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Background <p>Differentiating chondroid tumors is crucial for proper patient management. This study aimed to develop a deep learning model (DLM) for classifying enchondromas, atypical cartilaginous tumors (ACT), and high-grade chondrosarcomas using CT images.</p> Methods <p>This retrospective study analyzed chondroid tumors from two independent cohorts. Tumors were segmented on CT images. A 2D convolutional neural network was developed and tested using split-sample and geographical validation. Four radiologists blinded to patient data and the DLM results with various levels of experience performed readings of the external test dataset for comparison. Performance metrics included accuracy, sensitivity, specificity, and area under the curve (AUC).</p> Results <p>CTs from 344 patients (175 women; age = 50.3 ± 14.3 years;) with diagnosed enchondroma (<i>n</i> = 124), ACT (<i>n</i> = 92) or high-grade chondrosarcoma (<i>n</i> = 128) were analyzed. The DLM demonstrated comparable performance to radiologists (<i>p</i> &gt; 0.05), achieving an AUC of 0.88 for distinguishing enchondromas from chondrosarcomas and 0.82 for differentiating enchondromas from ACTs. The DLM and musculoskeletal expert showed similar performance in differentiating ACTs from high-grade chondrosarcomas (<i>p</i> = 0.26), with an AUC of 0.64 and 0.56, respectively.</p> Conclusions <p>The DLM reliably differentiates benign from malignant cartilaginous tumors and is particularly useful for the differentiation between ACTs and Enchondromas, which is challenging based on CT images only. However, the differentiation between ACTs and high-grade chondrosarcomas remains difficult, reflecting known diagnostic challenges in radiology.</p>

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A deep learning model for classification of chondroid tumors on CT images

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月28日

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  • Felix G. Gassert,
  • Daniel Lang,
  • Nina Hesse,
  • Hans Roland Dürr,
  • Alexander Klein,
  • Luca Kohll,
  • Florian Hinterwimmer,
  • Johanna Luitjens,
  • Stefan Weissinger,
  • Jan C. Peeken,
  • Carolin Mogler,
  • Carolin Knebel,
  • Stefan Bartzsch,
  • Florian T. Gassert,
  • Alexandra S. Gersing

摘要

Background

Differentiating chondroid tumors is crucial for proper patient management. This study aimed to develop a deep learning model (DLM) for classifying enchondromas, atypical cartilaginous tumors (ACT), and high-grade chondrosarcomas using CT images.

Methods

This retrospective study analyzed chondroid tumors from two independent cohorts. Tumors were segmented on CT images. A 2D convolutional neural network was developed and tested using split-sample and geographical validation. Four radiologists blinded to patient data and the DLM results with various levels of experience performed readings of the external test dataset for comparison. Performance metrics included accuracy, sensitivity, specificity, and area under the curve (AUC).

Results

CTs from 344 patients (175 women; age = 50.3 ± 14.3 years;) with diagnosed enchondroma (n = 124), ACT (n = 92) or high-grade chondrosarcoma (n = 128) were analyzed. The DLM demonstrated comparable performance to radiologists (p > 0.05), achieving an AUC of 0.88 for distinguishing enchondromas from chondrosarcomas and 0.82 for differentiating enchondromas from ACTs. The DLM and musculoskeletal expert showed similar performance in differentiating ACTs from high-grade chondrosarcomas (p = 0.26), with an AUC of 0.64 and 0.56, respectively.

Conclusions

The DLM reliably differentiates benign from malignant cartilaginous tumors and is particularly useful for the differentiation between ACTs and Enchondromas, which is challenging based on CT images only. However, the differentiation between ACTs and high-grade chondrosarcomas remains difficult, reflecting known diagnostic challenges in radiology.