Artificial intelligence in musculoskeletal oncology imaging: A critical review of current applications

Lacroix, Maxime; Aouad, Théodore; Feydy, Jean; Biau, David; Larousserie, Frédérique; Fournier, Laure; Feydy, A.

doi:10.1016/j.diii.2022.10.004

Cited by 13 publications

(2 citation statements)

References 59 publications

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“…The integration of deep learning into radiology-genomics represents a transformative advance in the field of oncology [106] , [107] . This mini-review paper highlights the transformative impact of deep learning integration in radiology-genomics, a significant advance in oncology.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning of radiology-genomics integration for computational oncology: A mini review

Wang,

Li,

Zeng

2024

Computational and Structural Biotechnology Journal

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Section: Discussionmentioning

confidence: 99%

Deep Learning of radiology-genomics integration for computational oncology: A mini review

Wang,

Li,

Zeng

2024

Computational and Structural Biotechnology Journal

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“…Artificial intelligence has also been used for the detection, segmentation, classification, and prognostic evaluation of focal bone lesions but with limited impact on current practice for now. 20…”

Section: Perspectivesmentioning

confidence: 99%

Initial Evaluation of Focal Bone Lesions: How Do We Do It?

Gondim Teixeira,

Lemore,

Vogt

et al. 2023

Semin Musculoskelet Radiol

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Recommender-based bone tumour classification with radiographs—a link to the past

Hinterwimmer,

Serena,

Wilhelm

et al. 2024

Eur Radiol

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Objectives To develop an algorithm to link undiagnosed patients to previous patient histories based on radiographs, and simultaneous classification of multiple bone tumours to enable early and specific diagnosis. Materials and methods For this retrospective study, data from 2000 to 2021 were curated from our database by two orthopaedic surgeons, a radiologist and a data scientist. Patients with complete clinical and pre-therapy radiographic data were eligible. To ensure feasibility, the ten most frequent primary tumour entities, confirmed histologically or by tumour board decision, were included. We implemented a ResNet and transformer model to establish baseline results. Our method extracts image features using deep learning and then clusters the k most similar images to the target image using a hash-based nearest-neighbour recommender approach that performs simultaneous classification by majority voting. The results were evaluated with precision-at-k, accuracy, precision and recall. Discrete parameters were described by incidence and percentage ratios. For continuous parameters, based on a normality test, respective statistical measures were calculated. Results Included were data from 809 patients (1792 radiographs; mean age 33.73 ± 18.65, range 3–89 years; 443 men), with Osteochondroma (28.31%) and Ewing sarcoma (1.11%) as the most and least common entities, respectively. The dataset was split into training (80%) and test subsets (20%). For k = 3, our model achieved the highest mean accuracy, precision and recall (92.86%, 92.86% and 34.08%), significantly outperforming state-of-the-art models (54.10%, 55.57%, 19.85% and 62.80%, 61.33%, 23.05%). Conclusion Our novel approach surpasses current models in tumour classification and links to past patient data, leveraging expert insights. Clinical relevance statement The proposed algorithm could serve as a vital support tool for clinicians and general practitioners with limited experience in bone tumour classification by identifying similar cases and classifying bone tumour entities. Key Points • Addressed accurate bone tumour classification using radiographic features. • Model achieved 92.86%, 92.86% and 34.08% mean accuracy, precision and recall, respectively, significantly surpassing state-of-the-art models. • Enhanced diagnosis by integrating prior expert patient assessments. Graphical abstract

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Artificial intelligence in musculoskeletal oncology imaging: A critical review of current applications

Cited by 13 publications

References 59 publications

Deep Learning of radiology-genomics integration for computational oncology: A mini review

Deep Learning of radiology-genomics integration for computational oncology: A mini review

Initial Evaluation of Focal Bone Lesions: How Do We Do It?

Recommender-based bone tumour classification with radiographs—a link to the past

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