Preoperative assessment of the histopathological growth patterns of colorectal liver metastasis on CT using artificial intelligence

Background: Histopathological growth patterns (HGP) are a biomarker for predicting survival and systemic treatment effectiveness in colorectal liver metastasis (CRLM). Currently, HGP assessment in CRLM requires the resection specimen. Predicting the HGP from preoperative medical imaging could allow more personalised care and better outcomes....

Classification of primary liver tumors with radiomics and deep learning based on multiphasic MRI

Primary liver cancer is a commonly diagnosed cancer and accurate diagnosis is crucial for treatment planning. To differentiate between malignant and benign liver tumors, contrast-enhanced MRI is typically used as it provides information over multiple contrast phases. However, diagnosis based on MRI is challenging. In this study, automatic...

QuantImage v2: a comprehensive and integrated physician-centered cloud platform for radiomics and machine learning research

Background: Radiomics, the field of image-based computational medical biomarker research, has experienced rapid growth over the past decade due to its potential to revolutionize the development of personalized decision support models. However, despite its research momentum and important advances toward methodological standardization, the...

Differentiation of Hypertrophic Cardiomyopathy Mutation Carriers without Left Ventricular Hypertrophy and Healthy Controls on CMR using Radiomics

Hypertrophic cardiomyopathy (HCM) is known as a frequent, genetic cardiovascular disease, often caused by mutations of sarcomere protein genes. HCM is primarily characterized by the presence of an increased left ventricular wall thickness, i.e. left ventricular hypertrophy (LVH). However, the disease appears to be asymptomatic in some patients,...

Classification of clinically significant prostate cancer on multi-parametric MRI: A validation study comparing deep learning and radiomics

The computer-aided analysis of prostate multiparametric MRI (mpMRI) could improve significant-prostate-cancer (PCa) detection. Various deep-learning-and radiomics-based methods for significant-PCa segmentation or classification have been reported in the literature. To be able to assess the generalizability of the performance of these methods,...

Differential Diagnosis and Molecular Stratification of Gastrointestinal Stromal Tumors on CT Images Using a Radiomics Approach

Treatment planning of gastrointestinal stromal tumors (GISTs) includes distinguishing GISTs from other intra-abdominal tumors and GISTs’ molecular analysis. The aim of this study was to evaluate radiomics for distinguishing GISTs from other intra-abdominal tumors, and in GISTs, predict the c-KIT, PDGFRA, BRAF mutational status, and mitotic...

Automatic algorithm selection and hyperparameter optimization for medical image classification

Recent years have shown a tremendous increase in the application of Artificial Intelligence to the field of radiology, often through the extraction and analysis of large numbers of quantitative features from medical images. These applications increase the demand for machine learning models to extract information from these images. To provide...

The influence of the exclusion of central necrosis on [¹⁸F]FDG PET radiomic analysis

Background: Central necrosis can be detected on [¹⁸F]FDG PET/CT as a region with little to no tracer uptake. Currently, there is no consensus regarding the inclusion of regions of central necrosis during volume of interest (VOI) delineation for radiomic analysis. The aim of this study was to assess how central necrosis affects...

A multi-center, multi-vendor study to evaluate the generalizability of a radiomics model for classifying prostate cancer: High grade vs. low grade

Radiomics applied in MRI has shown promising results in classifying prostate cancer lesions. However, many papers describe single-center studies without external validation. The issues of using radiomics models on unseen data have not yet been sufficiently addressed. The aim of this study is to evaluate the generalizability of radiomics...

Automated classification of significant prostate cancer on MRI: A systematic review on the performance of machine learning applications

Significant prostate carcinoma (sPCa) classification based on MRI using radiomics or deep learning approaches has gained much interest, due to the potential application in assisting in clinical decision-making. Objective: To systematically review the literature (i) to determine which algorithms are most frequently used for sPCa classification...

Differential diagnosis and mutation stratification of desmoid-type fibromatosis on MRI using radiomics

Purpose: Diagnosing desmoid-type fibromatosis (DTF) requires an invasive tissue biopsy with β-catenin staining and CTNNB1 mutational analysis, and is challenging due to its rarity. The aim of this study was to evaluate radiomics for distinguishing DTF from soft tissue sarcomas (STS), and in DTF, for predicting the CTNNB1 mutation types....

MRI prostate cancer radiomics: Assessment of effectiveness and perspectives

Prostate cancer is a disease with very high prevalence and mortality in the western world. An early accurate diagnosis can increase treatment efficiency. Current diagnosing techniques consist in systematic biopsy sampling. Radiomics can infer tumor's phenotypic differentiations from medical images, providing an accurate guide for biopsy sampling...