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

no code implementations • 22 Aug 2024 • Douwe J. Spaanderman, Matthew Marzetti, Xinyi Wan, Andrew F. Scarsbrook, Philip Robinson, Edwin H. G. Oei, Jacob J. Visser, Robert Hemke, Kirsten van Langevelde, David F. Hanff, Geert J. L. H. van Leenders, Cornelis Verhoef, Dirk J. Gruühagen, Wiro J. Niessen, Stefan Klein, Martijn P. A. Starmans

This systematic review provides an overview of Artificial Intelligence (AI) methods using radiological imaging for diagnosis and prognosis of these tumours, highlighting challenges in clinical translation, and evaluating study alignment with the Checklist for AI in Medical Imaging (CLAIM) and the FUTURE-AI international consensus guidelines for trustworthy and deployable AI to promote the clinical translation of AI methods.

Translation

Self-Contrastive Weakly Supervised Learning Framework for Prognostic Prediction Using Whole Slide Images

1 code implementation • 24 May 2024 • Saul Fuster, Farbod Khoraminia, Julio Silva-Rodríguez, Umay Kiraz, Geert J. L. H. van Leenders, Trygve Eftestøl, Valery Naranjo, Emiel A. M. Janssen, Tahlita C. M. Zuiverloon, Kjersti Engan

We present a pioneering investigation into the application of deep learning techniques to analyze histopathological images for addressing the substantial challenge of automated prognostic prediction.

Contrastive Learning Multiple Instance Learning +2

Minimally Interactive Segmentation of Soft-Tissue Tumors on CT and MRI using Deep Learning

no code implementations • 12 Feb 2024 • Douwe J. Spaanderman, Martijn P. A. Starmans, Gonnie C. M. van Erp, David F. Hanff, Judith H. Sluijter, Anne-Rose W. Schut, Geert J. L. H. van Leenders, Cornelis Verhoef, Dirk J. Grunhagen, Wiro J. Niessen, Jacob J. Visser, Stefan Klein

Next, the method was externally validated on a dataset including five unseen STT phenotypes in extremities, achieving 0. 81$\pm$0. 08 for CT, 0. 84$\pm$0. 09 for T1-weighted MRI, and 0. 88\pm0. 08 for previously unseen T2-weighted fat-saturated (FS) MRI.

Interactive Segmentation Segmentation

Reproducible radiomics through automated machine learning validated on twelve clinical applications

2 code implementations • 19 Aug 2021 • Martijn P. A. Starmans, Sebastian R. van der Voort, Thomas Phil, Milea J. M. Timbergen, Melissa Vos, Guillaume A. Padmos, Wouter Kessels, David Hanff, Dirk J. Grunhagen, Cornelis Verhoef, Stefan Sleijfer, Martin J. van den Bent, Marion Smits, Roy S. Dwarkasing, Christopher J. Els, Federico Fiduzi, Geert J. L. H. van Leenders, Anela Blazevic, Johannes Hofland, Tessa Brabander, Renza A. H. van Gils, Gaston J. H. Franssen, Richard A. Feelders, Wouter W. de Herder, Florian E. Buisman, Francois E. J. A. Willemssen, Bas Groot Koerkamp, Lindsay Angus, Astrid A. M. van der Veldt, Ana Rajicic, Arlette E. Odink, Mitchell Deen, Jose M. Castillo T., Jifke Veenland, Ivo Schoots, Michel Renckens, Michail Doukas, Rob A. de Man, Jan N. M. IJzermans, Razvan L. Miclea, Peter B. Vermeulen, Esther E. Bron, Maarten G. Thomeer, Jacob J. Visser, Wiro J. Niessen, Stefan Klein

In this study we propose a framework for automatically optimizing the construction of radiomics workflows per application.

Bayesian Optimization BIG-bench Machine Learning

Differential diagnosis and molecular stratification of gastrointestinal stromal tumors on CT images using a radiomics approach

1 code implementation • 14 Oct 2020 • Martijn P. A. Starmans, Milea J. M. Timbergen, Melissa Vos, Michel Renckens, Dirk J. Grünhagen, Geert J. L. H. van Leenders, Roy S. Dwarkasing, François E. J. A. Willemssen, Wiro J. Niessen, Cornelis Verhoef, Stefan Sleijfer, Jacob J. Visser, Stefan Klein

The aim of this study was to evaluate radiomics for distinguishing GISTs from other intra-abdominal tumors, and in GISTs, predict the c-KIT, PDGFRA, BRAF mutational status and mitotic index (MI).

Automated Detection of Cribriform Growth Patterns in Prostate Histology Images

no code implementations • 23 Mar 2020 • Pierre Ambrosini, Eva Hollemans, Charlotte F. Kweldam, Geert J. L. H. van Leenders, Sjoerd Stallinga, Frans Vos

In conclusion, the proposed deep learning method has high sensitivity for detecting cribriform growth patterns at the expense of a limited number of false positives.

Decision Making Ensemble Learning