Imperial College London
Alternate

ProfessorDanielRueckert

Faculty of Engineering, Department of Computing

Professor of Visual Information Processing
 
 
 
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Contact

 

+44 (0)20 7594 8333d.rueckert Website

 
 
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Location

 

568Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Hinterwimmer:2022:10.1007/s00330-022-08981-3,
author = {Hinterwimmer, F and Consalvo, S and Neumann, J and Rueckert, D and von, Eisenhart-Rothe R and Burgkart, R},
doi = {10.1007/s00330-022-08981-3},
journal = {Eur Radiol},
pages = {7173--7184},
title = {Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies-a scoping review.},
url = {http://dx.doi.org/10.1007/s00330-022-08981-3},
volume = {32},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Musculoskeletal malignancies are a rare type of cancer. Consequently, sufficient imaging data for machine learning (ML) applications is difficult to obtain. The main purpose of this review was to investigate whether ML is already having an impact on imaging-driven diagnosis of musculoskeletal malignancies and what the respective reasons for this might be. A scoping review was conducted by a radiologist, an orthopaedic surgeon and a data scientist to identify suitable articles based on the PRISMA statement. Studies meeting the following criteria were included: primary malignant musculoskeletal tumours, machine/deep learning application, imaging data or data retrieved from images, human/preclinical, English language and original research. Initially, 480 articles were found and 38 met the eligibility criteria. Several continuous and discrete parameters related to publication, patient distribution, tumour specificities, ML methods, data and metrics were extracted from the final articles. For the synthesis, diagnosis-oriented studies were further examined by retrieving the number of patients and labels and metric scores. No significant correlations between metrics and mean number of samples were found. Several studies presented that ML could support imaging-driven diagnosis of musculoskeletal malignancies in distinct cases. However, data quality and quantity must be increased to achieve clinically relevant results. Compared to the experience of an expert radiologist, the studies used small datasets and mostly included only one type of data. Key to critical advancement of ML models for rare diseases such as musculoskeletal malignancies is a systematic, structured data collection and the establishment of (inter)national networks to obtain substantial datasets in the future. KEY POINTS: • Machine learning does not yet significantly impact imaging-driven diagnosis for musculoskeletal malignancies compared to other disciplines such as lung, breast or CNS cancer. • R
AU  - Hinterwimmer,F
AU  - Consalvo,S
AU  - Neumann,J
AU  - Rueckert,D
AU  - von,Eisenhart-Rothe R
AU  - Burgkart,R
DO  - 10.1007/s00330-022-08981-3
EP  - 7184
PY  - 2022///
SP  - 7173
TI  - Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies-a scoping review.
T2  - Eur Radiol
UR  - http://dx.doi.org/10.1007/s00330-022-08981-3
UR  - https://www.ncbi.nlm.nih.gov/pubmed/35852574
VL  - 32
ER  -
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