Automatic segmentation of the prostate on Magnetic Resonance Imaging (MRI) is one of the topics on which research has focused in recent years as it is a fundamental first step in the building process ...of a Computer aided diagnosis (CAD) system for cancer detection. Unfortunately, MRI acquired in different centers with different scanners leads to images with different characteristics. In this work, we propose an automatic algorithm for prostate segmentation, based on a U-Net applying transfer learning method in a bi-center setting. First, T2w images with and without endorectal coil from 80 patients acquired at Center A were used as training set and internal validation set. Then, T2w images without endorectal coil from 20 patients acquired at Center B were used as external validation. The reference standard for this study was manual segmentation of the prostate gland performed by an expert operator. The results showed a Dice similarity coefficient >85% in both internal and external validation datasets.Clinical Relevance- This segmentation algorithm could be integrated into a CAD system to optimize computational effort in prostate cancer detection.
In the last decades, MRI was proven a useful tool for the diagnosis and characterization of Prostate Cancer (PCa). In the literature, many studies focused on characterizing PCa aggressiveness, but a ...few have distinguished between low-aggressive (Gleason Grade Group (GG) <=2) and high-aggressive (GG>=3) PCas based on biparametric MRI (bpMRI). In this study, 108 PCas were collected from two different centers and were divided into training, testing, and validation set. From Apparent Diffusion Coefficient (ADC) map and T2-Weighted Image (T2WI), we extracted texture features, both 3D and 2D, and we implemented three different methods of Feature Selection (FS): Minimum Redundance Maximum Relevance (MRMR), Affinity Propagation (AP), and Genetic Algorithm (GA). From the resulting subsets of predictors, we trained Support Vector Machine (SVM), Decision Tree, and Ensemble Learning classifiers on the training set, and we evaluated their prediction ability on the testing set. Then, for each FS method, we chose the best classifier, based on both training and testing performances, and we further assessed their generalization capability on the validation set. Between the three best models, a Decision Tree was trained using only two features extracted from the ADC map and selected by MRMR, achieving, on the validation set, an Area Under the ROC (AUC) equal to 81%, with sensitivity and specificity of 77% and 93%, respectively.Clinical Relevance- Our best model demonstrated to be able to distinguish low-aggressive from high-aggressive PCas with high accuracy. Potentially, this approach could help clinician to noninvasively distinguish between PCas that might need active treatment and those that could potentially benefit from active surveillance, avoiding biopsy-related complications.
Colorectal cancer (CRC) has the second-highest tumor incidence and is a leading cause of death by cancer. Nearly 20% of patients with CRC will have metastases (mts) at the time of diagnosis, and more ...than 50% of patients with CRC develop metastases during their disease. Unfortunately, only 45% of patients after a chemotherapy will respond to treatment. The aim of this study is to develop and validate a machine learning algorithm to predict response of individual liver mts, using CT scans. Understanding which mts will respond or not will help clinicians in providing a more efficient per-lesion treatment based on patient specific response and not only following a standard treatment. A group of 92 patients was enrolled from two Italian institutions. CT scans were collected, and the portal venous phase was manually segmented by an expert radiologist. Then, 75 radiomics features were extracted both from 7x7 ROIs that moved across the image and from the whole 3D mts. Feature selection was performed using a genetic algorithm. Results are presented as a comparison of the two different approaches of features extraction and different classification algorithms. Accuracy (ACC), sensitivity (SE), specificity (SP), negative and positive predictive values (NPV and PPV) were evaluated for all lesions (per-lesion analysis) and patients (per-patient analysis) in the construction and validation sets. Best results were obtained in the per-lesion analysis from the 3D approach using a Support Vector Machine as classifier. We reached on the training set an ACC of 81%, while on test set, we obtained SE of 76%, SP of 67%, PPV of 69% and NPV of 75%. On the validation set a SE of 61%, SP of 60%, PPV of 57% and NPV of 64% were reached. The promising results obtained in the validation dataset should be extended to a larger cohort of patient to further validate our method.Clinical Relevance- to develop a radiomics signatures predicting single liver mts response to therapy. A personalized mts approach is important to avoid unnecessary toxicity offering more suitable treatments and a better quality of life to oncological patients.
OBJECTIVES:The value of visual inspection of ventilator waveforms in detecting patient–ventilator asynchronies in the intensive care unit has never been systematically evaluated. This study aims to ...assess intensive care unit physiciansʼ ability to identify patient–ventilator asynchronies through ventilator waveforms.
DESIGN:Prospective observational study.
SETTING:Intensive care unit of a University Hospital.
PATIENTS:Twenty-four patients receiving mechanical ventilation for acute respiratory failure.
INTERVENTION:Forty-three 5-min reports displaying flow-time and airway pressure-time tracings were evaluated by 10 expert and 10 nonexpert, i.e., residents, intensive care unit physicians. The asynchronies identified by experts and nonexperts were compared with those ascertained by three independent examiners who evaluated the same reports displaying, additionally, tracings of diaphragm electrical activity.
