Abstract
Background
The pre-operative non-invasive differential diagnosis of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) mainly depends on imaging. However, the accuracy of conventional imaging and radiomics methods in differentiating between the two carcinomas is unsatisfactory. In this study, we aimed to establish a novel deep learning model based on computed tomography (CT) images to provide an effective and non-invasive pre-operative differential diagnosis method for HCC and ICC.
Materials and methods
We retrospectively investigated the CT images of 395 HCC patients and 99 ICC patients who were diagnosed based on pathological analysis. To differentiate between HCC and ICC we developed a deep learning model called CSAM-Net based on channel and spatial attention mechanisms. We compared the proposed CSAM-Net with conventional radiomic models such as conventional logistic regression, least absolute shrinkage and selection operator regression, support vector machine, and random forest models.
Results
With respect to differentiating between HCC and ICC, the CSAM-Net model showed area under the receiver operating characteristic curve (AUC) values of 0.987 (accuracy = 0.939), 0.969 (accuracy = 0.914), and 0.959 (accuracy = 0.912) for the training, validation, and test sets, respectively, which were significantly higher than those of the conventional radiomics models (0.736–0.913 [accuracy = 0.735–0.912], 0.602–0.828 [accuracy = 0.647–0.818], and 0.638–0.845 [accuracy = 0.618–0.849], respectively. The decision curve analysis showed a high net benefit of the CSAM-Net model, which suggests potential efficacy in differentiating between HCC and ICC in the diagnosis of liver cancers.
Conclusions
The proposed CSAM-Net model based on channel and spatial attention mechanisms provides an effective and non-invasive tool for the differential diagnosis of HCC and ICC on CT images, and has potential applications in diagnosis of liver cancers.
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Data availability
The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.
Abbreviations
- HCC:
-
Hepatocellular carcinoma
- ICC:
-
Intrahepatic cholangiocarcinoma
- CT:
-
Computed tomography
- MRI:
-
Magnetic resonance imaging
- CEUS:
-
Contrast-enhanced ultrasound
- AUC:
-
Area under the operating characteristic curve
- CNNs:
-
Convolutional neural networks
- LASSO:
-
Least absolute shrinkage and selection operator
- LR:
-
Logistic regression
- SVM:
-
Support vector machine
- RF:
-
Random forest
- GLSZM:
-
Gray-level size zone matrix
- GLRLM:
-
Gray-level run-length matrix
- GLDM:
-
Gray-level dependence matrix
- cHCC-ICC:
-
Combined HCC and ICC
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Acknowledgements
We thank the Department of Pathology of the First Affiliated Hospital of Nanchang University for helping us to collect pathological data.
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Conception and design of the research: JH, YS, ZW, HZ, JW, K-HZ. Acquisition of data: JH, GX, XZ. Analysis and interpretation of the data: ZW, HZ, XZ. Statistical analysis: YS, GX, JW, K-HZ. Writing of the manuscript: JH, YS, ZW, HZ. Critical revision of the manuscript for intellectual content: JW, K-HZ. All authors read and approved the final draft.
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This study was conducted in accordance with the declaration of Helsinki.This study was conducted with approval from the Ethics Committee of First Affiliated Hospital of Nanchang University (2022)CDYFYYLK(08-014).A written informed consent was obtained from all participants.
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This retrospective study was approved by the Ethics Committee of the First Affiliated Hospital of Nanchang University.
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Huang, Jl., Sun, Y., Wu, Zh. et al. Differential diagnosis of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on spatial and channel attention mechanisms. J Cancer Res Clin Oncol 149, 10161–10168 (2023). https://doi.org/10.1007/s00432-023-04935-4
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DOI: https://doi.org/10.1007/s00432-023-04935-4