Publications

(2024). Automated Assessment of T2-Weighted MRI to Differentiate Malignant and Benign Primary Solid Liver Lesions in Noncirrhotic Livers Usiong Radiomics. Academic Radiology.

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(2024). Radiology AI Deployment and Assessment Rubric (RADAR) to bring value-based AI into radiological practice. Insights into Imaging.

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(2024). Preoperative Classification of Peripheral Nerve Sheath Tumors on MRI Using Radiomics. Cancers.

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(2024). New implementation of data standards for AI research in precision oncology. Experience from EuCanImage.

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(2024). Minimally Interactive Segmentation of Soft-Tissue Tumors on CT and MRI using Deep Learning.

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(2024). MAMA-MIA: A Large-Scale Multi-Center Breast Cancer DCE-MRI Benchmark Dataset with Expert Segmentations.

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(2024). Comprehensive characterization of circulating tumor cells and cell-free DNA in patients with metastatic melanoma. Molecular Oncology.

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(2023). Obtained an AINed Personal Fellowship Grant!. AINed Personal Fellowship.

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(2023). Auditing Unfair Biases in CNN-based Diagnosis of Alzheimer’s Disease. Fairness of AI in Medical Imaging (FAIMI) MICCAI 2023 Workshop.

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(2023). Predicting survival after surgery for colorectal liver metastasis with deep learning.

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(2023). Multi-center External Validation of a Radiomics Model Differentiating Between ALT and Lipoma Using Automatic and Minimally Interactive Segmentation Methods.

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(2023). Interactive segmentation of Soft-Tissue Tumors on MRI and CT.

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(2023). Radiology AI Deployment and Assessment Rubric (RADAR) for value-based AI in Radiology.

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(2023). Bias and Fairness in Radiomics: A Comparative Analysis of Machine Learning Models on Four Oncology Datasets.

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(2023). Independent validation of CT radiomics models in colorectal liver metastases: predicting local tumour progression after ablation. Eruopean Radiology.

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(2023). Automated Assessment of T2-Weighted MRI to Differentiate Malignant and Benign Primary Solid Liver Lesions in Noncirrhotic Livers Usiong Radiomics. Academic Radiology.

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(2023). AI and image analysis of liver metastases. Liver Metastases Research Network (LMRN) Annual Meeting 2023.

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(2023). Zijn we dan artificieel intelligent met mammografie/MRI . Rijnmond Regional Borstkanker meeting.

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(2023). Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects. European Radiology Experimental.

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(2022). Comprehensive characterization of circulating tumor cells and cell-free DNA in patients with metastatic melanoma.

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(2022). Current status and future outlook on artificial intelligence in radiological imaging for liver metastases. Liver Metastases Research Network (LMRN) Annual Meeting 2022.

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(2022). Optimization of Preoperative Lymph Node Staging in Patients with Muscle-Invasive Bladder Cancer Using Radiomics on Computed Tomography. Bladder Cancer Lymph Node Staging with Radiomics.

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(2022). Automatic quantification of complex regional pain syndrome using radiomics and deep learning based on thermography images.

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(2022). Automated differentiation of malignant and benign primary solid liver lesions on MRI: an externally validated radiomics model.

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(2022). Visual Versus Automated Detection of Enlarged Perivascular Spaces and their Mimics.

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(2022). Differential diagnosis and molecular stratification of gastrointestinal stromal tumors on CT images using a radiomics approach. Journal of Digital Imaging.

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(2021). Classification of Clinically Significant Prostate Cancer on Multi-Parametric MRI: A Validation Study Comparing Deep Learning and Radiomics. Cancers.

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(2021). Reproducible radiomics through automated machine learning validated on twelve clinical applications.

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(2021). Predicting symptomatic mesenteric mass in small intestinal neuroendocrine tumors using radiomics. Endocrine-Related Cancer.

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(2021). Reproducible radiomics through automated machine learning validated on twelve clinical applications. Euro-Bioimaging User Forum 2021: Understanding and Fighting Cancer.

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(2021). The effect of preprocessing on convolutional neurnal networks for medical image segmentation. International Symposium on Biomedical Imaging (ISBI 2021).

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(2021). The BRAF P.V600E Mutation Status of Melanoma Lung Metastases Cannot Be Discriminated on Computed Tomography by LIDC Criteria nor Radiomics Using Machine Learning. Journal of Personalized Medicine.

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(2021). Multicentre studies for more robust radiomics signatures. ECR 2021.

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(2021). A Multi-Center, Multi-Vendor Study to Evaluate the Generalizability of a Radiomics Model for Classifying Prostate cancer: High Grade vs. Low Grade. Diagnostics.

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(2021). WORCDatabase. Zenodo.

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(2021). The WORC* database: MRI and CT scans, segmentations, and clinical labels for 930 patients from six radiomics studies.

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(2021). MesentericRadiomics. Zenodo.

