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Jyoti Mayadev Lab


The Mayadev Lab focuses on enhancing the cure through scientific discovery of locally advanced cervical cancer through translational science and clinical trials. The Mayadev lab has a passion to understand the mechanisms of therapeutic response and tumor resistance in cervical cancer. Her lab works on investigations of developmental therapeutic interventions, neoadjuvant strategies with chemoradiation, brachytherapy (internal radiation) optimization, and decreasing disparities in cervical cancer.

The mission of the Mayadev laboratory is to use our clinical experience with locally advanced cervical cancer to better understand and shed light on complex problems. Questions that drive our research include: “Why do certain patients recur with cancer and why do some experience toxicity from our treatment? How can be increase the cure rate for patients with large tumors or those with lymph node spread?” We then use all of our available scientific tools, technology, innovation, team science, and knowledge to answer these questions. Everything we do focuses on this strategy; we are sincerely dedicated to cervical cancer innovation.​

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FOCUSED PROJECTS AND GRANTS

FOCUSED PROJECTS AND GRANTS


                         
 

​​​CLINICAL TRIALS

CLINICAL TRIALS

                               
 

INDUSTRY CLINICAL TRIAL CONSULTING : CONTACTS

INDUSTRY CLINICAL TRIAL CONSULTING : CONTACTS

                                     
 

MEDIA

MEDIA


                         
 

Patient and Provider Resources

Patient and Provider Resources

                                
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DR. MEYERS’ RESEARCH IS SUPPORTED IN PART BY A 5-YEAR, $1.2M K08 GRANT FROM THE NATIONAL CANCER INSTITUTE OF THE NATIONAL INSTITUTE OF HEALTH​

Featured Articles

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Automated treatment planning framework for brachytherapy of cervical cancer

We describe an optimization framework to convert brachytherapy dose distributions directly into dwell times.

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Knowledge-based dose prediction models to inform gynecologic brachytherapy needle supplementation

Our knowledge-based dose prediction model accurately identified cases that could have met constraints without needle supplementation, suggesting that such models may be beneficial for applicator selection.​

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A knowledge-based organ dose prediction tool for brachytherapy treatment planning of patients with cervical cancer

A simple boundary distance-driven knowledge-based DVH estimation exhibited promising results in predicting critical brachytherapy dose metrics.​

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