MRI Follow-up of Astrocytoma: Automated Coregistration and Color-Coding of FLAIR Sequences Improves Diagnostic Accuracy With Comparable Reading Time.

BACKGROUND: MRI follow-up is widely used for longitudinal assessment of astrocytoma, yet reading can be tedious and error-prone, in particular when changes are subtle. PURPOSE/HYPOTHESIS: To determine the effect of automated, color-coded coregistration (AC) of fluid attenuated inversion recovery (FLAIR) sequences on diagnostic accuracy, certainty, and reading time compared to conventional follow-up MRI assessment of astrocytoma patients. STUDY TYPE: Retrospective. POPULATION: In all, 41 patients with neuropathologically confirmed astrocytoma. FIELD STRENGTH/SEQUENCE: 1.0-3.0T/FLAIR ASSESSMENT: The presence or absence of tumor progression was determined based on FLAIR sequences, contrast-enhanced T1 sequences, and clinical data. Three radiologists assessed 47 MRI study pairs in a conventional reading (CR) and in a second reading supported by AC after 6 weeks. Readers determined the presence/absence of tumor progression and indicated diagnostic certainty on a 5-point Likert scale. Reading time was recorded by an independent assessor. STATISTICAL TESTS: The Wilcoxon test was used to assess reading time and diagnostic certainty. Differences in diagnostic accuracy, sensitivity, and specificity were analyzed with the McNemar mid-p test. RESULTS: Readers attained significantly higher overall sensitivity (0.86 vs. 0.75; P < 0.05) and diagnostic accuracy (0.84 vs. 0.73; P < 0.05) for detection of progressive nonenhancing tumor burden when using AC compared to CR. There was a strong trend towards higher specificity within the AC-augmented reading, yet without statistical significance (0.83 vs. 0.71; P = 0.08). Sensitivity for unequivocal disease progression was similarly high in both approaches (AC: 0.94, CR: 0.92), while for marginal disease progressions, it was significantly higher in AC (AC: 0.78, CR: 0.58; P < 0.05). Reading time including application loading time was comparable (AC: 38.1 ± 16.8 sec, CR: 36.0 ± 18.9 s; P = 0.25). DATA CONCLUSION: Compared to CR, AC improves comparison of FLAIR signal hyperintensity at MRI follow-up of astrocytoma patients, allowing for a significantly higher diagnostic accuracy, particularly for subtle disease progression at a comparable reading time. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY STAGE: 6 J. Magn. Reson. Imaging 2020;52:1197-1206.

Follow-up MRI in multiple sclerosis patients: automated co-registration and lesion color-coding improves diagnostic accuracy and reduces reading time.

OBJECTIVES: In multiple sclerosis (MS), the heterogeneous and numerous appearances of lesions may impair diagnostic accuracy. This study investigates if a combined automated co-registration and lesion color-coding method (AC) improves assessment of MS follow-up MRI compared with conventional reading (CR). METHODS: We retrospectively assessed 70 follow-up MRI of 53 patients. Heterogeneous datasets of diverse scanners and institutions were used. Two readers determined presence of (a) progression, (b) regression, (c) mixed change, or (d) stable disease between the two examinations using corresponding FLAIR sequences in CR and AC-assisted reading. Consensus reference reading was provided by two blinded radiologists. Kappa statistics tested interrater agreement, McNemar's test dichotomous variables, and Wilcoxon's test continuous variables (statistical significance p ≤ 0.05). RESULTS: The cohort comprised 41 female and 12 male patients with a mean age of 40 (± 14) years. Average rating time was reduced from 78 (± 36) to 44 (±22) s with the AC approach (p < 0.001). The time needed to start and match datasets with AC was 14 (± 1) s. Compared with CR, AC improved interrater agreement, both between raters (0.52 vs. 0.67) and between raters and consensus reference reading (0.47/0.5 vs. 0.83/0.78). Compared with CR, the diagnostic accuracy increased from 67 to 90% (reader 1, p < 0.01) and from 70 to 87% (reader 2, p < 0.05) in the AC-assisted reading. CONCLUSIONS: Compared with CR, automated co-registration and lesion color-coding of MS-associated FLAIR-lesions in follow-up MRI increased diagnostic accuracy and reduced the time required for follow-up evaluation significantly. The AC algorithm therefore appears to be helpful to improve MS follow-up assessments in clinical routine. KEY POINTS: • Automated co-registration and lesion color-coding increases diagnostic accuracy in the assessment of MRI follow-up examinations in patients with multiple sclerosis. • Automated co-registration and lesion color-coding reduces reading time of MRI follow-up examinations in patients with multiple sclerosis. • Automated co-registration and lesion color-coding improved interrater agreement in the assessment of MRI follow-up examinations in patients with multiple sclerosis.