Purpose To compare the acceptability of computed tomographic (CT) colonography and flexible sigmoidoscopy (FS) screening and the factors predicting CT colonographic screening participation, targeting participants in a randomized screening trial. Materials and Methods Eligible individuals aged 58 years (n = 1984) living in Turin, Italy, were randomly assigned to be invited to screening for colorectal cancer with FS or CT colonography. After individuals who had died or moved away (n = 28) were excluded, 264 of 976 (27.0%) underwent screening with FS and 298 of 980 (30.4%) underwent CT colonography. All attendees and a sample of CT colonography nonattendees (n = 299) were contacted for a telephone interview 3-6 months after invitation for screening, and screening experience and factors affecting participation were investigated. Odds ratios (ORs) were computed by means of multivariable logistic regression. Results For the telephone interviews, 239 of 264 (90.6%) FS attendees, 237 of 298 (79.5%) CT colonography attendees, and 182 of 299 (60.9%) CT colonography nonattendees responded. The percentage of attendees who would recommend the test to friends or relatives was 99.1% among FS and 93.3% among CT colonography attendees. Discomfort associated with bowel preparation was higher among CT colonography than FS attendees (OR, 2.77; 95% confidence interval [CI]: 1.47, 5.24). CT colonography nonattendees were less likely to be men (OR, 0.36; 95% CI: 0.18, 0.71), retired (OR, 0.31; 95% CI: 0.13, 0.75), to report regular physical activity (OR, 0.37; 95% CI: 0.20, 0.70), or to have read the information leaflet (OR, 0.18; 95% CI: 0.08, 0.41). They were more likely to mention screening-related anxiety (mild: OR, 6.30; 95% CI: 2.48, 15.97; moderate or severe: OR, 3.63; 95% CI: 1.87, 7.04), erroneous beliefs about screening (OR, 32.15; 95% CI: 6.26, 165.19), or having undergone a recent fecal occult blood test (OR, 13.69; 95% CI: 3.66, 51.29). Conclusion CT colonography and FS screening are well accepted, but further reducing the discomfort from bowel preparation may increase CT colonography screening acceptability. Negative attitudes, erroneous beliefs about screening, and organizational barriers are limiting screening uptake; all these factors are modifiable and therefore potentially susceptible to interventions. (©) RSNA, 2017 Online supplemental material is available for this article.

To compare breast density (BD) assessment provided by an automated BD evaluator (ABDE) with that provided by a panel of experienced breast radiologists, on a multivendor dataset. Methods Twenty-one radiologists assessed 613 screening/ diagnostic digital mammograms from nine centers and six different vendors, using the BI-RADS a, b, c, and d density classification. The same mammograms were also evaluated by an ABDE providing the ratio between fibroglandular and total breast area on a continuous scale and, automatically, the BI-RADS score. A panel majority report (PMR) was used as reference standard. Agreement (κ) and accuracy (proportion of cases correctly classified) were calculated for binary (BI-RADS a-b versus c-d) and 4-class classification. Results While the agreement of individual radiologists with the PMR ranged from κ=0.483 to κ=0.885, the ABDE correctly classified 563/613 mammograms (92 %). A substantial agreement for binary classification was found for individual reader pairs (κ=0.620, standard deviation [SD]=0.140), individual versus PMR (κ=0.736, SD=0.117), and individual versus ABDE (κ=0.674, SD=0.095). Agreement between ABDE and PMR was almost perfect (κ=0.831). Conclusions The ABDE showed an almost perfect agreement with a 21-radiologist panel in binary BD classification on a multivendor dataset, earning a chance as a reproducible alternative to visual evaluation.

