Lung Texture in Serial Thoracic Computed Tomography Scans: Correlation of Radiomics-based Features With Radiation Therapy Dose and Radiation Pneumonitis Development

Abstract

Purpose To assess the relationship between radiation dose and change in a set of mathematical intensity- and texture-based features and to determine the ability of texture analysis to identify patients who develop radiation pneumonitis (RP). Methods and Materials A total of 106 patients who received radiation therapy (RT) for esophageal cancer were retrospectively identified under institutional review board approval. For each patient, diagnostic computed tomography (CT) scans were acquired before (0–168 days) and after (5–120 days) RT, and a treatment planning CT scan with an associated dose map was obtained. 32- × 32-pixel regions of interest (ROIs) were randomly identified in the lungs of each pre-RT scan. ROIs were subsequently mapped to the post-RT scan and the planning scan dose map by using deformable image registration. The changes in 20 feature values (ΔFV) between pre- and post-RT scan ROIs were calculated. Regression modeling and analysis of variance were used to test the relationships between ΔFV, mean ROI dose, and development of grade ≥2 RP. Area under the receiver operating characteristic curve (AUC) was calculated to determine each feature’s ability to distinguish between patients with and those without RP. A classifier was constructed to determine whether 2- or 3-feature combinations could improve RP distinction. Results For all 20 features, a significant ΔFV was observed with increasing radiation dose. Twelve features changed significantly for patients with RP. Individual texture features could discriminate between patients with and those without RP with moderate performance (AUCs from 0.49 to 0.78). Using multiple features in a classifier, AUC increased significantly (0.59–0.84). Conclusions A relationship between dose and change in a set of image-based features was observed. For 12 features, ΔFV was significantly related to RP development. This study demonstrated the ability of radiomics to provide a quantitative, individualized measurement of patient lung tissue reaction to RT and assess RP development.

Texture analysis of medical images for radiotherapy applications

“…Radiation pneumonitis is one of the drawbacks of lung irradiation, and it was investigated by Cunliffe et al 100 on patients affected by oesophageal cancer, considering CT images acquired before and after RT. A relationship between dose and texture variations was observed, and in particular, these changes were significantly related to radiation pneumonitis development in 12 features, consisting of the first and second order, fractal and Law's filter parameters.…”

Can radiomics features be reproducibly measured from CBCT images for patients with non‐small cell lung cancer?

“…Data mining and machine learning techniques are then used to build models and capture valuable insights from those imaging features. In the context of tumor analysis, univariate or multivariate models using these features have typically been built to diagnose lesions, [13][14][15] identify secondary effects, 16 or predict outcome. 6,12 Recent publications have demonstrated that a wide variety of radiomics features may predict NSCLC patient outcomes when extracted from computed tomography (CT), 6,12,17 contrast-enhanced CT, 18,19 or positron emission tomography (PET) [20][21][22] images.…”

Anatomic, functional and molecular imaging in lung cancer precision radiation therapy: treatment response assessment and radiation therapy personalization

“…These quantitative data extend beyond what is visible to the human eye and can be powerfully correlated with patient outcomes (99,100). The use of radiomics for lung cancer is an active area of study (101)(102)(103). For example, Van Timmeren and colleagues have shown that radiomic features of CBCT images are associated with survival after RT in NSCLC (104).…”