Non-invasive prediction of Central lymph node metastasis in papillary thyroid microcarcinoma with machine learning-based CT radiomics: a multicenter study.

Objectives: This study aimed to develop and validate a machine learning-based computed tomography (CT) radiomics method to preoperatively predict the presence of central lymph node metastasis (CLNM) in patients with papillary thyroid microcarcinoma (PTMC).

Methods: A total of 921 patients with histopathologically proven PTMC from three medical centers were included in this retrospective study and divided into training, internal validation, external test 1, and external test 2 sets. Radiomics features of thyroid tumors were extracted from CT images and selected for dimensional reduction. Five machine learning classifiers were applied, and the best classifier was selected to calculate radiomics scores (rad-scores). Then, the rad-scores and clinical factors were combined to construct a nomogram model.

Results: In the four sets, 35.18% (324/921) patients were CLNM+. The XGBoost classifier showed the best performance, with the highest average area under the curve (AUC) of 0.756 in the validation set. The nomogram model incorporating XGBoost-based rad-scores with age and sex showed better performance than the clinical model in the training [AUC: 0.847(0.809-0.879) vs. 0.706(0.660-0.748)], internal validation [AUC: 0.773(0.682-0.847) vs. 0.671(0.575-0.758)], external test 1 [AUC: 0.807(0.757-0.852) vs. 0.639(0.580-0.695)], and external test 2 [AUC: 0.746(0.645-0.830) vs. 0.608(0.502-0.707)] sets. Furthermore, the nomogram showed better clinical benefit than the clinical and radiomics models.

Conclusions: The nomogram model based on the XGBoost classifier exhibited favorable performance. This model provides a potential approach for the non-invasive diagnosis of CLNM in patients with PTMC.

Advances in knowledge: This study developed a potential surrogate of preoperative accurate evaluation of CLNM status, which is non-invasive and easy-to-use.