Automated Color-Coding of Lesion Changes in Contrast-Enhanced 3D T1-Weighted Sequences for MRI Follow-up of Brain Metastases

Abstract

BACKGROUND AND PURPOSE: MR imaging is the technique of choice for follow-up of patients with brain metastases, yet the radiologic assessment is often tedious and error-prone, especially in examinations with multiple metastases or subtle changes. This study aimed to determine whether using automated color-coding improves the radiologic assessment of brain metastases compared with conventional reading. MATERIALS AND METHODS: One hundred twenty-one pairs of follow-up examinations of patients with brain metastases were assessed. Two radiologists determined the presence of progression, regression, mixed changes, or stable disease between the follow-up examinations and indicated subjective diagnostic certainty regarding their decisions in a conventional reading and a second reading using automated color-coding after an interval of 8 weeks. RESULTS: The rate of correctly classified diagnoses was higher (91.3%, 221/242, versus 74.0%, 179/242, P, .01) when using automated color-coding, and the median Likert score for diagnostic certainty improved from 2 (interquartile range, 2–3) to 4 (interquartile range, 3–5) (P, .05) compared with the conventional reading. Interrater agreement was excellent (k = 0.80; 95% CI, 0.71–0.89) with automated color-coding compared with a moderate agreement (k = 0.46; 95% CI, 0.34–0.58) with the conventional reading approach. When considering the time required for image preprocessing, the overall average time for reading an examination was longer in the automated color-coding approach (91.5 [SD, 23.1] seconds versus 79.4 [SD, 34.7] seconds, P, .001). CONCLUSIONS: Compared with the conventional reading, automated color-coding of lesion changes in follow-up examinations of patients with brain metastases significantly increased the rate of correct diagnoses and resulted in higher diagnostic certainty.