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Improving the accuracy of prognosis for clinical stage I solid lung adenocarcinoma by radiomics models covering tumor per se and peritumoral changes on CT

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Abstract

Objectives

To assess methods to improve the accuracy of prognosis for clinical stage I solid lung adenocarcinoma using radiomics based on different volumes of interests (VOIs).

Methods

This retrospective study included patients with postoperative clinical stage I solid lung adenocarcinoma from two hospitals, center 1 and center 2. Three databases were generated: dataset A (training set from center 1), dataset B (internal test set from center 1), and dataset C (external validation test from center 2). Disease-free survival (DFS) data were collected. CT radiomics models were constructed based on four VOIs: gross tumor volume (GTV), 3 mm external to the tumor border (peritumoral volume [PTV]0~+3), 6 mm crossing tumor border (PTV−3~+3), and 6 mm external to the tumor border (PTV0~+6). The area under the receiver operating characteristic curve (AUC) was used to compare the model accuracies.

Results

A total of 334 patients were included (204 and 130 from centers 1 and 2). The model using PTV−3~+3 (AUC 0.81 [95% confidence interval {CI}: 0.75, 0.94], 0.81 [0.63, 0.90] for datasets B and C) outperformed the other three models, GTV (0.73 [0.58, 0.81], 0.73 [0.58, 0.83]), PTV0~+3 (0.76 [0.52, 0.87], 0.75 [0.60, 0.83]), and PTV0~+6 (0.72 [0.60, 0.81], 0.69 [0.59, 0.81]), in datasets B and C, all p < 0.05.

Conclusions

A radiomics model based on a VOI of 6 mm crossing tumor border more accurately predicts prognosis of clinical stage I solid lung adenocarcinoma than that based on VOIs including overall tumor or external rims of 3 mm and 6 mm.

Key Points

• Radiomics is a useful approach to improve the accuracy of prognosis for stage I solid adenocarcinoma.

• The radiomics model based on VOIs that includes 3 mm within and external to the tumor border (peritumoral volume [PTV] −3~+3 ) outperformed models that included either only the tumor itself or those that only included the peritumoral volume.

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Abbreviations

AUC:

Area under the receiver operating characteristic curve

CI :

Confidence interval

DFS:

Disease-free survival

GTV :

Gross tumor volume

LASSO :

Least absolute shrinkage and selection operator

PTV :

Peritumoral volume

ROC :

Receiver operating characteristic curve

ROI:

Region of interest

VOIs:

Volumes of interests

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Company names

Product IFoundry (Intelligence Foundry 1.1) GE Healthcare.

Funding

This study has received funding from the Guangdong Ministry of Education Industry-University-Research Project (2011A090200057).

Author information

Author notes

  1. Kunfeng Liu and Kunwei Li contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China

    Kunfeng Liu, Kunwei Li, Mingzhu Liang, Yinghua Zhong & Xueguo Liu

  2. Translational Medicine Team, GE Healthcare, Shanghai, China

    Tingfan Wu & Xin Li

  3. State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China

    Xiangyang Yu

  4. Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China

    Chuanmiao Xie

  5. Department of Thoracic Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China

    Lanjun Zhang

  6. Department of Radiology, Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China

    Xueguo Liu

Authors
  1. Kunfeng Liu
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  7. Xin Li
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Corresponding author

Correspondence to Xueguo Liu.

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Guarantor

The scientific guarantor of this publication is Xueguo Liu.

Conflict of interest

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

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported in “Quantitative radiomic model for predicting malignancy of small solid pulmonary nodules detected by low-dose CT screening.”

Methodology

• retrospective

• diagnostic or prognostic study

• multicenter study

Additional information

Kunfeng Liu and Kunwei Li are co-first authors.

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Liu, K., Li, K., Wu, T. et al. Improving the accuracy of prognosis for clinical stage I solid lung adenocarcinoma by radiomics models covering tumor per se and peritumoral changes on CT. Eur Radiol 32, 1065–1077 (2022). https://doi.org/10.1007/s00330-021-08194-0

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  • DOI: https://doi.org/10.1007/s00330-021-08194-0

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