Introduction
Tongue cancer affects the oral and maxillofacial area and is more common in men due to the higher frequency of tobacco and alcohol consumption. Treatment for early-stage tongue cancer usually involves surgery, radiation therapy, or a combination of both ( Coletta, Yeudall & Salo, 2020 ; Ansarin et al., 2019 ; Pfister et al., 2020 ). Cervical lymph node metastasis is a major concern in the early stage of tongue cancer and can reduce patient survival by almost half. It is currently the primary prognostic factor for tongue cancer ( Ren, Yuan & Tao, 2022 ; Imai et al., 2017 ; Li et al., 2023 ). Occult lymph node metastasis (OLNM) is diagnosed with the presence of micrometastasis in the lymph nodes through pathological examination after neck dissection, before which the results of palpation, ultrasound, CT, MRI, and other imaging tests are negative ( Baba et al., 2020 ). About 40–60% of the patients in the early stage of oral cancer have OLNM ( Yuan, Ren & Tao, 2021 ; Ren, Yuan & Tao, 2022 ; Doll et al., 2022 ). In addition, patients without pathological lymph node metastasis (pN0) who undergo neck dissection have to bear unnecessary intraoperative risks and postoperative complications such as shoulder dysfunction. On the other hand, a lack of effective treatment in patients without clinical lymph node metastasis (cN0) but with OLNM may lead to cervical lymph node metastasis or even extracapsular spread during follow-up, resulting in a worse prognosis ( Wang et al., 2022 ). Therefore, early detection and accurate prediction of OLNM are critical for determining the optimal treatment and improving patient outcomes.

In recent years, radiomics and deep learning have helped identify early signs of metastasis and predict the likelihood of cancer recurrence ( Ferreira-Junior et al., 2020 , Jalalifar et al., 2022 ; Bang et al., 2023 ). Radiomics involves the extraction of quantitative features from medical images to identify subtle patterns that are not visible to the naked eye ( Guiot et al., 2022 ; Currie & Rohren, 2022 ; Currie, Hawk & Rohren, 2023 ). Deep learning refers to training deep neural networks, such as conventional convolutional neural network (CNN) on large datasets, and fine-tuning them on smaller datasets to improve the predictive ability ( Hosny et al., 2018 ; Liu et al., 2020 ). It uses an optimization algorithm called back-propagation to adjust its internal parameters for the best image output prediction.

Combining radiomics with deep learning can help improve the accuracy of the prediction models for lymph node metastasis in early tongue cancer ( Gillies, Kinahan & Hricak, 2016 ; Leger et al., 2017 ). Machine learning algorithms can identify patterns indicative of metastasis by analyzing radiomics features, such as texture, shape, and intensity ( Tang et al., 2022 ). Furthermore, deep transfer learning can automatically extract more meaningful features from medical images to avoid massive engineering of radiomics for more accurate predictions in a shorter duration, achieving good performance ( Hosny et al., 2018 ).

Previous studies have used this approach on the intratumoral region without considering the peritumoral area. However, there is evidence that peritumoral features can improve diagnostic efficiency ( Zhou et al., 2018 ; Zhang et al., 2022 ; Zhao et al., 2023 ). Zhang et al. (2022) demonstrated that time-serial CT-based radiomics signature integrating intra- and peri-tumoral features offered the potential to predict progression-free survival for lung adenocarcinoma patients treated with epidermal growth factor receptor-tyrosine kinase inhibitors. Zhao et al. (2023) developed and validated a nomogram based on intratumoral and peritumoral radiomics signatures to predict distant metastasis-free survival in patients with locally advanced rectal cancer before the treatment with neoadjuvant chemoradiotherapy. In addition, Zhou et al. (2018) generated a radiogenomics map for non-small cell lung cancer to illustrate the relationship between molecular and imaging phenotypes.

This study aimed to combine radiomics and deep transfer learning to predict OLNM in patients with early-stage tongue cancer based on contrast-enhanced CT images. The peritumoral region was incorporated into the radiomics-based model to improve the predictive accuracy. In clinical settings, this model can minimize decision-making errors by surgeons and reduce the risk of unnecessary surgery. The workflow of the study is shown in Figure 1 .