Introduction to Quantitative Radiomics

Quantitative radiomics features were extracted from CECT images, and the least absolute shrinkage and selection operator (LASSO) method was applied to select radiomics features.

Background This study aimed to develope and validate a radiomics nomogram by integrating the quantitative radiomics characteristics of No.

The purpose of this study was to develop a robust predictive model for ECE using quantitative radiomics.

We also found that using machine learning classifiers such as k-nearest neighbors (kNN) and support vector machine (SVM) on CNN-derived quantitative radiomics features yielded comparable discriminative performance, with AUC of up to 0.

Purpose To investigate whether quantitative radiomics features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) could be used to differentiate triple-negative breast cancer (TNBC) and nontriple-negative breast cancer (non-TNBC).

All 155 patients underwent simulation CT before nCRT, Quantitative radiomics features were extracted from CT images of each patient.

CONCLUSIONS The CT-based quantitative radiomics analysis could enable high accuracy prediction of subsequent MaVI development in HCC with prognostic implications.

Conclusions: We evaluated quantitative radiomics features from MRI images and presented an externally validated radiomics-based clinical model, the LF model for the prediction of PHLF in HCC patients, which could assist clinicians in making treatment strategies before surgery.

Methods: Quantitative radiomics and dosiomics features were extracted from CIRT planning CTs with dose distribution (DD) and multiparametric MRIs (mpMRI, pre re-RT) of 141 patients (recurrent grade III: n=56 40%, grade IV: n=85 60%) treated with a median dose of 42 Gy (RBE) and a median fraction of 13.

We explored the noninvasive method to predict the prognosis of progression and Glasgow Outcome Scale (GOS) by using a quantitative radiomics approach and statistical analysis.

This current study aimed to develop risk models for small solid and subsolid pulmonary nodules based on clinical and quantitative radiomics features.

Quantitative radiomics metrics of the primary lesion were extracted from three sets of monochromatic images (40, 70, and 100 keV) at venous phase.

All axillary lymph nodes were manually delineated on the T2W MR images, and quantitative radiomics features were extracted from the delineated regions.

All volumes of interest of tumors were respectively segmented on T2-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient, artery phase, portal venous phase, and delay phase sequences, from which quantitative radiomics features were extracted and analyzed individually or combined.

Conclusions Quantitative radiomics imaging features extracted from digital renal ultrasound are fully capable of distinguishing IgA nephropathy from MN.

A total of 612 quantitative radiomics features were extracted from mammograms using the Pyradiomics software.

The purpose of this study is to develop and validate a MRI-based prognostic quantitative radiomics classifier in patients with newly diagnosed GBM and to evaluate whether the classifier allows stratification with improved accuracy over the clinical and qualitative imaging features risk models.

Quantitative radiomics features were extracted from tumor regions in 210 Glioblastoma (GBM) and 75 low-grade glioma (LGG) MRI subjects.

RATIONALE AND OBJECTIVES The objective of this study was to demonstrate improvement in distinguishing between benign lesions and luminal A breast cancers in a large clinical breast magnetic resonance imaging database by using quantitative radiomics over maximum linear size alone.

The purpose of this study was to assess the potential for using an automated, quantitative radiomics platform on magnetic resonance (MR) breast imaging for inferring underlying activity of clinically relevant gene pathways derived from RNA sequencing of invasive breast cancers prior to therapy.

RATIONALE AND OBJECTIVES To investigate whether quantitative radiomics features extracted from computed tomography (CT) can predict microsatellite instability (MSI) status in an Asian cohort of patients with stage Ⅱ colorectal cancer (CRC).

Breast tumor segmentation in MRI is an essential step for quantitative radiomics analysis, where automated and accurate tumor segmentation is needed but very challenging.

In the training set, the sensitivity, specificity, accuracy and area under the curve (AUC) values of the logistic regression model for evaluating HER-2 status based on quantitative radiomics features were 87.

Quantitative radiomics features, extracted from medical images, characterize tumour-phenotypes and have been shown to provide prognostic value in predicting clinical outcomes.

According to the time-signal-intensity curve, lesion segmentation was performed on the first and on the strongest phase of the enhancement on the original DCE-MRI images, and 1,029 quantitative radiomics features were calculated automatically from each lesion, wherein there were three datasets available (Dataset-F, Dataset-S and Dataset-FS).

All recruited patients underwent nonenhanced and dual-phase chest contrast-enhanced CT; 1160 quantitative radiomics features were calculated to build a radiomics classification model.

Quantitative radiomics allows for the extraction of more advanced shape features.

CT scans before any invasive operation were gathered and analyzed to extract quantitative radiomics features and build random forest classifiers to identify EGFR mutants from wild types.