Deep learning approach of diffusion-weighted imaging as an outcome predictor in laryngeal and hypopharyngeal cancer patients with radiotherapy-related curative treatment: a preliminary study

Objectives This preliminary study aimed to develop a deep learning (DL) model using diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps to predict local recurrence and 2-year progression-free survival (PFS) in laryngeal and hypopharyngeal cancer patients treated with various forms of radiotherapy-related curative therapy. Methods Seventy patients with laryngeal and hypopharyngeal cancers treated by radiotherapy, chemoradiotherapy, or induction-(chemo)radiotherapy were enrolled and divided into training ( N = 49) and test ( N = 21) groups based on presentation timeline. All patients underwent MR before and 4 weeks after the start of radiotherapy. The DL models that extracted imaging features on pre- and intra-treatment DWI and ADC maps were trained to predict the local recurrence within a 2-year follow-up. In the test group, each DL model was analyzed for recurrence prediction. Additionally, the Kaplan-Meier and multivariable Cox regression analyses were performed to evaluate the prognostic significance of the DL models and clinical variables. Results The highest area under the receiver operating characteristics curve and accuracy for predicting the local recurrence in the DL model were 0.767 and 81.0%, respectively, using intra-treatment DWI (DWI intra ). The log-rank test showed that DWI intra was significantly associated with PFS ( p = 0.013). DWI intra was an independent prognostic factor for PFS in multivariate analysis ( p = 0.023). Conclusion DL models using DWI intra may have prognostic value in patients with laryngeal and hypopharyngeal cancers treated by curative radiotherapy. The model-related findings may contribute to determining the therapeutic strategy in the early stage of the treatment. Key Points • Deep learning models using intra-treatment diffusion-weighted imaging have prognostic value in patients with laryngeal and hypopharyngeal cancers treated by curative radiotherapy. • The findings from these models may contribute to determining the therapeutic strategy at the early stage of the treatment.