LOMIA-T: A Transformer-based LOngitudinal Medical Image Analysis framework for predicting treatment response of esophageal cancer

Deep learning models based on medical images have made significant strides in predicting treatment outcomes. However, previous methods have primarily concentrated on single time-point images, neglecting the temporal dynamics and changes inherent in longitudinal medical images. Thus, we propose a Transformer-based longitudinal image analysis framework (LOMIA-T) to contrast and fuse latent representations from pre- and post-treatment medical images for predicting treatment response. Specifically, we first design a treatment response- based contrastive loss to enhance latent representation by discerning evolutionary processes across various disease stages. Then, we integrate latent representations from pre- and post-treatment CT images using a cross-attention mechanism. Considering the redundancy in the dual-branch output features induced by the cross-attention mechanism, we propose a clinically interpretable feature fusion strategy to predict treatment response. Experimentally, the proposed framework outperforms several state-of-the-art longitudinal image analysis methods on an in-house Esophageal Squamous Cell Carcinoma (ESCC) dataset, encompassing 170 pre- and post-treatment contrast-enhanced CT image pairs from ESCC patients underwent neoadjuvant chemoradiotherapy. Ablation experiments validate the efficacy of the proposed treatment response-based contrastive loss and feature fusion strategy. The codes will be made available at . ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study is supported by Beijing Natural Science Foundation (L232132) and National Nature Science Foundation of China (82102140). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study was approved by the Ethics Committee of the Fifth Affiliated Hospital of Sun Yat-sen University (2021-K62-1) and was conducted in accordance with the Declaration of Helsinki. Informed consent was waived due to the observational design of this study and the de-identifed nature of the data. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors