Assessment of CT to CBCT contour mapping for radiomic feature analysis in prostate cancer

Abstract

This study provides a quantitative assessment of the accuracy of a commercially available deformable image registration (DIR) algorithm to automatically generate prostate contours and additionally investigates the robustness of radiomic features to differing contours. Twenty-eight prostate cancer patients enrolled on an institutional review board (IRB) approved protocol were selected. Planning CTs (pCTs) were deformably registered to daily cone-beam CTs (CBCTs) to generate prostate contours (auto contours). The prostate contours were also manually drawn by a physician. Quantitative assessment of deformed versus manually drawn prostate contours on daily CBCT images was performed using Dice similarity coefficient (DSC), mean distance-to-agreement (MDA), difference in center-of-mass position (ΔCM) and difference in volume (ΔVol). Radiomic features from 6 classes were extracted from each contour. Lin’s concordance correlation coefficient (CCC) and mean absolute percent difference in radiomic feature-derived data (mean |%Δ|RF) between auto and manual contours were calculated. The mean (± SD) DSC, MDA, ΔCM and ΔVol between the auto and manual prostate contours were 0.90 ± 0.04, 1.81 ± 0.47 mm, 2.17 ± 1.26 mm and 5.1 ± 4.1% respectively. Of the 1,010 fractions under consideration, 94.8% of DIRs were within TG-132 recommended tolerance. 30 radiomic features had a CCC > 0.90 and 21 had a mean |%∆|RF < 5%. Auto-propagation of prostate contours resulted in nearly 95% of DIRs within tolerance recommendations of TG-132, leading to the majority of features being regarded as acceptably robust. The use of auto contours for radiomic feature analysis is promising but must be done with caution.

Radiomics in prostate cancer: an up-to-date review

“… 209 In particular, even for contour created by a trained radiologist, inter- and intra-observer contouring variability could significantly impact the successive radiomics extracted features, while in automatically generated contouring, despite promising results, current findings are still unripe. 210 , 211 Despite this could potentially lead to important limitations related to reproducibility, novel approaches assessing the sensitivity of RFs in relation to inter-observer variability (via the normalization and quantization of images before extraction or the use of morphological features) could limit this issue. 212 , 213 Different combination models between clinical features, imaging phenotypes, and radiomics gave better results in diagnosing PCa, but their use is still limited to clinical research.…”

Cone-beam CT delta-radiomics to predict genitourinary toxicities and international prostate symptom of prostate cancer patients: a pilot study

“…A larger patient cohort will make it difficult to replicate the results of this study due to time costs. Auto segmentation of the prostate contours through the use of artificial intelligence, deep learning, or deformable propagation could greatly reduce the time it takes run radiomics studies such as this 42 . Future work will investigate performance of automated contours in comparison to manually segmented contours for radiomic analysis.…”

“…These features are described in detail in Delgadillo et al . , including the Image Biomarker Standardization Initiative (IBSI) code equivalent 31 , 42 . The full list of RFs along with their IBSI code equivalent are shown in Supplementary Table S3 .…”

“…Textural features were calculated from five RF classes including gray-level co-occurrence matrices (GLCM), Neighborhood Gray-Tone Difference Matrix (NGTDM), gray-level run length matrices (GLRLM), and gray-level size zone matrices (GLSZM), and first order statistical features. These features are described in detail in Delgadillo et al, including the Image Biomarker Standardization Initiative (IBSI) code equivalent 31,42 . The full list of RFs along with their IBSI code equivalent are shown in Supplementary Table S3.…”

Assessing radiomics feature stability with simulated CT acquisitions

“…If acquisition details are not perfectly matched, two different images, even of the same tissue, will yield different measurements. A number of studies have reported the impact on CT radiomics analysis caused by the variability of acquisition parameters and post-process variables [15][16][17][18] . Any algorithm or analysis based on these measurements will therefore not be reliable for use with unseen scanners.…”