PURPOSE To assess the efficacy and toxicity of sunitinib monotherapy in palliative squamous cell carcinoma of the head and neck (SCCHN). PATIENTS AND METHODS Thirty-eight patients with SCCHN having evidence of progressive disease (PD) were treated with sunitinib 37.5 mg/d given continuously until PD or unacceptable toxicity. The primary end point was the rate of disease control, defined as stable disease (SD) or partial response (PR) at 6 to 8 weeks after treatment initiation (two-stage design, Simon). Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was performed in a subset of patients before and 6 to 8 weeks after treatment. The volume transfer constant of the contrast agent (K(trans)) was used to measure changes in the microcirculation blood flow and endothelial permeability of the tumor. Results A PR was observed in one patient, SD in 18, and PD in 19 (Response Evaluation Criteria in Solid Tumors [RECIST]), resulting in a disease control rate of 50%. Among the 18 patients with SD, there were five unconfirmed PRs and six additional minor responses. A significant decrease in K(trans) was seen in three of the four patients who received DCE-MRI monitoring. Grade 5 head and neck bleeds occurred in four patients. Local complications, including the appearance or worsening of tumor skin ulceration or tumor fistula, were recorded in 15 patients. CONCLUSION Sunitinib demonstrated modest activity in palliative SSCHN. The severity of some of the complications highlights the importance of improved patient selection for future studies with sunitinib in head and neck cancer. Sunitinib should not be used outside clinical trials in SSCHN.

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Brain blood barrier breakdown as assessed by contrast-enhanced (CE) T1-weighted MR imaging is currently the standard radiological marker of inflammatory activity in multiple sclerosis (MS) patients. Our objective was to evaluate the performance of an alternative model assessing the inflammatory activity of MS lesions by texture analysis of T2-weighted MR images. Twenty-one patients with definite MS were examined on the same 3.0T MR system by T2-weighted, FLAIR, diffusion-weighted and CE-T1 sequences. Lesions and mirrored contralateral areas within the normal appearing white matter (NAWM) were characterized by texture parameters computed from the gray level co-occurrence and run length matrices, and by the apparent diffusion coefficient (ADC). Statistical differences between MS lesions and NAWM were analyzed. ROC analysis and leave-one-out cross-validation were performed to evaluate the performance of individual parameters, and multi-parametric models using linear discriminant analysis (LDA), partial least squares (PLS) and logistic regression (LR) in the identification of CE lesions. ADC and all but one texture parameter were significantly different within white matter lesions compared to within NAWM (p < 0.0167). Using LDA, an 8-texture parameter model identified CE lesions with a sensitivity Se = 70% and a specificity Sp = 76%. Using LR, a 10-texture parameter model performed better with Se = 86% / Sp = 84%. Using PLS, a 6-texture parameter model achieved the highest accuracy with Se = 88% / Sp = 81%. Texture parameter from T2-weighted images can assess brain inflammatory activity with sufficient accuracy to be considered as a potential alternative to enhancement on CE T1-weighted images.