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

Hesham Elhalawani; Aasheesh Kanwar; Abdallah S.R. Mohamed; Aubrey White; James Zafereo; Andrew Wong; Joel Berends; Shady Abohashem; Bowman Williams; Jeremy M. Aymard; Subha Perni; Jay Messer; Ben Warren; Bassem Youssef; Pei Yang; Mohamed A.M. Meheissen; Mona Kamal; Baher Elgohari; Rachel B. Ger; Carlos E. Cardenas; Xenia Fave; Lifei Zhang; Dennis Mackin; G. Elisabeta Marai; David M. Vock; Guadalupe M. Canahuate; Stephen Y. Lai; G. Brandon Gunn; Adam S. Garden; David I. Rosenthal; Laurence Court; Clifton D. Fuller

doi:10.1038/s41598-017-14687-0

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

Hesham Elhalawani, Aasheesh Kanwar, Abdallah S.R. Mohamed, Aubrey White, James Zafereo, Andrew Wong, Joel Berends, Shady Abohashem, Bowman Williams, Jeremy M. Aymard, Subha Perni, Jay Messer, Ben Warren, Bassem Youssef, Pei Yang, Mohamed A.M. Meheissen, Mona Kamal, Baher Elgohari, Rachel B. Ger, Carlos E. CardenasXenia Fave, Lifei Zhang, Dennis Mackin, G. Elisabeta Marai, David M. Vock, Guadalupe M. Canahuate, Stephen Y. Lai, G. Brandon Gunn, Adam S. Garden, David I. Rosenthal, Laurence Court, Clifton D. Fuller

Biostatistics

Research output: Contribution to journal › Article › peer-review

24 Scopus citations

Abstract

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.

Original language	English (US)
Article number	1524
Journal	Scientific reports
Volume	8
Issue number	1
DOIs	https://doi.org/10.1038/s41598-017-14687-0
State	Published - Dec 1 2018

Bibliographical note

Funding Information:
Dr. Elhalawani was supported in part by the philanthropic donations from the Family of Paul W. Beach to Dr. G. Brandon Gunn, MD. This research was supported by the Andrew Sabin Family Foundation; Dr. Fuller is a Sabin Family Foundation Fellow. Drs Lai, Mohamed and Fuller receive funding support from the National Institutes of Health (NIH)/National Institute for Dental and Craniofacial Research (NIDCR) (1R01DE025248- 01/R56DE025248-01). Drs Canahuate, Marai, Vock, Mohamed and Fuller were previously funded via the National Science Foundation (NSF), Division of Mathematical Sciences, Joint NIH/NSF Initiative on Quantitative Approaches to Biomedical Big Data (QuBBD) Grant (NSF 1557679) and are currently supported by the NIH National Cancer Institute/Big Data to Knowledge (BD2K) Program (1R01CA214825-01) as well as NIH-NCIR01CA225190 QuBBD: Precision E -Radiomics for Dynamic Big Head & Neck Cancer Data. During the study execution and manuscript construction interval, Dr. Fuller received grant and/or salary support from the NIH/ NCI Head and Neck Specialized Programs of Research Excellence (SPORE) Developmental Research Program Career Development Award (P50CA097007-10); the NCI Paul Calabresi Clinical Oncology Program Award (K12 CA088084-06); a General Electric Healthcare/MD Anderson Center for Advanced Biomedical Imaging In-Kind Award; an Elekta AB/MD Anderson Department of Radiation Oncology Seed Grant; the Center for Radiation Oncology Research (CROR) at MD Anderson Cancer Center Seed Grant; and the MD Anderson Institutional Research Grant (IRG) Program. Dr. Fuller has received speaker travel funding from Elekta AB. Mr. Kanwar was supported by a 2016-2017 Radiological Society of North America (RSNA) Education and Research Foundation Research Medical Student Grant Award (RSNA RMS1618). This research was accomplished with infrastructure support provided under the auspices of the Oropharynx Program at The University of Texas MD Anderson Cancer Center and funded in part through the Stiefel Oropharyngeal Research Fund as part of the programmatic efforts of the Charles and Daneen Stiefel Center for Head and Neck Cancer. Supported in part by the NIH/NCI Cancer Center Support (Core) Grant CA016672 to The University of Texas MD Anderson Cancer Center (P30 CA016672).

