ADC values of cervical cancer after therapy showed significant differences compared with pretherapy values, particularly for responders.

A considerable number of unsuspected extrapulmonary malignancies can be detected in lung cancer screening trials. A careful evaluation of extrapulmonary structures, with particular attention to the kidneys and lymph nodes, is recommended.

Please cite this article as: Zilocchi AAE, , M., Fasani P, Giannitto C, Maccagnoni S, Maniglio M, Campoleoni M, Brambilla R, Casiraghi E, Biondetti PR, The value of precontrast thoraco-abdominopelvic CT in polytrauma patients., European Journal of Radiology (2015), http://dx.doi.org/10.1016/j.ejrad. 2015.02.015 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. CONCLUSIONS: precontrast CT acquisition did not provide significant information in trauma patients, exposing them to an unjustified radiation dose.

Digital breast tomosynthesis (DBT) studies were introduced as a successful help for the detection of calcification, which can be a primary sign of cancer. Expert radiologists are able to detect suspicious calcifications in DBT, but a high number of calcifications with non-malignant diagnosis at biopsy have been reported (false positives, FP). In this study, a radiomic approach was developed and applied on DBT images with the aim to reduce the number of benign calcifications addressed to biopsy and to give the radiologists a helpful decision support system during their diagnostic activity. This allows personalizing patient management on the basis of personalized risk. For this purpose, 49 patients showing microcalcifications on DBT images were retrospectively included, classified by BI-RADS (Breast Imaging-Reporting and Data System) and analyzed. After segmentation of microcalcifications from DBT images, radiomic features were extracted. Features were then selected with respect to their stability within different segmentations and their repeatability in test–retest studies. Stable radiomic features were used to train, validate and test (nested 10-fold cross-validation) a preliminary machine learning radiomic classifier that, combined with BI-RADS classification, allowed a reduction in FP of a factor of 2 and an improvement in positive predictive value of 50%.

The possibility of a metastatic lesion to the breast should be taken into account in any patient presenting with a breast lump and a previous history of melanoma. Breast involvement cannot be considered an isolated finding, as it might be the first manifestation of widespread disease.