Discussion
OLNM is an essential prognostic factor in early-stage tongue cancer (cT1-2N0M0) and influences treatment decisions ( Kubo et al., 2022 , Kwon, 2022 ). Thus, accurate prediction of OLNM can significantly impact clinical management and outcomes of tongue cancer patients ( Baba et al., 2019 ). In this study, we developed a decision-level fusion system for classifying CT images by integrating radiomics, clinical, and deep learning models with the stacking ensemble technique. A nomogram was employed to visualize the integrated prediction results and allow clinical interpretation. To our knowledge, this is also the first time that an integrated model capturing the features of CT images around the tumor has been used to predict OLNM in early-stage tongue cancer.

The integrated model exhibited superior performance compared with the individual radiomics, clinical, and deep learning models in terms of the AUC values, and this can be attributed to the combination of the complementary strengths of the three models that allowed diverse aspects of the data to be captured. For instance, the radiomics model allowed quantitative features to be extracted from CT images and subtle patterns that might be overlooked by human observers to be captured, while the clinical model provided additional demographic and clinical information. We also applied deep learning to predict the presence of OLNM in cT1-2N0 patients ( Jin et al., 2021 ). The deep learning model based on ResNet50 can directly analyze raw medical images and learn complex and high-level representations of the data ( Yu et al., 2020 ). However, the format of the raw images is a matter of debate. While some researchers advocate for directly incorporating 3D images into the model, others believe that the images should first undergo a flattening process before being fed into the model ( Harmon et al., 2020 ; Calabrese et al., 2022 ; Tian et al., 2020 ; Shi et al., 2022 ; Cho et al., 2022 ; Charles et al., 2017 ). However, direct use of 3D data for deep learning training often requires significant computational resources, such as high-performance graphic processing units (GPUs) and large memory capacities, making the training and deployment processes expensive and time-consuming. Furthermore, medical image datasets are often small and imbalanced, possibly leading to overfitting when 3D data are directly used for deep learning training. Overfitting can cause the model to perform well with the training data but underperform in real-world applications due to reduced generalization. Therefore, we converted the 3D CT images to 2D images, during which determination of the feature extraction range was required due to relatively fewer features. To extract more representative features, we adopted three cropping methods to obtain suitable images as the input for the deep learning model. The M1 dataset was first obtained by setting pixel values of the background to zero. Then, the largest cross-section from the 3D ROI was selected and expanded by five pixels to obtain dataset M2. Finally, the 3D ROI was evenly expanded by two voxels around the tumor, and the largest cross-section was cropped to generate the M3 dataset. Incorporating peri-tumoral information can prevent overfitting when deep learning models are applied to most small medical imaging datasets by increasing the number of features. Among the two peri-tumoral processing methods, M3 uniformly expanded a fixed length in 3D images, while M2 involved rectangular expansion in 2D images. Consequently, M3 encompassed more critical features than M2, eventually yielding superior results.

By combining the outputs of multiple base models, stacking leverages the strengths of each to achieve better predictive accuracy ( Mostafaei et al., 2020 ) and reduces overfitting by diversifying the model ensemble, leading to more robust and generalizable results. We compared the performance of our integrated model with the decision-making abilities of clinicians and found that the integrated model had superior accuracy. As already explained, this can be attributed to the ability of machine learning algorithms to detect subtle patterns and relationships in the data that may not be easily discernible by human experts. Our findings indicate that a targeted multiomics integration model may help clinical practitioners, especially those with less experience, attain a performance level similar to that of experts. Clinicians often need to analyze vast amounts of information, including medical images, clinical records, and laboratory results, within a limited time, which can lead to cognitive overload and increase the likelihood of errors. The integrated model can rapidly process and analyze a large number of data, thus reducing the time needed for diagnosis and treatment planning. Another benefit of the integrated model is its ability to continuously learn and adapt new data that become available. As medical knowledge and clinical practice evolve, clinicians need to update their knowledge and decision-making process accordingly. However, this process can be time-consuming and challenging. The integrated model can be retrained with new data, enabling it to stay up-to-date with the latest advancements in the field and provide more accurate predictions. While the expertise of clinicians is invaluable, personal biases and preferences can sometimes influence their decisions, potentially leading to sub-optimal patient outcomes. By providing an objective, data-driven approach, the integrated model can minimize the impact of subjectivity in decision-making, leading to more consistent and optimal patient care. The nomogram incorporated the predictions from the radiomics, clinical, and deep learning models, as well as the output from the meta-model, to provide a comprehensive and intuitive representation of the decision-level fusion process. This can enable more effective communication of the results to both clinical practitioners and researchers, facilitating better understanding and application of the model in real-world settings.

In clinical practice, clinical data and CT image features of patients with early tongue cancer can be collected before surgery. Through objective data analysis and processing, the integrated model can be used to predict OLNM more accurately in a short period, and unnecessary neck surgery can be minimized for patients with negative OLNM. During the follow-up period, new data can be continuously collected for prediction, patients with missed diagnosis or OLNM are detected as early as possible, and lymph node dissection can be completed in time to effectively improve the prognosis. For patients with positive OLNM, cervical surgery should be completed simultaneously to reduce the risk of cervical lymph node metastasis and spread during follow-up. The integrated model is beneficial to the standardized and individualized diagnosis and treatment of early tongue cancer and improves the curative effect and prognosis.

However, several limitations to our study should be acknowledged. First, the sample size used for training and validation was relatively small, and this may limit the generalizability of our findings. Future studies should include larger, more diverse patient cohorts and include predictive molecular biomarkers associated with tissue or blood to further optimize the synthesis model, and prospective studies are needed to verify its efficacy. Second, the choice of the meta-model in the stacking ensemble technique could potentially impact the results. In our study, we employed logistic regression as the meta-model due to its simplicity and interpretability. However, other meta-models, such as SVM or random forest, could be explored in the future to assess their impact on the performance of the integrated model.