Prediction of pituitary adenoma surgical consistency: radiomic data mining and machine learning on T2-weighted MRI.

ABSTRACT:

Purpose

Pituitary macroadenoma consistency can influence the ease of lesion removal during surgery, especially when using a transsphenoidal approach. Unfortunately, it is not assessable on standard qualitative MRI. Radiomic texture analysis could help in extracting mineable quantitative tissue characteristics. We aimed to assess the accuracy of texture analysis combined with machine learning in the preoperative evaluation of pituitary macroadenoma consistency in patients undergoing endoscopic endonasal surgery.

Methods

Data of 89 patients (68 soft and 21 fibrous macroadenomas) who underwent MRI and transsphenoidal surgery at our institution were retrospectively reviewed. After manual segmentation, radiomic texture features were extracted from original and filtered MR images.

SUBMITTER: Cuocolo R 

PROVIDER: S-EPMC7666676 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature