Monday, December 30, 2013

Minimizing Metastatic Risk in Radiotherapy Fractionation Schedules

Minimizing Metastatic Risk in Radiotherapy Fractionation Schedules

Metastasis is the process by which cells from a primary tumor disperse and form new tumors at distant anatomical locations. The treatment and prevention of metastatic cancer remains an extremely challenging problem. In this work, we consider the problem of developing fractionated irradiation schedules that minimize production of metastatic cancer cells. Interestingly we observe that the resulting fractionation schedules are significantly different than those that result from more standard objectives such as minimization of final primary tumor volume. Hypo-fractionation is suggested even in cases when the α/β value of the tumor is large. This work introduces a novel biologically motivated objective function to the radiation optimization community that takes into account metastatic risk instead of the status of the primary tumor.

Friday, December 20, 2013

Tumour Control Probability in Cancer Stem Cells Hypothesis

Tumour Control Probability in Cancer Stem 

Cells Hypothesis

The tumour control probability (TCP) is a formalism derived to compare various treatment regimens of radiation therapy, defined as the probability that given a prescribed dose of radiation, a tumour has been eradicated or controlled. In the traditional view of cancer, all cells share the ability to divide without limit and thus have the potential to generate a malignant tumour. However, an emerging notion is that only a sub-population of cells, the so-called cancer stem cells (CSCs), are responsible for the initiation and maintenance of the tumour. A key implication of the CSC hypothesis is that these cells must be eradicated to achieve cures, thus we define TCP_S as the probability of eradicating CSCs for a given dose of radiation. A cell surface protein expression profile, such as CD44high/CD24low for breast cancer, is often used as a biomarker to monitor CSCs enrichment. However, it is increasingly recognized that not all cells bearing this expression profile are necessarily CSCs, and in particular early generations of progenitor cells may share the same phenotype. Thus, due to the lack of a perfect biomarker for CSCs, we also define a novel measurable TCP_CD+, that is the probability of eliminating or controlling biomarker positive cells. Based on these definitions, we use stochastic methods and numerical simulations to compare the theoretical TCP_S and the measurable TCP_CD+. We also use the measurable TCP to compare the effect of various radiation protocols.


Wednesday, December 18, 2013

Radiotherapy planning for glioblastoma based on a tumor growth model: Improving target volume delineation

Jan Unkelbach, Bjoern H. Menze, Ender Konukoglu, Florian Dittmann, Matthieu Le, Nicholas Ayache, Helen A. Shih

Glioblastoma di ffer from many other tumors in the sense that they grow in filtratively into the brain tissue instead of forming a solid tumor mass with a de fined boundary. Only the part of the tumor with high tumor cell density can be localized through imaging directly. In contrast, brain tissue infi ltrated by tumor cells at low density appears normal on current imaging modalities. In current clinical practice, a uniform margin, typically two centimeters, is applied to account for microscopic spread of disease that is not directly assessable through imaging. The current treatment planning procedure can potentially be improved by accounting for the anisotropy of tumor growth, which arises from di fferent factors: Anatomical barriers such as the falx cerebri represent boundaries for migrating tumor cells. In addition, tumor cells primarily spread in white matter and in lfiltrate gray matter at lower rate. We investigate the use of a phenomenological tumor growth model for treatment planning. The model is based on the Fisher-Kolmogorov equation, which formalizes these growth characteristics and estimates the spatial distribution of tumor cells in normal appearing regions of the brain. The target volume for radiotherapy planning can be defi ned as an isoline of the simulated tumor cell density. This paper analyzes the model with respect to implications for target volume de finition and identifi es its most critical components. A retrospective study involving 10 glioblastoma patients treated at our institution has been performed.

To illustrate the main fi ndings of the study, a detailed case study is presented for a glioblastoma located close to the falx. In this situation, the falx represents a boundary for migrating tumor cells, whereas the corpus callosum provides a route for the tumor to spread to the contralateral hemisphere. We further discuss the sensitivity of the model with respect to the input parameters. Correct segmentation of the brain appears to be the most crucial model input. We conclude that the tumor growth model provides a method to account for anisotropic growth patterns of glioma, and may therefore provide a tool to make target delineation more objective and automated.

Wednesday, December 4, 2013

Inferring causal models of cancer progression with a shrinkage estimator and probability raising

Inferring causal models of cancer progression with a shrinkage estimator and probability raising

Existing techniques to reconstruct tree models of progression for accumulative processes such as cancer, seek to estimate causation by combining correlation and a frequentist notion of temporal priority. In this paper we define a novel theoretical framework to reconstruct such models based on the probabilistic notion of causation defined by Suppes, which differ fundamentally from that based on correlation. We consider a general reconstruction setting complicated by the presence of noise in the data, owing to the intrinsic variability of biological processes as well as experimental or measurement errors. To gain immunity to noise in the reconstruction performance we use a shrinkage estimator. On synthetic data, we show that our approach outperforms the state-of-the-art and, for some real cancer datasets, we highlight biologically significant differences revealed by the reconstructed progressions. Finally, we show that our method is efficient even with a relatively low number of samples and its performance quickly converges to its asymptote as the number of samples increases. Our analysis suggests the applicability of the method on small datasets of real patients.

Tuesday, December 3, 2013

Most viewed abstracts: inception through November

Taking the idea from Haldane's Sieve I've decided to start a monthly 'most viewed' post.  To start though, I'll list the top 5 viewed posts to date, from our first post on June 19th, 2013.

The number one post actually comprises two abstracts:

A recent physical approach to angiogenesis modeling: mixture models

Modeling the Dichotomy of the Immune Response to Cancer: Cytotoxic Effects and Tumor-Promoting Inflammation

A deterministic model for the occurrence and dynamics of multiple mutations in hierarchically organized tissues

Cooperation and competition in the dynamics of tissue architecture during homeostasis and tumorigenesis

Cancer initiation with epistatic interactions between driver and passenger mutations

Please, keep spreading the word, and let us know if you find any preprints that we've missed. With +PeerJ, the +bioRxiv, +F1000 and the +arXiv there are more and more out there!