Tumor Response Assessment in Diffuse Intrinsic Pontine Glioma: Comparison of Semiautomated Volumetric, Semiautomated Linear, and Manual Linear Tumor Measurement Strategies

Abstract

Background and purpose: 2D measurements of diffuse intrinsic pontine gliomas are limited by variability, and volumetric response criteria are poorly defined. Semiautomated 2D measurements may improve consistency; however, the impact on tumor response assessments is unknown. The purpose of this study was to compare manual 2D, semiautomated 2D, and volumetric measurement strategies for diffuse intrinsic pontine gliomas.

Materials and methods: This study evaluated patients with diffuse intrinsic pontine gliomas through a Phase I/II trial (NCT02607124). Clinical 2D cross-product values were derived from manual linear measurements (cross-product = long axis × short axis). By means of dedicated software (mint Lesion), tumor margins were traced and maximum cross-product and tumor volume were automatically derived. Correlation and bias between methods were assessed, and response assessment per measurement strategy was reported.

Results: Ten patients (median age, 7.6 years) underwent 58 MR imaging examinations. Correlation and mean bias (95% limits) of percentage change in tumor size from prior examinations were the following: clinical and semiautomated cross-product, r = 0.36, -1.5% (-59.9%, 56.8%); clinical cross-product and volume, r = 0.61, -2.1% (-52.0%, 47.8%); and semiautomated cross-product and volume, r = 0.79, 0.6% (-39.3%, 38.1%). Stable disease, progressive disease, and partial response rates per measurement strategy were the following: clinical cross-product, 82%, 18%, 0%; semiautomated cross-product, 54%, 42%, 4%; and volume, 50%, 46%, 4%, respectively.

Conclusions: Manual 2D cross-product measurements may underestimate tumor size and disease progression compared with semiautomated 2D and volumetric measurements.

Fig 1.

Sample case demonstrating measurement methods. A, Manual clinical transaxial (2D) measurements. Largest dimension identified on axial images and perpendicular short axis dimension performed and reported in the clinical radiology report and used for derivation of tumor response. B, Semiautomated 2D measurements. Tumor margins are traced (red outline) on each image, and automated 2D measurements (largest long axis dimension and perpendicular short axis dimension, blue lines) are automatically derived, along with tumor volume in the mint Lesion software package. C, Semiautomated 2D measurements and volumes performed during the treatment course. Imaging performed after cycles 2, 4, 6, and 8.

Fig 2.

Sample case demonstrating clinical 2D measurements (clinical CP) and semiautomated 2D and volumetric measurements (semiautomated CP) during the treatment course. Note that in this case, although there were differences in orientation of the measurements with the semiautomated process, response classification was the same compared with manual clinical CP measurements.

Fig 3.

Sample case demonstrating clinical 2D measurements (clinical CP) and semiautomated 2D and volumetric measurements (semiautomated CP) during the treatment course. In this case, per protocol, imaging progression based on clinical CP (A) was called after cycle 8 (33.2% increase in CP). With semiautomated CP (B), progressive disease would have been called (based solely on imaging) after cycle 2 (28% increase). This is due to a different section choice as a maximum transaxial dimension and slightly different measurement orientation (B, cycle 2). Subsequently, however, on the basis of a protocol comparing with smallest CP during treatment (baseline), stable disease would have been called. Note that although the CP increased 28% (PD) after cycle 2, the tumor volume only increased 9% (SD). Such discrepancies were common when comparing treatment-response strategies.

Fig 4.

Clinically derived tumor CP versus semiautomated software–derived tumor CP for all time points with a linear trendline (r = 0.74, P < .0001).

Fig 5.

Bland-Altman plot demonstrating bias between clinical and semiautomated tumor CP for all time points. The solid line indicates a mean bias between techniques. Dashed lines indicate ±2 SDs of the mean (95% limits of agreement). Overall, clinical CP measured less than semiautomated CP (mean bias, −2.5). Outliers (>1.96 SDs) were predominantly noted in larger tumors.

Fig 6.

Correlation of percentage change from prior examination in clinical CP versus semiautomated CP (r = 0.36, P = .011).

Fig 7.

Bland-Altman plot demonstrating bias between the percentage change in clinical and semiautomated tumor CP from a prior examination. The solid line indicates mean bias between techniques. Dashed lines indicate ±2 SDs of the mean (95% limits of agreement). Overall, percentage change in clinical CP was smaller than the percentage change in semiautomated CP (mean bias, −1.5%, 95% limits of agreement, −59.9, +56.8) between time points.