Martijn Pieter Anton Starmans

Assistant professor

Erasmus University Rotterdam

Netherlands

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Martijn Pieter Anton Starmans is an Assistant Professor at Erasmus University Rotterdam, Netherlands. His research areas include radiological imaging in oncology, particularly focusing on soft-tissue and bone tumors, as well as advanced methodologies in deep learning for medical imaging. His recent publications highlight significant contributions to the classification and segmentation of tumors and cancer characterization, demonstrating an emphasis on incorporating artificial intelligence into clinical practices.

Martijn Pieter Anton Starmans

Research Interests

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Articles (18)

FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare.

AI in radiological imaging of soft-tissue and bone tumours: a systematic review evaluating against CLAIM and FUTURE-AI guidelines

Minimally interactive segmentation of soft-tissue tumors on CT and MRI using deep learning

A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations

Size matters

Prognostic value of total tumor volume in patients with colorectal liver metastases

Independent validation of CT radiomics models in colorectal liver metastases

Preoperative Classification of Peripheral Nerve Sheath Tumors on MRI Using Radiomics

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

Automated Assessment of T2-Weighted MRI to Differentiate Malignant and Benign Primary Solid Liver Lesions in Noncirrhotic Livers Using Radiomics

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

ESR Essentials: how to get to valuable radiology AI: the role of early health technology assessment—practice recommendations by the European Society of Medical Imaging Informatics

Classifying histopathological growth patterns for resected colorectal liver metastasis with a deep learning analysis

Multi-center external validation of an automated method segmenting and differentiating atypical lipomatous tumors from lipomas using radiomics and deep-learning on MRI

Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects

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

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

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

Collaborators (14)

Wendy Rogers

Jayashree Kalpathy-Cramer

Tinashe Mutsvangwa

Stacy Carter

Walter Taal

Stefan Klein

Maciej Bobowicz

Saskia Wilting

Andrew Scarsbrook

JL Boormans

Hendrik Erenstein

Sean Benson

Haridimos Kondylakis

Jacob J. Visser

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