Convolutional Neural Networks for the segmentation of microcalcification in Mammography Imaging

Cluster of microcalcifications can be an early sign of breast cancer. In this paper we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work we used 283 mammograms to train and validate our model, obtaining an accuracy of 98.22% in the detection of preliminary suspect regions and of 97.47% in the segmentation task. Our results show how deep learning could be an effective tool to effectively support radiologists during mammograms examination.Comment: 13 pages, 7 figure

Massive upper gastrointestinal bleeding from a pancreatic pseudocyst rupture: a case report

INTRODUCTION: Bleeding from pancreatic pseudocyst's rupture into adjacent organs is a rare, but potentially fatal, complication of chronic pancreatitis requiring quick management. Timing of the rupture is unpredictable; early diagnosis and correct management is essential in preventing the bleeding. CASE PRESENTATION: We describe the case of a 53 years old male patient successfully treated with emergency surgery for massive hematemesis due to a rupture of a bleeding pseudocyst into the stomach. Patient underwent emergency laparotomy and suture of the bleeding vessel. At 5 years follow-up patient is in healthy condition. CONCLUSION: This case shows to surgeons that pancreatic pseudocyst cannot be managed strictly with one rule and prompt surgical treatment is mandatory in case of haemodinamic instability

Diffusion-Weighted MRI of the Breast in Women with a History of Mantle Radiation: Does Radiation Alter Apparent Diffusion Coefficient?

OBJECTIVE: Fibrosis from chest irradiation could lower the apparent diffusion coefficient (ADC) of breast tissue. ADC values of normal breast tissue in high-risk women who underwent mantle radiation before age 30 years were compared with a screening control group matched for breast fibroglandular tissue (FGT). METHODS: In this retrospective study, we reviewed 21 women with a history of mantle radiation who underwent breast MRI examinations between 2008 and 2013, and 20 nonirradiated patients (control group) imaged during the same period with matching FGT and similar age. The women were dichotomized into low FGT (10/20, 50%) and high-FGT (10/20, 50%) groups, based on BI-RADS descriptors. All MRI examinations included diffusion-weighted imaging (DWI) (b = 0, 1000); ADC maps were generated and evaluated on PACS workstations by two radiologists in agreement. Region of interest markers were placed on ADC maps in visualized breast tissue in the retroareolar region of each breast. The ADC value was averaged for the right and left breast in each patient included in the study. The Wilcoxon signed-rank test was used to compare the ADC values in the irradiated patients and the matched control patients. RESULTS: The median breast ADC was lower in the irradiated group (1.32 × 10-3mm2/sec) than in the control group (1.62 × 10-3mm2/sec; P = 0.0089). Low FGT in the irradiated group had a lower median ADC (1.25 × 10-3mm2/sec) than it did in the control group (1.53 × 10-3mm2/sec). Irradiated high-FGT breasts had a median ADC (1.52 × 10-3mm2/sec), as compared with nonirradiated control patients with high FGT (1.82 × 10-3mm2/sec). CONCLUSION: Previously irradiated breasts have lower ADC values than do nonirradiated breasts

Multicentric Cancer Detected at Breast MR Imaging and Not at Mammography: Important or Not?

PURPOSE: To review the magnetic resonance (MR) imaging and pathologic features of multicentric cancer detected only at MR imaging and to evaluate its potential biologic value. MATERIALS AND METHODS: This retrospective study was institutional review board approved and HIPAA compliant; informed consent was waived. A review of records from 2001 to 2011 yielded 2021 patients with newly diagnosed breast cancer who underwent biopsy after preoperative MR imaging, 285 (14%) of whom had additional cancer detected at MR imaging that was occult at mammography. In 73 patients (3.6%), MR imaging identified 87 cancers in different quadrants than the known index cancer, constituting the basis of this report. In 62 of 73 patients (85%; 95% confidence interval [CI]: 75, 92), one additional cancer was found, and in 11 of 73 (15%; 95% CI: 8, 25), multiple additional cancers were found. A χ(2) test with adjustment for multiple lesions was used to examine whether MR imaging and pathologic features differ between the index lesion and additional multicentric lesions seen only at MR imaging. RESULTS: Known index cancers were more likely to be invasive than MR imaging-detected multicentric cancers (88% vs 76%, P = .023). Ductal carcinoma in situ (21 of 87 lesions [24%]; 95% CI: 15, 36) represented a minority of additional MR imaging-detected multicentric cancers. Overall, the size of MR imaging-detected multicentric invasive cancers (median, 0.6 cm; range, 0.1-6.3 cm) was smaller than that of the index cancer (median, 1.2 cm; range, 0.05-7.0 cm; P = .023), although 17 of 73 (23%) (95% CI: 14, 35) patients had larger MR imaging-detected multicentric cancers than the known index lesion, and 18 of 73 (25%) (95% CI: 15, 36) had MR imaging-detected multicentric cancers larger than 1 cm. MR imaging-detected multicentric cancers and index cancers differed in histologic characteristics, invasiveness, and grade in 27 of 73 (37%) patients (95% CI: 26, 49). In four of 73 (5%) patients (95% CI: 2, 13), MR imaging-detected multicentric cancers were potentially more biologically relevant because of the presence of unsuspected invasion or a higher grade. CONCLUSION: Multicentric cancer detected only at MR imaging was invasive in 66 of 87 patients (76%), larger than 1 cm in 18 of 73 patients (25%), larger than the known index cancer in 17 of 73 patients (23%), and more biologically important in four of 73 women (5%). An unsuspected additional multicentric cancer seen only at MR imaging is likely clinically relevant disease

Convolutional Neural Networks for the Segmentation of Microcalcification in Mammography Imaging

Cluster of microcalcifications can be an early sign of breast cancer. In this paper, we propose a novel approach based on convolutional neural networks for the detection and segmentation of microcalcification clusters. In this work, we used 283 mammograms to train and validate our model, obtaining an accuracy of 99.99% on microcalcification detection and a false positive rate of 0.005%. Our results show how deep learning could be an effective tool to effectively support radiologists during mammograms examination

Coefficient of variation for repeated measurements of phantom ADC along each of the main orthogonal directions ( - <i>i</i> = 1, readout/left-right; <i>i</i> = 2, phase-encoding/anterior-posterior; <i>i</i> = 3, slice-selection/head-foot) for scanner-A, scanner-B and scanner-C.

<p>The bar charts depict the mean value ± standard deviation within ROI_ref.</p