International journal of radiation oncology, biology, physics · 2018
Radiomic Biomarkers to Refine Risk Models for Distant Metastasis in HPV-related Oropharyngeal Carcinoma.
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Kwan Jennifer Yin Yee, Su Jie, Huang Shao Hui, Ghoraie Laleh S, Xu Wei, Chan Biu, Yip Kenneth W, Giuliani Meredith, Bayley Andrew, Kim John, Hope Andrew J, Ringash Jolie, Cho John, McNiven Andrea, Hansen Aaron, Goldstein David, de Almeida John R, Aerts Hugo J, Waldron John N, Haibe-Kains Benjamin, O'Sullivan Brian, Bratman Scott V, Liu Fei-Fei
Аннотация
PURPOSE: Distant metastasis (DM) is the main cause of death for patients with human papillomavirus (HPV)-related oropharyngeal cancers (OPCs); yet, there are few reliable predictors of DM in this disease. The role of quantitative imaging (ie, radiomic) analysis was examined to determine whether there are primary tumor features discernible on imaging studies that are associated with a higher risk of DM developing.
METHODS AND MATERIALS: Radiation therapy planning computed tomography scans were retrieved for all nonmetastatic p16-positive OPC patients treated with radiation therapy or chemoradiation therapy at a single institution between 2005 and 2010. Radiomic biomarkers were derived from each gross tumor volume. The biomarkers included 4 representative radiomic features from tumor first-order statistics, shape, texture, and wavelet groups, as well as a combined 4-feature signature. Univariable Cox proportional hazards models for DM risk were identified. The discriminative performance of prognostic univariable and multivariable models was compared using the concordance index (C-index). Subgroup analyses were performed.
RESULTS: There were 300 HPV-related OPC patients who were eligible for the analysis. A total of 36 DM events occurred within a median follow-up period of 5 years. On univariable analysis, top results included the 4 representative radiomic features (C-index, 0.670-0.686; P < .001), the radiomic signature (C-index, 0.670; P < .001), tumor stage (C-index, 0.633; P < .001), tumor diameter (C-index, 0.653; P < .001), and tumor volume (C-index, 0.674; P < .001), which demonstrated moderate discrimination of DM risk. Combined clinical-radiomic models yielded significantly improved performance (C-index, 0.701-0.714; P < .05). In subgroup analyses, the radiomic biomarkers consistently stratified patients for DM risk, particularly for those cohorts with greater risks (C-index, 0.663-0.796), such as patients with stage III disease.
CONCLUSIONS: Radiomic biomarkers appear to classify DM risk for patients with nonmetastatic HPV-related OPC. Radiomic biomarkers could be used either alone or with other clinical characteristics in the assignment of DM risk in future HPV-related OPC clinical trials.
METHODS AND MATERIALS: Radiation therapy planning computed tomography scans were retrieved for all nonmetastatic p16-positive OPC patients treated with radiation therapy or chemoradiation therapy at a single institution between 2005 and 2010. Radiomic biomarkers were derived from each gross tumor volume. The biomarkers included 4 representative radiomic features from tumor first-order statistics, shape, texture, and wavelet groups, as well as a combined 4-feature signature. Univariable Cox proportional hazards models for DM risk were identified. The discriminative performance of prognostic univariable and multivariable models was compared using the concordance index (C-index). Subgroup analyses were performed.
RESULTS: There were 300 HPV-related OPC patients who were eligible for the analysis. A total of 36 DM events occurred within a median follow-up period of 5 years. On univariable analysis, top results included the 4 representative radiomic features (C-index, 0.670-0.686; P < .001), the radiomic signature (C-index, 0.670; P < .001), tumor stage (C-index, 0.633; P < .001), tumor diameter (C-index, 0.653; P < .001), and tumor volume (C-index, 0.674; P < .001), which demonstrated moderate discrimination of DM risk. Combined clinical-radiomic models yielded significantly improved performance (C-index, 0.701-0.714; P < .05). In subgroup analyses, the radiomic biomarkers consistently stratified patients for DM risk, particularly for those cohorts with greater risks (C-index, 0.663-0.796), such as patients with stage III disease.
CONCLUSIONS: Radiomic biomarkers appear to classify DM risk for patients with nonmetastatic HPV-related OPC. Radiomic biomarkers could be used either alone or with other clinical characteristics in the assignment of DM risk in future HPV-related OPC clinical trials.