Sylvester Comprehensive Cancer Center

Ivaylo Mihaylov, Ph.D.

Ivaylo Mihaylov, Ph.D.

Assoicate Professor of Radiation Oncology

Description of Research

Dr. Mihaylov’s research focuses on different aspects of radiotherapy treatment plan optimization. Cancer patients represent a challenging disease population, which faces rather poor prognosis with current treatment planning and delivery practices. Venues for a potential dose escalation and/or increased healthy tissue sparing, through innovative therapeutic approaches for those patients, are clearly needed. More specifically, current state of the art radiotherapy treatment planning relies on the dose-volume-histogram (DVH) paradigm, where doses to fractional (most often) or absolute volumes of anatomical structures are employed in both optimization and plan evaluation process. It has been argued however, that the effects of delivered dose seem to be more closely related to healthy tissue toxicity (and thereby to clinical outcomes) when dose-mass histograms (DMHs) are considered in treatment plan evaluation.

The above mentioned research has been funded by NIH. In the proposed research density information is explicitly incorporated into the cost functions of the inverse optimization process, thereby shifting from DVH to DMH treatment planning paradigm. This novel DMH-based intensity modulated radiotherapy (IMRT) optimization aims in minimization of radiation doses to a certain mass, rather than a volume, of healthy tissue. Our working hypothesis is that DMH- optimization will reduce doses to healthy tissue substantially. In certain cases, with extensive, difficult to treat disease, lower doses to healthy tissue can be used for isotoxic dose escalation.

The goal of the proposed research is to pave the road for a novel, dose-mass-histogram (DMH) based, inverse optimization for radiotherapy treatment planning. This novel optimization methodology will be applied for treatment planning for different anatomical sites. Successful development and validation of the proposed research will provide a general framework for generation of radiotherapy treatment plans, with substantially lower doses to healthy tissue, compared to current standard of care, realized through DVH- optimization.


  • Developed new treatment planning framework for radiotherapy inverse optimization.
  • The framework and its tallied observables would be applied to cohorts of patients in virtual clinical trials in order to validate the studies and suggest new treatment planning paradigm for radiotherapy.
  • Extended the framework through more sophisticated optimization functions which can further improve the therapeutic effects of radiation.
  • Those new treatment planning methods would be tailored to different treatment delivery techniques.
  • Selected Cancer-Related Publications

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