Individualized treatment effect inference of head and neck cancer with multimodal data

Estimating individualized treatment effects (ITE) is critical for personalized medicine, yet it remains a challenge due to retrospective observational data, which suffer from selection bias in clinical practice and the complexity of multimodal data used for patient status depiction. In this work, the authors develop an end-to-end deep learning (DL) framework that incorporates multimodal patient data and multiple treatments for accurate ITE inference in a retrospective head and neck cancer (HNC) study. A possible solution is concatenating the factors and adapting adversarial training, which has shown great promise on tabular data, to disentangle patient characteristics from patient status features to mitigate treatment selection bias. However, this approach suffers from instability when applied to complex multimodal patient data and multiple treatment options. For flexible and efficient treatment-conditioned information fusion, they propose a bi-stage adaptive instance normalization (Bi-AdaIN) to inject relevant factors into corresponding layers, an approach that is also robust to missing values. Furthermore, they propose to disentangle status features from the multi-treatment variable using mutual information (MI) regularization, enabling more accurate predictions of patient-specific outcomes for both factual and counterfactual data. The authors evaluated their model on the RADCURE dataset, comprising 3,346 HNC cases with CT scans and multiple clinical variables who received radiotherapy or additional chemotherapy and EGFRI. The Bias-Adjusted Treatment Effect (BATE) is substantially reduced compared to the conventional direct ITE method (which does not consider treatment bias) and to adversarial training, indicating a more robust estimation of causal effects. This work is one of the first DL-based studies to address ITE estimation using multimodal medical imaging, offering a promising approach to counterfactual reasoning in clinical oncology for decision support.