Intracellular Doppler spectroscopy and deep learning in clinical trials to personalize cancer chemotherapy

David Nolte; Ran An; John Turek

doi:10.1117/12.2616035

9 December 2021 Intracellular Doppler spectroscopy and deep learning in clinical trials to personalize cancer chemotherapy

David Nolte, Ran An, John Turek

Author Affiliations +

Proceedings Volume 11924, Optical Coherence Imaging Techniques and Imaging in Scattering Media IV; 119240I (2021) https://doi.org/10.1117/12.2616035
Event: European Conferences on Biomedical Optics, 2021, Online Only

Abstract

Clinical trials of coherence-gated Doppler infrared spectroscopy of intracellular dynamics in living tumor tissue seek to identify the efficacy of prescribed chemotherapy for cancer patients. Changes in intracellular dynamics have specific Doppler signatures that depend on the applied cancer drugs and the sensitivity of the patient to treatment. A challenging feature of these assays is a strong intra-tumor heterogeneity that poses a significant challenge to machine-learning classifiers. We train a Twin Deep Network (TDN) to identify these signatures in the presence of strong heterogeneous background to accurately predict patient response to therapy. The TDN is being applied to two ongoing clinical trials: a clinical trial of HER2neg breast-cancer patients, and esophageal cancer patients, all undergoing neoadjuvant therapy or chemoradiation therapy, respectively. This work provides insight into the value of Deep Learning for advanced data analytics as the volume and variety of data from optics-based assays grows

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David Nolte, Ran An, and John Turek "Intracellular Doppler spectroscopy and deep learning in clinical trials to personalize cancer chemotherapy", Proc. SPIE 11924, Optical Coherence Imaging Techniques and Imaging in Scattering Media IV, 119240I (9 December 2021); https://doi.org/10.1117/12.2616035

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KEYWORDS

Cancer

Clinical trials

Doppler effect

Tumors

Biopsy

Digital holography

Imaging systems

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