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Rationale and Objectives Propagation-based phase-contrast CT (PB-CT) is an advanced X-ray imaging technology that exploits both refraction and absorption of the transmitted X-ray beam. This study was aimed at optimizing the experimental conditions of PB-CT for breast cancer imaging and examined its performance relative to conventional absorption-based CT (AB-CT) in terms of image quality and radiation dose. Materials and Methods Surgically excised breast mastectomy specimens (n = 12) were scanned using both PB-CT and AB-CT techniques under varying imaging conditions. To evaluate the radiological image quality, visual grading characteristics (VGC) analysis was used in which 11 breast specialist radiologists compared the overall image quality of PB-CT images with respect to the corresponding AB-CT images. The area under the VGC curve was calculated to measure the differences between PB-CT and AB-CT images. Results The highest radiological quality was obtained for PB-CT images using a 32 keV energy X-ray beam and by applying the Homogeneous Transport of Intensity Equation phase retrieval with the value of its parameter γ set to one-half of the theoretically optimal value for the given materials. Using these optimized conditions, the image quality of PB-CT images obtained at 4 mGy and 2 mGy mean glandular dose was significantly higher than AB-CT images at 4 mGy (AUCVGC = 0.901, p = 0.001 and AUCVGC = 0.819, p = 0.011, respectively). Conclusion PB-CT achieves a higher radiological image quality compared to AB-CT even at a considerably lower mean glandular dose. Successful translation of the PB-CT technique for breast cancer imaging can potentially result in improved breast cancer diagnosis.

Purpose To investigate the relationship between breast cancer imaging features on magnetic resonance imaging (MRI) and histopathological characteristics. Methods and materials We prospectively enrolled 46 patients who underwent 1.5-T MRI with 68 breast malignant lesions from 2017 until 2019. Peritumoral edema was determined based on visual assessment on T2 weighted imaging. Lesions were categorized into two groups: A: with edema (48 lesions) and B: without edema (20 lesions). Results The tumor size was not different among two groups but multifocal-multicentric lesions were more common in the group A (70% vs. 35%). The axillary lymph nodes are most involved in group A. ER and PR positive lesions were more common in group B (90% vs. 56.3%) but in the group A, HER2 positive lesions were found to be more common (31.3% vs. 15%). The mean ADC value in tumors and peritumoral regions were lower (0.97 × 10−3 mm2/s, P = 0.023) and higher (1.85 × 10−3 mm2/s, P Conclusion Breast carcinomas with peritumoral edema were found to be more multifocal-multicentric, with higher prevalence of axillary lymph node involvement, more HER 2-positive, with lower prevalence of ER/PR-positive, lower tumoral ADC and higher peritumoral ADC values.

Purpose To propose a computer-assisted method for distinguishing uterine sarcoma from leiomyomas based on perfusion weighted magnetic resonance imaging (PWI). Materials and methods Forty-two women confirmed to have a total of 60 masses (10 uterine sarcomas and 50 benign leiomyomas) were included. The reference diagnosis was based on postoperative histopathological examination. All women underwent the standard MRI protocol with 3-Tesla MR imager (Magnetom Trio, Siemens, Erlangen, Germany) for assessment of myometrial masses, followed by PWI. For each mass, two regions of interest (ROI) were outlined manually by an experienced radiologist; one (ROIL) represented the entire tumor while the other (ROIs) was placed on the area of the lesion with the most marked contrast enhancement. Two additional ROIs with diameters similar to ROIs (3.0 to 3.1 mm) were placed on psoas muscle (ROIP) and myometrium (ROIM) in order to provide baselines for comparisons. The obtained ROIs of PWI images were then analyzed using the DCE Tool plug-in (version 2.0SP1) within ClearCanvas (Toronto, Ontario, Canada) framework. The DCE Tool provides seven parameters (Ktrans, kep, Vb, IAUC, initial slope, peak, the mean squared error) for modelling contrast uptake within an ROI using the modified Tofts model. Parameters extracted from the ROIs were fed into a decision tree ensemble, which classified the corresponding lesions either as malignant or benign. The leave-one-out cross validation (LOOCV) was utilized to evaluate the performance of the classifier. Results None of the parameters extracted from ROIL or ROIs differed significantly between uterine sarcoma and benign leiomyomas (all p > 0.05). The overall accuracy of 66.7% was obtained by feeding seven parameters extracted from ROIL to the classifier. When 21 features extracted from ROIL, ROIM, and ROIP were fed into the classifier an accuracy of 91.7%, sensitivity of 100%, and specificity of 90% were achieved in the optimal operating point of classifier. Conclusion Although none of the PWI parameters differed significantly between benign and malignant lesions, when the information provided by the extracted features was aggregated using a machine learning method, a promising discriminative power was obtained. This suggests that the proposed model for combining the PWI parameters is potentially useful for differentiating uterine sarcoma from leiomyomas.

