Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
Cancer recurrence times from a branching process model
by
Antal, Tibor
, Avanzini, Stefano
in
Analysis
/ Branching (mathematics)
/ Breast
/ Cancer
/ Cancer metastasis
/ Cancer recurrence
/ Colorectal cancer
/ Drug resistance
/ Forecasting - methods
/ Health aspects
/ Humans
/ Lung cancer
/ Markov processes
/ Medical prognosis
/ Medicine and Health Sciences
/ Metastases
/ Metastasis
/ Models, Theoretical
/ Mortality
/ Mutation
/ Neoplasm Metastasis - physiopathology
/ Neoplasm Recurrence, Local - physiopathology
/ Neoplasms - metabolism
/ Parameter estimation
/ Population
/ Probability distribution
/ Prostate
/ Prostate cancer
/ Recurrence (Disease)
/ Stochastic models
/ Surgery
/ Survival analysis
/ Tumors
2019
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Cancer recurrence times from a branching process model
by
Antal, Tibor
, Avanzini, Stefano
in
Analysis
/ Branching (mathematics)
/ Breast
/ Cancer
/ Cancer metastasis
/ Cancer recurrence
/ Colorectal cancer
/ Drug resistance
/ Forecasting - methods
/ Health aspects
/ Humans
/ Lung cancer
/ Markov processes
/ Medical prognosis
/ Medicine and Health Sciences
/ Metastases
/ Metastasis
/ Models, Theoretical
/ Mortality
/ Mutation
/ Neoplasm Metastasis - physiopathology
/ Neoplasm Recurrence, Local - physiopathology
/ Neoplasms - metabolism
/ Parameter estimation
/ Population
/ Probability distribution
/ Prostate
/ Prostate cancer
/ Recurrence (Disease)
/ Stochastic models
/ Surgery
/ Survival analysis
/ Tumors
2019
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Cancer recurrence times from a branching process model
by
Antal, Tibor
, Avanzini, Stefano
in
Analysis
/ Branching (mathematics)
/ Breast
/ Cancer
/ Cancer metastasis
/ Cancer recurrence
/ Colorectal cancer
/ Drug resistance
/ Forecasting - methods
/ Health aspects
/ Humans
/ Lung cancer
/ Markov processes
/ Medical prognosis
/ Medicine and Health Sciences
/ Metastases
/ Metastasis
/ Models, Theoretical
/ Mortality
/ Mutation
/ Neoplasm Metastasis - physiopathology
/ Neoplasm Recurrence, Local - physiopathology
/ Neoplasms - metabolism
/ Parameter estimation
/ Population
/ Probability distribution
/ Prostate
/ Prostate cancer
/ Recurrence (Disease)
/ Stochastic models
/ Surgery
/ Survival analysis
/ Tumors
2019
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Cancer recurrence times from a branching process model
2019
Request Book From Autostore
and Choose the Collection Method
Overview
As cancer advances, cells often spread from the primary tumor to other parts of the body and form metastases. This is the main cause of cancer related mortality. Here we investigate a conceptually simple model of metastasis formation where metastatic lesions are initiated at a rate which depends on the size of the primary tumor. The evolution of each metastasis is described as an independent branching process. We assume that the primary tumor is resected at a given size and study the earliest time at which any metastasis reaches a minimal detectable size. The parameters of our model are estimated independently for breast, colorectal, headneck, lung and prostate cancers. We use these estimates to compare predictions from our model with values reported in clinical literature. For some cancer types, we find a remarkably wide range of resection sizes such that metastases are very likely to be present, but none of them are detectable. Our model predicts that only very early resections can prevent recurrence, and that small delays in the time of surgery can significantly increase the recurrence probability.
Publisher
Public Library of Science,Public Library of Science (PLoS)
This website uses cookies to ensure you get the best experience on our website.