Robot-assisted partial nephrectomy using the novel Hugo™ RAS system: Feasibility, setting and perioperative outcomes of the first off-clamp series

INTRODUCTION

Hugo Robot-Assisted Surgery (RAS) System has been conceived with enhanced modularity but its role for nephron-sparing surgery setting still remains poorly explored. We aimed to describe our experience in robot-assisted partial nephrectomy (RAPN) with a three-arms setting for the first off-clamp series using the new Hugo RAS System.

METHODS

Patients were placed on an extended flank position at the margin of the surgical bed with a slightly flexion (45°). The first 11 mm robotic trocar (camera port) was placed along the pararectal line 14 ± 2 cm far from the umbilicus. The pneumoperitoneum was then induced through the AirSeal system (SurgiQuest, Milford, Connecticut, USA©). Two more 8 mm operative robotic ports were placed under direct vision, either 8 ± 1 cm far from optic's port. Two 12 mm laparoscopic ports for bed-assistant were placed between robotic ports. Monopolar curved shears, fenestrated grasper, and large needle driver were used in a three-instruments configuration.

RESULTS

Off-clamp RAPN was successfully performed in seven patients with cT1 renal masses using a trans-peritoneal route. Median port placement and docking time was 6 min (IQR, 4-8 min). Hemostasis was achieved through renorraphy using a single transfix stitch with sliding clips technique. There was no need for additional ports placement. No intraoperative complications occurred, no clashing of robotic instruments or between the robotic arms was observed. No technical failures of the system occurred. Median console time was 83 min (IQR, 68-115 min). Median estimated blood loss were 200 ml (IQR, 50-400 ml). All patients were discharged between post-operative day 2 and 3, without the need of hospital readmission. No complications were recorded within the first 30 post-operative days.

CONCLUSIONS

We performed the first series of off-clamp RAPN using the novel HUGO RAS System. This novel robotic platform showed an easy-friendly docking system, providing excellent perioperative outcomes with a simple three-arms configuration.

HER2-low expression in patients with advanced or metastatic solid tumors

BACKGROUND

Human epidermal growth factor receptor 2 (HER2)-low is a newly defined category with HER2 1+ or 2+ expression by immunohistochemistry (IHC) and lack of HER2 gene amplification measured by in situ hybridization (ISH). Much remains unknown about the HER2-low status across tumor types and changes in HER2 status between primary and metastatic samples.

PATIENTS AND METHODS

HER2 expression by IHC was evaluated in 4701 patients with solid tumors. We have evaluated the HER2 expression by IHC and amplification by ISH in paired breast and gastric/gastroesophageal (GEJ) primary and metastatic samples. HER2 expression was correlated with ERBB2 genomic alterations evaluated by next-generation sequencing (NGS) in non-breast, non-gastric/GEJ samples.

RESULTS

HER2 expression (HER2 IHC 1-3+) was found in half (49.8%) of the cancers, with HER2-low (1 or 2+) found in many tumor types: 47.1% in breast, 34.6% in gastric/GEJ, 50.0% in salivary gland, 46.9% in lung, 46.5% in endometrial, 46% in urothelial, and 45.5% of gallbladder cancers. The concordance evaluation of HER2 expression between primary and metastatic breast cancer samples showed that HER2 3+ remained unchanged in 87.1% with a strong agreement between primary and metastatic samples, with a weighted kappa (Κ) of 0.85 (95% confidence interval 0.79-0.91). ERBB2 alterations were identified in 117 (7.5%) patients with non-breast, non-gastric/GEJ solid tumors who had NGS testing. Of 1436 patients without ERBB2 alterations, 512 (35.7%) showed any level HER2 expression by IHC.

CONCLUSION

Our results show that HER2-low expression is frequently found across tumor types. These findings suggest that many patients with HER2-low solid tumors might benefit from HER2-targeted therapies.

T-pattern detection and analysis for the discovery of hidden features of behaviour

BACKGROUND

The behaviour of all living beings consists of hidden patterns in time; consequently, its nature and its underlying dynamics are intrinsically difficult to be perceived and detected by the unaided observer.

METHOD

Such a scientific challenge calls for improved means of detection, data handling and analysis. By using a powerful and versatile technique known as T-pattern detection and analysis (TPA) it is possible to unveil hidden relationships among the behavioural events in time.

RESULTS

TPA is demonstrated to be a solid and versatile tool to study the deep structure of behaviour in different experimental contexts, both in human and non human subjects.

CONCLUSION

This review deepens and extends contents recently published by adding new concepts and examples concerning the applications of TPA in the study of behaviour both in human and non-human subjects.

