This project aims to apply machine learning to routinely collected pathology and radiology images and help guide the frontline immune checkpoint inhibitor regimens selection for patients with metastatic non-small cell lung cancer (mNSCLC). The proposed studies will develop and test new pathomic-radiomic algorithms in a multi-center setting to predict which patients are likely to respond to immune checkpoint inhibitors alone or need to combine them with chemotherapy. The new compound pathomic-radiomic biomarkers will address unmet clinical needs in thoracic oncology by helping guide therapeutic decision making, ushering in a new era of precision immunooncology. The success of this project wil...

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This project aims to apply machine learning to routinely collected pathology and radiology images and help guide the frontline immune checkpoint inhibitor regimens selection for patients with metastatic non-small cell lung cancer (mNSCLC). The proposed studies will develop and test new pathomic-radiomic algorithms in a multi-center setting to predict which patients are likely to respond to immune checkpoint inhibitors alone or need to combine them with chemotherapy. The new compound pathomic-radiomic biomarkers will address unmet clinical needs in thoracic oncology by helping guide therapeutic decision making, ushering in a new era of precision immunooncology. The success of this project will enable the multidisciplinary team of providers to make a more reliable selection of metastatic lung cancer patients who may respond to and benefit from chemo-immune combination therapy based on routine radiographic and pathological images. The strength of this proposal is our ability to treat subjects, obtain and analyze high-quality radiographic and pathological images, and rapidly evaluate biomarkers by correlating to clinical outcomes in multi-center datasets. As routine radiologic and histologic images are readily available from nearly all patients, these radiopathomics markers will be much less impacted by insufficient biopsy samples or the long turnaround time and expense of next-generation sequencing approaches (e.g., TMB) and immune testing. Furthermore, the integrated imaging, histopathological, and immunogenomic analyses will provide new insight into the underlying biology of response to different immunotherapy regimens, potentially pinpoint novel targets for the development of future therapeutic strategies and streamline the next generation of immunotherapy clinical trials.

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