The reasons for discrepancies in target volume delineation: a SASRO study on head-and-neck and prostate cancer

But : comprendre les raisons des divergences observées dans le tracé des volumes cibles entre radio-oncologues. Matériel et méthodes : 18 centres suisses de radio-oncologie ont été invités à tracer les volumes pour un cas de cancer de la prostate et pour un cas de cancer de la sphère ORL. Nous avons également envoyé un questionnaire pour évaluer d'une part les différences dans la définition- des volumes (GTV [ gross tumor volume= volume tumoral macroscopique ], CTV [clinical tumor volume= volume de la tumeur clinique, macroscopique et microscopique], PTV [planning target volume= volume cible pour la planification], et d'autre part des variations dans les marges appliquées et les ganglions considérés à risque. Pour chaque centre, on a calculé la corrélation entre les marges qui ont été dessinées et celles qui ont été annoncées. Nous avons inclus dans le questionnaire une série de questions non spécifiques pour évaluer les méthodes de planification utilisées dans les différents centres. Résultats : Dans les 2 situations cliniques, on a mis en évidence de relativement grandes différences tant au niveau des volumes dessinés par les différents centres que dans leur définition des volumes. La corrélation entre les marges dessinées et définies était assez correcte dans le cas de la prostate mais médiocre dans le cas ORL. Le questionnaire a révélé d'importantes différences dans les méthodes de planification utilisées par les centres. Conclusion : Ces grandes différences peuvent être expliquées par, (1) une connaissance/interprétation variable des définitions ICRU, (2) des appréciations différentes de l'extension tumorale microscopique potentielle, (3) des difficultés dans l'identification du GTV, (4) des différences de concept, et (5) des différences entre la théorie (par ex. la description des marges) et la pratiqué (par ex. les marges dessinées).

Automatic filtering and substantiation of drug safety signals

Drug safety issues pose serious health threats to the population and constitute a major cause of mortality worldwide. Due to the prominent implications to both public health and the pharmaceutical industry, it is of great importance to unravel the molecular mechanisms by which an adverse drug reaction can be potentially elicited. These mechanisms can be investigated by placing the pharmaco-epidemiologically detected adverse drug reaction in an information-rich context and by exploiting all currently available biomedical knowledge to substantiate it. We present a computational framework for the biological annotation of potential adverse drug reactions. First, the proposed framework investigates previous evidences on the drug-event association in the context of biomedical literature (signal filtering). Then, it seeks to provide a biological explanation (signal substantiation) by exploring mechanistic connections that might explain why a drug produces a specific adverse reaction. The mechanistic connections include the activity of the drug, related compounds and drug metabolites on protein targets, the association of protein targets to clinical events, and the annotation of proteins (both protein targets and proteins associated with clinical events) to biological pathways. Hence, the workflows for signal filtering and substantiation integrate modules for literature and database mining, in silico drug-target profiling, and analyses based on gene-disease networks and biological pathways. Application examples of these workflows carried out on selected cases of drug safety signals are discussed. The methodology and workflows presented offer a novel approach to explore the molecular mechanisms underlying adverse drug reactions

On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.

On Automatic Diagnosis of Alzheimer's Disease based on Spontaneous Speech Analysis and Emotional Temperature

Alzheimer's disease is the most prevalent form of progressive degenerative dementia; it has a high socio-economic impact in Western countries. Therefore it is one of the most active research areas today. Alzheimer's is sometimes diagnosed by excluding other dementias, and definitive confirmation is only obtained through a post-mortem study of the brain tissue of the patient. The work presented here is part of a larger study that aims to identify novel technologies and biomarkers for early Alzheimer's disease detection, and it focuses on evaluating the suitability of a new approach for early diagnosis of Alzheimer’s disease by non-invasive methods. The purpose is to examine, in a pilot study, the potential of applying Machine Learning algorithms to speech features obtained from suspected Alzheimer sufferers in order help diagnose this disease and determine its degree of severity. Two human capabilities relevant in communication have been analyzed for feature selection: Spontaneous Speech and Emotional Response. The experimental results obtained were very satisfactory and promising for the early diagnosis and classification of Alzheimer’s disease patients.

