Atlas-based auto-segmentation for delineating the heart and cardiac substructures in breast cancer radiation therapy.

BACKGROUND: This study aimed to develop and validate an automatic multi-atlas segmentation method for delineating the heart and substructures in breast cancer radiation therapy (RT). MATERIAL AND METHODS: The atlas database consisted of non-contrast-enhanced planning CT scans from 42 breast cancer patients, each with one manual delineation of the heart and 22 cardiac substructures. Half of the patients were scanned during free-breathing, the rest were scanned during a deep inspiration breath-hold. The auto-segmentation was developed in the MIM software system and validated geometrically and dosimetrically in two steps: The first validation in a small dataset to ensure consistency of the atlas. This was succeeded by a final test where multiple manual delineations in CT scans of 12 breast cancer patients were compared to the auto-segmentation. For geometric evaluation, the dice similarity coefficient (DSC) and the mean surface distance (MSD) were used. For dosimetric evaluation, the RT doses to each substructure in the manual and the automatic delineations were compared. RESULTS: In the first validation, a high geometric and dosimetric performance between the automatic and manual delineations was observed for all substructures. The final test confirmed a high agreement between the automatic and manual delineations for the heart (DSC = 0.94) and the cardiac chambers (DSC: 0.75-0.86). The difference in MSD between the automatic and manual delineations was low (<4 mm) in all structures. Finally, a high correlation between mean RT doses for the automatic and the manual delineations was observed for the heart and substructures. CONCLUSIONS: An automatic segmentation tool for delineation of the heart and substructures in breast cancer RT was developed and validated with a high correlation between the automatic and manual delineations. The atlas is pivotal for large-scale evaluations of radiation-associated heart disease.

Density calibrated cone beam CT as a tool for adaptive radiotherapy.

BACKGROUND: Visual inspections of anatomical changes observed on daily cone-beam CT (CBCT) images are often used as triggers for radiotherapy plan adaptation to avoid unacceptable dose levels to the target or OARs. Direct CBCT dose calculations would improve the ability to adapt only those plans where dosimetric changes are observed. This study investigates the accuracy of dose calculations on CBCTs. MATERIALS AND METHODS: Calibration curves were obtained for CBCT imagers at nine identical accelerators. CBCT scans of a phantom with different density inserts were recorded for two scan modes (Head-Neck and Pelvis) and mean calibration curves were calculated. Subsequently, CBCT scans of the phantom with six different density inserts were recorded, the dose distributions on the CBCTs were calculated and compared to dose on the planning CT (pCT). The uncertainty was quantified by the dosimetric difference between the pCT and the CBCT. The two mean calibration curves were used to calculate the daily delivered CBCT dose for ten Head-Neck-, eleven Lung-, and ten pelvic patients. Additional patient calculations were performed using low-HU empirically corrected calibration curves. Patient doses were compared on target coverage and mean dose, and D1cc for OARs. RESULTS: The dose differences between pCT and CBCT for phantom data were small for all DVH parameters, with mean deviations below ±0.6% for both CBCT modes. For patient data, it was found that low-HU corrected calibration curves performed the best. The mean deviations for the mean dose and coverage of the target were 0.2%±0.7% and 0.1%±0.6%, across all patient groups. CONCLUSION: Dose calculation on CBCT images results in target coverage and mean dose with an accuracy of the order of 1%, which makes this acceptable for clinical use. The CBCT mode specific calibration curves can be used at all identical imaging devices and for all patient groups.

Dosimetric assessment of an Atlas based automated segmentation for loco-regional radiation therapy of early breast cancer in the Skagen Trial 1: A multi-institutional study.

The effect of Atlas-based automated segmentation (ABAS) on dose volume histogram (DVH) parameters compared to manual segmentation (MS) in loco-regional radiotherapy (RT) of early breast cancer was investigated in patients included in the Skagen Trial 1. This analysis supports implementation of ABAS in clinical practice and multi-institutional trials.

Internal and external validation of an ESTRO delineation guideline - dependent automated segmentation tool for loco-regional radiation therapy of early breast cancer.

BACKGROUND AND PURPOSE: To internally and externally validate an atlas based automated segmentation (ABAS) in loco-regional radiation therapy of breast cancer. MATERIALS AND METHODS: Structures of 60 patients delineated according to the ESTRO consensus guideline were included in four categorized multi-atlas libraries using MIM Maestro™ software. These libraries were used for auto-segmentation in two different patient groups (50 patients from the local institution and 40 patients from other institutions). Dice Similarity Coefficient, Average Hausdorff Distance, difference in volume and time were computed to compare ABAS before and after correction against a gold standard manual segmentation (MS). RESULTS: ABAS reduced the time of MS before and after correction by 93% and 32%, respectively. ABAS showed high agreement for lung, heart, breast and humeral head, moderate agreement for chest wall and axillary nodal levels and poor agreement for interpectoral, internal mammary nodal regions and LADCA. Correcting ABAS significantly improved all the results. External validation of ABAS showed comparable results. CONCLUSIONS: ABAS is a clinically useful tool for segmenting structures in breast cancer loco-regional radiation therapy in a multi-institutional setting. However, manual correction of some structures is important before clinical use. The ABAS is now available for routine clinical use in Danish patients.

Clinical validation of a 4D-CT based method for lung ventilation measurement in phantoms and patients.

BACKGROUND: Lung cancer patients referred to radiotherapy (RT) often present with regional lung function deficits, and it is therefore of interest to image their lung function prior to treatment. In this study a method was developed that uses a deformable image registration (DIR) between the peak-inhale and peak-exhale phases of a thoracic four-dimensional computed tomography (4D-CT) scan to extract ventilation information. The method calculates the displacement vector fields (DVFs) resulting from the DIR using the Jacobian map approach in order to extract information regarding regional lung volume change. MATERIAL AND METHODS: The DVFs resulting from DIRs were analysed to compute the Jacobian determinant of vectors in the field, thus obtaining a map of the vector gradients of the entire registered CT image, i.e. voxel-wise local volume change. Geometric and quantitative validation was achieved using images of both phantoms and patients. In the phantom studies, translations and deformations of known size and direction were introduced to validate both the DIR algorithm and the method as a whole. Furthermore, five patients underwent 4D-CT for planning of stereotactic body RT (SBRT). The patients were immobilised in a stereotactic body frame (SBF) and for each patient, two thoracic 4D-CT scans were acquired, one scan with respiration restricted by an abdominal compression plate and the other under free breathing. RESULTS: In the phantom studies deformation errors were found to be of the order of the expected precision of 3 mm, corresponding to the image slice distance, in lateral and vertical directions. For the longitudinal direction a more pronounced discrepancy was observed, with the algorithm predicting displacement lengths of less than half of the physically introduced deformation. Qualitatively the method performed as expected. In the patient study an inverse consistency test showed deviations of up to 5.8 mm, i.e. almost twice the image slice separation. Jacobian maps of the patient images indicated well-ventilated areas as anatomically expected. CONCLUSION: The established method provides a means of using a (commercially available) DIR algorithm to obtain a quantitative measure of local lung volume change. With further phantom and patient validation studies, quantitative maps of specific ventilation should be possible to produce and use in a clinical setting.