MEASUREMENTS AND MAIN RESULTS:Data were examined according to both breath-by-breath analysis and overall report analysis. Sensitivity, specificity, and positive and negative predictive values were determined. Sensitivity and positive predictive value were very low with breath-by-breath analysis (22% and 32%, respectively) and fairly increased with report analysis (55% and 44%, respectively). Conversely, specificity and negative predictive value were high with breath-by-breath analysis (91% and 86%, respectively) and slightly lower with report analysis (76% and 82%, respectively). Sensitivity was significantly higher for experts than for nonexperts for breath-by-breath analysis (28% vs. 16%, p < .05), but not for report analysis (63% vs. 46%, p = .15). The prevalence of asynchronies increased at higher ventilator assistance and tidal volumes (p < .001 for both), whereas it decreased at higher respiratory rates and diaphragm electrical activity (p < .001 for both). At higher prevalence, sensitivity decreased significantly (p < .001).
CONCLUSIONS:The ability of intensive care unit physicians to recognize patient–ventilator asynchronies was overall quite low and decreased at higher prevalence; expertise significantly increased sensitivity for breath-by-breath analysis, whereas it only produced a trend toward improvement for report analysis.
•Satellite interferometric data as tools for landslide intensity estimation.•Intensity as input for landslide potential loss calculation.•Regional scale approach fully relying on interferometric ...data.•Combination of interferometric data and gravitational process models.
Multi-Temporal Interferometric Synthetic Aperture Radar (MTInSAR) data offer a valuable support to landslide mapping and to landslide activity estimation in mountain environments, where in situ measures are sometimes difficult to gather. Nowadays, the interferometric approach is more and more used for wide-areas analysis, providing useful information for risk management actors but at the same time requiring a lot of efforts to correctly interpret what satellite data are telling us. In this context, hot-spot-like analyses that select and highlight the fastest moving areas in a region of interest, are a good operative solution for reducing the time needed to inspect a whole interferometric dataset composed by thousands or millions of points. In this work, we go beyond the concept of MTInSAR data as simple mapping tools by proposing an approach whose final goal is the quantification of the potential loss experienced by an element at risk hit by a potential landslide. To do so, it is mandatory to evaluate landslide intensity. Here, we estimate intensity using Active Deformation Areas (ADA) extracted from Sentinel-1 MTInSAR data. Depending on the localization of each ADA with respect to the urban areas, intensity is derived in two different ways. Once exposure and vulnerability of the elements at risk are estimated, the potential loss due to a landslide of a given intensity is calculated. We tested our methodology in the Eastern Valle d’Aosta (north-western Italy), along four lateral valleys of the Dora Baltea Valley. This territory is characterized by steep slopes and by numerous active and dormant landslides. The goal of this work is to develop a regional scale methodology based on satellite radar interferometry to assess the potential impact of landslides on the urban fabric.
Strain is an effective strategy to modulate the optoelectronic properties of 2D materials, but it has been almost unexplored in layered hybrid organic–inorganic metal halide perovskites (HOIPs) due ...to their complex band structure and mechanical properties. Here, we investigate the temperature-dependent microphotoluminescence (PL) of 2D (C6H5CH2CH2NH3)2Cs3Pb4Br13 HOIP subject to biaxial strain induced by a SiO2 ring platform on which flakes are placed by viscoelastic stamping. At 80 K, we found that a strain of <1% can change the PL emission from a single peak (unstrained) to three well-resolved peaks. Supported by micro-Raman spectroscopy, we show that the thermomechanically generated strain modulates the bandgap due to changes in the octahedral tilting and lattice expansion. Mechanical simulations demonstrate the coexistence of tensile and compressive strain along the flake. The observed PL peaks add an interesting feature to the rich phenomenology of photoluminescence in 2D HOIPs, which can be exploited in tailored sensing and optoelectronic devices.
Previous studies showed that the combination of an anti-Epidermal growth factor (EGFR) and a MEK-inhibitor is able to prevent the onset of resistance to anti-EGFR monoclonal antibodies in KRAS-wild ...type colorectal cancer (CRC), while the same combination reverts anti-EGFR primary resistance in KRAS mutated CRC cell lines. However, rapid onset of resistance is a limit to combination therapies in KRAS mutated CRC.
We generated four different KRAS mutated CRC cell lines resistant to a combination of cetuximab (an anti-EGFR antibody) and refametinib (a selective MEK-inhibitor) after continuous exposure to increasing concentration of the drugs. We characterized these resistant cell lines by evaluating the expression and activation status of a panel of receptor tyrosine kinases (RTKs) and intracellular transducers by immunoblot and qRT-PCR. Oncomine comprehensive assay and microarray analysis were carried out to investigate new acquired mutations or transcriptomic adaptation, respectively, in the resistant cell lines. Immunofluorescence assay was used to show the localization of RTKs in resistant and parental clones.
We found that PI3K-AKT pathway activation acts as an escape mechanism in cell lines with acquired resistance to combined inhibition of EGFR and MEK. AKT pathway activation is coupled to the activation of multiple RTKs such as HER2, HER3 and IGF1R, though its pharmacological inhibition is not sufficient to revert the resistant phenotype. PI3K pathway activation is mediated by autocrine loops and by heterodimerization of multiple receptors.
