Low-grade chondrosarcoma vs enchondroma: challenges in diagnosis and management

Wang, X. L.; Beuckeleer, L. H. De; Schepper, A. M. A. De; Marck, Eric Van

doi:10.1007/s003300000651

Cited by 47 publications

(42 citation statements)

References 9 publications

(41 reference statements)

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“…Conventional radiographs in two planes should always be the first imaging method used. MRI and CT should be used when diagnosis is difficult because they offer ability to visualize more clearly calcification, periosteal bone formation, cortical destruction or soft tissue involvement [23,[42][43][44][45][46][47].…”

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confidence: 99%

“…On computed tomography, characteristic features of malignancy were lytic areas, especially when pronounced in comparison with previously made radiographs, cortical lesions with a scalloping greater than 2/3 of the cortex or extension to soft tissue [29,[42][43][44][45][46][47][48].…”

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confidence: 99%

“…-Rearrangements of chromosome 6 and chromosome 12 -PTPN11 mutations -Alterations at some level in the pRb pathway -Other highly variable genetic alterations tion, edema surrounding the tumor on MR images, and soft tissue mass [2,29,[42][43][44][45][46][47]49]. Note that the use of scalloping in the diagnosis is limited when examining the metaphyseal region because the cortex is thin, especially in the proximal fibula [29].…”

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confidence: 99%

“…If malignancy is diagnosed, general staging should be carried out to assess the extent to which the disease has spread including bone scintigraphy and chest radiographs and CT [42][43][44][45][46][47][48]; small nodules are not specific for malignancy Whole body MRI and PET are under evaluation for both staging and treatment response evaluation.…”

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Insights into Enchondroma, Enchondromatosis and the risk of secondary Chondrosarcoma. Review of the literature with an emphasis on the clinical behaviour, radiology, malignant transformation and the follow up

Herget¹,

Strohm²,

Rottenburger³

et al. 2014

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confidence: 99%

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Insights into Enchondroma, Enchondromatosis and the risk of secondary Chondrosarcoma. Review of the literature with an emphasis on the clinical behaviour, radiology, malignant transformation and the follow up

Herget¹,

Strohm²,

Rottenburger³

et al. 2014

neo

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“…Distinguishing between a benign enchondroma and a low-grade chondrosarcoma is a common diagnostic challenge for orthopaedic oncologists [9,21,31]. Although multiple clinical and radiologic elements have been shown to aid in the diagnostic process [1,9,12,21], accurate differentiation between benign and malignant cartilaginous neoplasms can be difficult, even for trained specialists.…”

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Do Orthopaedic Oncologists Agree on the Diagnosis and Treatment of Cartilage Tumors of the Appendicular Skeleton?

Zamora

Urrutia

Schweitzer

et al. 2017

Clinical Orthopaedics &Amp; Related Research

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Background Distinguishing a benign enchondroma from a low-grade chondrosarcoma is a common diagnostic challenge for orthopaedic oncologists. Low interrater agreement has been observed for the diagnosis of cartilaginous neoplasms among radiologists and pathologists, but, to our knowledge, no study has evaluated inter-and intraobserver agreement among orthopaedic oncologists grading these lesions using initial clinical and imaging information. Determining such agreement is important since it reflects the certainty in the diagnosis by orthopaedic oncologists. Agreement also is important as it will guide future treatment and prognosis, considering that there is no gold standard for diagnosis of these lesions. Questions/Purposes (1) to determine inter-and intraobserver agreement among a multinational panel of expert orthopaedic oncologists in diagnosing cartilaginous neoplasms based on their assessment of clinical symptoms and imaging at diagnosis. (2) To describe the most important clinical and imaging features that experts use during the initial diagnostic process. (3) To determine interobserver agreement for proposed initial treatment strategies for cartilaginous neoplasms by this panel of evaluators. Methods Thirty-nine patients with intramedullary cartilaginous neoplasms of the appendicular skeleton of various histopathologic grades were selected and classified as having benign, low-grade malignant, or intermediate-or high-grade malignant neoplasms by 10 experienced orthopaedic oncologists based on clinical and imaging information. Additionally, they chose the three most important clinical or imaging features for the diagnosis of these neoplasms, and they proposed a treatment strategy for each patient. The Kappa coefficient (j) was used to determine inter-and intraobserver agreement. Results Inter-and intraobserver agreements were only fair to good, j = 0.44(95% CI, 0.41-0.48) and j = 0.62 (95% CI, 0.52-0.72), respectively. The three factors most frequently identified as helpful in making the diagnosis by our panel were cortical involvement in 65% of evaluations (253/390), neoplasm size in 51% (198/390), and pain in 50% (194/390). The interobserver agreement for the proposed initial treatment strategy after diagnosis was poor (j = 0.21; 95% CI, 0.18-0.24). Conclusions This study showed barely fair interobserver and fair to good intraobserver agreement for grading of intramedullary cartilaginous neoplasms by orthopaedic oncologists using initial clinical and imaging findings. These results reflect the insufficient guidance interpreting clinical and imaging features, and the limitations of the systems we use today when making these diagnoses. In the same way, they generate concern for the implications that this may have on different treatment strategies and the future prognosis of our patients. Future studies should build

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The use of radiomics and machine learning for the differentiation of chondrosarcoma from enchondroma

Erdem

Tamsel

Demirpolat

2023

J of Clinical Ultrasound

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Purpose To construct and compare machine learning models for differentiating chondrosarcoma from enchondroma using radiomic features from T1 and fat suppressed Proton density (PD) magnetic resonance imaging (MRI). Methods Eighty‐eight patients (57 with enchondroma, 31 with chondrosarcoma) were retrospectively included. Histogram matching and N4ITK MRI bias correction filters were applied. An experienced musculoskeletal radiologist and a senior resident in radiology performed manual segmentation. Voxel sizes were resampled. Laplacian of Gaussian filter and wavelet‐based features were used. One thousand eight hundred eighty‐eight features were obtained for each patient, with 944 from T1 and 944 from PD images. Sixty‐four unstable features were removed. Seven machine learning models were used for classification. Results Classification with all features showed neural network was the best model for both readers' datasets with area under the curve (AUC), classification accuracy (CA), and F1 score of 0.979, 0.984; 0.920, 0.932; and 0.889, 0.903, respectively. Four features, including one common to both readers, were selected using fast correlation based filter. The best performing models with selected features were gradient boosting for Fatih Erdem's dataset and neural network for Gülen Demirpolat's dataset with AUC, CA, and F1 score of 0.990, 0.979; 0.943, 0.955; 0.921, 0.933, respectively. Neural Network was the second‐best model for FE's dataset based on AUC (0.984). Conclusion Using pathology as a gold standard, this study defined and compared seven well‐performing models to distinguish enchondromas from chondrosarcomas and provided radiomic feature stability and reproducibility among the readers.

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Low-grade chondrosarcoma vs enchondroma: challenges in diagnosis and management

Cited by 47 publications

References 9 publications

Insights into Enchondroma, Enchondromatosis and the risk of secondary Chondrosarcoma. Review of the literature with an emphasis on the clinical behaviour, radiology, malignant transformation and the follow up

Insights into Enchondroma, Enchondromatosis and the risk of secondary Chondrosarcoma. Review of the literature with an emphasis on the clinical behaviour, radiology, malignant transformation and the follow up

Do Orthopaedic Oncologists Agree on the Diagnosis and Treatment of Cartilage Tumors of the Appendicular Skeleton?

The use of radiomics and machine learning for the differentiation of chondrosarcoma from enchondroma

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