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

Abstract

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.

Keywords: Texture analysis (TA); insufficiency fractures (IFs); radiation therapy (RT); side effects.

Figure 1

Examples of ROI and histograms of the pixel distribution (CT simulation DICOM images): (A) femoral head ROI; (B) sacrum ROI; (C) L5 ROI. ROI, regions of interest; DICOM, Digital Imaging and Communications in Medicine.

Figure 2

Box Plot in IF-p and C-p patients (univariate analysis). IF, insufficiency fracture. IF-p, insufficiency fracture cohort of patients; C-p, controlled patients cohort.

Figure 3

ROC curve generated from binary logistic regression. AUC 0.741 (95% CI: 0.627–0.855, P=0.001, S.E.: 0.058). ROC, receiver operating characteristic curve; AUC, area under the curve.