Computational imaging approaches to personalized gastric cancer treatment

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ABSTRACT Gastric cancer is a major global disease burden and leading cause of cancer mortality worldwide. Current treatment decision is made primarily on the basis of staging, which divides patients into several prognostic groups. For patients with localized and locally advanced disease, curative-intent surgery with chemotherapy is the standard treatment. However, survival outcomes vary widely, even among patients with disease of the same stage. Certain patients with early-stage disease have a sufficiently low risk of recurrence and may not benefit from, or could even be harmed by, chemotherapy given the associated toxicity and side effects. Conversely, many patients with aggressive tumors do not respond well to standard chemotherapy and still recur despite receiving extensive but ineffective treatment. Therefore, current one-size-fits-all approach is suboptimal, leading to over- and under-treatment in many patients. There is an unmet need for reliable prognostic and predictive models to guide personalized treatment of gastric cancer. To address this unmet need, we propose robust radiomics features of tumor morphology and spatial heterogeneity and establish their prognostic value. In addition, we will incorporate pathobiological knowledge into the design of deep learning models for predicting prognosis. Further, we will develop novel deep learning architecture to analyze longitudinal images for predicting pathologic response to neoadjuvant therapy. Finally, by leveraging the complementary value of imaging data, clinicopathologic variables and serial serum markers, we will construct integrative models to further improve prediction. If successful, the proposed models will be useful in two ways: (1), identify which patients with early gastric cancer may safely forego chemotherapy and avoid toxicity; (2), select the most effective chemotherapy regimen for a given patient. Further, the models can also identify patients with advanced disease who do not respond to standard chemotherapy and may benefit from novel targeted therapy or immunotherapy. The proposed computational imaging approaches are generally applicable for response monitoring and disease surveillance in many solid tumor types. Finally, the AI-based imaging technology developed here can bring benefit to underserved populations in minority groups and community settings. Progress made in gastric cancer will not only improve outcomes for patients in the US but also have global impact given its high incidence and mortality worldwide.