• September 8th, 2022

Radiomics' Event Package for ESMO

Here is the online version of our information package. Sign up to get access.

  • July 15th, 2022

New Beginnings - Radiomics 1st Newsletter

Radiomics is pleased to share the 1st edition of its newsletter series. Sign up to stay up to date with the latest news from Radiomics!

  • May 24th, 2022

Radiomics' Event Information Package for ASCO

Here is the online version of our information package. Sign up to get access.

  • April 21st, 2022

The clinical needs and regulatory requirements of an imaging-based companion diagnostic

The fourth part in the series of articles about imaging-based companion diagnostics will focus on the necessary regulations and considerations and is available to read here.

  • March 22nd, 2022

Technical considerations for an imaging-based companion diagnostic

The third part in the series of articles about imaging-based companion diagnostics will focus on their integration into clinics and is available to read here.

  • February 16th, 2022

9 in 10 pivotal clinical trials in oncology fail: Radiomics can help to make the best use of research investments

A recent review showed that 9 out of 10 clinical trials fail to test the therapy as effective, this begs the question if all potential insights from early-stage testing are being captured and used to guide the success in late-stage research and clinical use?

  • December 13th, 2021

Developing an imaging-based companion diagnostic tool with Radiomics

The second part in the series of articles about imaging-based companion diagnostics will focus on their development and is available to read here.

  • November 4th, 2021

What is an Imaging-based Companion Diagnostics and why is it needed?

Radiomics has produced a series of articles aimed at providing a complete overview of imaging companion diagnostics and their development pathway. The first one has now been released and is available by dropping an email to [email protected]

  • February 22nd, 2021

Binnenoor beter herkennen met kunstmatige intelligentie

De Belgische startup Radiomics heeft een nieuwe, op kunstmatige intelligentie gebaseerde, methode ontwikkeld om het binnenoor beter en nauwkeuriger in kaart te brengen tijdens een MRI-onderzoek. Een actie die nu nog handmatig uitgevoerd wordt door artsen. Bij de ontwikkeling heeft Radiomics nauw samengewerkt met artsen en wetenschappers van het Maastricht UMC+ (MUMC+), de Universiteit Maastricht, het Universitair Ziekenhuis Antwerpen (UZA) en de Universiteit Antwerpen.

  • February 22nd, 2021

Kunstmatige intelligentie vereenvoudigt onderzoek binnenoor

Artsen en wetenschappers van onder meer het Maastricht UMC+ en de Universiteit Maastricht hebben een methode ontwikkeld om het binnenoor in kaart te brengen met behulp van kunstmatige intelligentie.

  • February 22nd, 2021

Artificial intelligence helps doctors address inner ear problems

A new method to map the inner ear using artificial intelligence (AI). That is what the Belgian start-up Radiomics has developed in close collaboration with doctors and scientists from Maastricht UMC +, Maastricht University, Antwerp University Hospital (UZA) and the University of Antwerp.

  • February 18th, 2021

AI tool for the segmentation of the inner ear on MRI

Radiomics together with UZAntwerpen and UMaastricht, has developed a novel tool for the automatic identification of the inner ear on MRI

  • November 30th, 2020

ESTRO 2020

Presentation of our work on the prospective validation of a radiomics signature for chemoradiotherapy lung cancer patients


• A non-invasive, automated diagnosis of Menière’s disease using radiomics and machine learning on conventional magnetic resonance imaging: A multicentric, case-controlled feasibility study - October 2021 - La Radiologia Medica

The multi-layer perceptron model identifies patients with Menière’s disease based on radiomic features extracted from conventional T2-weighted MRI scans.

• Privacy preserving distributed learning classifiers – sequential learning with small sets of data - July 2021 - Computers in Biology and Medicine

We propose a privacy preserving distributed learning framework to exploit very small, siloed sets of clinical and imaging data to train AI models with performance comparable to centralized training

 A review in radiomics: Making personalized medicine a reality via routine imaging – July 2021 – Medicinal Research Reviews

Radiomics is the quantitative analysis of standard-of-care medical imaging; the information obtained can be applied to predictive and prognostic models.

 A Prospectively Validated Prognostic Model for Patients with Locally Advanced Squamous Cell Carcinoma of the Head and Neck Based on Radiomics of Computed Tomography Images – July 2021 – Cancers (MDPI)

We propose a multifactorial prognostic model including radiomics features to improve risk stratification for advanced HNSCC patients.

