Evaluation of RANO Criteria for the Assessment of Tumor Progression for Lower-Grade Gliomas

• Type of Material: Article
• Authors: Fabio Raman, University of Alabama; Alexander Mullen, University of Alabama; Matthew Byrd, University of Alabama; Sejong Bae, University of Alabama; Jinsuh Kim, Emory University; Houman Sotoudeh, University of Alabama; Fanny E Moron, Baylor College of Medicine; Hassan M Fathallah-Shaykh, University of Alabama
• Language: English
• Date: 2023-07-01
• Publisher: MDPI
• Publication Version: Final Published Version
• Copyright Statement: © 2023 by the authors.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.3390/cancers15133274
• Title of Journal or Parent Work: CANCERS
• Volume: 15
• Issue: 13
• Grant/Funding Information: S.B. was partially supported by the National Cancer Institute (P30CA013148).
• Keywords: RESPONSE ASSESSMENT; Life Sciences & Biomedicine; NEUROONCOLOGY; low-grade gliomas; Oncology; Science & Technology; statistical change-of-point method; RECIST; Response Assessment in Neuro-Oncology (RANO) criteria; CURVES
• Research Categories: Health Sciences, Oncology; Health Sciences, Radiology; Biology, Neuroscience

Abstract

Purpose: The Response Assessment in Neuro-Oncology (RANO) criteria for lower-grade gliomas (LGGs) define tumor progression as ≥25% change in the T2/FLAIR signal area based on an operator’s discretion of the perpendicular diameter of the largest tumor cross-section. Potential sources of error include acquisition inconsistency of 2D slices, operator selection variabilities in both representative tumor cross-section and measurement line locations, and the inability to quantify infiltrative tumor margins and satellite lesions. Our goal was to assess the accuracy and reproducibility of RANO in LG. Materials and Methods: A total of 651 FLAIR MRIs from 63 participants with LGGs were retrospectively analyzed by three blinded attending physicians and three blinded resident trainees using RANO criteria, 2D visual assessment, and computer-assisted 3D volumetric assessment. Results: RANO product measurements had poor-to-moderate inter-operator reproducibility (r2 = 0.28–0.82; coefficient of variance (CV) = 44–110%; mean percent difference (diff) = 0.4–46.8%) and moderate-to-excellent intra-operator reproducibility (r2 = 0.71–0.88; CV = 31–58%; diff = 0.3–23.9%). When compared to 2D visual ground truth, the accuracy of RANO compared to previous and baseline scans was 66.7% and 65.1%, with an area under the ROC curve (AUC) of 0.67 and 0.66, respectively. When comparing to volumetric ground truth, the accuracy of RANO compared to previous and baseline scans was 21.0% and 56.5%, with an AUC of 0.39 and 0.55, respectively. The median time delay at diagnosis was greater for false negative cases than for false positive cases for the RANO assessment compared to previous (2.05 > 0.50 years, p = 0.003) and baseline scans (1.08 > 0.50 years, p = 0.02). Conclusion: RANO-based assessment of LGGs has moderate reproducibility and poor accuracy when compared to either visual or volumetric ground truths.