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Artificial intelligence-based segmentation of small renal masses: a multi-center, multi-scanner, multi-sequence study
Mengqiu Cui1, Zilong Zeng2, Silu Chen3
1Department of Radiology, First Medical Center, Chinese PLA General Hospital, Beijing, China.
Objectives:
This study aims to develop an artificial intelligence (AI)-based automated segmentation method for small renal masses (SRMs) using multi-center, multi-scanner, multi-sequence MRI data.
Methods:
MR images from 988 pathologically confirmed SRM patients from three different centers were retrospectively included. Segmentation networks were independently developed for each MRI sequence using deep learning techniques. A GE dataset of 733 patients from Center 1 was used for training and validation. A GE test set, consisting of internal (99 from Center 1) and external test sets (81 from Center 2 and 3), was created for evaluation. Furthermore, a non-GE generalization set, consisting of 75 patients from Center 2 and 3, was used to assess the generalization ability. The method's performance was evaluated in terms of detection rate and segmentation accuracy (Dice similarity coefficient [DSC]). Subgroup analysis and multiple linear regression were used for further exploration.
Results:
Our method demonstrated promising results in the detection and segmentation of SRMs. All patients in the GE test set were correctly detected in at least one sequence. Our model achieved a median DSC of 0.769-0.855 across five MRI sequences and demonstrated reasonable generalization to non-GE scanners (median DSC range: 0.523-0.785).
Conclusions:
The implementation of automated segmentation achieved encouraging outcomes in both correct-detection rates and segmentation accuracy across a diverse cohort spanning multiple centers and scanners, suggesting its potential as a key component of future diagnostic pipelines for SRMs.
