Radiomics based on preoperative magnetic resonance imaging predict the cell lineages of nonfunctioning pituitary neuroendocrine tumors.

OBJECTIVE

Accurate preoperative predict the cell lineages of non-functioning pituitary neuroendocrine tumors (NFPitNETs) can help neurosurgeons develop treatment strategies. This study aimed to predict the three cell lineages of NFPitNETs using radiomics based on MRI.

METHODS

NFPitNETs patients from January 2019 and January 2023 were retrospectively enrolled, with adenoma lineages including SF-1 (n = 239), TPIT (n = 204), and PIT-1 (n = 100). Sagittal T1-weighted images (T1WI), contrast-enhanced (CE) sagittal T1WI, CE-coronal T1WI, and axial T2WI were obtained for tumor segmentation on ITK-SNAP. Pyradiomics was used for features extracted. Variance threshold method, t-test, and LASSO were used for feature selection. Support vector machine (SVM) and random forest (RF) were used to predict the three-lineages adenomas based on their radiomics and semantic features. Receiver operating characteristic curve-area under the curve (ROC-AUC) analysis was used to assess the model's performance.

RESULTS

A total of 543 patients with NFPitNETs (mean age, 49.46 ± 12.39) were included. Patients with SF-1 adenomas had a higher mean age than those with TPIT and PIT-1 adenomas (52.84 ± 11.56 vs 49.94 ± 10.54 vs 40.42 ± 13.41, p < 0.001). Female patients are more common in TPIT and PIT-1 adenomas than SF-1 ones (96.57% vs 69% vs 41%, p < 0.001). The SVM model incorporating semantic and radiomics features based on CE-coronal T1WI performed the best, with a macro-average AUC of 0.899. CE-coronal T1WI were the best among all the MR sequences for predicting the cell lineages of NFPitNETs.

CONCLUSION

Radiomics based on preoperative MRI can help predict the cell lineages of NFPitNETs, which prove useful to neurosurgeons to develop treatment strategies.