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(2021). MelaRadiomics. Zenodo.

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(2021). LiverRadiomics. Zenodo.

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(2021). Distinguishing pure histopathological growth patterns of colorectal liver metastases on CT using deep learning and radiomics: a pilot study. Clinical & Experimental Metastasis.

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(2021). CLMRadiomics. Zenodo.

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(2020). FAIR-compliant clinical, radiomics and DICOM metadata of RIDER, interobserver, Lung1 and head-Neck1 TCIA collections. Medical Physics.

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(2020). Differential diagnosis and mutation stratification of desmoid-type fibromatosis on MRI using radiomics. European Journal of Radiology.

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(2020). Multicentre studies for more robust radiomics signatures. ECR 2020.

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(2020). Radiomics model to predict hepatocellular carcinoma on liver MRI of high-risk patients in surveillance: a proof-of-concept study. Insights into Imaging.

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(2020). Prediction of histopathological growth patterns by radiomics and CT-imaging in patients with operable colorectal liver metastases: a proof-of-concept study. Insights into Imaging.

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(2020). Distinguishing well-differentiated liposarcomas from lipomas on MR images using a radiomics approach. Insights into Imaging.

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(2020). Distinguishing desmoid-type fibromatosis from soft tissue sarcoma on MRI using a radiomics approach. Insights into Imaging.

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(2020). Differential diagnosis and mutation stratification of gastrointestinal stromal tumours on CT images using a radiomics approach. Insights into Imaging.

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(2020). Radiomics of Gastrointestinal Stromal Tumors; Risk Classification Based on Computed Tomography Images – A Pilot Study. European Journal of Surgical Oncology.

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(2020). Radiomics: Data mining using quantitative medical image features. Handbook of Medical Image Computing and Computer Assisted Intervention.

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(2020). Prediction of Symptomatic Mesenteric Mass in Patients with Small Intestinal Neuroendocrine Tumors Using a CT Radiomics Approach. Neuroendocrinology.

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(2020). GISTRadiomics. Zenodo.

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(2020). Fully automatic construction of optimal radiomics workflows. Health-RI Conference.

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(2020). DMRadiomics. Zenodo.

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(2019). Radiomics approach to distinguish between well differentiated liposarcomas and lipomas on MRI. British Journal of Surgery.

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(2019). Predicting the 1p/19q Codeletion Status of Presumed Low-Grade Glioma with an Externally Validated Machine Learning Algorithm. Clinical Cancer Research.

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(2019). Radiomics of gastrointestinal stromal tumours, risk classification based on computed tomography images: A pilot study. Annals of Oncology.

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(2019). Differentiating well-differentiated liposarcomas from lipomas using a radiomics approach. Annals of Oncology.

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(2019). Multicenter CT phantoms public dataset for radiomics reproducibility studies. Radiotherapy and Oncology.

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(2019). Classification Of Prostate Cancer: High Grade Versus Low Grade Using A Radiomics Approach. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019).

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(2019). Are quality assurance phantoms useful to assess radiomics reproducibility? A multi-center study. Radiotherapy and Oncology.

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(2019). Multicenter CT phantoms public dataset for radiomics reproducibility tests. Medical Physics.

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(2019). Prediction of surgery requirement in mesenteric fibrosis on CT using a radiomics approach. Insights into Imaging.

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(2019). Mutation stratification of desmoid-type fibromatosis using a radiogenomics approach. European Journal of Surgical Oncology.

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(2019). Fully automatic construction of optimal radiomics workflows. Insights into Imaging.

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(2019). Classification of malignant and benign liver tumours using a radiomics approach. Insights into Imaging.

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(2019). Radiomics features for use in dementia diagnosis. 36th Annual Scientific Meeting of the ESMRMB.

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(2019). LipoRadiomicsFeatures. Zenodo.

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(2019). Fully automatic construction of optimal radiomics workflows. 7th Dutch Bio-Medical Engineering (BME) Conference.

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(2018). Classification of malignant and benign liver tumors using a radiomics approach. Medical Imaging 2018: Image Processing.

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(2018). Workflow for Optimal Radiomics Classification (WORC). Zenodo.

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(2018). Mutation stratification of desmoid-type fibromatosis using a radiomics approach – preliminary results. Desmoid Tumor Research Foundation (DTRF) meeting.

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(2018). Harmonizing radiomics among applications through adaptive workflow optimization. European Society of Medical Imaging Informatics (EuSoMII) Annual Meeting.

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(2017). A Radiomics Approach for Colorectal Liver Metastases Survival Prediction. Medical Image Computing and Computer Assisted Intervention 2017.

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(2017). Radiomics and liver tumors. Current and Future Perspectives in Primary Liver Tumors Symposium.

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(2017). Prediction of histopathological growth patterns in colorectal liver metastases using a Radiomics approach. Dutch Society for Pattern Recognition (NVPHBV) Spring Meeting.

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