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Determining the invasiveness of ground-glass nodules using a 3D multi-task network

  • Chest
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Abstract

Objectives

The aim of this study was to determine the invasiveness of ground-glass nodules (GGNs) using a 3D multi-task deep learning network.

Methods

We propose a novel architecture based on 3D multi-task learning to determine the invasiveness of GGNs. In total, 770 patients with 909 GGNs who underwent lung CT scans were enrolled. The patients were divided into the training (n = 626) and test sets (n = 144). In the test set, invasiveness was classified using deep learning into three categories: atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive pulmonary adenocarcinoma (IA). Furthermore, binary classifications (AAH/AIS/MIA vs. IA) were made by two thoracic radiologists and compared with the deep learning results.

Results

In the three-category classification task, the sensitivity, specificity, and accuracy were 65.41%, 82.21%, and 64.9%, respectively. In the binary classification task, the sensitivity, specificity, accuracy, and area under the ROC curve (AUC) values were 69.57%, 95.24%, 87.42%, and 0.89, respectively. In the visual assessment of GGN invasiveness of binary classification by the two thoracic radiologists, the sensitivity, specificity, and accuracy of the senior and junior radiologists were 58.93%, 90.51%, and 81.35% and 76.79%, 55.47%, and 61.66%, respectively.

Conclusions

The proposed multi-task deep learning model achieved good classification results in determining the invasiveness of GGNs. This model may help to select patients with invasive lesions who need surgery and the proper surgical methods.

Key Points

• The proposed multi-task model has achieved good classification results for the invasiveness of GGNs.

• The proposed network includes a classification and segmentation branch to learn global and regional features, respectively.

• The multi-task model could assist doctors in selecting patients with invasive lesions who need surgery and choosing appropriate surgical methods.

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Abbreviations

3D:

Three-dimension

AAH:

Atypical adenomatous hyperplasia

AIS:

Adenocarcinoma in situ

AUC:

Area under the curve

BN:

Batch normalization

CNN:

Convolutional neural network

CT:

Computed tomography

GGN:

Ground-glass nodule

GGO:

Ground-glass opacity

GPU:

Graphics processing unit

HIPAA:

Health Insurance Portability and Accountability Act

IA:

Invasive pulmonary adenocarcinoma

IRB:

Institutional review board

LLL:

Left lower lobe

LUL:

Left upper lobe

MCC:

Matthews correlation coefficient

MIA:

Minimally invasive adenocarcinoma

pGGN:

Pure ground-glass nodule

PSN:

Part-solid nodule

PTNB:

Percutaneous transthoracic needle lung biopsies

RLL:

Right lower lobe

RML:

Right middle lobe

ROC:

Receiver operating characteristic

RUL:

Right upper lobe

SGD:

Stochastic gradient descent

VATS:

Video-assisted thoracoscopic surgery

VGG:

Visual geometry group

WHO:

World Health Organization

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Funding

This study has received funding from Shanghai Shenkang Project (No. 16CR3024A), Shanghai Science and Technology Committee (No. 17441902700); Shanghai Science and Technology Committee (No. 18511102900, No. 18511102901); the Beijing Postdoctoral Research Foundation ZZ2019-88 and the National Natural Science Foundation of China (No. 81871508; No. 61572300; No. 61773246); and Taishan Scholar Program of Shandong Province of China (No. TSHW201502038); major program of Shandong Province Natural Science Foundation (No. ZR2018ZB0419).

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Author notes

  1. Ye Yu and Na Wang contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, China

    Ye Yu, Yicheng Fu, Xiaoqian Li, Huawei Wu, Jianrong Xu & Jiejun Cheng

  2. SenseTime Research, Shanghai, 200233, China

    Na Wang, Ning Huang & Xinglong Liu

  3. School of Information Science and Engineering at Shandong Normal University, Jinan, 250358, China

    Yuanjie Zheng

Authors
  1. Ye Yu
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  2. Na Wang
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  3. Ning Huang
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Corresponding authors

Correspondence to Jianrong Xu or Jiejun Cheng.

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The scientific guarantor of this publication is Jiejun Cheng.

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The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

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• retrospective

• diagnostic study

• performed at one institution

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Yu, Y., Wang, N., Huang, N. et al. Determining the invasiveness of ground-glass nodules using a 3D multi-task network. Eur Radiol 31, 7162–7171 (2021). https://doi.org/10.1007/s00330-021-07794-0

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  • DOI: https://doi.org/10.1007/s00330-021-07794-0

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