Within the next five years, most radiotherapy treatment schedules will be designed using spatiotemporal optimization

Kim, Min-Sun; Craft, David; Orton, Colin G.

doi:10.1118/1.4943383

Cited by 5 publications

(7 citation statements)

References 24 publications

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“…It is worthwhile to contrast formulation (P 0 t ) with the optimal stopping approach that we had originally envisioned in Dr Kim's doctoral dissertation (Kim 2010). In that formulation, the treatment planner (at the beginning of each session) decided whether or not to stop treatment.…”

Section: Discussionmentioning

confidence: 99%

“…The mathematical notations in this paper are standard in the literature on spatiobiologically integrated optimization (Kim 2010, Kim and Phillips 2016, Ajdari and Ghate 2016a, 2016b, Saberian et al 2016. It is redefined here for completeness.…”

Section: Problem Formulation and Solution Methodsmentioning

confidence: 99%

“…Another recent direction of theoretical research on the spatiobiologically integrated side focuses on adaptive planning of fluence-maps (Kim 2010, Ghate 2011, Kim and Phillips 2016, Saberian et al 2016. Unlike the traditional static-deterministic approach of adhering to a fluence-map that was calculated at the beginning of the treatment course, adaptive methods acknowledge that there is uncertainty in how tumors respond to radiation, and propose to recompute fluence-maps based on the observed evolution of the tumor's biological condition over multiple treatment sessions.…”

Section: Introductionmentioning

confidence: 99%

“…The primary research objective of this paper is to test the hypothesis that adaptive treatment-length planning, when combined with adaptive fluence-map planning, decreases the TNTCR as compared to only using adaptive fluence-map planning. We test this hypothesis by (1) developing a spatiobiolgically integrated optimization form ulation that attempts to minimize the TNTCR by adaptively selecting both the number of treatment sessions and the fluence-map, and (2) comparing the resulting TNTCR via computer simulations against an existing formulation from Kim (2010) and Kim and Phillips (2016) that also minimizes TNTCR by adaptively selecting only the fluence-map. These computer simulations are conducted on five well-studied phantom headand-neck cancer cases from (Saberian et al 2015a), Saberian et al (2016) and Saberian et al (2017), each with three different tumor proliferation rates.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive treatment-length optimization in spatiobiologically integrated radiotherapy

2018

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Recent theoretical research on spatiobiologically integrated radiotherapy has focused on optimization models that adapt fluence-maps to the evolution of tumor state, for example, cell densities, as observed in quantitative functional images acquired over the treatment course. We propose an optimization model that adapts the length of the treatment course as well as the fluence-maps to such imaged tumor state. Specifically, after observing the tumor cell densities at the beginning of a session, the treatment planner solves a group of convex optimization problems to determine an optimal number of remaining treatment sessions, and a corresponding optimal fluence-map for each of these sessions. The objective is to minimize the total number of tumor cells remaining (TNTCR) at the end of this proposed treatment course, subject to upper limits on the biologically effective dose delivered to the organs-at-risk. This fluence-map is administered in future sessions until the next image is available, and then the number of sessions and the fluence-map are re-optimized based on the latest cell density information. We demonstrate via computer simulations on five head-and-neck test cases that such adaptive treatment-length and fluence-map planning reduces the TNTCR and increases the biological effect on the tumor while employing shorter treatment courses, as compared to only adapting fluence-maps and using a pre-determined treatment course length based on one-size-fits-all guidelines.

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Section: Discussionmentioning

confidence: 99%

Section: Problem Formulation and Solution Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive treatment-length optimization in spatiobiologically integrated radiotherapy

2018

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“…An improved understanding of what drives poor outcomes in patients with factors like large tumors and co-morbid illness is needed. If RT delivery is considered in isolation, measures such as the optimization of fractionation schedules for a given patient, or spatiotemporal optimization of radiation dose, are unlikely to result in large improvements in outcomes [120]. …”

Section: Introductionmentioning

confidence: 99%

A critical review of recent developments in radiotherapy for non-small cell lung cancer

Baker¹,

et al. 2016

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Lung cancer is the leading cause of cancer mortality, and radiotherapy plays a key role in both curative and palliative treatments for this disease. Recent advances include stereotactic ablative radiotherapy (SABR), which is now established as a curative-intent treatment option for patients with peripheral early-stage NSCLC who are medically inoperable, or at high risk for surgical complications. Improved delivery techniques have facilitated studies evaluating the role of SABR in oligometastatic NSCLC, and encouraged the use of high-technology radiotherapy in some palliative settings. Although outcomes in locally advanced NSCLC remain disappointing for many patients, future progress may come about from an improved understanding of disease biology and the development of radiotherapy approaches that further reduce normal tissue irradiation. At the moment, the benefits, if any, of radiotherapy technologies such as proton beam therapy remain unproven. This paper provides a critical review of selected aspects of modern radiotherapy for lung cancer, highlights the current limitations in our understanding and treatment approaches, and discuss future treatment strategies for NSCLC.

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Mathematical Modeling in Radiation Oncology

Rockne

Frankel

2017

Cancer Treatment and Research

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The goal of precision medicine is to tailor treatments to the individual patient's disease. In radiation oncology, this means tailoring the dose to the boundaries of the tumor, but also to the unique biology of the patient's disease. In recent years, mathematical modeling has made inroads toward achieving these goals, through the optimization of radiation dose based on radiobiological parameters for individual patients. In this chapter, we review recent literature of mathematical models of tumor growth and response to radiation therapy (RT) and discuss the clinical utility of mathematical models, as well as provide a forward-looking perspective into how mathematical models may enhance patient outcomes through well-designed clinical trials.

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Within the next five years, most radiotherapy treatment schedules will be designed using spatiotemporal optimization

Cited by 5 publications

References 24 publications

Adaptive treatment-length optimization in spatiobiologically integrated radiotherapy

Adaptive treatment-length optimization in spatiobiologically integrated radiotherapy

A critical review of recent developments in radiotherapy for non-small cell lung cancer

Mathematical Modeling in Radiation Oncology

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