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T. J. A. van Nijnatten

Researcher at Maastricht University

Publications -  20
Citations -  379

T. J. A. van Nijnatten is an academic researcher from Maastricht University. The author has contributed to research in topics: Breast cancer & Sentinel lymph node. The author has an hindex of 8, co-authored 20 publications receiving 218 citations. Previous affiliations of T. J. A. van Nijnatten include Maastricht University Medical Centre.

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Routine use of standard breast MRI compared to axillary ultrasound for differentiating between no, limited and advanced axillary nodal disease in newly diagnosed breast cancer patients

TL;DR: Evaluation of axillary nodal status on standard breast MRI is comparable to dedicated axillary ultrasound in breast cancer patients, and in patients who underwent preoperative standard Breast MRI, axillary abortion is only required in case of suspicious nodal findings on MRI.
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The diagnostic performance of sentinel lymph node biopsy in pathologically confirmed node positive breast cancer patients after neoadjuvant systemic therapy: A systematic review and meta-analysis.

TL;DR: Based on current evidence it seems not justified to omit further axillary treatment in every clinically node positive breast cancer patients with a negative sentinel lymph node biopsy after neoadjuvant systemic therapy.
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Exploring breast cancer response prediction to neoadjuvant systemic therapy using MRI-based radiomics: A systematic review.

TL;DR: Large methodological heterogeneity for each step of the MRI-based radiomics workflow for tumor response prediction to NST in breast cancer patients is revealed, consequently, the (overall promising) results are difficult to compare.
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MRI-based radiomics in breast cancer: feature robustness with respect to inter-observer segmentation variability

TL;DR: The robustness of radiomics features extracted by two commonly used radiomics software with respect to variability in manual breast tumor segmentation on MRI were identified as robust and were independent of inter-observer manual segmentation variability.