a poor prognosis. We hypothesize that there are specific genomic alterations (GAs) associated with the progression from mCSPC to mCRPC. Methods: Patients (Pts) with mCSPC and mCRPC undergoing next-generation sequencing of cell-free DNA by a CLIA certified lab (G360, Guardant Health Inc., Redwood City, CA) as a part of routine care were retrospectively identified. Principal components analysis, an unsupervised ML algorithm, was used for data exploration and visualization. A combination of feature selection and supervised machine learning classification algorithms were used to identify genes associated with mCRPC. Gene Ontology enrichment analysis was used to identify pathways enriched for mCRPC-associated GAs. Patterns of mCRPC-associated GAs at a gene- and pathway-level were identified by Bayesian networks fitted using an exact structure learning algorithm. Results: 154 Pts with mCSPC and 187 Pts with mCRPC were included. A set of 16 GAs that robustly distinguished mCRPC from mCSPC (PPV = 94%, specificity = 91%) using supervised machine learning algorithms. These GAs, primarily amplifications, corresponded to AR, MAPK signaling, PI3K signaling, G1/S cell cycle, and receptor tyrosine kinases (RTKs). Positive statistical dependencies were observed between genes in these pathways. At a pathway-level, the presence of G1/S GAs in mCRPC samples increased the likelihood of harboring GAs in RTK, MAPK, and PI3K signaling. Limitations: The retrospective nature of our study means that unknown exposures could act as confounding variables, however this is representative of real-world clinical settings. Although the strength of this study is inclusion of clinically annotated patient samples, the limitation is that patients with mCSPC and mCRPC were unmatched. Conclusions: These results provide evidence that progression from mCSPC to mCRPC is associated with stereotyped concomitant gain-of-function in the RTK, PI3K, MAPK, and G1/S pathways in addition to AR. Upon external validation, these hypothesis generating data may warrant further investigation into combinatorial therapies that target these pathways.

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