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CIALAB cervical cancer research to be published in the Journal of Digital Imaging

Jeff Prescott, an MD/PhD student in the lab, has had his research accepted from publication in JDI. The article, entitled, "Temporal Analysis of Tumor Heterogeneity and Volume for Cervical Cancer Treatment Outcome Prediction: Preliminary Evaluation," explores the efficacy of using wavelet coefficient measures of tumor perfusion on dynamic contrast-enhanced MR images of cervical tumors to predict the outcome of radiation treatment of the tumors. Subscribers to JDI can access the full text online at http://dx.doi.org/10.1007/s10278-009-9179-7. The publication abstract appears below.

Temporal Analysis of Tumor Heterogeneity and Volume for Cervical Cancer Treatment Outcome Prediction: Preliminary Evaluation

In this paper we present a method of quantifying the heterogeneity of cervical cancer tumors for use in radiation treatment outcome prediction. Features based on the distribution of masked wavelet decomposition coefficients in the tumor region of interest (ROI) of temporal dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) studies were used along with the imaged tumor volume to assess the response of the tumors to treatment. The wavelet decomposition combined with ROI masking was used to extract local intensity variations in the tumor. The developed method was tested on a data set consisting of 23 patients with advanced cervical cancer who underwent radiation therapy; 18 of these patients had local control of the tumor and five had local recurrence. Each patient participated in two DCE-MRI studies: one prior to treatment, and another early into treatment (2 - 4 weeks). An outcome of local control or local recurrence of the tumor was assigned to each patient based on a post-therapy follow-up at least 2 years after the end of treatment. Three different supervised classifiers were trained on combinational subsets of the full wavelet and volume feature set. The best performing linear discriminant analysis (LDA) and support vector machine (SVM) classifiers each had mean prediction accuracies of 95.7%, with the LDA classifier being more sensitive (100% vs. 80%) and the SVM classifier being more specific (100% vs. 94.4%) in those cases. The K-nearest neighbor classifier performed the best out of all three classifiers, having multiple feature sets that were used to achieve 100% prediction accuracy. The use of distribution measures of the masked wavelet coefficients as features resulted in much better predictive performance than those of previous approaches based on tumor intensity values and their distributions or tumor volume alone.

Keywords: cervical cancer, treatment outcome prediction, dynamic contrast enhanced MRI, wavelet.
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