Metastases have been widely thought to arise from rare, selected, mutation-bearing cells in the primary tumor. Recently, however, it has been proposed that breast tumors are imprinted ab initio with metastatic ability. Thus, there is a debate over whether 'phenotypic' disease progression is really associated with 'molecular' progression. We profiled 26 matched primary breast tumors and lymph node metastases and identified 270 probesets that could discriminate between the two categories. We then used an independent cohort of breast tumors (81 samples) and unmatched distant metastases (32 samples) to validate and refine this list down to a 126-probeset list. A representative subset of these genes was subjected to analysis by in situ hybridization, on a third independent cohort (57 primary breast tumors and matched lymph node metastases). This not only confirmed the expression profile data, but also allowed us to establish the cellular origin of the signals. One-third of the analysed representative genes (4 of 11) were expressed by the epithelial component. The four epithelial genes alone were able to discriminate primary breast tumors from their metastases. Finally, engineered alterations in the expression of two of the epithelial genes (SERPINB5 and LTF) modified cell motility in vitro, in accordance with a possible causal role in metastasis. Our results show that breast cancer metastases are molecularly distinct from their primary tumors.

Abstract. In this work, we present a novel approach to mass detection in digital mammograms. The great variability of the masses appearance is the main obstacle of building a mass detection method. It is indeed demanding to characterize all the varieties of masses with a reduced set of features. Hence, in our approach we have chosen not to extract any feature, for the detection of the region of interest; on the contrary, we exploit all the information available on the image. A multiresolution overcomplete wavelet representation is performed, in order to codify the image with redundancy of information. The vectors of the very-large space obtained are then provided to a first SVM classifier. The detection task is here considered as a two-class pattern recognition problem: crops are classified as suspect or not, by using this SVM classifier. False candidates are eliminated with a second cascaded SVM. To further reduce the number of false positives, an ensemble of experts is applied: the final suspect regions are achieved by using a voting strategy. The sensitivity of the presented system is nearly 80% with a false-positive rate of 1.1 marks per image, estimated on images coming from the USF DDSM database.

In this paper we investigate the feasibility of using an SVM (support vector machine) classifier in our automatic system for the detection of clustered microcalcifications in digital mammograms. SVM is a technique for pattern recognition which relies on the statistical learning theory. It minimizes a function of two terms: the number of misclassified vectors of the training set and a term regarding the generalization classifier capability. We compare the SVM classifier with an MLP (multi-layer perceptron) in the false-positive reduction phase of our detection scheme: a detected signal is considered either microcalcification or false signal, according to the value of a set of its features. The SVM classifier gets slightly better results than the MLP one (Az value of 0.963 against 0.958) in the presence of a high number of training data; the improvement becomes much more evident (Az value of 0.952 against 0.918) in training sets of reduced size. Finally, the setting of the SVM classifier is much easier than the MLP one.

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The system shows a novel approach to the problem of lung nodule detection in CT scans: It relies on filtering techniques, image transforms, and descriptors rather than region growing and nodule segmentation, and the results are comparable to those of other recent systems in literature and show little dependency on the different types of nodules, which is a good sign of robustness.

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In Northern Italy the coronavirus infection has spread since February 2020: the increase in admissions of COVID-19 patients corresponded to a drastic decrease in admissions of regular patients to the Emergency Room (ER). This retrospective study was conducted by Academy of Emergency Medicine and Care (AcEMC). During the lockdown period the accesses were reduced by more than 50%, and in the following months of May and June 2020, there was a recovery clearly below (70%) previous year’s numbers. We have observed a drastic reduction in white and green codes, a fair reduction in yellow codes, while red codes remained stable. The decrease in access to the ER mainly concerned patients with low priority color codes, but also the reduction in the number of accesses of yellow and red codes, insignificant at a superficial glance, is notable. If we consider that yellow and red codes during the months of the lockdown included many patients with COVIDrelated respiratory insufficiency, it is evident that there was a clear reduction in the number of serious illnesses not COVID-related. This is certainly another serious consequence of the COVID-19 pandemic.