A New Metric for Detecting Change in Slowly Evolving Brain Tumors: Validation in Meningioma Patients

Abstract

Background Change detection is a critical component in the diagnosis and monitoring of many slowly evolving pathologies. Objective This article describes a semi-automatic monitoring approach using longitudinal medical images. We test the method on brain scans of meningioma patients, which experts found difficult to monitor as the tumor evolution is very slow and may be obscured by artifacts related to image acquisition. Methods We describe a semi-automatic procedure targeted towards identifying difficult-to-detect changes in brain tumor imaging. The tool combines input from a medical expert with state-of-the-art technology. The software is easy to calibrate and, in less than five minutes, returns the total volume of tumor change in mm3. We test the method on post-gadolinium, T1-weighted Magnetic Resonance Images of ten meningioma patients and compare our results to experts’ findings. We also perform benchmark testing with synthetic data. Results Our experiments indicated that experts’ visual inspections are not sensitive enough to detect subtle growth. Measurements based on experts’ manual segmentations were highly accurate but also labor intensive. The accuracy of our approach was comparable to the experts’ results. However, our approach required far less user input and generated more consistent measurements. Conclusion The sensitivity of experts’ visual inspection is often too low to detect subtle growth of meningiomas from longitudinal scans. Measurements based on experts’ segmentation are highly accurate but generally too labor intensive for standard clinical settings. We described an alternative metric that provides accurate and robust measurements of subtle tumor changes while requiring a minimal amount of user input.

Detection of small changes in medical and random-dot images comparing self-organizing map performance to human detection

“…Discussion [4] reported that an expert wrongly classified all cases with 1% artificial lesion growth, and only achieved an accuracy of 20% for cases with 5% growth. The same expert, however, correctly classified all cases with a 22% growth.…”

“…This is especially true for scans taken at relatively short intervals (less than a year). Visual inspection often misses the slow evolution because the change may be obscured by variations in body position, slice position, or intensity profile between scans, as noted by [4]. In some cases, the change can be too small to be noticed, leaving a patient to fate.…”

Assessment of pituitary adenoma volumetric change using longitudinal MR image registration

“…One of the fields that may particularly benefit from computer-aided methods is the assessment of changes in a patient's tumor burden especially when the tumor evolution is slow. This issue has recently been addressed with the development of a computerassisted semi-automated method applied on slow-growing meningiomas [14]. A benefit of a 3D volumetric approach compared to 2D-based volume analysis has recently been Fig.…”

Semiautomatic segmentation and follow-up of multicomponent low-grade tumors in longitudinal brain MRI studies

“…Quantification of longitudinal changes in brain MRI scans has gained much attention in recent years. [15][16][17][18][19][20][21][22][23][24] Some of these methods register follow-up MR scans and analyze the changes between the scans. Patriarche and Erickson 22 present a graylevel based change detection.…”

“…Chitphakdithai et al 16 simultaneously estimate the registration parameters and label the changes between two consecutive brain scans to track metastatic brain tumors. Pohl et al 23 present a pipeline method to segment a tumor in a set of longitudinal scans based on user guided segmentation of the first scan. Menze et al 10 and Konukoglu et al 25 present approaches for modeling tumor growth in longitudinal images based on a reaction-diffusion framework.…”