Funding Information:
Dr. Elhalawani was supported in part by the philanthropic donations from the Family of Paul W. Beach to Dr. G. Brandon Gunn, MD. This research was supported by the Andrew Sabin Family Foundation; Dr. Fuller is a Sabin Family Foundation Fellow. Drs Lai, Mohamed and Fuller receive funding support from the National Institutes of Health (NIH)/National Institute for Dental and Craniofacial Research (NIDCR) (1R01DE025248-01/R56DE025248-01). Drs Canahuate, Marai, Vock, Mohamed and Fuller were previously funded via the National Science Foundation (NSF), Division of Mathematical Sciences, Joint NIH/NSF Initiative on Quantitative Approaches to Biomedical Big Data (QuBBD) Grant (NSF 1557679) and are currently supported by the NIH National Cancer Institute/Big Data to Knowledge (BD2K) Program (1R01CA214825-01) as well as NIH-NCI-R01CA225190 QuBBD: Precision E –Radiomics for Dynamic Big Head & Neck Cancer Data. During the study execution and manuscript construction interval, Dr. Fuller received grant and/or salary support from the NIH/ NCI Head and Neck Specialized Programs of Research Excellence (SPORE) Developmental Research Program Career Development Award (P50CA097007-10); the NCI Paul Calabresi Clinical Oncology Program Award (K12 CA088084-06); a General Electric Healthcare/MD Anderson Center for Advanced Biomedical Imaging In-Kind Award; an Elekta AB/MD Anderson Department of Radiation Oncology Seed Grant; the Center for Radiation Oncology Research (CROR) at MD Anderson Cancer Center Seed Grant; and the MD Anderson Institutional Research Grant (IRG) Program. Dr. Fuller has received speaker travel funding from Elekta AB. Mr. Kanwar was supported by a 2016–2017 Radiological Society of North America (RSNA) Education and Research Foundation Research Medical Student Grant Award (RSNA RMS1618). This research was accomplished with infrastructure support provided under the auspices of the Oropharynx Program at The University of Texas MD Anderson Cancer Center and funded in part through the Stiefel Oropharyngeal Research Fund as part of the programmatic efforts of the Charles and Daneen Stiefel Center for Head and Neck Cancer. Supported in part by the NIH/NCI Cancer Center Support (Core) Grant CA016672 to The University of Texas MD Anderson Cancer Center (P30 CA016672).

Publisher Copyright:
© 2017 The Author(s).

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Elhalawani, H., Kanwar, A., Mohamed, A. S. R., White, A., Zafereo, J., Wong, A., Berends, J., Abohashem, S., Williams, B., Aymard, J. M., Perni, S., Messer, J., Warren, B., Youssef, B., Yang, P., Meheissen, M. A. M., Kamal, M., Elgohari, B., Ger, R. B., ... Fuller, C. D. (2018). Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients. Scientific reports, 8(1), Article 1524. https://doi.org/10.1038/s41598-017-14687-0

Elhalawani, H, Kanwar, A, Mohamed, ASR, White, A, Zafereo, J, Wong, A, Berends, J, Abohashem, S, Williams, B, Aymard, JM, Perni, S, Messer, J, Warren, B, Youssef, B, Yang, P, Meheissen, MAM, Kamal, M, Elgohari, B, Ger, RB, Cardenas, CE, Fave, X, Zhang, L, Mackin, D, Marai, GE, Vock, DM, Canahuate, GM, Lai, SY, Gunn, GB, Garden, AS, Rosenthal, DI, Court, L & Fuller, CD 2018, 'Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients', Scientific reports, vol. 8, no. 1, 1524. https://doi.org/10.1038/s41598-017-14687-0

