CT slice index and thickness: impact on organ contouring in radiation treatment planning for prostate cancer

Cardiac substructure delineation in radiation therapy - A state-of-the-art review

Delineation of cardiac substructures is crucial for a better understanding of radiation-related cardiotoxicities and to facilitate accurate and precise cardiac dose calculation for developing and applying risk models. This review examines recent advancements in cardiac substructure delineation in the radiation therapy (RT) context, aiming to provide a comprehensive overview of the current level of knowledge, challenges and future directions in this evolving field. Imaging used for RT planning presents challenges in reliably visualising cardiac anatomy. Although cardiac atlases and contouring guidelines aid in standardisation and reduction of variability, significant uncertainties remain in defining cardiac anatomy. Coupled with the inherent complexity of the heart, this necessitates auto-contouring for consistent large-scale data analysis and improved efficiency in prospective applications. Auto-contouring models, developed primarily for breast and lung cancer RT, have demonstrated performance comparable to manual contouring, marking a significant milestone in the evolution of cardiac delineation practices. Nevertheless, several key concerns require further investigation. There is an unmet need for expanding cardiac auto-contouring models to encompass a broader range of cancer sites. A shift in focus is needed from ensuring accuracy to enhancing the robustness and accessibility of auto-contouring models. Addressing these challenges is paramount for the integration of cardiac substructure delineation and associated risk models into routine clinical practice, thereby improving the safety of RT for future cancer patients.

Clinical Use of a Commercial Artificial Intelligence-Based Software for Autocontouring in Radiation Therapy: Geometric Performance and Dosimetric Impact

PURPOSE

When autocontouring based on artificial intelligence (AI) is used in the radiotherapy (RT) workflow, the contours are reviewed and eventually adjusted by a radiation oncologist before an RT treatment plan is generated, with the purpose of improving dosimetry and reducing both interobserver variability and time for contouring. The purpose of this study was to evaluate the results of application of a commercial AI-based autocontouring for RT, assessing both geometric accuracies and the influence on optimized dose from automatically generated contours after review by human operator.

MATERIALS AND METHODS

A commercial autocontouring system was applied to a retrospective database of 40 patients, of which 20 were treated with radiotherapy for prostate cancer (PCa) and 20 for head and neck cancer (HNC). Contours resulting from AI were compared against AI contours reviewed by human operator and human-only contours using Dice similarity coefficient (DSC), Hausdorff distance (HD), and relative volume difference (RVD). Dosimetric indices such as Dmean, D0.03cc, and normalized plan quality metrics were used to compare dose distributions from RT plans generated from structure sets contoured by humans assisted by AI against plans from manual contours. The reduction in contouring time obtained by using automated tools was also assessed. A Wilcoxon rank sum test was computed to assess the significance of differences. Interobserver variability of the comparison of manual vs. AI-assisted contours was also assessed among two radiation oncologists for PCa.

RESULTS

For PCa, AI-assisted segmentation showed good agreement with expert radiation oncologist structures with average DSC among patients ≥ 0.7 for all structures, and minimal radiation oncology adjustment of structures (DSC of adjusted versus AI structures ≥ 0.91). For HNC, results of comparison between manual and AI contouring varied considerably e.g., 0.77 for oral cavity and 0.11-0.13 for brachial plexus, but again, adjustment was generally minimal (DSC of adjusted against AI contours 0.97 for oral cavity, 0.92-0.93 for brachial plexus). The difference in dose for the target and organs at risk were not statistically significant between human and AI-assisted, with the only exceptions of D0.03cc to the anal canal and Dmean to the brachial plexus. The observed average differences in plan quality for PCa and HNC cases were 8% and 6.7%, respectively. The dose parameter changes due to interobserver variability in PCa were small, with the exception of the anal canal, where large dose variations were observed. The reduction in time required for contouring was 72% for PCa and 84% for HNC.

CONCLUSIONS

When an autocontouring system is used in combination with human review, the time of the RT workflow is significantly reduced without affecting dose distribution and plan quality.

Computed tomographic quantitative evaluation of common bile duct size in normal dogs: A reference range study considering body weight

Introduction

Common bile duct (CBD) measurements are important for the evaluation of biliary systemic disorders. However, in veterinary medicine, reference ranges for specific body weights (BW) and correlation between CBD diameter and BW have not been studied. This study aimed to establish normal reference ranges of CBD diameter for different BW groups and to analyse correlation between CBD diameter and BW in dogs without hepatobiliary disease. Additionally, normal reference ranges of CBD to aorta ratio (CBD: Ao ratio) were established which is not affected by BW.

