Radiomics features of the primary tumor fail to improve prediction of overall survival in large cohorts of CT- and PET-imaged head and neck cancer patients
Autoři:
Rachel B. Ger aff001; Shouhao Zhou aff002; Baher Elgohari aff004; Hesham Elhalawani aff004; Dennis M. Mackin aff001; Joseph G. Meier aff002; Callistus M. Nguyen aff001; Brian M. Anderson aff002; Casey Gay aff001; Jing Ning aff003; Clifton D. Fuller aff002; Heng Li aff001; Rebecca M. Howell aff001; Rick R. Layman aff002; Osama Mawlawi aff002; R. Jason Stafford aff002; Hugo Aerts aff006; Laurence E. Court aff001
Působiště autorů:
Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
aff001; MD Anderson Cancer Center UTHealth Science Center at Houston Graduate School of Biomedical Sciences, Houston, Texas, United States of America
aff002; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
aff003; Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
aff004; Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
aff005; Department of Radiation Oncology, Dana Farber Cancer Institute, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
aff006
Vyšlo v časopise:
PLoS ONE 14(9)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0222509
Souhrn
Radiomics studies require many patients in order to power them, thus patients are often combined from different institutions and using different imaging protocols. Various studies have shown that imaging protocols affect radiomics feature values. We examined whether using data from cohorts with controlled imaging protocols improved patient outcome models. We retrospectively reviewed 726 CT and 686 PET images from head and neck cancer patients, who were divided into training or independent testing cohorts. For each patient, radiomics features with different preprocessing were calculated and two clinical variables—HPV status and tumor volume—were also included. A Cox proportional hazards model was built on the training data by using bootstrapped Lasso regression to predict overall survival. The effect of controlled imaging protocols on model performance was evaluated by subsetting the original training and independent testing cohorts to include only patients whose images were obtained using the same imaging protocol and vendor. Tumor volume, HPV status, and two radiomics covariates were selected for the CT model, resulting in an AUC of 0.72. However, volume alone produced a higher AUC, whereas adding radiomics features reduced the AUC. HPV status and one radiomics feature were selected as covariates for the PET model, resulting in an AUC of 0.59, but neither covariate was significantly associated with survival. Limiting the training and independent testing to patients with the same imaging protocol reduced the AUC for CT patients to 0.55, and no covariates were selected for PET patients. Radiomics features were not consistently associated with survival in CT or PET images of head and neck patients, even within patients with the same imaging protocol.
Klíčová slova:
Research and analysis methods – Imaging techniques – Positron emission tomography – Biology and life sciences – Neuroscience – Neuroimaging – Organisms – Viruses – DNA viruses – Papillomaviruses – Human papillomavirus – Microbiology – Medical microbiology – Microbial pathogens – Viral pathogens – Medicine and health sciences – Diagnostic medicine – Diagnostic radiology – Tomography – Computed axial tomography – Radiology and imaging – Oncology – Cancers and neoplasms – Head and neck cancers – Head and neck tumors – Head and neck squamous cell carcinoma – Carcinomas – Squamous cell carcinomas – Cancer treatment – Pathology and laboratory medicine – Pathogens – Computer and information sciences – Software engineering – Preprocessing – Engineering and technology – Signal processing – Noise reduction
Zdroje
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Článek vyšel v časopise
PLOS One
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