PI3K activation plays a central role in the acquired resistance to the combination of anti-EGFR and MEK-inhibitor in KRAS mutated colorectal cancer cell lines. PI3K activation is cooperatively achieved through the activation of multiple RTKs such as HER2, HER3 and IGF1R.
Landslides in reservoir contexts are a well-recognised hazard that may lead to dangerous situations regarding infrastructures and people’s safety. Satellite-based radar interferometry is proving to ...be a reliable method to monitor the activity of landslides in such contexts. Here, we present a DInSAR (Differential Interferometric Synthetic Aperture Radar) analysis of Sentinel-1 images that exemplifies the usefulness of the technique to recognize and monitor landslides in the Rules Reservoir (Southern Spain). The integration of DInSAR results with a comprehensive geomorphological study allowed us to understand the typology, evolution and triggering factors of three active landslides: Lorenzo-1, Rules Viaduct and El Arrecife. We could distinguish between rotational and translational landslides and, thus, we evaluated the potential hazards related to these typologies, i.e., retrogression (Lorenzo-1 and Rules Viaduct landslides) or catastrophic slope failure (El Arrecife Landslide), respectively. We also observed how changes in the water level of the reservoir influence the landslide’s behaviour. Additionally, we were able to monitor the stability of the Rules Dam as well as detect the deformation of a highway viaduct that crosses a branch of the reservoir. Overall, we consider that other techniques must be applied to continue monitoring the movements, especially in the El Arrecife Landslide, in order to avoid future structural damages and fatalities.
In this paper we discuss the applicability of numerical descriptors and statistical physics concepts to characterize complex biological systems observed at microscopic level through organ on chip ...approach. To this end, we employ data collected on a microfluidic platform in which leukocytes can move through suitably built channels toward their target. Leukocyte behavior is recorded by standard time lapse imaging. In particular, we analyze three groups of human peripheral blood mononuclear cells (PBMC): heterozygous mutants (in which only one copy of the FPR1 gene is normal), homozygous mutants (in which both alleles encoding FPR1 are loss-of-function variants) and cells from 'wild type' donors (with normal expression of FPR1). We characterize the migration of these cells providing a quantitative confirmation of the essential role of FPR1 in cancer chemotherapy response. Indeed wild type PBMC perform biased random walks toward chemotherapy-treated cancer cells establishing persistent interactions with them. Conversely, heterozygous mutants present a weaker bias in their motion and homozygous mutants perform rather uncorrelated random walks, both failing to engage with their targets. We next focus on wild type cells and study the interactions of leukocytes with cancerous cells developing a novel heuristic procedure, inspired by Lyapunov stability in dynamical systems.
Assessment of fluid responsiveness is problematic in intensive care unit (ICU) patients, in particular for those undergoing modes of partial support, such as pressure support ventilation (PSV). We ...propose a new test, based on application of a ventilator-generated sigh, to predict fluid responsiveness in ICU patients undergoing PSV.
This was a prospective bi-centric interventional study conducted in two general ICUs. In 40 critically ill patients with a stable ventilatory PSV pattern and requiring volume expansion (VE), we assessed the variations in arterial systolic pressure (SAP), pulse pressure (PP) and stroke volume index (SVI) consequent to random application of 4-s sighs at three different inspiratory pressures. A radial arterial signal was directed to the MOSTCARE™ pulse contour hemodynamic monitoring system for hemodynamic measurements. Data obtained during sigh tests were recorded beat by beat, while all the hemodynamic parameters were averaged over 30 s for the remaining period of the study protocol. VE consisted of 500 mL of crystalloids over 10 min. A patient was considered a responder if a VE-induced increase in cardiac index (CI) ≥ 15% was observed.
The slopes for SAP, SVI and PP of were all significantly different between responders and non-responders (p < 0.0001, p = 0.0004 and p < 0.0001, respectively). The AUC of the slope of SAP (0.99; sensitivity 100.0% (79.4-100.0%) and specificity 95.8% (78.8-99.9%) was significantly greater than the AUC for PP (0.91) and SVI (0.83) (p = 0.04 and 0.009, respectively). The SAP slope best threshold value of the ROC curve was - 4.4° from baseline. The only parameter found to be independently associated with fluid responsiveness among those included in the logistic regression was the slope for SAP (p = 0.009; odds ratio 0.27 (95% confidence interval (CI
) 0.10-0.70)). The effects produced by the sigh at 35 cmH
0 (Sigh
) are significantly different between responders and non-responders. For a 35% reduction in PP from baseline, the AUC was 0.91 (CI
0.82-0.99), with sensitivity 75.0% and specificity 91.6%.
In a selected ICU population undergoing PSV, analysis of the slope for SAP after the application of three successive sighs and the nadir of PP after Sigh
reliably predict fluid responsiveness.
Australian New Zealand Clinical Trials Registry, ACTRN12615001232527 . Registered on 10 November 2015.
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