Wait-and-scan: an alternative for curettage in atypical cartilaginous tumours of the long bones

X-rays radiomics-based machine learning classification of atypical cartilaginous tumour and high-grade chondrosarcoma of long bones

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

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

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

X-rays radiomics-based machine learning accurately differentiates between ACT and high-grade CS of long bones.

FUNDING

AIRC Investigator Grant.

A Systematic Review of Adjuvant Chemotherapy in Localized Dedifferentiated Chondrosarcoma

Dedifferentiated chondrosarcoma (DDCS) is a high-grade subtype of chondrosarcoma with the bimorphic histological appearance of a conventional chondrosarcoma component with abrupt transition to a high-grade, non-cartilaginous sarcoma. DDCS can be radiographically divided into central and peripheral types. Wide resection is currently the main therapeutic option for localized DDCS. Moreover, the effectiveness of adjuvant chemotherapy remains controversial. Therefore, we performed a systematic review of available evidence to evaluate the effect of adjuvant chemotherapy on localized DDCS. The purpose was to compare the 5-year survival rate among patients treated with surgery plus adjuvant chemotherapy or surgery alone for localized DDCS. The search was conducted in PubMed, Embase, and Cochrane Central Register of Controlled Trials (CENTRAL) databases. Of the 217 studies shortlisted, 11 retrospective non-randomized studies (comprising 556 patients with localized DDCS) were selected. The 5-year survival rates were similar between the two treatment groups (28.2% (51/181) vs. 24.0% (90/375), respectively). The overall pooled odds ratio was 1.25 (95% confidence interval: 0.80-1.94; p = 0.324), and heterogeneity I2 was 2%. However, when limited to peripheral DDCS, adjuvant chemotherapy was associated with prolonged survival (p = 0.03). Due to the paucity of included studies and the absence of prospective comparative studies, no conclusions can be drawn regarding the effectiveness or ineffectiveness of adjuvant chemotherapy for localized DDCS.