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

abstract = "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.",

author = "Hesham Elhalawani and Aasheesh Kanwar and Mohamed, {Abdallah S.R.} and Aubrey White and James Zafereo and Andrew Wong and Joel Berends and Shady Abohashem and Bowman Williams and Aymard, {Jeremy M.} and Subha Perni and Jay Messer and Ben Warren and Bassem Youssef and Pei Yang and Meheissen, {Mohamed A.M.} and Mona Kamal and Baher Elgohari and Ger, {Rachel B.} and Cardenas, {Carlos E.} and Xenia Fave and Lifei Zhang and Dennis Mackin and Marai, {G. Elisabeta} and Vock, {David M.} and Canahuate, {Guadalupe M.} and Lai, {Stephen Y.} and Gunn, {G. Brandon} and Garden, {Adam S.} and Rosenthal, {David I.} and Laurence Court and Fuller, {Clifton D.}",

note = "Funding Information: Dr. Elhalawani was supported in part by the philanthropic donations from the Family of Paul W. Beach to Dr. G. Brandon Gunn, MD. This research was supported by the Andrew Sabin Family Foundation; Dr. Fuller is a Sabin Family Foundation Fellow. Drs Lai, Mohamed and Fuller receive funding support from the National Institutes of Health (NIH)/National Institute for Dental and Craniofacial Research (NIDCR) (1R01DE025248- 01/R56DE025248-01). Drs Canahuate, Marai, Vock, Mohamed and Fuller were previously funded via the National Science Foundation (NSF), Division of Mathematical Sciences, Joint NIH/NSF Initiative on Quantitative Approaches to Biomedical Big Data (QuBBD) Grant (NSF 1557679) and are currently supported by the NIH National Cancer Institute/Big Data to Knowledge (BD2K) Program (1R01CA214825-01) as well as NIH-NCIR01CA225190 QuBBD: Precision E -Radiomics for Dynamic Big Head & Neck Cancer Data. During the study execution and manuscript construction interval, Dr. Fuller received grant and/or salary support from the NIH/ NCI Head and Neck Specialized Programs of Research Excellence (SPORE) Developmental Research Program Career Development Award (P50CA097007-10); the NCI Paul Calabresi Clinical Oncology Program Award (K12 CA088084-06); a General Electric Healthcare/MD Anderson Center for Advanced Biomedical Imaging In-Kind Award; an Elekta AB/MD Anderson Department of Radiation Oncology Seed Grant; the Center for Radiation Oncology Research (CROR) at MD Anderson Cancer Center Seed Grant; and the MD Anderson Institutional Research Grant (IRG) Program. Dr. Fuller has received speaker travel funding from Elekta AB. Mr. Kanwar was supported by a 2016-2017 Radiological Society of North America (RSNA) Education and Research Foundation Research Medical Student Grant Award (RSNA RMS1618). This research was accomplished with infrastructure support provided under the auspices of the Oropharynx Program at The University of Texas MD Anderson Cancer Center and funded in part through the Stiefel Oropharyngeal Research Fund as part of the programmatic efforts of the Charles and Daneen Stiefel Center for Head and Neck Cancer. Supported in part by the NIH/NCI Cancer Center Support (Core) Grant CA016672 to The University of Texas MD Anderson Cancer Center (P30 CA016672). Funding Information: Dr. Elhalawani was supported in part by the philanthropic donations from the Family of Paul W. Beach to Dr. G. Brandon Gunn, MD. This research was supported by the Andrew Sabin Family Foundation; Dr. Fuller is a Sabin Family Foundation Fellow. Drs Lai, Mohamed and Fuller receive funding support from the National Institutes of Health (NIH)/National Institute for Dental and Craniofacial Research (NIDCR) (1R01DE025248-01/R56DE025248-01). Drs Canahuate, Marai, Vock, Mohamed and Fuller were previously funded via the National Science Foundation (NSF), Division of Mathematical Sciences, Joint NIH/NSF Initiative on Quantitative Approaches to Biomedical Big Data (QuBBD) Grant (NSF 1557679) and are currently supported by the NIH National Cancer Institute/Big Data to Knowledge (BD2K) Program (1R01CA214825-01) as well as NIH-NCI-R01CA225190 QuBBD: Precision E –Radiomics for Dynamic Big Head & Neck Cancer Data. During the study execution and manuscript construction interval, Dr. Fuller received grant and/or salary support from the NIH/ NCI Head and Neck Specialized Programs of Research Excellence (SPORE) Developmental Research Program Career Development Award (P50CA097007-10); the NCI Paul Calabresi Clinical Oncology Program Award (K12 CA088084-06); a General Electric Healthcare/MD Anderson Center for Advanced Biomedical Imaging In-Kind Award; an Elekta AB/MD Anderson Department of Radiation Oncology Seed Grant; the Center for Radiation Oncology Research (CROR) at MD Anderson Cancer Center Seed Grant; and the MD Anderson Institutional Research Grant (IRG) Program. Dr. Fuller has received speaker travel funding from Elekta AB. Mr. Kanwar was supported by a 2016–2017 Radiological Society of North America (RSNA) Education and Research Foundation Research Medical Student Grant Award (RSNA RMS1618). This research was accomplished with infrastructure support provided under the auspices of the Oropharynx Program at The University of Texas MD Anderson Cancer Center and funded in part through the Stiefel Oropharyngeal Research Fund as part of the programmatic efforts of the Charles and Daneen Stiefel Center for Head and Neck Cancer. Supported in part by the NIH/NCI Cancer Center Support (Core) Grant CA016672 to The University of Texas MD Anderson Cancer Center (P30 CA016672). Publisher Copyright: {\textcopyright} 2017 The Author(s).",

