A multicentric study of radiomics and artificial intelligence analysis on contrast-enhanced mammography to identify different histotypes of breast cancer.

OBJECTIVE: To evaluate the performance of radiomic analysis on contrast-enhanced mammography images to identify different histotypes of breast cancer mainly in order to predict grading, to identify hormone receptors, to discriminate human epidermal growth factor receptor 2 (HER2) and to identify luminal histotype of the breast cancer. METHODS: From four Italian centers were recruited 180 malignant lesions and 68 benign lesions. However, only the malignant lesions were considered for the analysis. All patients underwent contrast-enhanced mammography in cranium caudal (CC) and medium lateral oblique (MLO) view. Considering histological findings as the ground truth, four outcomes were considered: (1) G1 + G2 vs. G3; (2) HER2 + vs. HER2 - ; (3) HR + vs. HR - ; and (4) non-luminal vs. luminal A or HR + /HER2- and luminal B or HR + /HER2 + . For multivariate analysis feature selection, balancing techniques and patter recognition approaches were considered. RESULTS: The univariate findings showed that the diagnostic performance is low for each outcome, while the results of the multivariate analysis showed that better performances can be obtained. In the HER2 + detection, the best performance (73% of accuracy and AUC = 0.77) was obtained using a linear regression model (LRM) with 12 features extracted by MLO view. In the HR + detection, the best performance (77% of accuracy and AUC = 0.80) was obtained using a LRM with 14 features extracted by MLO view. In grading classification, the best performance was obtained by a decision tree trained with three predictors extracted by MLO view reaching an accuracy of 82% on validation set. In the luminal versus non-luminal histotype classification, the best performance was obtained by a bagged tree trained with 15 predictors extracted by CC view reaching an accuracy of 94% on validation set. CONCLUSIONS: The results suggest that radiomics analysis can be effectively applied to design a tool to support physician decision making in breast cancer classification. In particular, the classification of luminal versus non-luminal histotypes can be performed with high accuracy.

Radiomics and artificial intelligence analysis by T2-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging to predict Breast Cancer Histological Outcome.

OBJECTIVE: The objective of the study was to evaluate the accuracy of radiomics features obtained by MR images to predict Breast Cancer Histological Outcome. METHODS: A total of 217 patients with malignant lesions were analysed underwent MRI examinations. Considering histological findings as the ground truth, four different types of findings were used in both univariate and multivariate analyses: (1) G1 + G2 vs G3 classification; (2) presence of human epidermal growth factor receptor 2 (HER2 + vs HER2 -); (3) presence of the hormone receptor (HR + vs HR -); and (4) presence of luminal subtypes of breast cancer. RESULTS: The best accuracy for discriminating HER2 + versus HER2 - breast cancers was obtained considering nine predictors by early phase T1-weighted subtraction images and a decision tree (accuracy of 88% on validation set). The best accuracy for discriminating HR + versus HR - breast cancers was obtained considering nine predictors by T2-weighted subtraction images and a decision tree (accuracy of 90% on validation set). The best accuracy for discriminating G1 + G2 versus G3 breast cancers was obtained considering 16 predictors by early phase T1-weighted subtraction images in a linear regression model with an accuracy of 75%. The best accuracy for discriminating luminal versus non-luminal breast cancers was obtained considering 27 predictors by early phase T1-weighted subtraction images and a decision tree (accuracy of 94% on validation set). CONCLUSIONS: The combination of radiomics analysis and artificial intelligence techniques could be used to support physician decision-making in prediction of Breast Cancer Histological Outcome.

Irisin prevents microgravity-induced impairment of osteoblast differentiation in vitro during the space flight CRS-14 mission.

Understanding molecular mechanisms responsible for bone cells unbalance in microgravity would allow the development of better countermeasures for astronauts, and eventually advancing terrestrial osteoporosis treatments. We conduct a unique investigation by using a controlled 3D in vitro cell model to mimic the bone microenvironment in microgravity aboard the SpaceX Dragon cargo ferry to the ISS. Osteoblasts (OBs), osteoclasts (OCs), and endothelial cells (ECs), seeded on Skelite discs, were cultured w/ or w/o rec-Irisin and exposed to 14 days of microgravity in the eOSTEO hardware. Gene expression analysis was assessed, and results were compared to ground controls treated within identical payloads. Our results show that the microgravity-induced downregulation of mRNA levels of genes encoding for OB key transcription factors (Atf4 -75%, P < .01; RunX2 -87%, P < .001, Osterix -95%, P < .05 vs ground) and proteins (Collagen I -84%, P < .05; Osteoprotegerin -94%, P < .05) were prevented by irisin. Despite it was not effective in preventing Trap and Cathepsin K mRNA increase, irisin induced a 2.8-fold increase of Osteoprotegerin (P < .05) that might act for reducing osteoclastogenesis in microgravity. Our results provide evidence that irisin supports OB differentiation and activity in microgravity and it might represent a countermeasure to prevent bone loss in astronauts.

