14 research outputs found
Cancer recurrence times from a branching process model
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.Comment: 26 pages, 9 figures, 4 table
Comparison between software volumetric breast density estimates in breast tomosynthesis and digital mammography images in a large public screening cohort
Inter-laboratory comparison of channelized hotelling observer computation.
The task-based assessment of image quality using model observers is increasingly used for the assessment of different imaging modalities. However, the performance computation of model observers needs standardization as well as a well-established trust in its implementation methodology and uncertainty estimation. The purpose of this work was to determine the degree of equivalence of the channelized Hotelling observer performance and uncertainty estimation using an intercomparison exercise.
Image samples to estimate model observer performance for detection tasks were generated from two-dimensional CT image slices of a uniform water phantom. A common set of images was sent to participating laboratories to perform and document the following tasks: (a) estimate the detectability index of a well-defined CHO and its uncertainty in three conditions involving different sized targets all at the same dose, and (b) apply this CHO to an image set where ground truth was unknown to participants (lower image dose). In addition, and on an optional basis, we asked the participating laboratories to (c) estimate the performance of real human observers from a psychophysical experiment of their choice. Each of the 13 participating laboratories was confidentially assigned a participant number and image sets could be downloaded through a secure server. Results were distributed with each participant recognizable by its number and then each laboratory was able to modify their results with justification as model observer calculation are not yet a routine and potentially error prone.
Detectability index increased with signal size for all participants and was very consistent for 6 mm sized target while showing higher variability for 8 and 10 mm sized target. There was one order of magnitude between the lowest and the largest uncertainty estimation.
This intercomparison helped define the state of the art of model observer performance computation and with thirteen participants, reflects openness and trust within the medical imaging community. The performance of a CHO with explicitly defined channels and a relatively large number of test images was consistently estimated by all participants. In contrast, the paper demonstrates that there is no agreement on estimating the variance of detectability in the training and testing setting