An Innovative Approach to Scalp Segmentation in Pediatric Brain Radiotherapy: An Atlas-Based Study

PURPOSE/OBJECTIVE(S)

This study aims to develop an atlas-based method for segmentation of the scalp in pediatric patients that received brain radiotherapy. The study focuses on the development of a model that utilizes CT scans of pediatric medulloblastoma (MB) patients who underwent craniospinal-irradiation (CSI) and a boost in the posterior cranial fossa (PCF).

MATERIALS/METHODS

A team of experts created the Alopecia_Hope (A_H) contouring protocol on the CT scan using the 2018 EPTN consensus-based atlas and by defining for the manual adjustments the anatomic bone landmarks to differentiate between the scalp and the facial and neck skin. Using RayStation version 12A-SP1, the system was running his latest available software version at the time of study. Automated scripts were created to identify bone tissue and skin as a subtraction volume between bone and body. The soft tissue CT window was then manually used to increase or decrease the thickness of the skin, following the landmarks outlined in the protocol. The entire head scalp was defined as WScalp (WS), and a duplicate and reduced version was also defined as PCFScalp (PCFS). We randomly chose 40 patients to create both WS and PCF atlas with RayStation and 8 patients were used as testers for both volumes, the patients with major bone changes after neurosurgery were excluded. The contouring time was recorded, the mean time was calculated, and the contouring results were analyzed by experts. Standard similarity metrics of 3D DICE similarity coefficient and mean distance to agreement (MDA) were calculated.

RESULTS

A total of 52 patients were selected for the study, with a median contouring time of 19 minutes using the guidelines, the A_H protocol and automatic scripting. Three patients were excluded. Of the 8 testers, including both volumes (WS and PCFS), the mean contouring time for the expert operator was 17.5 min. While the contouring undergone by the automatic atlas was 11 min and the mean time of the expert correction was 4.5 min. Considering the work done by the atlas is in background, this resulted in a time savings of 80% for the operator. Table 1 contains mean and one standard deviation data for the metric, both WS and PCFS tested.

CONCLUSION

Atlas-based self-segmentation and the Alopecia_Hope protocol offer a more efficient and accurate method to identify specific areas of the scalp that may be affected by post-radiotherapy hair loss. This study demonstrates the effectiveness and efficiency in reducing radiotherapy workload and improving accuracy, with possible future application in the prevention of permanent alopecia and in improving the quality of life of patients undergoing whole brain radiotherapy.

Measurement of liver iron overload: noninvasive calibration of MRI-R2* by magnetic iron detector susceptometer

An accurate assessment of body iron accumulation is essential for the diagnosis and therapy of iron overload in diseases such as thalassemia or hemochromatosis. Magnetic iron detector susceptometry and MRI are noninvasive techniques capable of detecting iron overload in the liver. Although the transverse relaxation rate measured by MRI can be correlated with the presence of iron, a calibration step is needed to obtain the liver iron concentration. Magnetic iron detector provides an evaluation of the iron overload in the whole liver. In this article, we describe a retrospective observational study comparing magnetic iron detector and MRI examinations performed on the same group of 97 patients with transfusional or congenital iron overload. A biopsy-free linear calibration to convert the average transverse relaxation rate in iron overload (R(2) = 0.72), or in liver iron concentration evaluated in wet tissue (R(2) = 0.68), is presented. This article also compares liver iron concentrations calculated in dry tissue using MRI and the existing biopsy calibration with liver iron concentrations evaluated in wet tissue by magnetic iron detector to obtain an estimate of the wet-to-dry conversion factor of 6.7 ± 0.8 (95% confidence level).