In the literature, both a "hot-spot" and "whole-tumor method for ROI placement are reported [ 36].
The following data items were recorded from each study: first author, year of publication, time of follow-up, region, number of patients with positive and negative tumors, method of detection, positive rate, results of univariate and multivariate survival analyses, HRs and 95%% CIs.
To assess the performance of RAE, we compared its results with those of a recently developed method on two large studies totaling 512 solid tumors (Methods S1).
An example of an alternative tumor eradication method is SDT.
Therefore, the development of reproducible and automatic tumor segmentation method is needed.
In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs).
The purpose of this study was to develop and assess the performance of a tumor tracking method designed for application in radiation therapy.
Our study demonstrates that single value threshold tumor contouring method using 18F-FDG-PET can reliably delineate the viable portion of a tumor, as illustrated by a mean AUC of 0.934 (CI of 0.911 0.954) (Fig. 5).
The purpose of our study was to design a validation system for FDG-PET tumor contouring method using corresponding cross-sectional pathology based on frozen tumor sections as the gold standard.
We propose to take into account the predicted information, which is used in combination with the random walks (RW) algorithm, to develop an automatic tumor segmentation method on PET images.
The most widely used tumor ablation method is thermal ablation.
