A random forest classifier predicts recurrence risk in patients with ovarian cancer

Abstract Background Tumor-infiltrating immune cell compositions have been previously correlated to encouragement or inhibition of tumor growth. This association highlights immune-landscape profiling through non-invasive methods as a crucial step in approaches to treatment of patients with meningioma (MNG), a prevalent primary intracranial tumor. Genome-wide DNA methylation patterns can aid in definition and assessment of cell compositions in liquid biopsy serum specimens, and allow for development of machine-learning models with predictive capabilities. Methods We profiled the cfDNA methylome (EPIC array) in liquid biopsy specimens from patients with MNG (n = 63) and nontumor controls (n = 6). We conducted both unsupervised epigenome-wide and supervised analyses of the meningioma methylome. Estimation of immune cell composition was conducted using Python-based methodology, where a reference methylome atlas of chosen cell types (B-cells, CD4- and CD8T-cells, neutrophils, natural killer cells, monocytes, cortical neuron, vascular endothelial cells, and healthy meninge) was used to deconvolute the MNG samples. Recurrence risk was estimated using an existing methylation-based Random-Forest classifier previously reported and validated, adapted to our serum-based cohort through employment of translatable meningioma subgroup-specific methylation markers (differentially methylated probes). Results We identified four distinct genome-wide methylation subgroups (k-clusters) of MNG which presented differential tumor micro-environments across all cell types investigated. Application of the DNA methylation-based Random-Forest classifier allowed for categorization of primary MNG serum samples into estimated recurrence-risk subgroups. Significantly contrasting micro-environments for the subgroups were observed across several cell-types, with those MNG more likely to recur displaying depletion in cell types reported to improve anti-tumoral response in many tumors (e.g. T-Cells). Conclusions DNA methylation based deconvolution allowed for detection of contrasting tumor microenvironment compositions across MNG methylation subtypes and recurrence-risk estimation subgroups. These results suggest that microenvironment profiling can be informative of probable tumor behavior and prognostic outcomes, helping guide therapeutic approaches towards treatment of patients with MNG.

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Using Lexicon and Random Forest Classifier for Twitter Sentiment Analysis

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i6.591594 ◽

2019 ◽

Vol 7 (6) ◽

pp. 591-594

Author(s):

M. Thenmozhi ◽

R. Indira ◽

R. Dharani

Keyword(s):

Random Forest ◽

Sentiment Analysis ◽

Random Forest Classifier

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The Performance Improvement on 4-FB Face using Random Forest Classifier

Scientific Visualization ◽

10.26583/sv.10.5.01 ◽

2018 ◽

Vol 10 (5) ◽

pp. 1-12

Author(s):

B. Nassih ◽

A. Amine ◽

M. Ngadi ◽

D. Naji ◽

N. Hmina

Keyword(s):

Random Forest ◽

Performance Improvement ◽

Random Forest Classifier

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Perbandingan CART dan Random Forest untuk Deteksi Kanker berbasis Klasifikasi Data Microarray

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v4i5.2083 ◽

2020 ◽

Vol 4 (5) ◽

pp. 805-812

Author(s):

Riska Chairunisa ◽

Adiwijaya ◽

Widi Astuti

Keyword(s):

Breast Cancer ◽

Lung Cancer ◽

Ovarian Cancer ◽

Random Forest ◽

Classification And Regression Tree ◽

Classification Method ◽

Discrete Wavelet ◽

Prostate Tumors ◽

Cancer Data ◽

Colon Tumors

Cancer is one of the deadliest diseases in the world with a mortality rate of 57,3% in 2018 in Asia. Therefore, early diagnosis is needed to avoid an increase in mortality caused by cancer. As machine learning develops, cancer gene data can be processed using microarrays for early detection of cancer outbreaks. But the problem that microarray has is the number of attributes that are so numerous that it is necessary to do dimensional reduction. To overcome these problems, this study used dimensions reduction Discrete Wavelet Transform (DWT) with Classification and Regression Tree (CART) and Random Forest (RF) as classification method. The purpose of using these two classification methods is to find out which classification method produces the best performance when combined with the DWT dimension reduction. This research use five microarray data, namely Colon Tumors, Breast Cancer, Lung Cancer, Prostate Tumors and Ovarian Cancer from Kent-Ridge Biomedical Dataset. The best accuracy obtained in this study for breast cancer data were 76,92% with CART-DWT, Colon Tumors 90,1% with RF-DWT, lung cancer 100% with RF-DWT, prostate tumors 95,49% with RF-DWT, and ovarian cancer 100% with RF-DWT. From these results it can be concluded that RF-DWT is better than CART-DWT.

