Artificial intelligence in musculoskeletal oncology imaging: A critical review of current applications - 04/01/23

Doi : 10.1016/j.diii.2022.10.004

Maxime Lacroix ^a,^b,^c,^⁎, Theodore Aouad ^d, Jean Feydy ^e, David Biau ^b,^f, Frédérique Larousserie ^b,^g, Laure Fournier ^a,^b,^c, Antoine Feydy ^b,^h
^a Department of Radiology, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, Paris, 75015, France
^b Université Paris Cité, Faculté de Médecine, Paris, 75006, France
^c PARCC UMRS 970, INSERM, Paris 75015, France
^d Université Paris-Saclay, CentraleSupélec, Inria, Centre for Visual Computing, 91190, Gif-sur-Yvette, France
^e Université Paris Cité, HeKA team, Inria Paris, Inserm, 75006, Paris, France
^f Department of Orthopedic Surgery, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris, 75014, France
^g Department of Pathology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris, 75014, France
^h Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris, 75014, France

^⁎Corresponding author: maxime.lacroix@aphp.fr

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Highlights

•	Machine learning, deep learning and radiomic models can be applied to musculoskeletal oncology imaging.
•	One limitation of radiomic studies is the lack of assessement of robustness of extracted features.
•	Further efforts are needed for a routine use of artificial intelligence in musculoskeletal oncology imaging.

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Abstract

Artificial intelligence (AI) is increasingly being studied in musculoskeletal oncology imaging. AI has been applied to both primary and secondary bone tumors and assessed for various predictive tasks that include detection, segmentation, classification, and prognosis. Still, in the field of clinical research, further efforts are needed to improve AI reproducibility and reach an acceptable level of evidence in musculoskeletal oncology. This review describes the basic principles of the most common AI techniques, including machine learning, deep learning and radiomics. Then, recent developments and current results of AI in the field of musculoskeletal oncology are presented. Finally, limitations and future perspectives of AI in this field are discussed.

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Keywords : Artificial intelligence, Bone tumor, Deep learning, Machine learning, Metastases

Abbreviations : ADC, AI, AUC, CNN, CT, DL, DSC, FDG, PET/CT, ML, MRI, PET-CT, RF, ROC, SPECT, SVM

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Introduction

Basic principles and process

Why do we do “learning”?

Overfitting and generalization

Conventional statistics

Overcoming the “curse of dimensionality” with machine learning (ML)

Processing markers into hand-crafted features with radiomics

Learning task-specific features with deep learning

Statistical analysis and regularization

Applying machine learning for segmentation

Current applications

Primary bone lesions

Secondary bone lesions

Main limitations of AI

CRediT authorship contribution statement

Esportazione

Vol 104 - N° 1

P. 18-23 - gennaio 2023 Ritorno al numero

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Artificial intelligence in musculoskeletal oncology imaging: A critical review of current applications - 04/01/23

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