Proliferation Saturation Index Predicts Oropharyngeal Squamous Cell Cancer Gross Tumor Volume Reduction to Prospectively Identify Patients for Adaptive Radiation Therapy

Lewin, Thomas D.; Kim, J.; Latifi, Kujtim; Poleszczuk, Jan; Bull, Joshua A.; Byrne, Helen; Torres-Roca, Javier F.; Moros, Eduardo G.; Gatenby, Robert A.; Harrison, Louis B.; Heukelom, J.; Mohamed, A.S.R.; Rosenthal, D.I.; Fuller, C.D.; Caudell, Jimmy J.; Enderling, Heiko

doi:10.1016/j.ijrobp.2015.12.116

Cited by 4 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For demonstration purpose we show that these different dynamic responses can be observed in oropharyngeal cancer patients with comparable stage and size at beginning of radiotherapy (43.7 cm 3 , 48.5 cm 3 , 63.3 cm 3 and 48.7 cm 3 , figure 3). The data shown is from a cohort of 51 oropharyngeal cancer patients comprised of 32 who were treated at Moffitt Cancer Center, Florida and 19 at MD Anderson, Texas (Lewin et al 2016). Each patient received the standard fractionation protocol with 2 Gy fractions administered daily Monday-Friday.…”

Section: Tumour Dynamics In Response To Fractionated Radiotherapymentioning

confidence: 99%

“…Mathematical modelling of radiotherapy may be used to provide a better understanding of the factors which are important in determining tumour response dynamics. There are a number of approaches that may be taken to model radiotherapy response, including compartmental ordinary differential equation (ODE) models (Sachs et al 2001, Chvetsov et al 2009, Wang and Feng 2013, Chvetsov 2013, Chvetsov et al 2014, Prokopiou et al 2015, Tariq et al 2016, continuum partial differential equation (PDE) models (Rockne et al 2009, Rockne et al 2015, Lewin et al 2018, computational agent-based models (Richard et al 2007, Powathil et al 2013, 2016, Alfonso et al 2014 and probabilistic approaches (Zaider and Minerbo 2000, Hanin 2004, O'Rourke et al 2009, Zaider and Hanin 2011, Gong et al 2013, Bobadilla et al 2018. The simplest formulations view the tumour volume as a single, homogeneous compartment (Wheldon et al 1977, Sachs et al 2001, McAneney and O'Rourke 2007, Prokopiou et al 2015, Poleszczuk et al 2018 and are often targeted towards clinical application due to the limited data typically available for model calibration (Wang et al 2009, Stevens et al 2010, Sharma et al 2016.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The importance of dead material within a tumour on the dynamics in response to radiotherapy

et al. 2020

View full text Add to dashboard Cite

In vivo tumours are highly heterogeneous, often comprising regions of hypoxia and necrosis. Radiotherapy significantly alters the intratumoural composition. Moreover, radiation-induced cell death may occur via a number of different mechanisms that act over different timescales. Dead material may therefore occupy a significant portion of the tumour volume for some time after irradiation and may affect the subsequent tumour dynamics. We present a three phase tumour growth model that accounts for the effects of radiotherapy and use it to investigate how dead material within the tumour may affect the spatio-temporal tumour response dynamics. We use numerical simulation of the model equations to characterise qualitatively different tumour volume dynamics in response to fractionated radiotherapy. We demonstrate examples, and associated parameter values, for which the properties of the dead material significantly alter the observed tumour volume dynamics throughout treatment. These simulations suggest that for some cases it may not be possible to accurately predict radiotherapy response from pre-treatment, gross tumour volume measurements without consideration of the dead material within the tumour.

show abstract

Section: Tumour Dynamics In Response To Fractionated Radiotherapymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The importance of dead material within a tumour on the dynamics in response to radiotherapy

et al. 2020

View full text Add to dashboard Cite

show abstract

“…While data repositories collected from cohorts of subjects are available, existing advanced risk prediction models capture in relatively crude fashion oncological outcomes through the use of standard regression-based models [4], [5]. These models do not handle incomplete demographic or laboratory data.…”

Section: Background and Related Workmentioning

confidence: 99%

Precision Risk Analysis of Cancer Therapy with Interactive Nomograms and Survival Plots

Marai

Burks

et al. 2019

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

We present the design and evaluation of an integrated problem solving environment for cancer therapy analysis. The environment intertwines a statistical martingale model and a K Nearest Neighbor approach with visual encodings, including novel interactive nomograms, in order to compute and explain a patient's probability of survival as a function of similar patient results. A coordinated views paradigm enables exploration of the multivariate, heterogeneous and few-valued data from a large head and neck cancer repository. A visual scaffolding approach further enables users to build from familiar representations to unfamiliar ones. Evaluation with domain experts show how this visualization approach and set of streamlined workflows enable the systematic and precise analysis of a patient prognosis in the context of cohorts of similar patients. We describe the design lessons learned from this successful, multi-site remote collaboration.

show abstract

“…While promising, this study was limited by the lack of patient data. Further work on this concept has been encouraging in predicting treatment early response in patients with oropharyngeal carcinoma [37] however, clinical evaluation is necessary to ascertain the potential benefit of this concept in alternative fractionation schedules. The most recent work published on this concept highlights the dependence of the model of tumour growth law utilised for the calculations [38].…”

Section: Hypoxia Targetingmentioning

confidence: 99%

An overview on personalisation of radiotherapy prescriptions in locally advanced non-small cell lung cancer: Are we there yet?

Barrett

Hanna

Marignol

2018

Radiotherapy and Oncology

View full text Add to dashboard Cite

Standard of care radiotherapy in LA-NSCLC is 60-66 Gy in 30-33 fractions. However outcomes for these patients are poor with 5-year survival in the range of 10-20%. Randomised controlled trials have shown that dose escalation in a linear fashion does not improve outcomes for all patients, thus there is a need to tailor the prescription to the individual patient. This review assesses the strategies published to personalise the radiation therapy dose prescription in LA-NSCLC. A systematic and scoping search of the literature was performed to identify studies that met the inclusion criteria. 19 relevant studies were identified ranging from prospective clinical trials to mathematically modelled concept studies. Heterogeneity existed between all clinical studies. Nine heterogeneous publications proposed methodology to adapt the dose prescription to the individual patient. A number of encouraging strategies have been identified but fall short of the evidence level required to influence clinical practice.

show abstract

Proliferation Saturation Index Predicts Oropharyngeal Squamous Cell Cancer Gross Tumor Volume Reduction to Prospectively Identify Patients for Adaptive Radiation Therapy

Abstract: tumor board checklist with review of all newly diagnosed head and neck patients prior to and after implementation.

Cited by 4 publications

References 0 publications

The importance of dead material within a tumour on the dynamics in response to radiotherapy

The importance of dead material within a tumour on the dynamics in response to radiotherapy

Precision Risk Analysis of Cancer Therapy with Interactive Nomograms and Survival Plots

An overview on personalisation of radiotherapy prescriptions in locally advanced non-small cell lung cancer: Are we there yet?

Contact Info

Product

Resources

About