Methods

CBD diameter was measured at three different sites: porta hepatis (PH), duodenal papilla (DP) level and mid-portion (Mid) between these points using computed tomography (CT) in 283 dogs without hepatobiliary disease.

Results

The reference range of CBD diameter at PH level: 1.69 ± 0.29 mm (Class 1; 1 kg ≤ BW < 5 kg), 1.92 ± 0.35 mm (Class 2; 5 kg ≤ BW < 10 kg), 2.20 ± 0.43 mm (Class 3; 10 kg ≤ BW < 15 kg), 2.79 ± 0.49 mm (Class 4; 15 kg ≤ BW < 30 kg); Mid-level: 2.06 ± 0.25 mm (Class 1), 2.43 ± 0.37 mm (Class 2), 2.74 ± 0.52 mm (Class 3), 3.14 ± 0.44 mm (Class 4); DP level: 2.33 ± 0.34 mm (Class 1), 2.90 ± 0.36 mm (Class 2), 3.35 ± 0.49 mm (Class 3), and 3.83 ± 0.50 mm (Class 4). There was a significant difference in CBD diameter at each level among all BW groups. Furthermore, BW and CBD diameter showed positive linear correlation at each level. We devised CBD: Ao ratio at each level that showed no significant difference between the different BW groups; PH level: 0.34 ± 0.05; Mid-level: 0.42 ± 0.06; DP level: 0.47 ± 0.06.

Conclusion

In conclusion, since the CBD diameter for each BW is significantly different, different normal reference ranges of CBD diameter should be applied for each BW, and the CBD: Ao ratio can be used regardless of the BW.

Impact of slice thickness, pixel size, and CT dose on the performance of automatic contouring algorithms

Purpose

To investigate the impact of computed tomography (CT) image acquisition and reconstruction parameters, including slice thickness, pixel size, and dose, on automatic contouring algorithms.

Methods

Eleven scans from patients with head‐and‐neck cancer were reconstructed with varying slice thicknesses and pixel sizes. CT dose was varied by adding noise using low‐dose simulation software. The impact of these imaging parameters on two in‐house auto‐contouring algorithms, one convolutional neural network (CNN)‐based and one multiatlas‐based system (MACS) was investigated for 183 reconstructed scans. For each algorithm, auto‐contours for organs‐at‐risk were compared with auto‐contours from scans with 3 mm slice thickness, 0.977 mm pixel size, and 100% CT dose using Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean surface distance (MSD).

Results

Increasing the slice thickness from baseline value of 3 mm gave a progressive reduction in DSC and an increase in HD and MSD on average for all structures. Reducing the CT dose only had a relatively minimal effect on DSC and HD. The rate of change with respect to dose for both auto‐contouring methods is approximately 0. Changes in pixel size had a small effect on DSC and HD for CNN‐based auto‐contouring with differences in DSC being within 0.07. Small structures had larger deviations from the baseline values than large structures for DSC. The relative differences in HD and MSD between the large and small structures were small.

Conclusions

Auto‐contours can deviate substantially with changes in CT acquisition and reconstruction parameters, especially slice thickness and pixel size. The CNN was less sensitive to changes in pixel size, and dose levels than the MACS. The results contraindicated more restrictive values for the parameters should be used than a typical imaging protocol for head‐and‐neck.

Assessment of Radiation Dose Delivered and Volume Measurement By Low- and High-Dose Diagnostic Computed Tomography: Anthropomorphic Liver Phantom Study

Aim:

Liver volume measurement is a mandatory test before measure liver surgeries and transplantation. We aimed a study on the difference in volume measurement and radiation dose to an anthropomorphic liver phantom using high-dose and low-dose diagnostic computed tomography (CT).

Materials and Methods:

Several measurements of the manual total volume measurement done on an anthropomorphic liver phantom mounted with thermoluminescent dosimeter. We exposed the phantom with diagnostic CT, low-dose CT, and a low-dose CT with copper filter.

Results:

Phantom underwent ten scanning for each exposure. There was no significant difference in the total volume measurement in comparison to the phantom volume. The volume of phantom measured by low-dose CT, low-dose CT with copper phantom, and high-dose CT were 1869 ± 18 cm³, 1852 ± 24 cm³, and 1908 ± 12 cm³, (P = 0.3), respectively. However, the radiation dose delivered was significantly different (1.54 mGy, 0.77 mGy, and 5.84 mGy [P = 0.001], respectively).

