Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients

Autor:	M.A.M. Meheissen, David I. Rosenthal, James Zafereo, Stephen Y. Lai, Rachel B. Ger, Joel Berends, Clifton D. Fuller, David M. Vock, Mona Kamal, Adam S. Garden, Abdallah S.R. Mohamed, Carlos E. Cardenas, Ben Warren, Subha Perni, Aasheesh Kanwar, A. White, Bassem Youssef, Pei Yang, Hesham Elhalawani, Lifei Zhang, X Fave, Bowman Williams, J.A. Messer, Dennis Stephen Mackin, Jeremy M. Aymard, Baher Elgohari, Shady Abohashem, G. Elisabeta Marai, Guadalupe Canahuate, Laurence E. Court, Andrew J. Wong, G. Brandon Gunn
Jazyk:	angličtina
Rok vydání:	2018
Předmět:	Adult Male medicine.medical_specialty Science Texture (geology) Article 030218 nuclear medicine & medical imaging Machine Learning 03 medical and health sciences 0302 clinical medicine Image Processing Computer-Assisted medicine Humans Aged Retrospective Studies Aged 80 and over Multidisciplinary business.industry Head and neck cancer Middle Aged Prognosis medicine.disease Primary tumor 3. Good health Oropharyngeal Neoplasms 030220 oncology & carcinogenesis Medicine Female Radiology Neoplasm Recurrence Local Tomography X-Ray Computed business
Zdroj:	Scientific Reports, Vol 8, Iss 1, Pp 1-13 (2018) Scientific Reports
ISSN:	2045-2322
Popis:	Radiomics is one such “big data” approach that applies advanced image refining/data characterization algorithms to generate imaging features that can quantitatively classify tumor phenotypes in a non-invasive manner. We hypothesize that certain textural features of oropharyngeal cancer (OPC) primary tumors will have statistically significant correlations to patient outcomes such as local control. Patients from an IRB-approved database dispositioned to (chemo)radiotherapy for locally advanced OPC were included in this retrospective series. Pretreatment contrast CT scans were extracted and radiomics-based analysis of gross tumor volume of the primary disease (GTVp) were performed using imaging biomarker explorer (IBEX) software that runs in Matlab platform. Data set was randomly divided into a training dataset and test and tuning holdback dataset. Machine learning methods were applied to yield a radiomic signature consisting of features with minimal overlap and maximum prognostic significance. The radiomic signature was adapted to discriminate patients, in concordance with other key clinical prognosticators. 465 patients were available for analysis. A signature composed of 2 radiomic features from pre-therapy imaging was derived, based on the Intensity Direct and Neighbor Intensity Difference methods. Analysis of resultant groupings showed robust discrimination of recurrence probability and Kaplan-Meier-estimated local control rate (LCR) differences between “favorable” and “unfavorable” clusters were noted.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::29e0c623b9ec382f4d1c4cefd24a83dc https://doaj.org/article/14dc704120a64401851bd9962500904a Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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