 Radiomics in Lung Diseases Imaging: State-of-the-Art for Clinicians – June 2021 – Journal of Personalized Medicine (MDPI)

Radiomic Analysis in Chest Computed Tomography: A Clinician’s Perspective

 The application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset – May 2021 – Plos One

Demonstration that the reproducibility of a given radiomics feature is not constant but is dependent on the heterogeneity found in the data under analysis.

 Deep learning for the fully automated segmentation of the inner ear on MRI – February 2021 – Scientific Reports

3D U-net model for the fully automated segmentation of the inner ear in MRI that is equivalent to human readers. 



• Development and Validation of an Automated Radiomic CT Signature for Detecting COVID-19 – December 2020 – Diagnostics (MDPI)

COVID-19 diagnosis in chest CT using fully automated AI framework

• Non-invasive imaging prediction of tumour hypoxia: A novel developed and externally validated CT and FDG-PET-based radiomic signature – December 2020 – Radiotherapy and Oncology

Development and validation of a disease-agnostic and disease-specific CT (+FDG-PET) based radiomics hypoxia classification signature.

• Diagnosis of Invasive Lung Adenocarcinoma Based on Chest CT Radiomic Features of Part-Solid Pulmonary Nodules: A Multicenter Study – November 2020 – Radiology

A radiomic signatures of part-solid nodules in CT scan for the detection of invasive lung adenocarcinoma in PSNs.

 Blockchain for Privacy Preserving and Trustworthy Distributed Machine Learning in Multicentric Medical Imaging (C-DistriM) – October 2020 – IEEE Access

A novel distributed learning that combines sequential learning with a blockchain-based platform, namely Chained Distributed Machine learning C-DistriM

 MRI-based radiomics in breast cancer: feature robustness with respect to inter-observer segmentation variability – August 2020 – Scientific Reports

The robustness of radiomics features extracted by two commonly used radiomics software with respect to variability in manual breast tumor segmentation on MRI

 Computed tomography-derived radiomic signature of head and neck squamous cell carcinoma (peri)tumoral tissue for the prediction of locoregional recurrence and distant metastasis after concurrent chemo-radiotherapy – May 2020 – PlosOne

Radiomic analysis of peritumoral tissue may detect invasion of surrounding tissues indicating a higher chance of locoregional recurrence and distant metastases in head and neck squamous cell carcinoma

• The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping – May 2020 – Radiology

The Image Biomarker Standardization Initiative validated consensus-based reference values for 169 radiomics features, enabling calibration and verification of radiomics software.

• Systematic Review of Privacy-Preserving Distributed Machine Learning From Federated Databases in Health Care – March 2020 - JCO Clinical Cancer Informatics

A review of the state-of-the-art in distributed learning in health care

 Preoperative CT-based radiomics combined with intraoperative frozen section is predictive of invasive adenocarcinoma in pulmonary nodules: a multicenter study – January 2020 – European Radiology

Development of a CT-based radiomics signature combined with frozen section (FS) and clinical data to identify invasive adenocarcinomas

• The Emerging Role of Radiomics in COPD and Lung Cancer– January 2020 – Respiration

Radiomics in COPD and lung cancer: A review and potential application in lung cancer evaluation



• Computed Tomography-based Radiomics for Risk Stratification in Prostate Cancer – October 2019 – International Journal of Radiation Oncology – Biology – Physics

A study of the role of CT-based radiomics features in prostate cancer risk stratification using cross-validation

 Radiomics Analysis for Clinical Decision Support in Nuclear Medicine – September 2019 – Seminars in Nuclear Medicine

Radiomics can be used to aid clinical decision support systems in order to build diagnostic, prognostic, and predictive models.

• Challenges and caveats of a multi-center retrospective radiomics study: an example of early treatment response assessment for NSCLC patients using FDG-PET/CT radiomics – June 2019 – PlosOne

Prognostic models based on individual patient characteristics can improve treatment decisions and outcome in the future.

