The location and extent of white matter lesions on magnetic resonance imaging (MRI) are important criteria for diagnosis, follow-up and prognosis of multiple sclerosis (MS). Clinical trials have shown that quantitative values, such as lesion volumes, are meaningful in MS prognosis. Manual lesion delineation for the segmentation of lesions is, however, time-consuming and suffers from observer variability. In this paper, we propose MSmetrix, an accurate and reliable automatic method for lesion segmentation based on MRI, independent of scanner or acquisition protocol and without requiring any training data. In MSmetrix, 3D T1-weighted and FLAIR MR images are used in a probabilistic model to detect white matter (WM) lesions as an outlier to normal brain while segmenting the brain tissue into grey matter, WM and cerebrospinal fluid. The actual lesion segmentation is performed based on prior knowledge about the location (within WM) and the appearance (hyperintense on FLAIR) of lesions. The accuracy of MSmetrix is evaluated by comparing its output with expert reference segmentations of 20 MRI datasets of MS patients. Spatial overlap (Dice) between the MSmetrix and the expert lesion segmentation is 0.67 ± 0.11. The intraclass correlation coefficient (ICC) equals 0.8 indicating a good volumetric agreement between the MSmetrix and expert labelling. The reproducibility of MSmetrix' lesion volumes is evaluated based on 10 MS patients, scanned twice with a short interval on three different scanners. The agreement between the first and the second scan on each scanner is evaluated through the spatial overlap and absolute lesion volume difference between them. The spatial overlap was 0.69 ± 0.14 and absolute total lesion volume difference between the two scans was 0.54 ± 0.58 ml. Finally, the accuracy and reproducibility of MSmetrix compare favourably with other publicly available MS lesion segmentation algorithms, applied on the same data using default parameter settings.

Human epidermal growth factor receptor (HER)2/neu kinase domain mutations are found in approximately 1-4% of lung adenocarcinomas with a similar phenotype to tumors with epidermal growth factor receptor (EGFR) mutations. Afatinib is a potent irreversible ErbB family blocker. We determined the tumor genomic status of the EGFR and HER2 genes in non- or light smokers with lung adenocarcinoma in patients who were entered into an exploratory Phase II study with afatinib. Five patients with a non-smoking history and metastatic lung adenocarcinomas bearing mutations in the kinase domain of HER2 gene were identified, three of which were evaluable for response. Objective response was observed in all three patients, even after failure of other EGFR- and/or HER2-targeted treatments; the case histories of these patients are described in this report. These findings suggest that afatinib is a potential novel treatment option for this subgroup of patients, even when other EGFR and HER2 targeting treatments have failed.

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