Abstract
This study aimed to develop an automated model to extract temporal features from DCE-MRI in head-and-neck (HN) cancers to localize significant tumor subvolumes having low blood volume (LBV) for predicting local and regional failure after chemoradiation therapy. Temporal features were extracted from time-intensity curves to build classification model for differentiating voxels with LBV from those with high BV. Support vector machine (SVM) classification was trained on the extracted features for voxel classification. Subvolumes with LBV were then assembled from the classified voxels with LBV. The model was trained and validated on independent datasets created from 456 873 DCE curves. The resultant subvolumes were compared to ones derived by a 2-step method via pharmacokinetic modeling of blood volume, and evaluated for classification accuracy and volumetric similarity by DSC. The proposed model achieved an average voxel-level classification accuracy and DSC of 82% and 0.72, respectively. Also, the model showed tolerance on different acquisition parameters of DCE-MRI. The model could be directly used for outcome prediction and therapy assessment in radiation therapy of HN cancers, or even supporting boost target definition in adaptive clinical trials with further validation. The model is fully automatable, extendable, and scalable to extract temporal features of DCE-MRI in other tumors.
Highlights
Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) (DCE-MRI; Table 1) has been widely explored and applied in clinical studies for diagnosis, treatment planning, and monitoring therapy response of diseases [1], in cancers [2, 3]
Our results showed that the new approach reached high voxel classification accuracy of the tumor with low blood volume (LBV), which has the potential to automatically analyze the DCE data and create significant tumor behavior metrics for supporting adaptive RT in advanced HN cancers
To compute the Dice similarity coefficient (DSC), the contiguous voxels classified as LBV but smaller than 1 cc were excluded, which was a criterion used in a clinical trial
Summary
Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) (DCE-MRI; Table 1) has been widely explored and applied in clinical studies for diagnosis, treatment planning, and monitoring therapy response of diseases [1], in cancers [2, 3]. The conventional analysis of DCE data is to quantify kinetic parameters such as perfusion, microvascular volume, vessel permeability, and volume of the extravascular extracellular space by fitting the data to a pharmacokinetic (PK) model (eg, Tofts model) [1, 2, 4]. To further use this technique for cancer prognosis and therapy monitoring, a 2-step analysis is often applied, in which a metric(s) is extracted from physiological parametric maps and modeled for prediction of a clinical endpoint of interest. These 2-step processes are timeconsuming for processing a large amount of patient data and supporting real-time clinical decision-making
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