Electrophysiological Indexes of Incongruent Audiovisual Phonemic Processing: Unraveling the McGurk Effect

In this study the timing of electromagnetic signals recorded during incongruent and congruent audiovisual (AV) stimulation in 14 Italian healthy volunteers was examined. In a previous study (Proverbio et al., 2016) we investigated the McGurk effect in the Italian language and found out which visual and auditory inputs provided the most compelling illusory effects (e.g., bilabial phonemes presented acoustically and paired with non-labials, especially alveolar-nasal and velar-occlusive phonemes). In this study EEG was recorded from 128 scalp sites while participants observed a female and a male actor uttering 288 syllables selected on the basis of the previous investigation (lasting approximately 600 ms) and responded to rare targets (/re/, /ri/, /ro/, /ru/). In half of the cases the AV information was incongruent, except for targets that were always congruent. A pMMN (phonological Mismatch Negativity) to incongruent AV stimuli was identified 500 ms after voice onset time. This automatic response indexed the detection of an incongruity between the labial and phonetic information. SwLORETA (Low-Resolution Electromagnetic Tomography) analysis applied to the difference voltage incongruent-congruent in the same time window revealed that the strongest sources of this activity were the right superior temporal (STG) and superior frontal gyri, which supports their involvement in AV integration.

GPCALMA: a Grid-based Tool for Mammographic Screening

Objectives: The next generation of high energy physics (HEP) experiments requires a GRID approach to a distributed computing system: the key concept is the Virtual Organisation (VO), a group of distributed users with a common goal and the will to share their resources.

Methods: A similar approach, applied to a group of hospitals that joined the GPCALMA project (Grid Platform for Computer Assisted Library for MAmmography), will allow common screening programs for early diagnosis of breast and, in the future, lung cancer. The application code makes use of neural networks for the image analysis and is useful in improving the radiologists' diagnostic performance. GRID services allow remote image analysis and interactive online diagnosis, with a potential for a relevant reduction of the delays presently associated with screening programs.

Results and Conclusions: A prototype of the system, based on AliEn GRID Services [1], is already available, with a central server running common services [2] and several clients connecting to it. Mammograms can be acquired in any location; the related informatio required to select and access them at any time is stored in a common service called Data Catalogue, which can be queried by any client. Thanks to the PROOF facility [3], the result of a query can be used as input for analy-sis algorithms, which are executed on the nodes where the input images are stored,. The selected approach avoids data transfers for all the images with a negative diagnosis and allows an almost real time diagnosis for the set of images with high cancer probability.

Expression profiling stratifies mesothelioma tumors and signifies deregulation of spindle checkpoint pathway and microtubule network with therapeutic implications

BACKGROUND

Malignant pleural mesothelioma (MPM) is a lethal neoplasm exhibiting resistance to most treatment regimens and requires effective therapeutic options. Though an effective strategy in many cancer, targeted therapy is relatively unexplored in MPM because the therapeutically important oncogenic pathways and networks in MPM are largely unknown.

MATERIALS AND METHODS

We carried out gene expression microarray profiling of 53 surgically resected MPMs tumors along with paired normal tissue. We also carried out whole transcriptomic sequence (RNA-seq) analysis on eight tumor specimens. Taqman-based quantitative Reverse-transcription polymerase chain reaction (qRT-PCR), western analysis and immunohistochemistry (IHC) analysis of mitotic arrest deficient-like 1 (MAD2L1) was carried out on tissue specimens. Cell viability assays of MPM cell lines were carried out to assess sensitivity to specific small molecule inhibitors.

RESULTS

Bioinformatics analysis of the microarray data followed by pathway analysis revealed that the mitotic spindle assembly checkpoint (MSAC) pathway was most significantly altered in MPM tumors with upregulation of 18 component genes, including MAD2L1 gene. We validated the microarray data for MAD2L1 expression using quantitative qRT-PCR and western blot analysis on tissue lysates. Additionally, we analyzed expression of the MAD2L1 protein by IHC using an independent tissue microarray set of 80 MPM tissue samples. Robust clustering of gene expression data revealed three novel subgroups of tumors, with unique expression profiles, and showed differential expression of MSAC pathway genes. Network analysis of the microarray data showed the cytoskeleton/spindle microtubules network was the second-most significantly affected network. We also demonstrate that a nontaxane small molecule inhibitor, epothilone B, targeting the microtubules have great efficacy in decreasing viability of 14 MPM cell lines.

CONCLUSIONS

Overall, our findings show that MPM tumors have significant deregulation of the MSAC pathway and the microtubule network, it can be classified into three novel molecular subgroups of potential therapeutic importance and epothilone B is a promising therapeutic agent for MPM.

Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models

We propose a computer-aided detection (CAD) system which can detect small-sized (from 3mm) pulmonary nodules in spiral CT scans. A pulmonary nodule is a small lesion in the lungs, round-shaped (parenchymal nodule) or worm-shaped (juxtapleural nodule). Both kinds of lesions have a radio-density greater than lung parenchyma, thus appearing white on the images. Lung nodules might indicate a lung cancer and their early stage detection arguably improves the patient survival rate. CT is considered to be the most accurate imaging modality for nodule detection. However, the large amount of data per examination makes the full analysis difficult, leading to omission of nodules by the radiologist. We developed an advanced computerized method for the automatic detection of internal and juxtapleural nodules on low-dose and thin-slice lung CT scan. This method consists of an initial selection of nodule candidates list, the segmentation of each candidate nodule and the classification of the features computed for each segmented nodule candidate.The presented CAD system is aimed to reduce the number of omissions and to decrease the radiologist scan examination time. Our system locates with the same scheme both internal and juxtapleural nodules. For a correct volume segmentation of the lung parenchyma, the system uses a Region Growing (RG) algorithm and an opening process for including the juxtapleural nodules. The segmentation and the extraction of the suspected nodular lesions from CT images by a lung CAD system constitutes a hard task. In order to solve this key problem, we use a new Stable 3D Mass-Spring Model (MSM) combined with a spline curves reconstruction process. Our model represents concurrently the characteristic gray value range, the directed contour information as well as shape knowledge, which leads to a much more robust and efficient segmentation process. For distinguishing the real nodules among nodule candidates, an additional classification step is applied; furthermore, a neural network is applied to reduce the false positives (FPs) after a double-threshold cut. The system performance was tested on a set of 84 scans made available by the Lung Image Database Consortium (LIDC) annotated by four expert radiologists. The detection rate of the system is 97% with 6.1 FPs/CT. A reduction to 2.5 FPs/CT is achieved at 88% sensitivity. We presented a new 3D segmentation technique for lung nodules in CT datasets, using deformable MSMs. The result is a efficient segmentation process able to converge, identifying the shape of the generic ROI, after a few iterations. Our suitable results show that the use of the 3D AC model and the feature analysis based FPs reduction process constitutes an accurate approach to the segmentation and the classification of lung nodules.

Modelling the geographical distribution of co-infection risk from single-disease surveys

BACKGROUND

The need to deliver interventions targeting multiple diseases in a cost-effective manner calls for integrated disease control efforts. Consequently, maps are required that show where the risk of co-infection is particularly high. Co-infection risk is preferably estimated via Bayesian geostatistical multinomial modelling, using data from surveys screening for multiple infections simultaneously. However, only few surveys have collected this type of data.

METHODS

Bayesian geostatistical shared component models (allowing for covariates, disease-specific and shared spatial and non-spatial random effects) are proposed to model the geographical distribution and burden of co-infection risk from single-disease surveys. The ability of the models to capture co-infection risk is assessed on simulated data sets based on multinomial distributions assuming light- and heavy-dependent diseases, and a real data set of Schistosoma mansoni-hookworm co-infection in the region of Man, Côte d'Ivoire. The data were restructured as if obtained from single-disease surveys. The estimated results of co-infection risk, together with independent and multinomial model results, were compared via different validation techniques.

RESULTS

The results showed that shared component models result in more accurate estimates of co-infection risk than models assuming independence in settings of heavy-dependent diseases. The shared spatial random effects are similar to the spatial co-infection random effects of the multinomial model for heavy-dependent data.

CONCLUSIONS

In the absence of true co-infection data geostatistical shared component models are able to estimate the spatial patterns and burden of co-infection risk from single-disease survey data, especially in settings of heavy-dependent diseases.

Integrating microRNA and mRNA expression profiling using a novel algorithm identified a small set of unique genes upregulated in malignant pleural mesothelioma (MPM)