year = "2018",

month = dec,

day = "1",

doi = "10.1038/s41598-017-14687-0",

language = "English (US)",

volume = "8",

journal = "Scientific reports",

issn = "2045-2322",

publisher = "Nature Publishing Group",

number = "1",

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TY - JOUR

T1 - Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients

AU - Elhalawani, Hesham

AU - Kanwar, Aasheesh

AU - Mohamed, Abdallah S.R.

AU - White, Aubrey

AU - Zafereo, James

AU - Wong, Andrew

AU - Berends, Joel

AU - Abohashem, Shady

AU - Williams, Bowman

AU - Aymard, Jeremy M.

AU - Perni, Subha

AU - Messer, Jay

AU - Warren, Ben

AU - Youssef, Bassem

AU - Yang, Pei

AU - Meheissen, Mohamed A.M.

AU - Kamal, Mona

AU - Elgohari, Baher

AU - Ger, Rachel B.

AU - Cardenas, Carlos E.

AU - Fave, Xenia

AU - Zhang, Lifei

AU - Mackin, Dennis

AU - Marai, G. Elisabeta

AU - Vock, David M.

AU - Canahuate, Guadalupe M.

AU - Lai, Stephen Y.

AU - Gunn, G. Brandon

AU - Garden, Adam S.

AU - Rosenthal, David I.

AU - Court, Laurence

AU - Fuller, Clifton D.

