Deep learning enhanced MRI radiomics in predicting pathological response of head and neck squamous carcinoma to...

Neoadjuvant chemoimmunotherapy (NACI) has become one of the most widely adopted therapies for head and neck squamous cell carcinoma (HNSCC) before surgery. However, the accurate prediction of patients responding to this therapy has been challenging owing to the lack of predictive biomarkers.

In the present study, histologically confirmed HNSCC patients with complete clinicopathological data, who received chemotherapy plus programmed cell death protein 1 inhibitor as the NACI regimen for 2-3 cycles before radical surgery between 2021 and 2023, were screened, and both clinicopathological and magnetic resonance imaging (MRI) data were collected and divided into training, testing, and external validation cohorts. Both traditional radiomics and deep-learning techniques were employed to extract features from MRI, followed by feature selection using both Spearman correlation and least absolute shrinkage and selection operator analysis. The selected features were incorporated into predictive models using a logistic regression classifier for pathologic complete response.

The results demonstrated that three out of seven features extracted from MRI were deep-learning features. Notably, the integration of deep-learning features with clinicopathological and radiomics features increased the area under the curve in the training, testing, and external validation cohorts to 0.781, 0.759, and 0.740, respectively. Moreover, multimodal prediction for patient stratification can significantly improve the prognosis of HNSCC patients undergoing NACI.

In conclusion, deep-learning features from MRI can augment traditional imaging analysis to uncover hidden predictive patterns reflecting responsiveness to NACI in HNSCC patients, and integration of different data types provides a more robust prediction strategy.