scholarly journals PTEN and DNA Ploidy Status by Machine Learning in Prostate Cancer

Cancers ◽  
2021 ◽  
Vol 13 (17) ◽  
pp. 4291
Author(s):  
Karolina Cyll ◽  
Andreas Kleppe ◽  
Joakim Kalsnes ◽  
Ljiljana Vlatkovic ◽  
Manohar Pradhan ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Dna Ploidy ◽  
Area Under The Curve ◽  
Image Cytometry ◽  
Primary Analysis ◽  
Pten Protein ◽  
Protein Status ◽  
Automated Method ◽  
Ploidy Status

Machine learning (ML) is expected to improve biomarker assessment. Using convolution neural networks, we developed a fully-automated method for assessing PTEN protein status in immunohistochemically-stained slides using a radical prostatectomy (RP) cohort (n = 253). It was validated according to a predefined protocol in an independent RP cohort (n = 259), alone and by measuring its prognostic value in combination with DNA ploidy status determined by ML-based image cytometry. In the primary analysis, automatically assessed dichotomized PTEN status was associated with time to biochemical recurrence (TTBCR) (hazard ratio (HR) = 3.32, 95% CI 2.05 to 5.38). Patients with both non-diploid tumors and PTEN-low had an HR of 4.63 (95% CI 2.50 to 8.57), while patients with one of these characteristics had an HR of 1.94 (95% CI 1.15 to 3.30), compared to patients with diploid tumors and PTEN-high, in univariable analysis of TTBCR in the validation cohort. Automatic PTEN scoring was strongly predictive of the PTEN status assessed by human experts (area under the curve 0.987 (95% CI 0.968 to 0.994)). This suggests that PTEN status can be accurately assessed using ML, and that the combined marker of automatically assessed PTEN and DNA ploidy status may provide an objective supplement to the existing risk stratification factors in prostate cancer.

scholarly journals Predicting the Grade of Prostate Cancer Based on a Biparametric MRI Radiomics Signature

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Li Zhang ◽  
Xia Zhe ◽  
Min Tang ◽  
Jing Zhang ◽  
Jialiang Ren ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Logistic Regression ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Low Grade ◽  
High Grade ◽  
Random Forest Tree

Purpose. This study aimed to investigate the value of biparametric magnetic resonance imaging (bp-MRI)-based radiomics signatures for the preoperative prediction of prostate cancer (PCa) grade compared with visual assessments by radiologists based on the Prostate Imaging Reporting and Data System Version 2.1 (PI-RADS V2.1) scores of multiparametric MRI (mp-MRI). Methods. This retrospective study included 142 consecutive patients with histologically confirmed PCa who were undergoing mp-MRI before surgery. MRI images were scored and evaluated by two independent radiologists using PI-RADS V2.1. The radiomics workflow was divided into five steps: (a) image selection and segmentation, (b) feature extraction, (c) feature selection, (d) model establishment, and (e) model evaluation. Three machine learning algorithms (random forest tree (RF), logistic regression, and support vector machine (SVM)) were constructed to differentiate high-grade from low-grade PCa. Receiver operating characteristic (ROC) analysis was used to compare the machine learning-based analysis of bp-MRI radiomics models with PI-RADS V2.1. Results. In all, 8 stable radiomics features out of 804 extracted features based on T2-weighted imaging (T2WI) and ADC sequences were selected. Radiomics signatures successfully categorized high-grade and low-grade PCa cases ( P < 0.05 ) in both the training and test datasets. The radiomics model-based RF method (area under the curve, AUC: 0.982; 0.918), logistic regression (AUC: 0.886; 0.886), and SVM (AUC: 0.943; 0.913) in both the training and test cohorts had better diagnostic performance than PI-RADS V2.1 (AUC: 0.767; 0.813) when predicting PCa grade. Conclusions. The results of this clinical study indicate that machine learning-based analysis of bp-MRI radiomic models may be helpful for distinguishing high-grade and low-grade PCa that outperformed the PI-RADS V2.1 scores based on mp-MRI. The machine learning algorithm RF model was slightly better.

Needle Biopsy DNA Ploidy Status Predicts Grade Shifting in Prostate Cancer

1999 ◽  
Vol 23 (3) ◽  
pp. 296-301 ◽  
Author(s):  
Jeffrey S. Ross ◽  
Christine E. Sheehan ◽  
Robert A. Ambros ◽  
Tipu Nazeer ◽  
Timothy A. Jennings ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Needle Biopsy ◽  
Dna Ploidy ◽  
Ploidy Status

scholarly journals Machine learning for differentiating metastatic and completely responded sclerotic bone lesion in prostate cancer: a retrospective radiomics study

2019 ◽  
Vol 92 (1101) ◽  
pp. 20190286 ◽  
Author(s):  
Emine Acar ◽  
Asım Leblebici ◽  
Berat Ender Ellidokuz ◽  
Yasemin Başbınar ◽  
Gamze Çapa Kaya
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Texture Analysis ◽  
Area Under The Curve ◽  
Ct Images ◽  
Metastatic Lesions ◽  
Psma Pet ◽  
Pet Ct ◽  
Sclerotic Lesions ◽  
Psma Expression

