Related papers: MPRAD: A Multiparametric Radiomics Framework
Background. Radiomic features, derived from a region of interest (ROI) in medical images, are valuable as prognostic factors. Selecting an appropriate ROI is critical, and many recent studies have focused on leveraging multiple ROIs by…
Radiomics analysis has emerged as a promising approach for extracting quantitative features from medical images to aid in cancer diagnosis and treatment. However, radiomics research currently lacks standardization, and radiomics features…
Chest X-ray becomes one of the most common medical diagnoses due to its noninvasiveness. The number of chest X-ray images has skyrocketed, but reading chest X-rays still have been manually performed by radiologists, which creates huge…
Background and Purpose: Radiomics features are used to identify disease types and predict therapy outcomes. However, how the radiomics features are different among different anatomical structures has never been investigated. Hence, we…
MR-derived radiomic features have demonstrated substantial predictive utility in modeling different prognostic factors of glioblastomas and other brain cancers. However, the biological relationship underpinning these predictive models has…
Background: The high dimensionality of radiomic feature sets, the variability in radiomic feature types and potentially high computational requirements all underscore the need for an effective method to identify the smallest set of…
With a high rate of morbidity and mortality, colorectal cancer (CRC) ranks third in mortality among cancers. By analyzing the texture properties of images and quantifying the heterogeneity of tumors, radiomics and radiogenomics are…
To develop and externally validate integrated ultrasound nomograms combining BIRADS features and quantitative morphometric characteristics, and to compare their performance with expert radiologists and state of the art large language models…
Patients with metastatic breast cancer (mBC) undergo continuous medical imaging during treatment, making accurate lesion detection and monitoring over time critical for clinical decisions. Predicting drug response from post-treatment data…
A new paradigm is beginning to emerge in Radiology with the advent of increased computational capabilities and algorithms. This has led to the ability of real time learning by computer systems of different lesion types to help the…
Artificial intelligence based radiomics models for thyroid ultrasound (US) often achieve strong diagnostic performance but remain difficult to interpret, limiting clinical trust and adoption. We developed and validated an interpretable…
Breast cancer as a medical condition and mammograms as images exhibit many dimensions of variability across the population. Similarly, the way diagnostic systems are used and maintained by clinicians varies between imaging centres and…
Classical radiomic features are designed to quantify image appearance and intensity patterns. Compared with end-to-end deep learning (DL) models trained for disease classification, radiomics pipelines with low-dimensional parametric…
Current imaging methods for diagnosing BC are associated with limited sensitivity and specificity and modest positive predictive power. The recent progress in image analysis using artificial intelligence (AI) has created great promise to…
Motivated by the question, "Can we generate tumors with desired attributes?'' this study leverages radiomics features to explore the feasibility of generating synthetic tumor images. Characterized by its low-dimensional yet biologically…
Breast cancer is the most prevalent cancer among women and predicting pathologic complete response (pCR) after anti-cancer treatment is crucial for patient prognosis and treatment customization. Deep learning has shown promise in medical…
Radiomics and deep learning both offer powerful tools for quantitative medical imaging, but most existing fusion approaches only leverage global radiomic features and overlook the complementary value of spatially resolved radiomic…
The feature extraction methods of radiomics are mainly based on static tomographic images at a certain moment, while the occurrence and development of disease is a dynamic process that cannot be fully reflected by only static…
The current cancer treatment practice collects multimodal data, such as radiology images, histopathology slides, genomics and clinical data. The importance of these data sources taken individually has fostered the recent raise of radiomics…
Classical radiomic features have been designed to describe image appearance and intensity patterns. These features are directly interpretable and readily understood by radiologists. Compared with end-to-end deep learning (DL) models, lower…