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An ordinal radiomic model to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma based on low-dose computed tomography in lung cancer screening

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Abstract

Objectives

To construct a radiomic model of low-dose CT (LDCT) to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma (IPA) and compare its diagnostic performance with quantitative-semantic model and radiologists.

Methods

A total of 682 pulmonary nodules were divided into the primary cohort (181 grade 1; 254 grade 2; 64 grade 3) and validation cohort (69 grade 1; 99 grade 2; 15 grade 3) according to scanners. The radiomic and quantitative-semantic models were built using ordinal logistic regression. The diagnostic performance of the models and radiologists was assessed by the area under the curve (AUC) of the receiver operating characteristic curve and accuracy.

Results

The radiomic model demonstrated excellent diagnostic performance in the validation cohort (AUC, 0.900 (95%CI: 0.847–0.939) for Grade 1 vs. Grade 2/Grade 3; AUC, 0.929 (95%CI: 0.882–0.962) for Grade 1/Grade 2 vs. Grade 3; accuracy, 0.803 (95%CI: 0.737–0.857)). No significant difference in diagnostic performance was found between the radiomic model and radiological expert (AUC, 0.840 (95%CI: 0.779–0.890) for Grade 1 vs. Grade 2/Grade 3, p = 0.130; AUC, 0.852 (95%CI: 0.793–0.900) for Grade 1/Grade 2 vs. Grade 3, p = 0.170; accuracy, 0.743 (95%CI: 0.673–0.804), p = 0.079), but the radiomic model outperformed the quantitative-semantic model and inexperienced radiologists (all p < 0.05).

Conclusions

The radiomic model of LDCT can be used to predict the differentiation grade of IPA in lung cancer screening, and its diagnostic performance is comparable to that of radiological expert.

Key Points

• Early identifying the novel differentiation grade of invasive non-mucinous pulmonary adenocarcinoma may provide guidance for further surveillance, surgical strategy, or more adjuvant treatment.

• The diagnostic performance of the radiomic model is comparable to that of a radiological expert and superior to that of the quantitative-semantic model and inexperienced radiologists.

• The radiomic model of low-dose CT can be used to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma in lung cancer screening.

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Abbreviations

AI:

Artificial intelligence

AIC:

Akaike’s information criterion

AUC:

Area under the curve

CI:

Confidence intervals

GLCM:

Grey level co-occurrence matrix

GLRLM:

Grey level run length matrix

GLSZM:

Grey level size zone matrix

IASLC:

International Association for the Study of Lung Cancer Pathology Committee

IBSI:

Imaging Biomarker Standardization Initiative

IPA:

Invasive non-mucinous pulmonary adenocarcinoma

LDCT:

Low-dose computed tomography

mRMR:

Minimum redundancy-maximum relevance

NGLDM:

Neighborhood grey level difference matrix

NGTDM:

Neighborhood grey tone difference matrix

ROC:

Receiver operating characteristic

ROI:

Region of interest

TRIPOD:

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis.

VIF:

Variance inflation factor

WHO:

World Health Organization

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Funding

This study has received funding from the National Natural Science Foundation of China (82202141), the Sichuan Science and Technology Program (2021YFS0075, 2021YFS0225), and the Chengdu Science and Technology Program (2021-YF05-01507-SN).

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Author notes

  1. Yong Li and Jieke Liu contributed equally to this work.

Authors and Affiliations

  1. Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No. 55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China

    Yong Li, Jieke Liu, Xi Yang, Ai Wang, Chi Zang, Lu Wang, Changjiu He, Libo Lin, Haomiao Qing, Jing Ren & Peng Zhou

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  1. Yong Li
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Correspondence to Peng Zhou.

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Li, Y., Liu, J., Yang, X. et al. An ordinal radiomic model to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma based on low-dose computed tomography in lung cancer screening. Eur Radiol 33, 3072–3082 (2023). https://doi.org/10.1007/s00330-023-09453-y

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