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MRI-Based Radiomics Differentiates Skull Base Chordoma and Chondrosarcoma: A Preliminary Study
| Content Provider | MDPI |
|---|---|
| Author | Yamazawa, Erika Takahashi, Satoshi Shin, Masahiro Tanaka, Shota Takahashi, Wataru Nakamoto, Takahiro Suzuki, Yuichi Takami, Hirokazu Saito, Nobuhito |
| Copyright Year | 2022 |
| Description | Chordoma and chondrosarcoma share common radiographic characteristics yet are distinct clinically. A radiomic machine learning model differentiating these tumors preoperatively would help plan surgery. MR images were acquired from 57 consecutive patients with chordoma (N = 32) or chondrosarcoma (N = 25) treated at the University of Tokyo Hospital between September 2012 and February 2020. Preoperative T1-weighted images with gadolinium enhancement (GdT1) and T2-weighted images were analyzed. Datasets from the first 47 cases were used for model creation, and those from the subsequent 10 cases were used for validation. Feature extraction was performed semi-automatically, and 2438 features were obtained per image sequence. Machine learning models with logistic regression and a support vector machine were created. The model with the highest accuracy incorporated seven features extracted from GdT1 in the logistic regression. The average area under the curve was 0.93 ± 0.06, and accuracy was 0.90 (9/10) in the validation dataset. The same validation dataset was assessed by 20 board-certified neurosurgeons. Diagnostic accuracy ranged from 0.50 to 0.80 (median 0.60, 95% confidence interval 0.60 ± 0.06%), which was inferior to that of the machine learning model (p = 0.03), although there are some limitations, such as the risk of overfitting and the lack of an extramural cohort for truly independent final validation. In summary, we created a novel MRI-based machine learning model to differentiate skull base chordoma and chondrosarcoma from multiparametric signatures. |
| Starting Page | 3264 |
| e-ISSN | 20726694 |
| DOI | 10.3390/cancers14133264 |
| Journal | Cancers |
| Issue Number | 13 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-07-03 |
| Access Restriction | Open |
| Subject Keyword | Cancers Radiology, Nuclear Medicine and Imaging Chondrosarcoma Chordoma Diagnostic Performance Machine Learning Model Mri Radiomics |
| Content Type | Text |
| Resource Type | Article |
