A predictive model for lymph node involvement with malignancy on PET/CT in non-small-cell lung cancer

Malcolm D. Mattes; Wolfgang A. Weber; Amanda Foster; Ariella B. Moshchinsky; Salma Ahsanuddin; Zhigang Zhang; Weiji Shi; Nabil P. Rizk; Abraham J. Wu; Hani Ashamalla; Andreas Rimner

doi:10.1097/JTO.0000000000000601

A predictive model for lymph node involvement with malignancy on PET/CT in non-small-cell lung cancer

Malcolm D. Mattes
, Wolfgang A. Weber
, Amanda Foster
, Ariella B. Moshchinsky
, Salma Ahsanuddin
, Zhigang Zhang
, Weiji Shi
, Nabil P. Rizk
, Abraham J. Wu
, Hani Ashamalla
, Andreas Rimner

West Virginia University
Departments of Radiology
Weill Cornell Medical College
New York Methodist Hospital
Departments of Epidemiology and Biostatistics

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

30 Scopus citations

Abstract

Introduction: Accurate assessment of lymph node (LN) involvement with malignancy is critical to staging and management of non-smallcell lung cancer. The goal of this retrospective study was to determine the tumor and imaging characteristics independently associated with malignant involvement of LNs visualized on positron emission tomography/computed tomography (PET/CT). Methods: From 2002 to 2011, 172 patients with newly diagnosed non- small-cell lung cancer underwent PET/CT within 31 days before LN biopsy. Among these patients, 504 anatomically defined, pathologyconfirmed LNs were visualized on PET/CT. Logistic regression analysis was used to determine the associations between nodal involvement with malignancy and several clinical and imaging variables, including tumor histology, tumor grade, LN risk category in relation to the primary tumor location, pathologic findings from additional biopsied LNs, interval between PET/CT and biopsy, primary tumor largest dimension, primary tumor standardized uptake value (SUV_max), LN short-Axis dimension, and LN SUV_max. Results: On univariate analysis, adenocarcinoma histology (p = 0.010), high LN risk category (p < 0.001), larger LN short-Axis dimension (p < 0.001), and higher LN SUV_max (p < 0.001) all correlated with nodal involvement. On multivariate analysis, adenocarcinoma histology (p = 0.003), high LN risk category (p = 0.005), and higher LN SUV_max (p < 0.001) correlated with nodal involvement, whereas LN short-Axis dimension was no longer statistically significant (p = 0.180). A nomogram developed for clinical application based on this analysis had excellent concordance between predicted and observed results (concordance index, 0.95). Conclusion: Adenocarcinoma histology, higher LN SUV_max, and higher LN risk category independently correlate with nodal involvement with malignancy and may be used in a model to accurately predict the risk of a node's involvement with malignancy.

Original language	English
Pages (from-to)	1207-1212
Number of pages	6
Journal	Journal of Thoracic Oncology
Volume	10
Issue number	8
DOIs	https://doi.org/10.1097/JTO.0000000000000601
State	Published - 1 Aug 2015
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Lymph node
NSCLC
PET
Staging

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10.1097/JTO.0000000000000601

Cite this

@article{b11ea56ceb8d4648bbb0f06e903b15e0,

title = "A predictive model for lymph node involvement with malignancy on PET/CT in non-small-cell lung cancer",

abstract = "Introduction: Accurate assessment of lymph node (LN) involvement with malignancy is critical to staging and management of non-smallcell lung cancer. The goal of this retrospective study was to determine the tumor and imaging characteristics independently associated with malignant involvement of LNs visualized on positron emission tomography/computed tomography (PET/CT). Methods: From 2002 to 2011, 172 patients with newly diagnosed non- small-cell lung cancer underwent PET/CT within 31 days before LN biopsy. Among these patients, 504 anatomically defined, pathologyconfirmed LNs were visualized on PET/CT. Logistic regression analysis was used to determine the associations between nodal involvement with malignancy and several clinical and imaging variables, including tumor histology, tumor grade, LN risk category in relation to the primary tumor location, pathologic findings from additional biopsied LNs, interval between PET/CT and biopsy, primary tumor largest dimension, primary tumor standardized uptake value (SUVmax), LN short-Axis dimension, and LN SUVmax. Results: On univariate analysis, adenocarcinoma histology (p = 0.010), high LN risk category (p < 0.001), larger LN short-Axis dimension (p < 0.001), and higher LN SUVmax (p < 0.001) all correlated with nodal involvement. On multivariate analysis, adenocarcinoma histology (p = 0.003), high LN risk category (p = 0.005), and higher LN SUVmax (p < 0.001) correlated with nodal involvement, whereas LN short-Axis dimension was no longer statistically significant (p = 0.180). A nomogram developed for clinical application based on this analysis had excellent concordance between predicted and observed results (concordance index, 0.95). Conclusion: Adenocarcinoma histology, higher LN SUVmax, and higher LN risk category independently correlate with nodal involvement with malignancy and may be used in a model to accurately predict the risk of a node's involvement with malignancy.",

keywords = "Lymph node, NSCLC, PET, Staging",

author = "Mattes, \{Malcolm D.\} and Weber, \{Wolfgang A.\} and Amanda Foster and Moshchinsky, \{Ariella B.\} and Salma Ahsanuddin and Zhigang Zhang and Weiji Shi and Rizk, \{Nabil P.\} and Wu, \{Abraham J.\} and Hani Ashamalla and Andreas Rimner",

note = "Publisher Copyright: Copyright {\textcopyright} 2015 by the International Association for the Study of Lung Cancer.",

year = "2015",

month = aug,

day = "1",

doi = "10.1097/JTO.0000000000000601",

language = "English",

volume = "10",

pages = "1207--1212",

journal = "Journal of Thoracic Oncology",

issn = "1556-0864",

publisher = "Elsevier Inc.",

number = "8",

}

TY - JOUR

T1 - A predictive model for lymph node involvement with malignancy on PET/CT in non-small-cell lung cancer

AU - Mattes, Malcolm D.

