Cone-Beam Computed Tomography

Following the institutional review board approval for the study (number: 119/2019; Muğla Sıtkı Koçman University Ethical Committee), a retrospective cohort analysis was performed using the medical records of patients. For the current study, patient consent is not required. All procedures executed involving human participants were in accordance with the ethical standards of the institutional ethical committee and with the 1964 Helsinki declaration.
A total of 146 patients who applied to the neurosurgery outpatient clinic with a recent abdominal CT (max three months) because of a lower back pain complaint were included in the study. Patients with a previous history of surgery or a vertebral fracture were excluded. After excluded patients, a total of 146 patients were included in the study, of whom 90 were female (61.6%) and 56 were male (38.4%). The mean age of the patients was 51.42±13.91 (20-82) years.
Lumbar vertebra CT scans of all patients were reviewed retrospectively. CT images at the level from L3-L4 intervertebral disc were analyzed for body composition of fat tissue and muscle mass volume through the dedicated CT software (Syngo.via, SOMATOM Definition Flash: Siemens Healthcare, Forchheim, Germany). The L3-L4 level was selected in sagittal reformat CT images with the software (Figure 1).
The density range of -200, -40 HU was selected for the fat density measurement in the cross-section with the "region grooving" application in the angled axial images obtained parallel to the disc plane at this level. First, the fat volume in the whole section was measured (visceral and subcutaneous). Then, only the visceral adipose tissue volume was calculated by drawing borders to exclude subcutaneous adipose tissue (Figure 2). The subcutaneous fat tissue volume was obtained by subtracting the visceral fat tissue volume from the total fat volume (Figure 3).
With the same application, muscle density was selected and paravertebral muscle tissue volume was calculated (bilateral musculus psoas major, musculus quadratus lumborum, musculus iliocostalis, musculus longissimus, musculus multifidus volumes). A Spearman correlation model was used to analyze visceral adiposity, subcutaneous fat, and muscle mass.
In CT images, each intervertebral disc space was evaluated in terms of the presence of osteophytes, loss of disc height, sclerosis in the end plates, and spinal stenosis (spinal canal narrowing under 15 mm AP diameter) to investigate the presence of degeneration. Each level was scored according to the presence of findings, with 1 point for the presence of osteophytes, loss of disc height, sclerosis in the end plates, and spinal stenosis. The total score at all levels (L1-S1) was calculated for each patient.
Statistical analyses were performed using IBM SPSS version 20.0 software (IBM Corp., Armonk, NY). The conformity of the data to normal distribution was assessed using the Shapiro-Wilk test. Normally distributed variables were presented as mean±standard deviation and those not showing normal distribution as median (minimum-maximum) values. Categorical variables were presented as numbers (n) and percentages (%). The Spearman's rank correlation coefficient test was used to determine the correlation between the measured parameters in various vertebral pathologies. Continuous variables were compared using the Mann-Whitney U test. The receiver operating characteristic (ROC) analysis was used to detect the area under the curve (AUC) and define the cutoff values with their sensitivities and specificities of the measurements. An alpha value of p<0.05 was accepted as statistically significant.

All SBRT treatments were performed using a Helical Tomotherapy (HT) Hi-Art Treatment System (Accuray, Madison, WI, USA). The HT-SBRT technique and treatment planning were performed as previously described according to our institutional protocol [16 (link)]. The gross tumor volume (GTV) was delineated as a lesion observed at the lung window level on the enhanced CT and/or FDG-PET. The clinical target volume was equal to gross tumor volume. The internal target volume (ITV) was contoured based on the extension of GTVs at the all phases (5 inspiratory, 5 expiratory, and 1 resting phase) of the respiratory cycle on the four-dimensional CT (4D-CT) (Siemens Somatom Sensation, Siemens Healthineers Corporation, Germany) scanning to include the full movement of the tumor. To compensate for the uncertainty in tumor position and changes in tumor motion caused by breathing, the planning target volume (PTV) was extended by a margin of 0.5 cm from the ITV. Cone beam CT was implemented before each treatment to confirm the position of the target was achieved. The main factors determining the dose/fractionation scheme were tumor location, tumor size, and lung function parameters. In general, a total dose of 50 Gy/5 fractions (biologically effective dose [BED] = 100 Gy) was delivered for patients with peripherally located tumors and 60 Gy/10 fractions (BED = 96 Gy) was delivered for patients with centrally located tumors or tumors with extensive adherence to the chest wall. Dose constraints for the OARs were implemented according to the experience of the Radiation Therapy Oncology Group (RTOG) 0236 guidelines [2 (link)].

All images were displayed using a Christie Mirage S+6K DLP projector and Starglass screen (58”x78”) for both monoscopic and stereoscopic viewing. The direct volume rendering technique was used for displaying the volumetric CT data [1 (link)]. Viewing software was implemented in Python programming language using the Visualization Toolkit (VTK) open-source software module (Kitware Inc., Clifton Park, New York, USA) [20 ], and Qt graphical user interface library (The Qt Company, Espoo, Finland). The scans were rendered with an NVIDIA Quadro K5200 graphics processor using a ray casting technique, which performs an image-order rendering. All the images were rendered using the same customized color and opacity transfer functions, which were edited by an experienced radiologist, and could not be altered for this study. In the opacity transfer function, higher opacity was assigned for intensity values corresponding to cardiac tissues, and lower opacity was for intensities corresponding to other organs such as lungs to make them transparent. The color transfer function was edited interactively to enhance the aesthetics of cardiac tissues and vessels. The refresh rate for the project was set to 90 Hz, and no lag in rendering was observed during the interactions such as rotation and scaling. 3D scans were viewed with active shutter glasses, and users manipulated the images using a mouse.