Observational study of coagulation activation in early breast cancer: development of a prognostic model based on data from the real world setting.

BACKGROUND: Cancer and coagulation activation are tightly related. The extent to which factors related to both these pathologic conditions concur to patient prognosis intensely animates the inherent research areas. The study herein presented aimed to the development of a tool for the assessment and stratification of risk of death and disease recurrence in early breast cancer. METHODS: Between 2008 and 2010, two hundreds thirty-five (N: 235) patients diagnosed with stage I-IIA breast cancer were included. Data on patient demographics and clinic-pathologic features were collected in course of face-to-face interviews or actively retrieved from clinical charts. Plasma levels of plasminogen activator inhibitor type 1 (PAI-1), fragment 1 + 2 (F1 + 2), thrombin antithrombin complex (TAT), factor VIII (FVIII), and D-dimer (DD) were measured at breast cancer diagnosis and prior to any therapeutic procedure, including breast surgery. The risk of death was computed in terms of overall survival (OS), which was the primary outcome. For a subset of patients (N = 62), disease free survival (DFS) was also assessed as a measure of risk of disease recurrence. RESULTS: Median follow up was 95 months (range 6-112 months). Mean age at diagnosis was 60.3 ± 13.4 years. Cancer cases were more commonly intraductal carcinomas (N: 204; 86.8%), pT1 (131; 55.7%), pN0 (141; 60%) and G2 (126; 53.6%). Elevated levels of PAI-1 (113; 48.1%) represented the most frequent coagulation abnormality, followed by higher levels of F1 + 2 (97; 41.3%), DD (63; 27.0%), TAT (34; 40%), and FVIII (29; 12.3%). In univariate models of OS, age, pT, DD, FVIII were prognostically relevant. In multivariate models of OS, age (p = 0.043), pT (p = 0.001), levels of DD (p = 0.029) and FVIII (p = 0.087) were confirmed. In the smaller subgroup of 62 patients, lymph node involvement, percent expression of estrogen receptors and levels of FVIII impacted DFS significantly. CONCLUSIONS: We developed a risk assessment tool for OS including patient- and cancer-related features along with biomarkers of coagulation activation in a cohort of early BC patients. Further studies are warranted to validate our prognostic model in the early setting and eventually extend its application to risk evaluation in the advanced setting for breast and other cancers.

Grape seed extract suppresses MDA-MB231 breast cancer cell migration and invasion.

BACKGROUND AND AIM: Breast cancer remains a leading cause of mortality among women. In metastasis, cascade migration of cancer cells and invasion of extracellular matrix (ECM) represent critical steps. Urokinase-type plasminogen activator (uPA), as well as metalloproteinases MMP-2 and MMP-9, strongly contribute to ECM remodelling, thus becoming associated with tumour migration and invasion. In addition, the high expression of cytoskeletal (CSK) proteins, as fascin, has been correlated with clinically aggressive metastatic tumours, and CSK proteins are thought to affect the migration of cancer cells. Consumption of fruits and vegetables, characterized by high procyanidin content, has been associated to a reduced mortality for breast cancer. Therefore, we investigated the biological effect of grape seed extract (GSE) on the highly metastatic MDA-MB231 breast cancer cell line, focusing on studying GSE ability in inhibiting two main metastatic processes, i.e., cell migration and invasion. METHODS: After MDA-MB231 breast cancer cells stimulated with GSE migration and invasion were evaluated by means of trans-well assays and uPA as well as MMPs activity was detected by gelatin zymography. Fascin, ß-catenin and nuclear factor-κB (NF-κB) expression were determined using western blot technique. ß-Catenin localization was observed by confocal microscopy. RESULTS: We observed that high concentrations of GSE inhibited cell proliferation and apoptosis. Conversely, low GSE concentration decreased cell migration and invasion, likely by hampering ß-catenin expression and localization, fascin and NF-κB expression, as well as by decreasing the activity of uPA, MMP-2 and MMP-9. CONCLUSIONS: These results make GSE a powerful candidate for developing preventive agents against cancer metastasis.

Special Issue: 'Advances in Space Biology'.

As we enter a new era of space exploration, space biology is at the forefront of both robotic and human space programs [...].

Sensitive colorimetric immunosensor using AuNP-functionalized polymer film for picogram-level detection of Tau protein intermediate aggregates.