e22111

Background: We employed a global profiling strategy using miRNA microarrays in MPM cell lines and archival tumor tissue. Methods: We isolated total RNA from 4 MPM cell lines, 2 control cell lines, and 16 tissue specimens from patients with resected MPM (n=8) and normal counterpart (n=8) patients as controls. Total RNA was labeled with Cyanine 3, then hybridized with Agilent human miRNA microarray v1 slides. Results: Preliminary miRNA profiles show up-regulation of 44 versus down-regulation of 29 miRNA's in MSTO-211H cancer cells compared to HCT-4012 (pleural telomerase-transformed control). Profiling of 16 tissue specimens (8 normal vs 8 MPM) revealed down-regulation of 11 miRNA's in MPM tumor tissue. To focus on relevant miRNA that regulate genes involved in carcinogenesis and progression, we identified > 1000 unique genes using the online targetscan 4.2 program ( http://www.targetscan.org ), which predicts biological targets of miRNAs by identifying the presence of conserved 8-mer and 7-mer sites that match the seed region of each miRNA. We then explored a novel screening strategy, which combines mRNA expression dataset with the miRNA dataset, to narrow the list of relevant miRNA's. We conducted gene expression profiling on the cell lines and MPM tissue samples with Affymetrix U133 plus 2.0 chips. Bioinformatic analysis was conducted with MeV: MultiExperiment Viewer software, data reduction techniques (Correspondance Analysis), hierarchical clustering methods, and Serial Analysis for Microarray (SAM), and showed up-regulation of ∼300 genes in MPM compared to normal tissues. We then computed of the ∼300 mRNA's up-regulated in MPM only 32 are recognized by the 11 down-regulated miRNA's using the targetscan 4.2 algorithm. Most of the miRNA's regulate single messages while ∼20 % of the messages are regulated by more than 1 miRNA's. Some of these targets include Ets variant 1 and Protein kinase C - epsilon. Conclusions: This innovative approach of selecting highly relevant miRNA is feasible and enables discovery of novel genes based on their ability to be bound by single or multiple miRNA's. Validation of our profiling studies using real-time PCR and protein analysis methods will be presented.

No significant financial relationships to disclose.

Computer-assisted diagnosis (CAD) in mammography: comparison of diagnostic accuracy of a new algorithm (Cyclopus, Medicad) with two commercial systems

PURPOSE

The study compares the diagnostic accuracy (correct identification of cancer) of a new computer-assisted diagnosis (CAD) system (Cyclopus) with two other commercial systems (R2 and CADx).

MATERIALS AND METHODS

Cyclopus was tested on a set of 120 mammograms on which the two compared commercial systems had been previously tested. The set consisted of mammograms reported as negative, preceding 31 interval cancers reviewed as screening error or minimal sign, and of 89 verified negative controls randomly selected from the same screening database.

RESULTS

Cyclopus sensitivity was 74.1% (R2=54.8%; CADx=41.9%) and was higher for interval cancers reviewed as screening error (90.9%; R2=54.5%; CADx=81.8%) compared with those reviewed as minimal sign (65.0%; R2=55.0%; CADx=20.0%). Specificity was 15.7% (R2=29.2%; CADx=17.9%). Overall accuracy was 30.8% (R2=35.8%; CADx=24.1%). The positive predictive value of a case with CAD marks [regions of interest (ROI)] was 23.4% (23/98; R2=16.0%; CADx=15.1%). Average ROI number per view among negative controls was 1.13 (R2=0.93; CADx=0.99). Cyclopus was more sensitive for masses compared with isolated microcalcifications (208 vs 62 ROI; R2=90 vs 213; CADx=192 vs 130).

CONCLUSIONS

Compared with two other commercial systems, Cyclopus was more sensitive (R2 p=0.14; CADx p=0.02) and less specific (R2 p=0.02; CADx p=0.64).

Comparison of two portable solid state detectors with an improved collimation and alignment device for mammographic x-ray spectroscopy

We describe a portable system for mammographic x-ray spectroscopy, based on a 2 X 2 X 1 mm3 cadmium telluride (CdTe) solid state detector, that is greatly improved over a similar system based on a 3 X 3 X 2 mm3 cadmium zinc telluride (CZT) solid state detector evaluated in an earlier work. The CdTe system utilized new pinhole collimators and an alignment device that facilitated measurement of mammographic x-ray spectra. Mammographic x-ray spectra acquired by each system were comparable. Half value layer measurements obtained using an ion chamber agreed closely with those derived from the x-ray spectra measured by either detector. The faster electronics and other features of the CdTe detector allowed its use with a larger pinhole collimator than could be used with the CZT detector. Additionally, the improved pinhole collimator and alignment features of the apparatus permitted much more rapid setup for acquisition of x-ray spectra than was possible on the system described in the earlier work. These improvements in detector technology, collimation and ease of alignment, as well as low cost, make this apparatus attractive as a tool for both laboratory research and advanced mammography quality control.

