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Radiomic and quantitative-semantic models of low-dose computed tomography for predicting the poorly differentiated invasive non-mucinous pulmonary adenocarcinoma

  • Chest Radiology
  • Published:
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Abstract

Purpose

Poorly differentiated invasive non-mucinous pulmonary adenocarcinoma (IPA), based on the novel grading system, was related to poor prognosis, with a high risk of lymph node metastasis and local recurrence. This study aimed to build the radiomic and quantitative-semantic models of low-dose computed tomography (LDCT) to preoperatively predict the poorly differentiated IPA in nodules with solid component, and compare their diagnostic performance with radiologists.

Materials and methods

A total of 396 nodules from 388 eligible patients, who underwent LDCT scan within 2 weeks before surgery and were pathologically diagnosed with IPA, were retrospectively enrolled between July 2018 and December 2021. Nodules were divided into two independent cohorts according to scanners: primary cohort (195 well/moderate differentiated and 64 poorly differentiated) and validation cohort (104 well/moderate differentiated and 33 poorly differentiated). The radiomic and quantitative-semantic models were built using multivariable logistic regression. The diagnostic performance of the models and radiologists was assessed by area under curve (AUC) of receiver operating characteristic (ROC) curve, accuracy, sensitivity, and specificity.

Results

No significant differences of AUCs were found between the radiomic and quantitative-semantic model in primary and validation cohorts (0.921 vs. 0.923, P = 0.846 and 0.938 vs. 0.911, P = 0.161). Both the models outperformed three radiologists in the validation cohort (all P < 0.05).

Conclusions

The radiomic and quantitative-semantic models of LDCT, which could identify the poorly differentiated IPA with excellent diagnostic performance, might provide guidance for therapeutic decision making, such as choosing appropriate surgical method or adjuvant chemotherapy.

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Abbreviations

AI:

Artificial intelligence

AIC:

Akaike’s information criterion

AUC:

Area under the curve

CT:

Computed tomography

CTR:

Consolidation/tumor ratio

DCA:

Decision curve analysis

IASLC:

International Association for the Study of Lung Cancer Pathology Committee

IBSI:

Imaging Biomarker Standardization Initiative

ICC:

Intraclass correlation coefficient

IPA:

Invasive non-mucinous pulmonary adenocarcinoma

LASSO:

Least absolute shrinkage and selection operator

LDCT:

Low-dose computed tomography

mRMR:

Minimum redundancy-maximum relevance

RFS:

Recurrence-free survival

ROC:

Receiver operating characteristic

TRIPOD:

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

WHO:

World Health Organization

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Funding

This study was supported by the National Natural Science Foundation of China (82202141), 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, Fuyang Xu, Lu Wang, Changjiu He, Libo Lin, Haomiao Qing, Jing Ren & Peng Zhou

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Contributions

All authors contributed to the study conception and design. YL and JL involved in conceptualization, data curation, formal analysis, methodology, and writing—original draft; XY, FX, and LW performed the data curation and validation. The study was founded by JL and PZ. Writing—review & editing was performed by HQ, JR, and PZ. All authors read and approved the final manuscript.

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Correspondence to Peng Zhou.

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The study was approved by the Institutional Review Board of Sichuan Cancer Hospital & Institute, and the patient consent was waived because anonymized information was used for this retrospective study.

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Li, Y., Liu, J., Yang, X. et al. Radiomic and quantitative-semantic models of low-dose computed tomography for predicting the poorly differentiated invasive non-mucinous pulmonary adenocarcinoma. Radiol med 128, 191–202 (2023). https://doi.org/10.1007/s11547-023-01591-z

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