AIMC Topic: Neoplasm Grading

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Imaging-Based Algorithm for the Local Grading of Glioma.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Gliomas are highly heterogeneous tumors, and optimal treatment depends on identifying and locating the highest grade disease present. Imaging techniques for doing so are generally not validated against the histopathologic crit...

A deep learning image-based intrinsic molecular subtype classifier of breast tumors reveals tumor heterogeneity that may affect survival.

Breast cancer research : BCR
BACKGROUND: Breast cancer intrinsic molecular subtype (IMS) as classified by the expression-based PAM50 assay is considered a strong prognostic feature, even when controlled for by standard clinicopathological features such as age, grade, and nodal s...

Distinguishing between paediatric brain tumour types using multi-parametric magnetic resonance imaging and machine learning: A multi-site study.

NeuroImage. Clinical
The imaging and subsequent accurate diagnosis of paediatric brain tumours presents a radiological challenge, with magnetic resonance imaging playing a key role in providing tumour specific imaging information. Diffusion weighted and perfusion imaging...

The diagnostic value of quantitative texture analysis of conventional MRI sequences using artificial neural networks in grading gliomas.

Clinical radiology
AIM: To explore the value of quantitative texture analysis of conventional magnetic resonance imaging (MRI) sequences using artificial neural networks (ANN) for the differentiation of high-grade gliomas (HGG) and low-grade gliomas (LGG).

Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study.

The Lancet. Oncology
BACKGROUND: An increasing volume of prostate biopsies and a worldwide shortage of urological pathologists puts a strain on pathology departments. Additionally, the high intra-observer and inter-observer variability in grading can result in overtreatm...

Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study.

The Lancet. Oncology
BACKGROUND: The Gleason score is the strongest correlating predictor of recurrence for prostate cancer, but has substantial inter-observer variability, limiting its usefulness for individual patients. Specialised urological pathologists have greater ...

Prediction of IDH and TERT promoter mutations in low-grade glioma from magnetic resonance images using a convolutional neural network.

Scientific reports
Identification of genotypes is crucial for treatment of glioma. Here, we developed a method to predict tumor genotypes using a pretrained convolutional neural network (CNN) from magnetic resonance (MR) images and compared the accuracy to that of a di...

One-slice CT image based kernelized radiomics model for the prediction of low/mid-grade and high-grade HNSCC.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
An accurate grade prediction can help to appropriate treatment strategy and effective diagnosis to Head and neck squamous cell carcinoma (HNSCC). Radiomics has been studied for the prediction of carcinoma characteristics in medical images. The succes...

Prediction of lower-grade glioma molecular subtypes using deep learning.

Journal of neuro-oncology
INTRODUCTION: It is useful to know the molecular subtype of lower-grade gliomas (LGG) when deciding on a treatment strategy. This study aims to diagnose this preoperatively.