Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review.

Autor: Lejla Alic, Wiro J Niessen, Jifke F Veenland

Publikováno v: PLoS ONE, Vol 9, Iss 10, p e110300 (2014)

BACKGROUND: Many techniques are proposed for the quantification of tumor heterogeneity as an imaging biomarker for differentiation between tumor types, tumor grading, response monitoring and outcome prediction. However, in clinical practice these met

Externí odkaz: https://doaj.org/article/1608b1222a0d4342a5020739f96661d3

Facilitating tumor functional assessment by spatially relating 3D tumor histology and in vivo MRI: image registration approach.

Autor: Lejla Alic, Joost C Haeck, Karin Bol, Stefan Klein, Sandra T van Tiel, Piotr A Wielepolski, Marion de Jong, Wiro J Niessen, Monique Bernsen, Jifke F Veenland

Publikováno v: PLoS ONE, Vol 6, Iss 8, p e22835 (2011)

BACKGROUND: Magnetic resonance imaging (MRI), together with histology, is widely used to diagnose and to monitor treatment in oncology. Spatial correspondence between these modalities provides information about the ability of MRI to characterize canc

Externí odkaz: https://doaj.org/article/91efff2896ff42bba99236d12da9df6e

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

Autor: Jose M. Castillo T., Martijn P. A. Starmans, Muhammad Arif, Wiro J. Niessen, Stefan Klein, Chris H. Bangma, Ivo G. Schoots, Jifke F. Veenland

Publikováno v: Diagnostics, Vol 11, Iss 2, p 369 (2021)

Radiomics applied in MRI has shown promising results in classifying prostate cancer lesions. However, many papers describe single-center studies without external validation. The issues of using radiomics models on unseen data have not yet been suffic

Externí odkaz: https://doaj.org/article/91ce6068e6a74db7963ecbbda152fca8

Clinically significant prostate cancer detection and segmentation in low-risk patients using a convolutional neural network on multi-parametric MRI

Autor: Wiro J. Niessen, Gabriel P. Krestin, Jose Castillo Tovar, Jifke F. Veenland, Muhammad Arif, Chris H. Bangma, Monique J. Roobol, Ivo G. Schoots

Publikováno v: European Radiology, 30(12), 6582-6592. Springer-Verlag
European Radiology

Objectives To develop an automatic method for identification and segmentation of clinically significant prostate cancer in low-risk patients and to evaluate the performance in a routine clinical setting. Methods A consecutive cohort (n = 292) from a

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b9bc38b3d5846a69723a6d25916227d4
https://doi.org/10.1007/s00330-020-07008-z

Classification of clinically significant prostate cancer on multi-parametric MRI

Autor: Jose M. Castillo T., Muhammad Arif, Martijn P. A. Starmans, Wiro J. Niessen, Chris H. Bangma, Ivo G. Schoots, Jifke F. Veenland

Publikováno v: Cancers, Vol 14, Iss 12, p 12 (2022)
Cancers; Volume 14; Issue 1; Pages: 12
Cancers, 14(1):12. Multidisciplinary Digital Publishing Institute (MDPI)
Cancers
Cancers, 14(1)

Simple Summary Computer-aided diagnosis systems to improve significant prostate cancer (PCa) diagnoses are being reported in the literature. These methods are based on either deep learning or radiomics. However, there is a lack of scientific evidence

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f68d1062ceaeb853b39acd539a7b356
http://resolver.tudelft.nl/uuid:0b615445-9f7d-4735-bc0a-4a21a5f9b0a6