Conclusion:

Total liver volume measurement provides essential clinical information in several clinical conditions. We recommended that the volume measured by a low-dose CT has an excellent correlation with the diagnostic quality CT and should be a routine in the routine clinical practice. CT volumetry achieves the same result while using very less radiation exposure. It may also be used with functional imaging to give complete information.

The dosimetric impact of stabilizing spinal implants in radiotherapy treatment planning with protons and photons: standard titanium alloy vs. radiolucent carbon-fiber-reinforced PEEK systems

Background

Throughout the last years, carbon‐fibre‐reinforced PEEK (CFP) pedicle screw systems were introduced to replace standard titanium alloy (Ti) implants for spinal instrumentation, promising improved radiotherapy (RT) treatment planning accuracy. We compared the dosimetric impact of both implants for intensity modulated proton (IMPT) and volumetric arc photon therapy (VMAT), with the focus on uncertainties in Hounsfield unit assignment of titanium alloy.

Methods

Retrospective planning was performed on CT data of five patients with Ti and five with CFP implants. Carbon‐fibre‐reinforced PEEK systems comprised radiolucent pedicle screws with thin titanium‐coated regions and titanium tulips. For each patient, one IMPT and one VMAT plan were generated with a nominal relative stopping power (SP) (IMPT) and electron density (ρ) (VMAT) and recalculated onto the identical CT with increased and decreased SP or ρ by ±6% for the titanium components.

Results

Recalculated VMAT dose distributions hardly deviated from the nominal plans for both screw types. IMPT plans resulted in more heterogeneous target coverage, measured by the standard deviation σ inside the target, which increased on average by 7.6 ± 2.3% (Ti) vs 3.4 ± 1.2% (CFP). Larger SPs lead to lower target minimum doses, lower SPs to higher dose maxima, with a more pronounced effect for Ti screws.

Conclusions

While VMAT plans showed no relevant difference in dosimetric quality between both screw types, IMPT plans demonstrated the benefit of CFP screws through a smaller dosimetric impact of CT‐value uncertainties compared to Ti. Reducing metal components in implants will therefore improve dose calculation accuracy and lower the risk for tumor underdosage.

The effect of longitudinal CT resolution and pixel size (FOV) on target delineation and treatment planning in stereotactic radiosurgery

The acquisition of high-quality, anatomic images is essential for the accurate delineation of tumor volumes and critical structures used for stereotactic radiosurgery (SRS) treatment planning. This study investigates the effect of CT slice thickness and field of view (FOV), i.e., longitudinal and axial CT resolution, on volume delineation and treatment planning in SRS and suggests optimal CT acquisition parameters for brain SRS simulation. Optimization of such parameters will maximize clinical efficacy, alter data storage requirements, reduce dosimetric uncertainties, and may ultimately facilitate more favorable clinical outcomes. Changes in the extent, shape and the absolute volume of the GTV were recorded when the longitudinal and axial CT resolution were modified. These changes ultimately impacted the PTV dose coverage. Reducing CT slice thickness from 2mm to 1mm resulted in an average decrease of 8.6%±13.9% (max=52.2%) and 3.0 %±4.3% (max=13.1%) in PTV D_min and PTV D95, respectively. Increasing CT slice thickness from 2mm to 3mm resulted in an average decrease of 10%±9.9% (max=26.8%) and 5.8%±5.8% (max=17.4%) in PTV D_min and PTV D95, respectively. Similarly, on average, PTV coverage decreased when FOV decreased. The average decrease in PTV D_min and PTV D95 for a 350cm FOV was 5.2%±7.2% (max=21.4%) and 1.9%±3.2% (max=7.5%), respectively. Decreasing FOV to 250cm yielded similar results with the average decrease of 5.6%±5.0% (max=13.2%) and 1.6%±2.6% (max=6.3%) in PTV D_min and PTV D95, respectively. These results suggest that the slice thickness and FOV of CT images affect target delineation and may potentially compromise the quality of the target coverage.

Low tube voltage CT for improved detection of pancreatic cancer: detection threshold for small, simulated lesions

BACKGROUND

Pancreatic ductal adenocarcinoma is associated with dismal prognosis. The detection of small pancreatic tumors which are still resectable is still a challenging problem.The aim of this study was to investigate the effect of decreasing the tube voltage from 120 to 80 kV on the detection of pancreatic tumors.

