Tumor radiomics signature for artificial neural network-assisted detection of neck metastasis in patient with tongue cancer

Background and purpose: To determine the neck management of tongue cancer, this study attempted to construct an artificial neural network (ANN)-assisted model based on computed tomography (CT) radiomics of primary tumors to predict neck lymph node (LN) status in patients with tongue squamous cell carcinoma (SCC).

Materials and methods: Three hundred thirteen patients with tongue SCC were retrospectively included and randomly divided into training (60%), validation (20%) and internally independent test (20%) sets. In total, 1673 feature values were extracted after the semiautomatic segmentation of primary tumors and set as input layers of a classical 3-layer ANN incorporated with or without clinical LN (cN) status after dimension reduction. The receiver operating characteristic (ROC) curve, accuracy (ACC), sensitivity (SEN), specificity (SPE), area under curve (AUC) and Net Reclassification Index (NRI), were used to evaluate and compare the models.

Results: Four models with different settings were constructed. The ACC, SEN, SPE and AUC reached 84.1%, 93.1%, 76.5% and 0.943 (95% confidence interval: 0.891-0.996, p<.001), respectively, in the test set. The NRI of models compared with radiologists reached 40% (p<.001). The occult nodal metastasis rate was reduced from 30.9% to a minimum of 12.7% in the T1-2 group.

Conclusion: ANN-based models that incorporated CT radiomics of primary tumors with traditional LN evaluation were constructed and validated to more precisely predict neck LN metastasis in patients with tongue SCC than with naked eyes, especially in early-stage cancer.