Vertical Delineation, 2014

Vertical delineators are intended to warn drivers of an approaching curve while providing them with a better appreciation of the sharpness of a curve. Drivers can then select an appropriate speed before entering the curve. Delineation can also provide continuous tracking information once drivers are within the curve to help position their vehicles within the travel lane while traversing the curve. The most common type of vertical delineation is post mounted delineators (PMDs). These devices are usually flexible or rigid posts with some amount of reflective surface mounted along the roadside to provide additional delineation. Another treatment that has been used is to provide additional delineation on chevron posts.

Improving a leaves automatic recognition process using PCA

In this work we present a simulation of a recognition process with perimeter characterization of a simple plant leaves as a unique discriminating parameter. Data coding allowing for independence of leaves size and orientation may penalize performance recognition for some varieties. Border description sequences are then used, and Principal Component Analysis (PCA) is applied in order to study which is the best number of components for the classification task, implemented by means of a Support Vector Machine (SVM) System. Obtained results are satisfactory, and compared with [4] our system improves the recognition success, diminishing the variance at the same time.

Automatic Recognition of Leaves by Shape Detection Pre-Processing with Ica

In this work we present a simulation of a recognition process with perimeter characterization of a simple plant leaves as a unique discriminating parameter. Data coding allowing for independence of leaves size and orientation may penalize performance recognition for some varieties. Border description sequences are then used to characterize the leaves. Independent Component Analysis (ICA) is then applied in order to study which is the best number of components to be considered for the classification task, implemented by means of an Artificial Neural Network (ANN). Obtained results with ICA as a pre-processing tool are satisfactory, and compared with some references our system improves the recognition success up to 80.8% depending on the number of considered independent components.

Alzheimer disease diagnosis based on automatic spontaneous speech analysis

Alzheimer’s disease (AD) is the most prevalent form of progressive degenerative dementia and it has a high socio-economic impact in Western countries, therefore is one of the most active research areas today. Its diagnosis is sometimes made by excluding other dementias, and definitive confirmation must be done trough a post-mortem study of the brain tissue of the patient. The purpose of this paper is to contribute to im-provement of early diagnosis of AD and its degree of severity, from an automatic analysis performed by non-invasive intelligent methods. The methods selected in this case are Automatic Spontaneous Speech Analysis (ASSA) and Emotional Temperature (ET), that have the great advantage of being non invasive, low cost and without any side effects.

Plagiarism meets paraphrasing: insights for the new generation in automatic plagiarism detection

Although paraphrasing is the linguistic mechanism underlying many plagiarism cases, little attention has been paid to its analysis in the framework of automatic plagiarism detection. Therefore, state-of-the-art plagiarism detectors find it difficult to detect cases of paraphrase plagiarism. In this article, we analyse the relationship between paraphrasing and plagiarism, paying special attention to which paraphrase phenomena underlie acts of plagiarism and which of them are detected by plagiarism detection systems. With this aim in mind, we created the P4P corpus, a new resource which uses a paraphrase typology to annotate a subset of the PAN-PC-10 corpus for automatic plagiarism detection. The results of the Second International Competition on Plagiarism Detection were analysed in the light of this annotation. The presented experiments show that (i) more complex paraphrase phenomena and a high density of paraphrase mechanisms make plagiarism detection more difficult, (ii) lexical substitutions are the paraphrase mechanisms used the most when plagiarising, and (iii) paraphrase mechanisms tend to shorten the plagiarized text. For the first time, the paraphrase mechanisms behind plagiarism have been analysed, providing critical insights for the improvement of automatic plagiarism detection systems.

Computed tomography commissioning programmes: how to obtain a reliable MTF with an automatic approach?

The purpose of this study was to assess the spatial resolution of a computed tomography (CT) scanner with an automatic approach developed for routine quality controls when varying CT parameters. The methods available to assess the modulation transfer functions (MTF) with the automatic approach were Droege's and the bead point source (BPS) methods. These MTFs were compared with presampled ones obtained using Boone's method. The results show that Droege's method is not accurate in the low-frequency range, whereas the BPS method is highly sensitive to image noise. While both methods are well adapted to routine stability controls, it was shown that they are not able to provide absolute measurements. On the other hand, Boone's method, which is robust with respect to aliasing, more resilient to noise and provides absolute measurements, satisfies the commissioning requirements perfectly. Thus, Boone's method combined with a modified Catphan 600 phantom could be a good solution to assess CT spatial resolution in the different CT planes.