METHODS

Three scanning protocols was used; one using the standard tube voltage (120 kV) and current (160 mA) and two using 80 kV but with different tube currents (500 and 675 mA) to achieve equivalent dose (15 mGy) and noise (15 HU) as that of the standard protocol.Tumors were simulated into collected CT phantom images. The attenuation in normal parenchyma at 120 kV was set at 130 HU, as measured previously in clinical examinations, and the tumor attenuation was assumed to differ 20 HU and was set at 110HU. By scanning and measuring of iodine solution with different concentrations the corresponding tumor and parenchyma attenuation at 80 kV was found to be 185 and 219 HU, respectively.To objectively evaluate the differences between the three protocols, a multi-reader multi-case receiver operating characteristic study was conducted, using three readers and 100 cases, each containing 0-3 lesions.

RESULTS

The highest reader averaged figure-of-merit (FOM) was achieved for 80 kV and 675 mA (FOM=0,850), and the lowest for 120 kV (FOM=0,709). There was a significant difference between the three protocols (p<0,0001), when making an analysis of variance (ANOVA). Post-hoc analysis (students t-test) shows that there was a significant difference between 120 and 80 kV, but not between the two levels of tube currents at 80 kV.

CONCLUSION

We conclude that when decreasing the tube voltage there is a significant improvement in tumor conspicuity.

Interobserver variability in target volume delineation in postoperative radiochemotherapy for gastric cancer. A pilot prospective study

INTRODUCTION

The aim of this study is to determine the interobserver variability (IV) between radiation oncologists (RO) in target volume delineation for postoperative gastric cancer (GC) radiotherapy planning.

MATERIALS AND METHODS

Four physicians were asked to delimitate clinical target volume (CTV) on the same 3D CT images in 9 postoperative radiochemotherapy GC patients. Instructions were given to include tumour bed, remaining stomach, anastomosis, duodenal loop and local lymph nodes. The principal variable was spatial volume discrepancy between the main observer (called "A") and other observers (all called "B"), which were compared using the mathematical formula A⌣B/A⌢B, applied to the 3D CT images using Boolean operators. Analysis of variance with two random effects (observers and patients) was performed.

RESULTS

Mean volumes were 1410 cm(3) for OBA, 1231 cm(3) for OB2, 734.6 cm(3) for OB3 and 1350 cm(3) for OB4. Discrepancies were 519.9±431.6 cm(3) for OB2, 652.1±294.36 cm(3) for OB3 and 225.90±237.07 cm(3) for OB4. Standard deviation ascribed to patients as random effect was 898.6 cm(3) and that ascribed to observers was 198.10 cm(3), considered as a statistically significant difference.

CONCLUSIONS

A significant IV in target delineation that can be attributed to many factors depends more on patients' characteristics than RO delineating decisions.

Computed tomography liver volumetry using 3-dimensional image data in living donor liver transplantation: effects of the slice thickness on the volume calculation

The purpose of this study was to evaluate the relationship between the slice thickness and the calculated volume in computed tomography (CT) liver volumetry through the comparison of the results from images [including 3-dimensional (3D) images] with various slice thicknesses. Twenty potential adult liver donors (12 men and 8 women) with a mean age of 39 years (range = 24-64 years) underwent CT with a 64-section multidetector row CT scanner after the intravenous injection of a contrast material. Four image sets with slice thicknesses of 0.625, 2.5, 5, and 10 mm were used. First, a program developed in our laboratory for automated liver extraction was applied to the CT images, and the liver boundaries were determined automatically. Then, an abdominal radiologist reviewed all images onto which automatically extracted boundaries had been superimposed and then edited the boundaries on each slice to enhance the accuracy. The liver volumes were determined via the counting of the voxels within the liver boundaries. The mean whole liver volumes estimated with CT were 1322.5 cm(3) from 0.625-mm images, 1313.3 cm(3) from 2.5-mm images, 1310.3 cm(3) from 5-mm images, and 1268.2 cm(3) from 10-mm images. The volumes calculated from 3D (0.625-mm) images were significantly larger than the volumes calculated from thicker images (P < 0.001). The partial liver volumes of right lobes, left lobes, and lateral segments were evaluated in a similar manner. The estimated maximum difference in the calculated volumes of lateral segments was -10.9 cm(3) (-4.63%) between 0.625- and 5-mm images. In conclusion, liver volumes calculated from 2.5-mm-thick or thicker images are significantly smaller than liver volumes calculated from 3D images. If a maximum error of 5% in the calculated graft volume will not have a significant clinical impact, 5-mm-thick images are acceptable for CT volumetry. If the impact is significant, 3D images could be essential.