A convolutional attention model for predicting response to chemo-immunotherapy from ultrasound elastography in mouse tumor models

Abstract Background. In the era of personalized cancer treatment, understanding the intrinsic heterogeneity of tumors is crucial. Despite some patients responding favorably to a particular treatment, others may not benefit, resulting in varied efficacy of standard therapies. This study focuses on the prediction of tumor response to chemo-immunotherapy, exploring the potential of tumor mechanics and medical imaging as predictive biomarkers. We have extensively studied "desmoplastic" tumors, characterized by a dense and very stiff stroma, which presents a significant challenge for treatment. The increased stiffness of such tumors can be restored through pharmacological intervention with mechanotherapeutics. Methods. Here, we developed a deep learning methodology based on shear wave elastography (SWE) images, which involved a convolutional neural network (CNN) model enhanced with attention modules. The model was developed and evaluated as a predictive biomarker in the setting of detecting responsive, stable and non-responsive tumors to chemotherapy, immunotherapy or the combination, following mechanotherapeutics administration. A dataset of 1365 SWE images was obtained from 630 tumors from our previous experiments and used to train and successfully evaluate our methodology. SWE, in combination with deep learning models, has demonstrated promising results in disease diagnosis and tumor classification but their potential for predicting tumor response prior to therapy is not yet fully realized. Here we show, strong evidence that integrating SWE-derived biomarkers with automatic tumor segmentation algorithms enables accurate tumor detection and prediction of therapeutic outcomes, Conclusions. This approach can enhance personalized cancer treatment by providing non-invasive, reliable predictions of therapeutic outcomes.