Department of Surgical Oncology, Erasmus MC Cancer Institute Rotterdam, the Netherlands; Department of Medical Oncology, Erasmus MC Cancer Institute Rotterdam, the Netherlands.
Department of Medical Informatics, Erasmus MC, Rotterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands.
Eur J Radiol. 2020 Oct;131:109266. doi: 10.1016/j.ejrad.2020.109266. Epub 2020 Sep 8.
Diagnosing desmoid-type fibromatosis (DTF) requires an invasive tissue biopsy with β-catenin staining and CTNNB1 mutational analysis, and is challenging due to its rarity. The aim of this study was to evaluate radiomics for distinguishing DTF from soft tissue sarcomas (STS), and in DTF, for predicting the CTNNB1 mutation types.
Patients with histologically confirmed extremity STS (non-DTF) or DTF and at least a pretreatment T1-weighted (T1w) MRI scan were retrospectively included. Tumors were semi-automatically annotated on the T1w scans, from which 411 features were extracted. Prediction models were created using a combination of various machine learning approaches. Evaluation was performed through a 100x random-split cross-validation. The model for DTF vs. non-DTF was compared to classification by two radiologists on a location matched subset.
The data included 203 patients (72 DTF, 131 STS). The T1w radiomics model showed a mean AUC of 0.79 on the full dataset. Addition of T2w or T1w post-contrast scans did not improve the performance. On the location matched cohort, the T1w model had a mean AUC of 0.88 while the radiologists had an AUC of 0.80 and 0.88, respectively. For the prediction of the CTNNB1 mutation types (S45 F, T41A and wild-type), the T1w model showed an AUC of 0.61, 0.56, and 0.74.
Our radiomics model was able to distinguish DTF from STS with high accuracy similar to two radiologists, but was not able to predict the CTNNB1 mutation status.
诊断韧带样型纤维瘤病（DTF）需要进行侵袭性组织活检，进行β-连环蛋白染色和 CTNNB1 突变分析，由于其罕见性，诊断具有挑战性。本研究旨在评估放射组学在区分 DTF 与软组织肉瘤（STS）中的作用，并在 DTF 中预测 CTNNB1 突变类型。
回顾性纳入经组织学证实的四肢 STS（非 DTF）或 DTF 患者，且至少有预处理 T1 加权（T1w）MRI 扫描。在 T1w 扫描上对肿瘤进行半自动标注，从中提取 411 个特征。使用各种机器学习方法的组合创建预测模型。通过 100 次随机分割交叉验证进行评估。将 DTF 与非 DTF 的模型与在位置匹配子集中的两位放射科医生的分类进行比较。
该数据包括 203 名患者（72 例 DTF，131 例 STS）。在全数据集上，T1w 放射组学模型的平均 AUC 为 0.79。添加 T2w 或 T1w 对比扫描并不能提高性能。在位置匹配队列中，T1w 模型的平均 AUC 为 0.88，而放射科医生的 AUC 分别为 0.80 和 0.88。对于 CTNNB1 突变类型（S45F、T41A 和野生型）的预测，T1w 模型的 AUC 分别为 0.61、0.56 和 0.74。
我们的放射组学模型能够以类似于两位放射科医生的高准确性区分 DTF 和 STS，但无法预测 CTNNB1 突变状态。
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