Development and Validation of a Nomogram for Preoperative Prediction of Lymph Node Metastasis in Lung Adenocarcinoma Based on Radiomics Signature and Deep Learning Signature.

Jia Ran,Jiumei Cai,Tao Yu,Zhongliang Wang,Ran Cao,Dan Zhao

doi:10.3389/fonc.2021.585942

Abstract

Background and PurposeThe preoperative LN (lymph node) status of patients with LUAD (lung adenocarcinoma) is a key factor for determining if systemic nodal dissection is required, which is usually confirmed after surgery. This study aimed to develop and validate a nomogram for preoperative prediction of LN metastasis in LUAD based on a radiomics signature and deep learning signature.Materials and MethodsThis retrospective study included a training cohort of 200 patients, an internal validation cohort of 40 patients, and an external validation cohort of 60 patients. Radiomics features were extracted from conventional CT (computed tomography) images. T-test and Extra-trees were performed for feature selection, and the selected features were combined using logistic regression to build the radiomics signature. The features and weights of the last fully connected layer of a CNN (convolutional neural network) were combined to obtain a deep learning signature. By incorporating clinical risk factors, the prediction model was developed using a multivariable logistic regression analysis, based on which the nomogram was developed. The calibration, discrimination and clinical values of the nomogram were evaluated.ResultsMultivariate logistic regression analysis showed that the radiomics signature, deep learning signature, and CT-reported LN status were independent predictors. The prediction model developed by all the independent predictors showed good discrimination (C-index, 0.820; 95% CI, 0.762 to 0.879) and calibration (Hosmer-Lemeshow test, P=0.193) capabilities for the training cohort. Additionally, the model achieved satisfactory discrimination (C-index, 0.861; 95% CI, 0.769 to 0.954) and calibration (Hosmer-Lemeshow test, P=0.775) when applied to the external validation cohort. An analysis of the decision curve showed that the nomogram had potential for clinical application.ConclusionsThis study presents a prediction model based on radiomics signature, deep learning signature, and CT-reported LN status that can be used to predict preoperative LN metastasis in patients with LUAD.

Highlights

Lung cancer is the most common cancer worldwide and the leading cause of cancer-related death [1]
Inclusion criteria: (a) the lymph node (LN) status was confirmed by operation and pathology reports, (b) the focus was single nodal mass type, (c) the time interval between computed tomography (CT) scan and operation was no more than 1 month, (d) the slice thickness of CT plain scan image was 5 mm
Because the data were divided into the different cohorts in equal proportions, the probability of LN metastasis was 50% in all the cohorts (P= 1.000)

Summary

Introduction

Lung cancer is the most common cancer worldwide and the leading cause of cancer-related death [1]. SND (systematic nodal dissection), as a core method for evaluating node involvement levels at the mediastinal and hilar, has been accepted by the IASLC (International Association for Lung Cancer Research) as a key component of intrathoracic staging [6]. SND prevents the lymphatic fluid in the influenced area from being discharged, thereby resulting in lymphedema. It is important to develop a preoperative, non-invasive, and effective method to predict the extent of LN involvement. The preoperative LN (lymph node) status of patients with LUAD (lung adenocarcinoma) is a key factor for determining if systemic nodal dissection is required, which is usually confirmed after surgery. This study aimed to develop and validate a nomogram for preoperative prediction of LN metastasis in LUAD based on a radiomics signature and deep learning signature

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Conclusion