What is Radiomics?
Turning medical images into data
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Publications
All Publications
Predictive value of baseline and longitudinal MRI radiomics for intracranial progression-free survival in ALK-positive NSCLC patients with brain metastases
Predicting intracranial progression-free survival (iPFS) is crucial for managing ALK-positive NSCLC patients with brain metastases…
Society for Immunotherapy of Cancer (SITC) consensus statement on essential biomarkers for immunotherapy clinical protocols
Immunotherapy of cancer is now an essential pillar of treatment for patients with…
Exploratory radiomics analysis in unresectable hepatocellular carcinoma treated with durvalumab alone or combined with tremelimumab or bevacizumab
We analyzed baseline and end-of-treatment (EOT) abdominal CT scans to explore associations between image features and clinical outcomes…
Baseline and early follow-up radiomics biomarkers for tumor growth in uveal melanoma patients treated with roginolisib (IOA-244)
This study investigated lesion-level radiomic features from baseline and early follow-up scans to identify individual tumor growth profiles…
Quantitative radiomics for the detection of symptomatic pneumonitis following chemoradiotherapy in patients with stage III unresectable NSCLC
A proprietary radiomic assessment with an auto segmentation model was applied to CT images to identify 4 abnormality patterns…
Roginolisib (IOA-244), a first oral allosteric modulator of phosphoinositide 3-kinase inhibitor delta (PI3Kδ)…
Radiomic assessment in UM patients detects mixed responses in…
Lung tumour vascularity is a risk factor for survival in NSCLC patients undergoing surgery
Vascularization is known to be linked to tumour growth. We explored the potential…
Lesion-specific radiomics analysis shows promising results for early-stage efficacy assessment…
Radiomics is an image based approach that allows for characterization and quantification of tumor lesions…
A review in radiomics: Making personalized medicine a reality via routine imaging
Radiomics is the quantitative analysis of standard‐of‑care medical imaging; the information obtained can be applied within clinical decision support systems…
White Paper | Leveraging AI-Enabled Tumor Assessment Tools on Radiological Images to Evaluate Treatment Effect and Support Clinical Trial Endpoints in Solid Tumors
Accurate and consistent tumor measurement is fundamental to evaluating treatment response in oncology clinical trials. For more than 25 years…
Towards texture accurate slice interpolation of medical images using PixelMiner
Quantitative image analysis models are used for medical imaging tasks such as registration, classification…
A Multimodal Imaging-Supported Down Syndrome Mouse Model of RSV Infection
Individuals with Down syndrome (DS) are more prone to develop severe respiratory tract infections. Although a RSV infection has a high clinical impact…
Radiomics and Delta-Radiomics Signatures to Predict Response and Survival…
The aim of our study was to determine the potential role of CT-based radiomics in predicting treatment response and survival in patients with advanced NSCLC…
Automatic segmentation of mediastinal pathological lymph nodes with Deep Learning
The importance of accurate manual segmentations as ground truths
Machine Learning to Identify Patients at Risk of Developing New-Onset Atrial Fibrillation after Coronary Artery Bypass
This study aims to get an effective machine learning (ML) prediction model of new-onset postoperative atrial fibrillation (POAF)…
Deep learning based identification of bone scintigraphies containing metastatic bone disease foci
Metastatic bone disease (MBD) is the most common form of metastases, most frequently deriving from prostate cancer.