AU - Weber, Wolfgang A.

AU - Foster, Amanda

AU - Moshchinsky, Ariella B.

AU - Ahsanuddin, Salma

AU - Zhang, Zhigang

AU - Shi, Weiji

AU - Rizk, Nabil P.

AU - Wu, Abraham J.

AU - Ashamalla, Hani

AU - Rimner, Andreas

PY - 2015/8/1

Y1 - 2015/8/1

N2 - Introduction: Accurate assessment of lymph node (LN) involvement with malignancy is critical to staging and management of non-smallcell lung cancer. The goal of this retrospective study was to determine the tumor and imaging characteristics independently associated with malignant involvement of LNs visualized on positron emission tomography/computed tomography (PET/CT). Methods: From 2002 to 2011, 172 patients with newly diagnosed non- small-cell lung cancer underwent PET/CT within 31 days before LN biopsy. Among these patients, 504 anatomically defined, pathologyconfirmed LNs were visualized on PET/CT. Logistic regression analysis was used to determine the associations between nodal involvement with malignancy and several clinical and imaging variables, including tumor histology, tumor grade, LN risk category in relation to the primary tumor location, pathologic findings from additional biopsied LNs, interval between PET/CT and biopsy, primary tumor largest dimension, primary tumor standardized uptake value (SUVmax), LN short-Axis dimension, and LN SUVmax. Results: On univariate analysis, adenocarcinoma histology (p = 0.010), high LN risk category (p < 0.001), larger LN short-Axis dimension (p < 0.001), and higher LN SUVmax (p < 0.001) all correlated with nodal involvement. On multivariate analysis, adenocarcinoma histology (p = 0.003), high LN risk category (p = 0.005), and higher LN SUVmax (p < 0.001) correlated with nodal involvement, whereas LN short-Axis dimension was no longer statistically significant (p = 0.180). A nomogram developed for clinical application based on this analysis had excellent concordance between predicted and observed results (concordance index, 0.95). Conclusion: Adenocarcinoma histology, higher LN SUVmax, and higher LN risk category independently correlate with nodal involvement with malignancy and may be used in a model to accurately predict the risk of a node's involvement with malignancy.

AB - Introduction: Accurate assessment of lymph node (LN) involvement with malignancy is critical to staging and management of non-smallcell lung cancer. The goal of this retrospective study was to determine the tumor and imaging characteristics independently associated with malignant involvement of LNs visualized on positron emission tomography/computed tomography (PET/CT). Methods: From 2002 to 2011, 172 patients with newly diagnosed non- small-cell lung cancer underwent PET/CT within 31 days before LN biopsy. Among these patients, 504 anatomically defined, pathologyconfirmed LNs were visualized on PET/CT. Logistic regression analysis was used to determine the associations between nodal involvement with malignancy and several clinical and imaging variables, including tumor histology, tumor grade, LN risk category in relation to the primary tumor location, pathologic findings from additional biopsied LNs, interval between PET/CT and biopsy, primary tumor largest dimension, primary tumor standardized uptake value (SUVmax), LN short-Axis dimension, and LN SUVmax. Results: On univariate analysis, adenocarcinoma histology (p = 0.010), high LN risk category (p < 0.001), larger LN short-Axis dimension (p < 0.001), and higher LN SUVmax (p < 0.001) all correlated with nodal involvement. On multivariate analysis, adenocarcinoma histology (p = 0.003), high LN risk category (p = 0.005), and higher LN SUVmax (p < 0.001) correlated with nodal involvement, whereas LN short-Axis dimension was no longer statistically significant (p = 0.180). A nomogram developed for clinical application based on this analysis had excellent concordance between predicted and observed results (concordance index, 0.95). Conclusion: Adenocarcinoma histology, higher LN SUVmax, and higher LN risk category independently correlate with nodal involvement with malignancy and may be used in a model to accurately predict the risk of a node's involvement with malignancy.

KW - Lymph node

KW - NSCLC

KW - PET

KW - Staging

UR - https://www.scopus.com/pages/publications/84942901530

U2 - 10.1097/JTO.0000000000000601

DO - 10.1097/JTO.0000000000000601

M3 - Article

C2 - 26200276

AN - SCOPUS:84942901530

SN - 1556-0864

VL - 10

SP - 1207

EP - 1212

JO - Journal of Thoracic Oncology

JF - Journal of Thoracic Oncology

IS - 8

ER -

A predictive model for lymph node involvement with malignancy on PET/CT in non-small-cell lung cancer

Abstract

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Keywords

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