Bone structure texture analysis as a potential tool to estimate radiation induced insufficiency fracture risk

MRI Radiomic Analysis of IMRT-Induced Bladder Wall Changes in Prostate Cancer Patients: A Relationship with Radiation Dose and Toxicity

BACKGROUND

The main purpose of this study was to assess the structural changes in the bladder wall of prostate cancer patients treated with intensity-modulated radiation therapy using magnetic resonance imaging texture features analysis and to correlate image texture changes with radiation dose and urinary toxicity.

METHODS

Ethical clearance was granted to enroll 33 patients into this study who were treated with intensity-modulated radiation therapy for prostate cancer. All patients underwent two magnetic resonance imagings before and after radiation therapy (RT). A total of 274 radiomic features were extracted from MR-T2W-weighted images. Wilcoxon singed rank-test was performed to assess significance of the change in mean radiomic features post-RT relative to pre-RT values. The relationship between radiation dose and feature changes was assessed and depicted. Cystitis was recorded as urinary toxicity. Area under receiver operating characteristic curve of a logistic regression-based classifier was used to find correlation between radiomic features with significant changes and radiation toxicity.

RESULTS

Thirty-three bladder walls were analyzed, with 11 patients developing grade ≥2 urinary toxicity. We showed that radiomic features may predict radiation toxicity and features including S5.0SumVarnc, S2.2SumVarnc, S1.0AngScMom, S0.4SumAverg, and S5. _5InvDfMom with area under receiver operating characteristic curve 0.75, 0.69, 0.65, 0.63, and 0.62 had highest correlation with toxicity, respectively. The results showed that most of the radiomic features were changed with radiation dose.

CONCLUSION

Feature changes have a good correlation with radiation dose and radiation-induced urinary toxicity. These radiomic features can be identified as being potentially important imaging biomarkers and also assessing mechanisms of radiation-induced bladder injuries.

Texture analysis as a predictor of radiation-induced xerostomia in head and neck patients undergoing IMRT

PURPOSE

Image texture analysis (TA) is a heterogeneity quantifying approach that cannot be appreciated by the naked eye, and early evidence suggests that TA has great potential in the field of oncology. The aim of this study is to evaluate parotid gland texture analysis (TA) combined with formal dosimetry as a factor for predicting severe late xerostomia in patients undergoing radiation therapy for head and neck cancers.

METHODS

We performed a retrospective analysis of patients treated at our Radiation Oncology Unit between January 2010 and December 2015, and selected the patients whose normal dose constraints for the parotid gland (mean dose < 26 Gy for the bilateral gland) could not be satisfied due to the presence of positive nodes close to the parotid glands. The parotid gland that showed the higher V30 was contoured on CT simulation and analysed with LifeX Software©. TA parameters included features of grey-level co-occurrence matrix (GLCM), neighbourhood grey-level dependence matrix (NGLDM), grey-level run length matrix (GLRLM), grey-level zone length matrix (GLZLM), sphericity, and indices from the grey-level histogram. We performed a univariate and multivariate analysis between all the texture parameters, the volume of the gland, the normal dose parameters (V30 and Mean Dose), and the development of severe chronic xerostomia.

RESULTS

Seventy-eight patients were included and 25 (31%) developed chronic xerostomia. The TA parameters correlated with severe chronic xerostomia included V30 (OR 5.63), Dmean (OR 5.71), Kurtosis (OR 0.78), GLCM Correlation (OR 1.34), and RLNU (OR 2.12). The multivariate logistic regression showed a significant correlation between V30 (0.001), GLCM correlation (p: 0.026), RLNU (p: 0.011), and chronic xerostomia (p < 0.001, R2:0.664).

CONCLUSIONS

Xerostomia represents an important cause of morbidity for head and neck cancer survivors after radiation therapy, and in certain cases normal dose constraints cannot be satisfied. Our results seem promising as texture analysis could enhance the normal dose constraints for the prediction of xerostomia.

3D bone texture analysis as a potential predictor of radiation-induced insufficiency fractures

BACKGROUND

The aim of our work is to assess the potential role of texture analysis (TA), applied to computed tomography (CT) simulation scans, in relation to the development of insufficiency fractures (IFs) in patients undergoing radiation therapy (RT) for pelvic malignancies.

METHODS

We analyzed patients undergoing pelvic RT from Jan-2010 to Dec-2016, 31 of whom had developed IFs of the pelvis. We analyzed CT simulation scans using LifeX Software©, and in particular we selected three regions of interest (ROI): L5 body, the sacrum and both the femoral heads. The ROI were automatically contoured using the treatment planning software Raystation©. TA parameters included parameters from the gray-level histogram, indices from sphericity and from the matrix of GLCM (gray level co-occurrence matrix). The IFs patients were matched (1:1 ratio) with control patients who had not developed IFs, and were matched for age, sex, type of tumor, menopausal status, RT dose and use of chemotherapy. Univariate and multivariate analyses (logistic regression) were used for statistical analysis.

RESULTS

Significant TA parameters on univariate analysis included both parameters from the histogram distribution, as well from the matrix of GLCM. On logistic regression analysis the significant parameters were L5-energy [P=0.033, odds ratio (OR): 1.997, 95% CI: 1.059-3.767] and FH-Skewness (P=0.014, OR: 2.338, 95% CI: 1.191-4.591), with a R2: 0.268. A ROC curve was generated from the binary logistic regression, and the AUC was 0.741 (95% CI: 0.627-0.855, P=0.001, S.E.: 0.058).

CONCLUSIONS

In our experience, 3D-bone CT TA can be used to stratify the risk of the patients to develop radiation-induced IFs. A prospective study will be conducted to validate these findings.

Bone texture analysis using CT-simulation scans to individuate risk parameters for radiation-induced insufficiency fractures

This study deals with the role of texture analysis as a predictive factor of radiation-induced insufficiency fractures in patients undergoing pelvic radiation.

INTRODUCTION

This study aims to assess the texture analysis (TA) of computed tomography (CT) simulation scans as a predictive factor of insufficiency fractures (IFs) in patients with pelvic malignancies undergoing radiation therapy (RT).

METHODS

We performed an analysis of patients undergoing pelvic RT from January 2010 to December 2014, 24 of whom had developed pelvic bone IFs. We analyzed CT-simulation images using ImageJ macro software and selected two regions of interest (ROIs), which are L5 body and the femoral head. TA parameters included mean (m), standard deviation (SD), skewness (sk), kurtosis (k), entropy (e), and uniformity (u). The IFs patients were compared (1:2 ratio) with controlled patients who had not developed IFs and matched for sex, age, menopausal status, type of tumor, use of chemotherapy, and RT dose. A reliability test of intra- and inter-reader ROI TA reproducibility with the intra-class correlation coefficient (ICC) was performed. Univariate and multivariate analyses (logistic regression) were applied for TA parameters observed both in the IFs and the controlled groups.

RESULTS

Inter- and intra-reader ROI TA was highly reproducible (ICC > 0.90). Significant TA parameters on paired t test included L5 m (p = 0.001), SD (p = 0.002), k (p = 0.006), e (p = 0.004), and u (p = 0.015) and femoral head m (p < 0.001) and SD (p = 0.001), whereas on logistic regression analysis, L5 e (p = 0.003) and u (p = 0.010) and femoral head m (p = 0.027), SD (p = 0.015), and sex (p = 0.044).

CONCLUSIONS

In our experience, bone CT TA could be correlated to the risk of radiation-induced IFs. Studies on a large patient series and methodological refinements are warranted.