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Methodology for Malware Classification using a Random Forest Classifier

2018 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) ◽

10.1109/ropec.2018.8661441 ◽

2018 ◽

Cited By ~ 4

Author(s):

Carlos Domenick Morales-Molina ◽

Diego Santamaria-Guerrero ◽

Gabriel Sanchez-Perez ◽

Hector Perez-Meana ◽

Aldo Hernandez-Suarez

Keyword(s):

Random Forest ◽

Random Forest Classifier ◽

Malware Classification

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Amide proton transfer weighted (APTw) imaging based radiomics allows for the differentiation of gliomas from metastases

Scientific Reports ◽

10.1038/s41598-021-85168-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Elisabeth Sartoretti ◽

Thomas Sartoretti ◽

Michael Wyss ◽

Carolin Reischauer ◽

Luuk van Smoorenburg ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Brain Tumors ◽

Proton Transfer ◽

Multilayer Perceptron ◽

Random Forest Classifier ◽

Amide Proton ◽

Low Grade ◽

Who Grade ◽

Amide Proton Transfer

AbstractWe sought to evaluate the utility of radiomics for Amide Proton Transfer weighted (APTw) imaging by assessing its value in differentiating brain metastases from high- and low grade glial brain tumors. We retrospectively identified 48 treatment-naïve patients (10 WHO grade 2, 1 WHO grade 3, 10 WHO grade 4 primary glial brain tumors and 27 metastases) with either primary glial brain tumors or metastases who had undergone APTw MR imaging. After image analysis with radiomics feature extraction and post-processing, machine learning algorithms (multilayer perceptron machine learning algorithm; random forest classifier) with stratified tenfold cross validation were trained on features and were used to differentiate the brain neoplasms. The multilayer perceptron achieved an AUC of 0.836 (receiver operating characteristic curve) in differentiating primary glial brain tumors from metastases. The random forest classifier achieved an AUC of 0.868 in differentiating WHO grade 4 from WHO grade 2/3 primary glial brain tumors. For the differentiation of WHO grade 4 tumors from grade 2/3 tumors and metastases an average AUC of 0.797 was achieved. Our results indicate that the use of radiomics for APTw imaging is feasible and the differentiation of primary glial brain tumors from metastases is achievable with a high degree of accuracy.

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Fully automated analysis combining [18F]-FET-PET and multiparametric MRI including DSC perfusion and APTw imaging: a promising tool for objective evaluation of glioma progression

European Journal of Nuclear Medicine and Molecular Imaging ◽

10.1007/s00259-021-05427-8 ◽

2021 ◽

Author(s):

K. J. Paprottka ◽

S. Kleiner ◽

C. Preibisch ◽

F. Kofler ◽

F. Schmidt-Graf ◽

...

Keyword(s):

Random Forest ◽

Tumor Progression ◽

Automated Analysis ◽

Random Forest Classifier ◽

Imaging Modalities ◽

Amide Proton ◽

Tumor Board ◽

Dynamic Susceptibility ◽

Fet Pet ◽

Promising Tool

Abstract Purpose To evaluate diagnostic accuracy of fully automated analysis of multimodal imaging data using [18F]-FET-PET and MRI (including amide proton transfer-weighted (APTw) imaging and dynamic-susceptibility-contrast (DSC) perfusion) in differentiation of tumor progression from treatment-related changes in patients with glioma. Material and methods At suspected tumor progression, MRI and [18F]-FET-PET data as part of a retrospective analysis of an observational cohort of 66 patients/74 scans (51 glioblastoma and 23 lower-grade-glioma, 8 patients included at two different time points) were automatically segmented into necrosis, FLAIR-hyperintense, and contrast-enhancing areas using an ensemble of deep learning algorithms. In parallel, previous MR exam was processed in a similar way to subtract preexisting tumor areas and focus on progressive tumor only. Within these progressive areas, intensity statistics were automatically extracted from [18F]-FET-PET, APTw, and DSC-derived cerebral-blood-volume (CBV) maps and used to train a Random Forest classifier with threefold cross-validation. To evaluate contribution of the imaging modalities to the classifier’s performance, impurity-based importance measures were collected. Classifier performance was compared with radiology reports and interdisciplinary tumor board assessments. Results In 57/74 cases (77%), tumor progression was confirmed histopathologically (39 cases) or via follow-up imaging (18 cases), while remaining 17 cases were diagnosed as treatment-related changes. The classification accuracy of the Random Forest classifier was 0.86, 95% CI 0.77–0.93 (sensitivity 0.91, 95% CI 0.81–0.97; specificity 0.71, 95% CI 0.44–0.9), significantly above the no-information rate of 0.77 (p = 0.03), and higher compared to an accuracy of 0.82 for MRI (95% CI 0.72–0.9), 0.81 for [18F]-FET-PET (95% CI 0.7–0.89), and 0.81 for expert consensus (95% CI 0.7–0.89), although these differences were not statistically significant (p > 0.1 for all comparisons, McNemar test). [18F]-FET-PET hot-spot volume was single-most important variable, with relevant contribution from all imaging modalities. Conclusion Automated, joint image analysis of [18F]-FET-PET and advanced MR imaging techniques APTw and DSC perfusion is a promising tool for objective response assessment in gliomas.

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