Abstract
Purpose
To investigate the performance of deep learning and radiomics features of intra-tumoral region (ITR) and peri-tumoral region (PTR) in the diagnosing of breast cancer lung metastasis (BCLM) and primary lung cancer (PLC) with low-dose CT (LDCT).
Methods
We retrospectively collected the LDCT images of 100 breast cancer patients with lung lesions, comprising 60 cases of BCLM and 40 cases of PLC. We proposed a fusion model that combined deep learning features extracted from ResNet18-based multi-input residual convolution network with traditional radiomics features. Specifically, the fusion model adopted a multi-region strategy, incorporating the aforementioned features from both the ITR and PTR. Then, we randomly divided the dataset into training and validation sets using fivefold cross-validation approach. Comprehensive comparative experiments were performed between the proposed fusion model and other eight models, including the intra-tumoral deep learning model, the intra-tumoral radiomics model, the intra-tumoral deep-learning radiomics model, the peri-tumoral deep learning model, the peri-tumoral radiomics model, the peri-tumoral deep-learning radiomics model, the multi-region radiomics model, and the multi-region deep-learning model.
Results
The fusion model developed using deep-learning radiomics feature sets extracted from the ITR and PTR had the best classification performance, with the area under the curve of 0.913 (95% CI 0.840–0.960). This was significantly higher than that of the single region’s radiomics model or deep learning model.
Conclusions
The combination of radiomics and deep learning features was effective in discriminating BCLM and PLC. Additionally, the analysis of the PTR can mine more comprehensive tumor information.
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Data availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Abbreviations
- LDCT:
-
Low-dose computed tomography
- ITR:
-
Intra-tumoral region
- PTR:
-
Peri-tumoral region
- MR:
-
Two regions of the intra-tumoral region and peri-tumoral region
- BCLM:
-
Breast cancer lung metastasis
- PLC:
-
Primary lung cancer
- LASSO:
-
Least absolute shrinkage and selection operator
- LDA:
-
Linear discriminant analysis
- ROI:
-
Region of interest
- CI:
-
Confidence interval
- ICCs:
-
Intra-class correlation coefficients
- AUC:
-
Area under the curve
- HU:
-
Hounsfield unit
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Acknowledgements
We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.
Funding
This work was supported by the Suzhou science and technology plan project (no. SZS2022008); the Harbin Medical University Cancer Hospital Haiyan Fund Youth Funding Project (no. JJQN2020-13); the Scientific Research Project of Heilongjiang Health Commission (no. 2020-073) and the Guizhou Provincial People’s Hospital Talent Fund (Yunsong Peng) under Grant Hospital Talent Project (no. [2022]-5).
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Conception and design of the research were performed by LL and XZ. Material preparation and data collection were performed by XZ, YL and TL. Analysis of data and statistical analysis were performed by LL, JZ and WC. The manuscript was written by LL. The manuscript was reviewed by GY and YP. All authors read and approved the final manuscript.
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This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Harbin Medical University ethics. Informed consent was obtained from all individual participants included in the study.
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Appropriate informed consent for the retrospective investigation was obtained from each patient. The authors affirm that human research participants provided informed consent for publication of the images in Fig. 1.
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Li, L., Zhou, X., Cui, W. et al. Combining radiomics and deep learning features of intra-tumoral and peri-tumoral regions for the classification of breast cancer lung metastasis and primary lung cancer with low-dose CT. J Cancer Res Clin Oncol 149, 15469–15478 (2023). https://doi.org/10.1007/s00432-023-05329-2
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DOI: https://doi.org/10.1007/s00432-023-05329-2