A completely automated CAD system for mass detection in a large mammographic database

Mass localization plays a crucial role in computer-aided detection (CAD) systems for the classification of suspicious regions in mammograms. In this article we present a completely automated classification system for the detection of masses in digitized mammographic images. The tool system we discuss consists in three processing levels: (a) Image segmentation for the localization of regions of interest (ROIs). This step relies on an iterative dynamical threshold algorithm able to select iso-intensity closed contours around gray level maxima of the mammogram. (b) ROI characterization by means of textural features computed from the gray tone spatial dependence matrix (GTSDM), containing second-order spatial statistics information on the pixel gray level intensity. As the images under study were recorded in different centers and with different machine settings, eight GTSDM features were selected so as to be invariant under monotonic transformation. In this way, the images do not need to be normalized, as the adopted features depend on the texture only, rather than on the gray tone levels, too. (c) ROI classification by means of a neural network, with supervision provided by the radiologist's diagnosis. The CAD system was evaluated on a large database of 3369 mammographic images [2307 negative, 1062 pathological (or positive), containing at least one confirmed mass, as diagnosed by an expert radiologist]. To assess the performance of the system, receiver operating characteristic (ROC) and free-response ROC analysis were employed. The area under the ROC curve was found to be Az = 0.783 +/- 0.008 for the ROI-based classification. When evaluating the accuracy of the CAD against the radiologist-drawn boundaries, 4.23 false positives per image are found at 80% of mass sensitivity.

Spatial risk prediction and mapping of Schistosoma mansoni infections among schoolchildren living in western Côte d'Ivoire

The objectives of this study were (1) to examine risk factors for Schistosoma mansoni infection among schoolchildren living in western Côte d'Ivoire, and (2) to carry forward spatial risk prediction and mapping at non-sampled locations. First, demographic and socio-economic data were obtained from 3818 children, aged 6-16 years, from 55 schools. Second, a single stool sample was examined from each child by the Kato-Katz technique to assess infection status of S. mansoni and its intensity. Third, remotely sensed environmental data were derived from satellite imagery and digitized ground maps. With these databases a comprehensive geographical information system was established. Bayesian variogram models were applied for spatial risk modelling and prediction. The infection prevalence of S. mansoni was 38.9%, ranging from 0% to 89.3% among schools. Results showed that age, sex, the richest wealth quintile, elevation and rainfall explained the geographical variation of the school prevalences of S. mansoni infection. The goodness of fit of different spatial models revealed that age, sex and socio-economic status had a stronger influence on infection prevalence than environmental covariates. The generated risk map can be used by decision-makers for the design and implementation of schistosomiasis control in this setting. If successfully validated elsewhere, this approach can guide control programmes quite generally.

GPCALMA: a Grid-based tool for mammographic screening

OBJECTIVES

The next generation of high energy physics (HEP) experiments requires a GRID approach to a distributed computing system: the key concept is the Virtual ORGANISATION (VO), a group of distributed users with a common goal and the will to share their resources.

METHODS

A similar approach, applied to a group of hospitals that joined the GPCALMA project (Grid Platform for Computer Assisted Library for MAmmography), will allow common screening programs for early diagnosis of breast and, in the future, lung cancer. The application code makes use of neural networks for the image analysis and is useful in improving the radiologists' diagnostic performance. GRID services allow remote image analysis and interactive online diagnosis, with a potential for a relevant reduction of the delays presently associated with screening programs.

RESULTS AND CONCLUSIONS

A prototype of the system, based on AliEn GRID Services [1], is already available, with a central server running common services [2] and several clients connecting to it. Mammograms can be acquired in any location; the related information required to select and access them at any time is stored in a common service called Data Catalogue, which can be queried by any client. Thanks to the PROOF facility [3], the result of a query can be used as input for analysis algorithms, which are executed on the nodes where the input images are stored,. The selected approach avoids data transfers for all the images with a negative diagnosis and allows an almost real time diagnosis for the set of images with high cancer probability.

Particle transport in a karst aquifer: natural and artificial tracer experiments with bacteria, bacteriophages and microspheres

Fast changes in spring water quality in karst areas are a major concern for production of drinking water and require detailed knowledge of the complex interaction between karst aquifer, transport behavior of microorganisms and water treatment. We have conducted artificial and natural particle transport experiments at a karst spring with bacteria, bacteriophages, microspheres, and pathogens. Transport of the investigated microorganisms, turbid matter and chemical pullutants as well as increase in discharge are strongly related to precipitation and the heterogeneity of the aquifer. The indicator bacteria E. coli revealed a significant correlation to verotoxin-producing E coli and Cryptosporidium spp. We conclude that artificial particle tracers can help identify 'hot spots' for microbial recharge and that system parameters in spring water such as turbidity, UV-extinction and increase in discharge can be key parameters for efficient raw water management.