Research Focus

The Machine Learning Department and its members conduct a wide array of research involving the development and application of artificial intelligence (AI), machine and deep learning (ML/DL) algorithms to translational cancer research and discovery that include quantitative image analysis (radiomics), digital pathology (pathomics), cancer diagnosis and prognosis, information retrieval and data integration, outcomes and behavioral sciences, molecular and computational biology. This is in addition to basic ML/DL research in pertinent areas to oncology such as visual analytics and explainable AI, automated ML/DL, and information-theoretic approaches.

Research projects include: 

Optimal Decision Making in Radiotherapy Using Panomics Analytics

Funding resource: NIH/NCI R01 CA233487

The long-term goal of this project is to overcome barriers related to prediction uncertainties and human-computer interactions, which are currently limiting the ability to make personalized clinical decisions for real-time response-based adaptation in radiotherapy from available data.

Patient Knowledge and Response graphic

Combined Radiation Acoustics and Ultrasound Imaging for Real-Time Guidance in Radiotherapy

Funding resource: NIH/NCI R37 CA222215

To develop an evaluated and integrated tomographic feedback system that uses X-ray acoustics (XACT) and advanced ultrasound (US) images to monitor a patient’s present status during radiotherapy delivery.

Radiation Acoustics Linac Machine graphic

Cerenkov Multi-Spectral Imaging (CMSI) for Adaptation and Real-Time Imaging in Radiotherapy

Funding resource: NIH/NCI R41 CA243722

Sight of Radiation and Sound of Radiation graphicSponsor: Endectra LLC

In this STTR proposal, Endectra will work with oncology researchers at Moffitt to develop and evaluate a novel Cerenkov Multi-Spectral Imaging (CMSI) technique using new solid-state on-body probes to conduct routine optical measurements of radiation dose and molecular imaging during cancer radiotherapy delivery. This approach is expected to provide more accurate tumor physiological representation and dose adaptation during treatment, reduce overall patient exposure to radiation, and allow for ongoing assessment of tumor physiological parameters. If successful, Endectra will develop CMSI as an alternative cost-saving and effective molecular imaging/targeting modality for routine radiotherapy applications, greatly improving radiotherapy outcomes and yielding a major impact on public health.

Read more about these research projects on our Lab page