PURPOSE: To describe and test a new fully automatic lesion detection system for breast DCE-MRI. MATERIALS AND METHODS: Studies were collected from two institutions adopting different DCE-MRI sequences, one with and the other one without fat-saturation. The detection pipeline consists of (i) breast segmentation, to identify breast size and location; (ii) registration, to correct for patient movements; (iii) lesion detection, to extract contrast-enhanced regions using a new normalization technique based on the contrast-uptake of mammary vessels; (iv) false positive (FP) reduction, to exclude contrast-enhanced regions other than lesions. Detection rate (number of system-detected malignant and benign lesions over the total number of lesions) and sensitivity (system-detected malignant lesions over the total number of malignant lesions) were assessed. The number of FPs was also assessed. RESULTS: Forty-eight studies with 12 benign and 53 malignant lesions were evaluated. Median lesion diameter was 6 mm (range, 5-15 mm) for benign and 26 mm (range, 5-75 mm) for malignant lesions. Detection rate was 58/65 (89%; 95% confidence interval [CI] 79%-95%) and sensitivity was 52/53 (98%; 95% CI 90%-99%). Mammary median FPs per breast was 4 (1st-3rd quartiles 3-7.25). CONCLUSION: The system showed promising results on MR datasets obtained from different scanners producing fat-sat or non-fat-sat images with variable temporal and spatial resolution and could potentially be used for early diagnosis and staging of breast cancer to reduce reading time and to improve lesion detection. Further evaluation is needed before it may be used in clinical practice.

A basic mesoscopic model of MTS growth is proposed. Taking advantage of the spherical symmetry, the model allows to fully explore the parameter space and investigate alternative interaction mechanisms, in view also of comparisons with macroscopic \"universal laws\". Preliminary results are in good agreement with available experimental data.

In today's world, large volumes of medical data are being continuously generated, but their value is severely undermined by our inability to translate them into knowledge and, ultimately, actions. Data mining techniques allow the extraction of previously unknown interesting patterns from large datasets, but their complexity limits their practical diffusion. Data-driven analysis is a multi-step process, in which health care professionals define analysis goals and assess extracted knowledge, while computer scientists tackle the non trivial task of driving the miner system analysis activity. This paper addresses the mining activity from a different perspective. We believe that mining systems should be able to devise which knowledge could be most interesting to users and extract actionable knowledge from large medical datasets, with minimal user intervention. More specifically, mining systems should be capable of (i) devising viable end-goals for a specific dataset (i.e., that yield actionable knowledge to the user and are feasible given the dataset characteristics and size), based on the expertise acquired during the analysis of previous datasets, and (ii) extracting a manageable set of knowledge. Automated data analysis should fuel the next generation of medical mining systems, thus enabling users to automatically mine massive data repositories

Automatic segmentation of the breast and axillary region is an important preprocessing step for automatic lesion detection in breast MR and dynamic contrast-enhanced-MR studies. In this paper, we present a fully automatic procedure based on the detection of the upper border of the pectoral muscle. Compared with previous methods based on thresholding, this method is more robust to noise and field inhomogeneities. The method was quantitatively evaluated on 31 cases acquired from two centers by comparing the results with a manual segmentation. Results indicate good overall agreement within the reference segmentation (overlap=0.79±0.09, recall=0.95± 0.02, precision=0.82 ± 0.1).

The design of accurate in silico cancer models capable of quantitatively predicting tumor growth is an important goal in cancer research today. Mesoscopic models have shown great promise in this scenario; however, their use is often inhibited by the difficulty in correctly assigning parameter values. In this paper, enabled by an extremely computationally efficient mesoscopic model, we propose a Genetic Algorithms' (GAs) approach to the exploration of parameter space. Analysis of the results suggest that this novel application of GAs to tumor growth models both facilitates the attribution of parameter values to the fitting of experimental data and, more importantly, lends insight to the role played by the different parameters in regulating the tumor model growth.

A novel neural network-based technique for segmentation of single-channel magnetic resonance images is presented. The segmentation of single-channel magnetic resonance images is a daunting task due to the relatively little information available at each pixel site. The proposed algorithm is based on unsupervised clustering by means of a Kohonen Self-Organizing Map: unsupervised segmentation algorithms are highly desirable in order to eliminate intra- and interobserver variability. Particular attention has been devoted to the choice of suitable features, in order to ensure an accurate and reliable segmentation: in particular, a feature set extracted from the neighborhood of each pixel has been evaluated. The proposed technique has been tested on simulated magnetic resonance images to assess its stability against the presence of noise and intensity inhomogeneities. Moreover, it has been tested on real magnetic resonance images of both volunteers and brain tumor patients. The preliminary results presented make the proposed technique a promising alternative for the segmentation of single-channel magnetic resonance images and encourage further investigation.