Purpose: Our goal was to define tumor and saliva gene methylation profile of head and neck squamous cell carcinoma and to evaluate its prognostic significance and its biomarker potential for early detection of relapse. Experimental Design: We prospectively analyzed 11 genes by methylation-specific PCR on primary tumors, histologically normal adjacent mucosa, and saliva from 90 French patients at diagnosis and during follow-up as well as on 30 saliva specimens from control-matched patients with nonmalignant head and neck pathology. Five additional genes were analyzed on 50 tumors of the series. Results: Methylation ofTIMP3, ECAD, p16, MGMT, DAPK, and RASSF1was the most frequently observed in tumors and paired saliva samples were analyzed at diagnosis, with an excellent agreement between both samples. At least one of these six genes was methylated in >75% of the samples without additional positive samples when other genes were analyzed. Methylation profile was similar in newly diagnosed and second primary cancers. Aberrant methylation was not associated with a worse prognosis. Ninety percent of normal adjacent mucosa and all control saliva samples were negative.Twenty-two patients were followed after treatment; abnormal methylation was detectable in the saliva of five patients few months before clinical and 2-deoxy-2[ 18 F]fluoro-D-glucose-positron emission tomography signs of relapse, allowing curable surgery. Saliva samples were negative for the17 other patients: 16 were in remission and only1relapsed. Conclusions: Gene methylation in saliva is a promising biomarker for the follow-up and early detection of still curable relapses of head and neck squamous cell carcinoma patients.

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Basaloid squamous cell carcinoma (BSCC) is a rare variant of squamous cell carcinoma (SCC) of the head and neck. Wain's criteria (peripheral palisading, association with SCC, high nuclear-cytoplasmic ratio, high mitotic rate, solid growth), anti-34BE12 and CK 5/6 staining, and absence of neuroendocrine markers are mandatory for the diagnosis of BSCC. Its increasing incidence parallels that of human papilloma virus (HPV)-positive tumours for the oropharyngeal subsite. On the other hand, BSCC is frequently considered a high-grade carcinoma of poorer prognosis than its SCC counterparts, mostly due to a higher rate of distant metastases. However, BSCC has similar or better locoregional control rates and a relatively better radiosensitivity than SCC. BSCC seems to have a dual behaviour depending, at least partly, on its recently described association with HPV. The basaloid subtype of SCC, owing to its particular behaviour, should be systematically investigated along with HPV and smoking status, as those factors may be determinant in the response to treatment.

NIR image-guided surgery improved the quality of surgery for peritoneal carcinomatosis by doubling the number of nodules detected and significantly reducing the duration of surgery.

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Feature extraction is a challenging problem in radar target identification. In this paper, we propose a new approach of feature extraction by using Matrix Pencil Method in Frequency Domain (MPMFD). The proposed method takes into account not only the magnitude of the signal, but also its phase, so that all the physical characteristics of the target will be considered. With this method, the separation between the early time and the late time is not necessary. The proposed method is compared to Matrix Pencil Method in Time Domain (MPMTD). The methods are applied on UWB backscattered signal from three canonical targets (thin wire, sphere, and cylinder). MPMFD is applied on a complex field (real and imaginary parts of the signal). To the best of our knowledge, this comparison and the reconstruction of the complex electromagnetic field by MPMFD have not been done before. We show the effect of the two extraction methods on the accuracy of three different classifiers: Naïve bayes (NB), K-Nearest Neighbor (K-NN), and Support Vector Machine (SVM). The results show that the accuracy of classification is better when using extracted features by MPMFD with SVM.

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