Materials and Methods
A retrospective study was conducted using data on patients diagnosed with tongue cancer between 2015 and 2022 at the Tianjin Medical University Cancer Institute and Hospital and Zhengzhou Central Hospital Affiliated to Zhengzhou University. A total of 125 patients were randomly divided into the training set (100 cases) and the test set (25 cases). The study was approved by the ethics committee of Zhengzhou Central Hospital (202336) and conducted in line with the Declaration of Helsinki. Due to the retrospective nature of the study, the need for informed consent was waived by the committee.

The inclusion criteria were as follows: (1) The participants must have a pathological diagnosis of tongue squamous cell carcinoma, (2) and were treated with planned surgery and elective neck dissection, (3) with a clinical diagnosis of cT1-2N0M0 based on palpation and imaging tests. (4) The contrast-enhanced CT scans of the head and neck area were performed within 30 days prior to surgery, and (5) the complete clinical information was available. Patients with a history of previous malignancy, or with unidentifiable lesions or artifacts-induced radiological interference on CT scans were excluded. The CT pictures from hospital were collected using a GE Medical Systems Light Speed Pro 12 CT scanner with 120 kVp and 150 mAs. The parameters for the CT scan were as follows: scanning slice thickness 1.5 mm, pitch of 1.5 mm, and matrix of 512 × 512. The baseline characteristics including gender, sex, T stage, tumor size, smoking history, and alcohol consumption were retrieved from the databases of the hospitals.

Two radiologists blinded to the clinicopathological information of the patients independently conducted tumor segmentation on the CT dataset using ITK-SNAP version 3.8.0 ( http://www.itksnap.org ). A senior radiologist verified all delineation tags and corrected any errors. Since the images came from two CT scanners with different parameters, resampling was done before the features could be extracted from the ROI of the CT. The image space was normalized to 1 × 1 × 1. The inter-observer reliability and intra-observer repeatability of the extraction of radiomic characteristics were generally evaluated using inter- and intra-class correlation coefficients (ICCs). Features with ICC > 0.75 were retained for good consistency. The extracted image features were then normalized by adjusting the distribution of all features to the same range. The data were standardized according to the formula: Z = (X−μ)/σ , where µ is the feature’s mean and σ is the feature’s standard deviation.

The radiomics features were classified based on geometry, intensity, and texture to describe different aspects of the tumors, specifically from the following aspects: (I) geometry—a three-dimensional form of the tumor; (II) intensity—first-order statistical distribution of the voxel intensities within the tumor; (III) texture—the patterns, or second- and high-order spatial distributions of the intensities. The neighborhood gray-tone difference matrix, gray-level co-occurrence matrix, gray-level run length matrix, gray level size zone matrix, and other techniques were used to extract the texture features ( Figure 2A ).

The radiomics features with p values less than 0.05 in the Mann-Whitney U test were screened. The correlation between these features was analyzed by Spearman’s method, and those with a correlation coefficient >0.9 were selected. A greedy recursive deletion strategy was used for feature filtering, and the feature with the highest level of duplication in the current set was removed each time to preserve the ability to display features as fully as possible. Finally, 115 features were retained and subjected to least absolute shrinkage and selection operator (LASSO) regression with the scikit-learn package in Python using 10-fold cross validation with minimum criteria. The features with non-zero coefficients in the regression model were used to construct the radiomics signature, and a linear combination of retained features weighted by their model coefficients was used to calculate the radiomics score for each patient ( Figs. 2B – 2D ).

To construct deep transfer learning models, the most representative images were identified. The images were processed through three steps: M1: Assuming that the image background would create interference, the background pixels were removed and the primary lesion area was extracted along with the area surrounding the tumor. M2: The largest ROI was selected from the CT images and enlarged by five pixels. M3: In the 3D image, the tumor area was expanded evenly by two voxels to obtain the peritumoral region, and the image with the largest ROI was selected ( Figure 2E ).

Radiomics model: The final features were input into various machine learning models such as support vector machine (SVM), k-nearest neighbor (kNN), random forest, extra trees, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), multilayer perceptron (MLP), and logistic regression (LR) to construct the radiomics model ( Cortes & Vapnik, 1995 ; Geurts, Ernst & Wehenkel, 2006 ; Chen & Guestrin, 2016 ; Ke et al., 2017 ).

Clinical model: The preoperative clinical data included age, gender, T stage, tumor size, drinking and smoking history. The same methodology described above was used to construct the clinical model.

Deep transfer learning model: Based on literature review and preliminary experimental testing, ResNet50 ( He et al., 2016 ) was selected as the deep transfer learning model, and three datasets were incorporated for training. The stochastic gradient descent (SGD) optimizer was adopted with a learning rate of 0.001 and 100 epochs. A fully connected layer, followed by a dropout layer with a 0.5 drop rate, was employed to prevent overfitting. The optimal dataset was selected for training, and the structured data were extracted from the average pooling layer ( i.e ., the penultimate layer). The deep learning model was then constructed using the same methodology mentioned above. Finally, the three base models were integrated using the stacking strategy to obtain the integrated model.

Student’s t-test or the Mann-Whitney U test was used to compare continuous variables, and the chi-squared test was used for categorical variables. All statistical tests were two-tailed and P < 0.05 was considered statistically significant. Python 3.7 was used for statistical analysis.