Prognostic value of pre-treatment CT texture analysis in combination with change in size of the primary tumor in response to induction chemotherapy for HPV-positive oropharyngeal squamous cell carcinoma.

BACKGROUND: To determine the additive value of quantitative radiomic texture features in predicting progression in human papillomavirus (HPV)-positive oropharyngeal squamous cell carcinoma (OPSCC) based on pre-treatment CT. METHODS: Retrospective analysis of a single-center cohort of adult patients enrolled in a response-adapted radiation volume de-escalation trial treated with induction chemotherapy. Texture analysis of HPV-positive OPSCC was performed via primary tumor site contouring on pre-treatment contrast-enhanced CT scans. Percent change in size of the tumor in response to induction chemotherapy based on RECIST 1.1 criteria and progression free survival were clinically determined for this cohort. Receiver operating characteristic (ROC) analysis was performed to compare the accuracy of percent change in tumor size after induction chemotherapy with a combination of change in tumor size and radiomic texture features for predicting tumor progression. RESULTS: Radiomic texture analysis of the primary tumors in 38 patients with OPSCC depicted on pre-treatment neck CT scans using skewness and entropy in combination with percent change in tumor size after induction chemotherapy yielded a statistically significant increase in accuracy for predicting tumor progression over change in tumor size alone, with an area under the curve of 0.80 versus 0.56 (one-tailed P=0.0087). CONCLUSIONS: This pilot study suggests that disease progression in patients with HPV-positive OPSCC is more accurately predicted using a combination of texture features on pre-treatment CT scans, along with change in tumor size compared to change in tumor size alone and could therefore serve as a radiomic texture signature.

Variation in algorithm implementation across radiomics software.

Given the increased need for consistent quantitative image analysis, variations in radiomics feature calculations due to differences in radiomics software were investigated. Two in-house radiomics packages and two freely available radiomics packages, MaZda and IBEX, were utilized. Forty 256 × 256 - pixel regions of interest (ROIs) from 40 digital mammograms were studied along with 39 manually delineated ROIs from the head and neck (HN) computed tomography (CT) scans of 39 patients. Each package was used to calculate first-order histogram and second-order gray-level co-occurrence matrix (GLCM) features. Friedman tests determined differences in feature values across packages, whereas intraclass-correlation coefficients (ICC) quantified agreement. All first-order features computed from both mammography and HN cases (except skewness in mammography) showed significant differences across all packages due to systematic biases introduced by each package; however, based on ICC values, all but one first-order feature calculated on mammography ROIs and all but two first-order features calculated on HN CT ROIs showed excellent agreement, indicating the observed differences were small relative to the feature values but the bias was systematic. All second-order features computed from the two databases both differed significantly and showed poor agreement among packages, due largely to discrepancies in package-specific default GLCM parameters. Additional differences in radiomics features were traced to variations in image preprocessing, algorithm implementation, and naming conventions. Large variations in features among software packages indicate that increased efforts to standardize radiomics processes must be conducted.