Here we demonstrate for the first time that an antibody-gold nanoparticles (AuNPs)-polymer conjugate thin-film biosensor can easily be fabricated to selectively capture Tau protein. Gold nanoparticles (AuNPs) are employed as sensing elements, thus capitalizing on their propensity to undergo assembly or disassembly in response to the adsorption or conjugation of various biomolecules on their surface, thereby forming robust interactions with the target analyte. We show that the Tau protein in its different aggregation phases can be detected, by restricting the reaction area on the solid thin polymer film and thus reducing the diffusion effects usually encountered in immunosensors. A limit of detection (LOD) of 460 pg/mL was reached, demonstrating a great potential for detecting Tau in aggregation states. This sensor based on thin polymer film could open new routes for sensing and monitoring Tau protein in biological assays and biomedical diagnosis.

Highly sensitive detection of the neurodegenerative biomarker Tau by using the concentration effect of the pyro-electrohydrodynamic jetting.

It is largely documented that neurodegenerative diseases can be effectively treated only if early diagnosed. In this context, the structural changes of some biomolecules such as Tau, seem to play a key role in neurodegeneration mechanism becoming eligible targets for an early diagnosis. Post-translational modifications are responsible to drive the Tau protein towards a transition phase from a native disorder conformation into a preaggregation state, which then straight recruits the final fibrillization process. Here, we show for the first time the detection of pre-aggregated Tau in artificial urine at femto-molar level, through the concentration effect of the pyro-electrohydrodynamic jet (p-jet) technique. An excellent linear calibration curve is demonstrated at the femto-molar level with a limit of detection (LOD) of 130 fM. Moreover, for the first time we show here the structure stability of the protein after p-jet application through a deep spectroscopic investigation. Thanks to the small volumes required and the relatively compact and cost-effective characteristics, this technique represents an innovative breakthrough in monitoring the early stage associated to neurodegeneration syndromes in different scenarios of point of care (POC) and such as for example in long-term human space exploration missions.

Clinical-radiomic models based on digital breast tomosynthesis images: a preliminary investigation of a predictive tool for cancer diagnosis.

Objective: This study aimed to develop a clinical-radiomic model based on radiomic features extracted from digital breast tomosynthesis (DBT) images and clinical factors that may help to discriminate between benign and malignant breast lesions. Materials and methods: A total of 150 patients were included in this study. DBT images acquired in the setting of a screening protocol were used. Lesions were delineated by two expert radiologists. Malignity was always confirmed by histopathological data. The data were randomly divided into training and validation set with an 80:20 ratio. A total of 58 radiomic features were extracted from each lesion using the LIFEx Software. Three different key methods of feature selection were implemented in Python: (1) K best (KB), (2) sequential (S), and (3) Random Forrest (RF). A model was therefore produced for each subset of seven variables using a machine-learning algorithm, which exploits the RF classification based on the Gini index. Results: All three clinical-radiomic models show significant differences (p < 0.05) between malignant and benign tumors. The area under the curve (AUC) values of the models obtained with three different feature selection methods were 0.72 [0.64,0.80], 0.72 [0.64,0.80] and 0.74 [0.66,0.82] for KB, SFS, and RF, respectively. Conclusion: The clinical-radiomic models developed by using radiomic features from DBT images showed a good discriminating power and hence may help radiologists in breast cancer tumor diagnoses already at the first screening.

Non-destructive real-time analysis of plant metabolite accumulation in radish microgreens under different LED light recipes.

Introduction: The future of human space missions relies on the ability to provide adequate food resources for astronauts and also to reduce stress due to the environment (microgravity and cosmic radiation). In this context, microgreens have been proposed for the astronaut diet because of their fast-growing time and their high levels of bioactive compounds and nutrients (vitamins, antioxidants, minerals, etc.), which are even higher than mature plants, and are usually consumed as ready-to-eat vegetables. Methods: Our study aimed to identify the best light recipe for the soilless cultivation of two cultivars of radish microgreens (Raphanus sativus, green daikon, and rioja improved) harvested eight days after sowing that could be used for space farming. The effects on plant metabolism of three different light emitting diodes (LED) light recipes (L1-20% red, 20% green, 60% blue; L2-40% red, 20% green, 40% blue; L3-60% red, 20% green, 20% blue) were tested on radish microgreens hydroponically grown. A fluorimetric-based technique was used for a real-time non-destructive screening to characterize plant methabolism. The adopted sensors allowed us to quantitatively estimate the fluorescence of flavonols, anthocyanins, and chlorophyll via specific indices verified by standardized spectrophotometric methods. To assess plant growth, morphometric parameters (fresh and dry weight, cotyledon area and weight, hypocotyl length) were analyzed. Results: We observed a statistically significant positive effect on biomass accumulation and productivity for both cultivars grown under the same light recipe (40% blue, 20% green, 40% red). We further investigated how the addition of UV and/or far-red LED lights could have a positive effect on plant metabolite accumulation (anthocyanins and flavonols). Discussion: These results can help design plant-based bioregenerative life-support systems for long-duration human space exploration, by integrating fluorescence-based non-destructive techniques to monitor the accumulation of metabolites with nutraceutical properties in soilless cultivated microgreens.