• Longitudinal radiomics of cone-beam CT images from non-small cell lung cancer patients: Evaluation of the added prognostic value for overall survival and locoregional recurrence – April 2019 – Radiotherapy and Oncology

A retrospective study on the potential added prognostic value of a longitudinal radiomics approach using cone-beam computed tomography (CBCT) for NSCLC

• Decision Support Systems in Oncology – February 2019 - JCO Clinical Cancer Informatics

Multifactorial Decision Support Systems for Oncology:Challenges, Opportunities, and Capacity for Precision Medicine



• Applicability of a prognostic CT-based radiomic signature model trained on stage I-III non-small cell lung cancer in stage IV non-small cell lung cancer – October 2018 – Lung Cancer

Prognostic radiomic signature for overall survival in stage IV non-small cell lung cancer patients undergoing chemotherapy

• A review on radiomics and the future of theranostics for patient selection in precision medicine – July 2018 - British Journal of Radiology

Radiomics: A Precision Medicine Decision Support Tool for Oncology Treatment

• Pre-treatment CT radiomics to predict 3-year overall survival following chemoradiotherapy of esophageal cancer – August 2018 – Acta Oncologica

Predicting 3-year overall survival of esophageal cancer patients after chemoradiotherapy using pre-treatment CT radiomic features

• Radiomics for clinical decision support system in oncology – August 2018 - Physica Medica

The process of radiomics, its challenges, opportunities, and capacity to improve clinical decision making

• 18F-fluorodeoxyglucose positron-emission tomography (FDG-PET)-Radiomics of metastatic lymph nodes and primary tumor in non-small cell lung cancer (NSCLC) - A prospective externally validated study – March 2018 – PlosOne

Prognostic model for non-small cell lung cancer with FDG-PET-Radiomics features from LNs

• Development and validation of a radiomic signature to predict HPV (p16) status from standard CT imaging: a multicenter study – February 2018 – British Journal of Radiology

Evaluation of the use of a radiomic approach to identify the HPV status of oropharyngeal squamous cell carcinoma.

• Noninvasive Glioblastoma Testing: Multimodal Approach to Monitoring and Predicting Treatment Response – January 2018 – Disease Markers

Predicting and Monitoring Treatment Response in Glioblastoma with Improved Noninvasive Tests



 Post-radiochemotherapy PET radiomics in head and neck cancer - The influence of radiomics implementation on the reproducibility of local control tumor models – December 2017 – Radiotherapy and Oncology

A comparison of post-radiochemotherapy PET radiomics with local tumor control in head and neck squamous cell carcinoma

 Big Data in radiation therapy: challenges and opportunities – December 2017 – British Journal of Radiology

Data collected and generated by radiation oncology can be classified by the Volume, Variety, Velocity and Veracity.

 Radiomics: the bridge between medical imaging and personalized medicine – October 2017 - Nature Reviews Clinical Oncology

Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging, is gaining importance in cancer research

 Influence of gray level discretization on radiomic feature stability for different CT scanners, tube currents and slice thicknesses: a comprehensive phantom study – September 2017 – Acta Oncologica

Radiomic feature values and their stability: The effect of CT-scanner, slice thicknesses, exposures and gray-level discretization

 Feature selection methodology for longitudinal cone-beam CT radiomics – August 2017 – Acta Oncologica

Radiomic feature values for CT images with different slice thicknesses, exposures and gray-level discretization

 4DCT imaging to assess radiomics feature stability: An investigation for thoracic cancers - August 2017 - Radiotherapy and Oncology

4DCT-scans can be used as alternative to eliminate unstable radiomics features as first step in a feature selection procedure.

 Defining the biological basis of radiomic phenotypes in lung cancer – July 2017 – eLife

Identification of previously undescribed associations between radiomic imaging features, molecular pathways, and clinical factors to enable phenotypic profiling of tumors

 Predictive and prognostic value of CT based radiomics signature in locally advanced head and neck cancers patients treated with concurrent chemoradiotherapy or bioradiotherapy and its added value to Human Papillomavirus status – June 2017 – Oral Oncology

Prognostic and predictive value of radiomics in patients with locally advanced head and neck squamous cell carcinomas treated with concurrent chemoradiotherapy

 Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care: euroCAT – June 2017 – Clinical and Translational Radiation Oncology

A proof-of-principle for future ‘big data’ infrastructures and distributed learning studies

 Decision support systems for personalized and participative radiation oncology – January 2017 – Advanced Drug Delivery Reviews

An overview of the factors that are associated with outcome in radiation oncology and the methodology behind the development of accurate prediction models of treatment outcome