DMH-Based Plan Evaluation and Inverse Optimization in Radiotherapy

The hypotheses of the study are as follows: Mass-based inverse optimization in radiotherapy treatment planning will result in a reduction of normal tissue and organs at risk (OAR) doses for desired prescription therapeutic doses to the targets. Dose-mass histograms (DMHs) may be more relevant to radiotherapy treatment planning and treatment plan assessment than the standard of care, realized through dose-volume histograms (DVHs)

Cancer patients continue to 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. 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 investigators propose the incorporation of mass and density information explicitly 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. The investigators' 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, which may result in an increase in estimated loco-regional tumor control probability. To test the study hypothesis, the investigators will pursue the following specific aims: (1) Develop the theoretical and computational framework of the DMH-based IMRT optimization. This framework will incorporate 3D and 4D IMRT as well as 3D volumetric modulated arc (VMAT) planning for different anatomical sites. (2) Investigate different parametric forms for DMH-optimization functions. The ultimate goal would be the simultaneous minimization of healthy tissue doses and/or escalation of therapeutic doses, without violating the established dosimetric tolerances for healthy anatomical structures. (3) Practical implementation and application of this novel optimization paradigm, where virtual clinical trials for cohorts of lung, head-and-neck, and prostate cancer cases will be performed. Statistical significance of the DMH-optimization dosimetric improvements over standard of care DVH-optimization will be quantified. Prospective 3D and 4D CT data collection will be used to study the interactions between tumor time-trending changes and DMH-based optimization results. 4D CT data will also be used to investigate and quantify the correlation between DMH-based end points and the loss of pulmonary function during and after radiotherapy treatment. The deliverability (with the existing radiotherapy treatment equipment) of the investigators' 3D VMAT and 3D/4D IMRT plans will be experimentally verified, thereby paving the road for initiation of clinical trials.

Study participants being treated according to the standard of care with intensity modulated radiotherapy (IMRT). Several CT scans will be performed for each enrolled subject: one before the radiotherapy course for patient treatment planning purposes (as part of the standard of care), one during the radiotherapy treatment course (between fraction 10 and 20), and one at follow up visit or at least 6 weeks post-radiotherapy treatment (whichever comes first).

The study is computational in nature. A new treatment planning paradigm is proposed, where from the newly proposed treatment plans, and the treatment plans generated with the standard of care, radiation doses to different organs and tissues would be derived. Radiotherapy toxicity (to healthy human tissue) is proportional to radiation dose - more radiation dose results in higher toxicity. Thereby, if radiation dose is decreased, the toxicity would also be decreased. The dosimetric differences which the investigators observe between the standard of care and their novel optimization approach are reported as percent change with respect to the standard of care.

Inclusion Criteria: Patients must have histologically confirmed head-and-neck, lung, or prostate tumors. Patients who will be treated with radiation therapy or concurrent chemoradiation therapy. Gross Tumor Volume (GTV) or resection cavity must be visible on CT such that it can be delineated as a target for radiotherapy. Patients who are able to understand the investigational nature of this study and agree to sign a written informed consent document. Exclusion Criteria: Pregnant or nursing women will not participate. Women of reproductive potential must be offered a pre-treatment pregnancy test and informed of the need to practice an effective contraceptive method during the therapy. Patients younger than 18 years. Patients whose size and weight would not allow CT scanning. No vulnerable populations (fetuses, pregnant women, children, prisoners) will be included in this study.