Background and Objective Breast cancer is a major cause of mortality among women if not treated in early stages. Previous works developed non-invasive diagnosis methods using imaging data, focusing on specific sets of features that can be called spatial features or temporal features. However, limited set of features carry limited information, requiring complex classification methods to diagnose the disease. For non-invasive diagnosis, different imaging modalities can be used. DCE-MRI is one of the best imaging techniques that provides temporal information about the kinetics of the contrast agent in suspicious lesions along with acceptable spatial resolution. Methods We have extracted and studied a comprehensive set of features from spatiotemporal space to obtain maximum available information from the DCE-MRI data. Then, we have applied a feature fusion technique to remove common information and extract a feature set with maximum information to be used by a simple classification method. We have also implemented conventional feature selection and classification methods and compared them with our proposed approach. Results Experimental results obtained from DCE-MRI data of 26 biopsy or short-term follow-up proven patients illustrate that the proposed method outperforms alternative methods. The proposed method achieves a classification accuracy of 99% without missing any of the malignant cases. Conclusions The proposed method may help physicians determine the likelihood of malignancy in breast cancer using DCE-MRI without biopsy.

Cell free diagnosis of cancer is one of the crucial fields in new generation of medical technology. In this regard, cancer detection based on coastal fluids secreted from the tissues (named as secretome) has attracted a lot of attention. Lipids are important macromolecules could be found with much higher concentrations in secretome of cancer tissues vs. normal ones. On the other hand, lipids are the main dielectric components of the secretome with respect to proteins and ions. Here for the first time we introduced an electrochemical lipidomics based on electrical impedance spectroscopy (EIS) of the secretomes to detect the cancerous samples due to the lipidic content of their secretions. The EIS sensor was fabricated by multiwall carbon nanotube (MWCNT) arrays as conductive and super hydrophobic materials to have great interactive surface with the lipidic content of the solution. Results of the tests on the secretions of more than 100 human biopsied breast tissues showed the promising match between the charge transfer resistance (RCT) of samples’ secretions and pathological states of the tissues with meaningful boundary (up to 8 kΩ for normal and more than 13 kΩ for cancer samples). Mass spectroscopic analyses confirmed the higher content of lipids in cancer secretomes. Electrical lipidomics of the secretome shed new lights in cell free cancer diagnosis and could be applied as a complementary clinical approach in all of biopsy based diagnoses in future.

A microfluidic electrical actuator in the form of a cytological slide has been developed to separate and relocate atypical/cancer cells of liquid cytology samples (such as fine needle aspiration, uterine cervical smear and bone marrow aspiration) in a limited space for fast and precise pathological evaluation. The mechanism of this system named smart cytological slide (SCS) was based on the tendency of atypical/neoplastic cells, which have significant negative membrane charges, to positive pole region of direct electric field applied by the patterned gold microelectrodes of the actuator. Results of SCS on cytological samples of 70 patients showed better separating profile with much greater efficacy and selectivity than conventional cytological slides (CCS). Moreover, SCS detected the false negative responses of CCS in 5% of the samples while we used from the residues of the samples in SCS tests.