To develop and investigate a method to identify, from dynamic contrast enhanced (DCE) MRI, significant subvolumes of tumors related to treatment outcomes. A method, called global-initiated regularized local fuzzy clustering, was proposed to identify subvolumes of head-and-neck cancers (HNC) from heterogeneous distributions of tumor blood volume (BV) and blood flow (BF) for assessment of therapy response. BV and BF images, derived from DCE MRI, of 14 patients with advanced HNC were obtained before treatment and 2 weeks after the start of 7-week chemoradiation therapy (chemo-RT). The delineated subvolumes of tumors with low BV or BF before and during treatment were evaluated for their associations with local failure (LF). Receiver operating characteristic (ROC) analysis was used to assess performance of the method for prediction of local failure of HNC. The sizes of the subvolumes of primary tumors with low BV, delineated by our method before and week 2 during treatment, were significantly greater in the patients with LF than with local control (LC) (p = 0.02 for pre-RT and 0.01 for week 2). While the total primary tumor volumes were reduced from baseline to week 2 during therapy to a similar extent for both the patients with LF and LC, the percentage decreases in the subvolumes of the primary tumors with low BV in the same time interval were significantly smaller for the patients with LF than those with LC (p < 0.05). ROC analysis shows that for any given sensitivity, the subvolume of the tumor with low BV week 2 during treatment has greater specificity for prediction of local failure than the pretreatment total tumor volume, the percentage change in the tumor volume week 2 during treatment, or the change in the averaged BV values of the entire tumor week 2 during therapy. We developed a method to identify the significant subvolumes of primary tumors related to local failure. Large poorly perfused subvolumes of primary or nodal HNC before treatment and persisting during the early course of chemo-RT have the potential for prediction of local or regional failure, and could be candidates for local dose intensification.
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