X-rays radiomics-based machine learning classification of atypical cartilaginous tumour and high-grade chondrosarcoma of long bones.
Artificial intelligence
Atypical cartilaginous tumour
Bone neoplasm
Chondrosarcoma
Radiomics
Journal
EBioMedicine
ISSN: 2352-3964
Titre abrégé: EBioMedicine
Pays: Netherlands
ID NLM: 101647039
Informations de publication
Date de publication:
Mar 2024
Mar 2024
Historique:
received:
05
11
2023
revised:
03
02
2024
accepted:
04
02
2024
medline:
18
3
2024
pubmed:
21
2
2024
entrez:
20
2
2024
Statut:
ppublish
Résumé
Atypical cartilaginous tumour (ACT) and high-grade chondrosarcoma (CS) of long bones are respectively managed with active surveillance or curettage and wide resection. Our aim was to determine diagnostic performance of X-rays radiomics-based machine learning for classification of ACT and high-grade CS of long bones. This retrospective, IRB-approved study included 150 patients with surgically treated and histology-proven lesions at two tertiary bone sarcoma centres. At centre 1, the dataset was split into training (n = 71 ACT, n = 24 high-grade CS) and internal test (n = 19 ACT, n = 6 high-grade CS) cohorts, respectively, based on the date of surgery. At centre 2, the dataset constituted the external test cohort (n = 12 ACT, n = 18 high-grade CS). Manual segmentation was performed on frontal view X-rays, using MRI or CT for preliminary identification of lesion margins. After image pre-processing, radiomic features were extracted. Dimensionality reduction included stability, coefficient of variation, and mutual information analyses. In the training cohort, after class balancing, a machine learning classifier (Support Vector Machine) was automatically tuned using nested 10-fold cross-validation. Then, it was tested on both the test cohorts and compared to two musculoskeletal radiologists' performance using McNemar's test. Five radiomic features (3 morphology, 2 texture) passed dimensionality reduction. After tuning on the training cohort (AUC = 0.75), the classifier had 80%, 83%, 79% and 80%, 89%, 67% accuracy, sensitivity, and specificity in the internal (temporally independent) and external (geographically independent) test cohorts, respectively, with no difference compared to the radiologists (p ≥ 0.617). X-rays radiomics-based machine learning accurately differentiates between ACT and high-grade CS of long bones. AIRC Investigator Grant.
Sections du résumé
BACKGROUND
BACKGROUND
Atypical cartilaginous tumour (ACT) and high-grade chondrosarcoma (CS) of long bones are respectively managed with active surveillance or curettage and wide resection. Our aim was to determine diagnostic performance of X-rays radiomics-based machine learning for classification of ACT and high-grade CS of long bones.
METHODS
METHODS
This retrospective, IRB-approved study included 150 patients with surgically treated and histology-proven lesions at two tertiary bone sarcoma centres. At centre 1, the dataset was split into training (n = 71 ACT, n = 24 high-grade CS) and internal test (n = 19 ACT, n = 6 high-grade CS) cohorts, respectively, based on the date of surgery. At centre 2, the dataset constituted the external test cohort (n = 12 ACT, n = 18 high-grade CS). Manual segmentation was performed on frontal view X-rays, using MRI or CT for preliminary identification of lesion margins. After image pre-processing, radiomic features were extracted. Dimensionality reduction included stability, coefficient of variation, and mutual information analyses. In the training cohort, after class balancing, a machine learning classifier (Support Vector Machine) was automatically tuned using nested 10-fold cross-validation. Then, it was tested on both the test cohorts and compared to two musculoskeletal radiologists' performance using McNemar's test.
FINDINGS
RESULTS
Five radiomic features (3 morphology, 2 texture) passed dimensionality reduction. After tuning on the training cohort (AUC = 0.75), the classifier had 80%, 83%, 79% and 80%, 89%, 67% accuracy, sensitivity, and specificity in the internal (temporally independent) and external (geographically independent) test cohorts, respectively, with no difference compared to the radiologists (p ≥ 0.617).
INTERPRETATION
CONCLUSIONS
X-rays radiomics-based machine learning accurately differentiates between ACT and high-grade CS of long bones.
FUNDING
BACKGROUND
AIRC Investigator Grant.
Identifiants
pubmed: 38377797
pii: S2352-3964(24)00053-7
doi: 10.1016/j.ebiom.2024.105018
pmc: PMC10884340
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
105018Informations de copyright
Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of interests Matteo Interlenghi: CTO and employee of DeepTrace Technologies. DeepTrace Technologies is a spin-off of Scuola Universitaria Superiore IUSS, Pavia, Italy; shareholder in DeepTrace Technologies. Christian Salvatore: CEO of DeepTrace Technologies. DeepTrace Technologies is a spin-off of Scuola Universitaria Superiore IUSS, Pavia, Italy; shareholder in DeepTrace Technologies. Isabella Castiglioni: Shareholder in DeepTrace Technologies. All other authors declare that they have no conflicts of interest to disclose.