The development of an automatic recognition system for earmark and earprint comparisons

The value of earmarks as an efficient means of personal identification is still subject to debate. It has been argued that the field is lacking a firm systematic and structured data basis to help practitioners to form their conclusions. Typically, there is a paucity of research guiding as to the selectivity of the features used in the comparison process between an earmark and reference earprints taken from an individual. This study proposes a system for the automatic comparison of earprints and earmarks, operating without any manual extraction of key-points or manual annotations. For each donor, a model is created using multiple reference prints, hence capturing the donor within source variability. For each comparison between a mark and a model, images are automatically aligned and a proximity score, based on a normalized 2D correlation coefficient, is calculated. Appropriate use of this score allows deriving a likelihood ratio that can be explored under known state of affairs (both in cases where it is known that the mark has been left by the donor that gave the model and conversely in cases when it is established that the mark originates from a different source). To assess the system performance, a first dataset containing 1229 donors elaborated during the FearID research project was used. Based on these data, for mark-to-print comparisons, the system performed with an equal error rate (EER) of 2.3% and about 88% of marks are found in the first 3 positions of a hitlist. When performing print-to-print transactions, results show an equal error rate of 0.5%. The system was then tested using real-case data obtained from police forces.

Automatic quality assessment in structural brain magnetic resonance imaging

MRI has evolved into an important diagnostic technique in medical imaging. However, reliability of the derived diagnosis can be degraded by artifacts, which challenge both radiologists and automatic computer-aided diagnosis. This work proposes a fully-automatic method for measuring image quality of three-dimensional (3D) structural MRI. Quality measures are derived by analyzing the air background of magnitude images and are capable of detecting image degradation from several sources, including bulk motion, residual magnetization from incomplete spoiling, blurring, and ghosting. The method has been validated on 749 3D T(1)-weighted 1.5T and 3T head scans acquired at 36 Alzheimer's Disease Neuroimaging Initiative (ADNI) study sites operating with various software and hardware combinations. Results are compared against qualitative grades assigned by the ADNI quality control center (taken as the reference standard). The derived quality indices are independent of the MRI system used and agree with the reference standard quality ratings with high sensitivity and specificity (>85%). The proposed procedures for quality assessment could be of great value for both research and routine clinical imaging. It could greatly improve workflow through its ability to rule out the need for a repeat scan while the patient is still in the magnet bore.

Further delineation of the facial 13q14 deletion syndrome in 13 retinoblastoma patients.

Thirteen years ago, Motegi and colleagues (J Med Genet 1987;24:696-697) summarized the specific facial phenotype of six Japanese retinoblastoma patients with interstitial 13q14 deletions. Among a series of 228 propositi with retinoblastoma referred to the Lausanne Retinoblastoma Clinic for treatment and genetic counseling between 1986 and 1997, 13 (5.7%) were diagnosed with a cytogenetic de-novo 13q14 deletion. We confirm the presence of the reported facial phenotype in our population of Caucasian patients and describe additional clinical traits, thus extending the facial phenotype associated with the 13q14 deletion. Del(13q14) comprises, among others, cranial anomalies, frontal bossing, deeply grooved and long philtrum, depressed and broad nasal bridge, bulbous tip of the nose, thick lower lip, thin upper lip, broad cheeks, and large ears and lobules. Recognition of this particular facial appearance was instrumental in the genetic diagnosis of 13q deletions and in the presymptomatic diagnosis of retinoblastoma in a significant number of our cases. Identification of this phenotype in a retinoblastoma patient allows for efficient diagnosis of recurrence in his progeny and/or sibship, while its ignorance will compromise genetic counseling due to the possible difficulties in detecting large deletions by standard molecular mutation analysis. Recognition of this syndrome in newborns without known familial risk for retinoblastoma is even more important as it is a clear warning sign that indicates immediate ophthalmic examination.

Repeatability in automatic sorting of curved Norway spruce saw logs

Abstract

A survey on automatic recommenders in the context of institutional repositories

Peer-reviewed