Objective:Using CT texture analysis and machine learning methods, this study aims to distinguish the lesions imaged via 68Ga-prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/CT as metastatic and completely responded in patients with known bone metastasis and who were previously treated.Methods:We retrospectively reviewed the 68Ga-PSMA PET/CT images of 75 patients after treatment, who were previously diagnosed with prostate cancer and had known bone metastasis. A texture analysis was performed on the metastatic lesions showing PSMA expression and completely responded sclerotic lesions without PSMA expression through CT images. Textural features were compared in two groups. Thus, the distinction of metastasis/completely responded lesions and the most effective parameters in this issue were determined by using various methods [decision tree, discriminant analysis, support vector machine (SVM), k-nearest neighbor (KNN), ensemble classifier] in machine learning.Results:In 28 of the 35 texture analysis findings, there was a statistically significant difference between the two groups. The Weighted KNN method had the highest accuracy and area under the curve, has been chosen as the best model. The weighted KNN algorithm was succeeded to differentiate sclerotic lesion from metastasis or completely responded lesions with 0.76 area under the curve. GLZLM_SZHGE and histogram-based kurtosis were found to be the most important parameters in differentiating metastatic and completely responded sclerotic lesions.Conclusions:Metastatic lesions and completely responded sclerosis areas in CT images, as determined by 68Ga-PSMA PET, could be distinguished with good accuracy using texture analysis and machine learning (Weighted KNN algorithm) in prostate cancer.Advances in knowledge:Our findings suggest that, with the use of newly emerging software, CT imaging can contribute to identifying the metastatic lesions in prostate cancer.

scholarly journals DNA Aneuploidy in Malignant Salivary Gland Neoplasms is Independent of USP44 Protein Expression

2017 ◽  
Vol 28 (2) ◽  
pp. 148-151 ◽  
Author(s):  
Vanessa Fátima Bernardes ◽  
Edward W Odell ◽  
Ricardo Santiago Gomez ◽  
Carolina Cavalieri Gomes
Keyword(s):  
Salivary Gland ◽  
Protein Expression ◽  
Tumor Size ◽  
Chromosomal Instability ◽  
Dna Ploidy ◽  
Image Cytometry ◽  
Mitotic Checkpoint ◽  
Salivary Gland Neoplasms ◽  
Ploidy Status

Chromosomal instability, leading to aneuploidy, is one of the hallmarks of human cancers. USP44 (ubiquitin specific peptidase 44) is an important molecule that plays a regulatory role in the mitotic checkpoint and USP44 loss causes chromosome mis-segregation, aneuploidy and tumorigenesis in vivo. In this study, it was investigated the immunoexpression of USP44 in 28 malignant salivary gland neoplasms and associated the results with DNA ploidy status assessed by image cytometry. USP44 protein was widely expressed in most of the tumor samples and no clear association could be established between its expression and DNA ploidy status or tumor size. On this basis, it may be concluded that the aneuploidy of the salivary gland cancers included in this study was not driven by loss of USP44 protein expression.

The combination of DNA ploidy status and PTEN/6q15 deletions to provide strong and independent prognostic information in prostate cancer.

2015 ◽  
Vol 33 (15_suppl) ◽  
pp. 5027-5027
Author(s):  
Maximilian Lennartz ◽  
Sarah Minner ◽  
Martina Kluth ◽  
Sophie Brasch ◽  
Hilko Wittmann ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Dna Ploidy ◽  
Prognostic Information ◽  
Ploidy Status

scholarly journals The Combination of DNA Ploidy Status and PTEN/6q15 Deletions Provides Strong and Independent Prognostic Information in Prostate Cancer

2016 ◽  
Vol 22 (11) ◽  
pp. 2802-2811 ◽  
Author(s):  
Maximilian Lennartz ◽  
Sarah Minner ◽  
Sophie Brasch ◽  
Hilko Wittmann ◽  
Leonard Paterna ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Dna Ploidy ◽  
Prognostic Information ◽  
Ploidy Status

scholarly journals Early Prediction of Seven-Day Mortality in Intensive Care Unit Using a Machine Learning Model: Results from the SPIN-UTI Project

2021 ◽  
Vol 10 (5) ◽  
pp. 992
Author(s):  
Martina Barchitta ◽  
Andrea Maugeri ◽  
Giuliana Favara ◽  
Paolo Marco Riela ◽  
Giovanni Gallo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Support Vector ◽  
Icu Admission ◽  
Risk Of Death ◽  
Saps Ii ◽  
Svm Algorithm

Patients in intensive care units (ICUs) were at higher risk of worsen prognosis and mortality. Here, we aimed to evaluate the ability of the Simplified Acute Physiology Score (SAPS II) to predict the risk of 7-day mortality, and to test a machine learning algorithm which combines the SAPS II with additional patients’ characteristics at ICU admission. We used data from the “Italian Nosocomial Infections Surveillance in Intensive Care Units” network. Support Vector Machines (SVM) algorithm was used to classify 3782 patients according to sex, patient’s origin, type of ICU admission, non-surgical treatment for acute coronary disease, surgical intervention, SAPS II, presence of invasive devices, trauma, impaired immunity, antibiotic therapy and onset of HAI. The accuracy of SAPS II for predicting patients who died from those who did not was 69.3%, with an Area Under the Curve (AUC) of 0.678. Using the SVM algorithm, instead, we achieved an accuracy of 83.5% and AUC of 0.896. Notably, SAPS II was the variable that weighted more on the model and its removal resulted in an AUC of 0.653 and an accuracy of 68.4%. Overall, these findings suggest the present SVM model as a useful tool to early predict patients at higher risk of death at ICU admission.

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