(1) Background: In oral cancer staging, ultrasonography (US), computed tomography (CT), magnetic resonance imaging (MRI), and 2-deoxy-2-[fluorine-18]fluoro-D-glucose (FDG) with positron emission tomography/computed tomography (PET/CT) are routinely used in clinical practice. The present study is a retrospective examination of the diagnostic accuracy of cervical lymph node metastasis using US, CT, MRI, and PET/CT, with histopathological diagnosis as a reference, to compare the different diagnostic imaging modalities. (2) Methods: The participants included 16 patients with oral squamous cell carcinoma who underwent US-, CT-, MRI-, and PET/CT-based preoperative diagnostic imaging and simultaneous primary lesion resection and neck dissection, including 82 level regions and 424 lymph nodes. We compared the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of each imaging modality based on the imaging results and the pathology results of metastasis. (3) Results: Of the four diagnostic imaging modalities, PET/CT exhibited the highest sensitivity but the lowest specificity and accuracy. US, CT, and MRI had high specificities. Comparing each level region and lymph node showed that differences were observed in PET/CT. (4) Conclusions: PET/CT to diagnose lymph node metastasis requires a comprehensive evaluation because it produces more false positives than other diagnostic imaging modalities. Using US, CT, and MRI, which have excellent spatial resolution, improves diagnostic accuracy at the lymph node level.

The histological grade of oral squamous cell carcinoma affects the prognosis. In the present study, we performed a radiomics analysis to extract features from ¹⁸F-FDG PET image data, created machine learning models from the features, and verified the accuracy of the prediction of the histological grade of oral squamous cell carcinoma. The subjects were 191 patients in whom an ¹⁸F-FDG-PET examination was performed preoperatively and a histopathological grade was confirmed after surgery, and their tumor sizes were sufficient for a radiomics analysis. These patients were split in a 70%/30% ratio for use as training data and testing data, respectively. We extracted 2993 radiomics features from the PET images of each patient. Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Naïve Bayes (NB), and K-Nearest Neighbor (KNN) machine learning models were created. The areas under the curve obtained from receiver operating characteristic curves for the prediction of the histological grade of oral squamous cell carcinoma were 0.72, 0.71, 0.84, 0.74, and 0.73 for LR, SVM, RF, NB, and KNN, respectively. We confirmed that a PET radiomics analysis is useful for the preoperative prediction of the histological grade of oral squamous cell carcinoma.

Background/Objectives: Oral squamous cell carcinoma (OSCC) carries a risk of late metastasis not only in advanced stages but also in early stages. In this study, we built and tested radiomics-based machine learning (ML) models for predicting the risk of metastasis from early OSCC on ¹⁸F-FDG positron emission tomography (PET). Methods: Patients diagnosed with T1 or T2 squamous cell carcinoma who underwent a preoperative ¹⁸F-FDG PET-CT examination at a single institution between 2016 and December 2022 were included in this retrospective study. The presence or absence of late cervical lymph node metastasis was confirmed for all patients. Among the radiomics features extracted from the images, we selected those that were useful for predicting late metastasis and used them to create ML models. We then verified the prediction accuracy of the models. Results: A total of 109 subjects were included, of which 31 had late lymph node metastasis and 78 were without metastasis. The most accurate ML model created using radiomics features selected from the subject cases had an area under the curve of 0.977 and accuracy of 87.5%. Conclusions: We confirmed that ML models using radiomics features extracted from PET images can be useful for predicting late metastasis in patients with early-stage OSCC.