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. 2018 Feb 27;13(2):e0193403.
doi: 10.1371/journal.pone.0193403. eCollection 2018.

A decision tree model for predicting mediastinal lymph node metastasis in non-small cell lung cancer with F-18 FDG PET/CT

Kyoungjune Pak  1   2 , Keunyoung Kim  1   2 , Mi-Hyun Kim  2   3 , Jung Seop Eom  2   3 , Min Ki Lee  2   3 , Jeong Su Cho  2   4 , Yun Seong Kim  5   6 , Bum Soo Kim  6   7 , Seong Jang Kim  6   7 , In Joo Kim  1   2
Affiliations

A decision tree model for predicting mediastinal lymph node metastasis in non-small cell lung cancer with F-18 FDG PET/CT

Kyoungjune Pak et al. PLoS One. .

Abstract

We aimed to develop a decision tree model to improve diagnostic performance of positron emission tomography/computed tomography (PET/CT) to detect metastatic lymph nodes (LN) in non-small cell lung cancer (NSCLC). 115 patients with NSCLC were included in this study. The training dataset included 66 patients. A decision tree model was developed with 9 variables, and validated with 49 patients: short and long diameters of LNs, ratio of short and long diameters, maximum standardized uptake value (SUVmax) of LN, mean hounsfield unit, ratio of LN SUVmax and ascending aorta SUVmax (LN/AA), and ratio of LN SUVmax and superior vena cava SUVmax. A total of 301 LNs of 115 patients were evaluated in this study. Nodular calcification was applied as the initial imaging parameter, and LN SUVmax (≥3.95) was assessed as the second. LN/AA (≥2.92) was required to high LN SUVmax. Sensitivity was 50% for training dataset, and 40% for validation dataset. However, specificity was 99.28% for training dataset, and 96.23% for validation dataset. In conclusion, we have developed a new decision tree model for interpreting mediastinal LNs. All LNs with nodular calcification were benign, and LNs with high LN SUVmax and high LN/AA were metastatic Further studies are needed to incorporate subjective parameters and pathologic evaluations into a decision tree model to improve the test performance of PET/CT.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. A decision tree model.
The model consists of 3 decision nodes: nodular calcification, LN SUVmax, and LN/AA. Nodular calcification was applied as the initial imaging parameter, and LN SUVmax was assessed as the second. LN/AA was required only to high LN SUVmax (≥3.95).
Fig 2
Fig 2. Diagnostic performance of a decision tree model with both training and validation datasets.
See this image and copyright information in PMC

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