N1 - Funding Information: Dr. Elhalawani was supported in part by the philanthropic donations from the Family of Paul W. Beach to Dr. G. Brandon Gunn, MD. This research was supported by the Andrew Sabin Family Foundation; Dr. Fuller is a Sabin Family Foundation Fellow. Drs Lai, Mohamed and Fuller receive funding support from the National Institutes of Health (NIH)/National Institute for Dental and Craniofacial Research (NIDCR) (1R01DE025248- 01/R56DE025248-01). Drs Canahuate, Marai, Vock, Mohamed and Fuller were previously funded via the National Science Foundation (NSF), Division of Mathematical Sciences, Joint NIH/NSF Initiative on Quantitative Approaches to Biomedical Big Data (QuBBD) Grant (NSF 1557679) and are currently supported by the NIH National Cancer Institute/Big Data to Knowledge (BD2K) Program (1R01CA214825-01) as well as NIH-NCIR01CA225190 QuBBD: Precision E -Radiomics for Dynamic Big Head & Neck Cancer Data. During the study execution and manuscript construction interval, Dr. Fuller received grant and/or salary support from the NIH/ NCI Head and Neck Specialized Programs of Research Excellence (SPORE) Developmental Research Program Career Development Award (P50CA097007-10); the NCI Paul Calabresi Clinical Oncology Program Award (K12 CA088084-06); a General Electric Healthcare/MD Anderson Center for Advanced Biomedical Imaging In-Kind Award; an Elekta AB/MD Anderson Department of Radiation Oncology Seed Grant; the Center for Radiation Oncology Research (CROR) at MD Anderson Cancer Center Seed Grant; and the MD Anderson Institutional Research Grant (IRG) Program. Dr. Fuller has received speaker travel funding from Elekta AB. Mr. Kanwar was supported by a 2016-2017 Radiological Society of North America (RSNA) Education and Research Foundation Research Medical Student Grant Award (RSNA RMS1618). This research was accomplished with infrastructure support provided under the auspices of the Oropharynx Program at The University of Texas MD Anderson Cancer Center and funded in part through the Stiefel Oropharyngeal Research Fund as part of the programmatic efforts of the Charles and Daneen Stiefel Center for Head and Neck Cancer. Supported in part by the NIH/NCI Cancer Center Support (Core) Grant CA016672 to The University of Texas MD Anderson Cancer Center (P30 CA016672). Funding Information: Dr. Elhalawani was supported in part by the philanthropic donations from the Family of Paul W. Beach to Dr. G. Brandon Gunn, MD. This research was supported by the Andrew Sabin Family Foundation; Dr. Fuller is a Sabin Family Foundation Fellow. Drs Lai, Mohamed and Fuller receive funding support from the National Institutes of Health (NIH)/National Institute for Dental and Craniofacial Research (NIDCR) (1R01DE025248-01/R56DE025248-01). Drs Canahuate, Marai, Vock, Mohamed and Fuller were previously funded via the National Science Foundation (NSF), Division of Mathematical Sciences, Joint NIH/NSF Initiative on Quantitative Approaches to Biomedical Big Data (QuBBD) Grant (NSF 1557679) and are currently supported by the NIH National Cancer Institute/Big Data to Knowledge (BD2K) Program (1R01CA214825-01) as well as NIH-NCI-R01CA225190 QuBBD: Precision E –Radiomics for Dynamic Big Head & Neck Cancer Data. During the study execution and manuscript construction interval, Dr. Fuller received grant and/or salary support from the NIH/ NCI Head and Neck Specialized Programs of Research Excellence (SPORE) Developmental Research Program Career Development Award (P50CA097007-10); the NCI Paul Calabresi Clinical Oncology Program Award (K12 CA088084-06); a General Electric Healthcare/MD Anderson Center for Advanced Biomedical Imaging In-Kind Award; an Elekta AB/MD Anderson Department of Radiation Oncology Seed Grant; the Center for Radiation Oncology Research (CROR) at MD Anderson Cancer Center Seed Grant; and the MD Anderson Institutional Research Grant (IRG) Program. Dr. Fuller has received speaker travel funding from Elekta AB. Mr. Kanwar was supported by a 2016–2017 Radiological Society of North America (RSNA) Education and Research Foundation Research Medical Student Grant Award (RSNA RMS1618). This research was accomplished with infrastructure support provided under the auspices of the Oropharynx Program at The University of Texas MD Anderson Cancer Center and funded in part through the Stiefel Oropharyngeal Research Fund as part of the programmatic efforts of the Charles and Daneen Stiefel Center for Head and Neck Cancer. Supported in part by the NIH/NCI Cancer Center Support (Core) Grant CA016672 to The University of Texas MD Anderson Cancer Center (P30 CA016672). Publisher Copyright: © 2017 The Author(s).

PY - 2018/12/1

Y1 - 2018/12/1

N2 - 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.

AB - 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.

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ER -

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

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