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Radiotherapy (RT) for head and neck cancer is now guided by cone-beam computed tomography (CBCT). We aim to identify a CBCT radiomic signature predictive of progression to RT. A cohort of 93 patients was split into training (n = 60) and testing (n = 33) sets. A total of 88 features were extracted from the gross tumor volume (GTV) on each CBCT. Receiver operating characteristic (ROC) curves were used to determine the power of each feature at each week of treatment to predict progression to radio(chemo)therapy. Only features with AUC > 0.65 at each week were pre-selected. Absolute differences were calculated between features from each weekly CBCT and baseline CBCT1 images. The smallest detectable change (C = 1.96 �� SD, SD being the standard deviation of differences between feature values calculated on CBCT1 and CBCTn) with its confidence interval (95% confidence interval [CI]) was determined for each feature. The features for which the change was larger than C for at least 5% of patients were then selected. A radiomics-based model was built at the time-point that showed the highest AUC and compared with models relying on clinical variables. Seven features had an AUC > 0.65 at each week, and six exhibited a change larger than the predefined CI 95%. After exclusion of inter-correlated features, only one parameter remains, Coarseness. Among clinical variable, only hemoglobin value was significant. AUC for predicting the treatment response were 0.78 (p = .006), 0.85 (p p p We described a feature selection methodology for delta-radiomics that is able to select reproducible features which are informative due to their change during treatment. A selected delta radiomics feature may improve clinical-based prediction models.

Radiomics is a field that has been growing rapidly for the past ten years in medical imaging and more particularly in oncology where the primary objective is to contribute to personalised and predictive medicine. This short review aimed at providing some insights regarding the potential value of radiomics for cancer patients treated with radiotherapy. Radiomics may contribute to each stage of the patients’ management: diagnosis, planning, treatment monitoring and post-treatment follow-up (toxicity and response). However, its applicability in clinical routine is currently hindered by several factors, including lack of automation, standardisation and harmonisation. A major effort must be carried out to automate the workflow, standardise radiomics good practices and carry out large-scale studies before any transfer to daily clinical practice., La radiomique est un champ de recherche qui s’est incroyablement développé durant cette dernière décennie, en particulier en oncologie, où le but est principalement de contribuer à une médecine personnalisée et prédictive. Cette courte revue a pour but de fournir un aperçu de la valeur potentielle de la radiomique pour la prise en charge des patients atteints de cancer traités par irradiation. Le bénéfice de la radiomique se dessine en effet à chacune des étapes de la prise en charge, depuis le diagnostic jusqu’au suivi, en passant par la planification et la prédiction de la toxicité et de la réponse thérapeutique. Néanmoins, le transfert en routine clinique de la radiomique est pour l’instant ralenti par plusieurs facteurs, dont le manque d’automatisation, de standardisation et d’harmonisation. Un effort majeur doit être mené afin d’automatiser le processus, standardiser les bonnes pratiques et réaliser des études à grande échelle, avant tout transfert en routine clinique.

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Purpose The aim of this paper is to define the requirements and describe the design and implementation of a standard benchmark tool for evaluation and validation of PET-auto-segmentation (PET-AS) algorithms. This work follows the recommendations of Task Group 211 (TG211) appointed by the American Association of Physicists in Medicine (AAPM). Methods The recommendations published in the AAPM TG211 report were used to derive a set of required features and to guide the design and structure of a benchmarking software tool. These items included the selection of appropriate representative data and reference contours obtained from established approaches and the description of available metrics. The benchmark was designed in a way that it could be extendable and open to the implementation of bespoke segmentation methods, while maintaining its main purpose of being a standard testing platform for newly developed PET-AS methods. An example of implementation of the proposed framework, named PETASset, was built. In this work, a selection of PET-AS methods representing common approaches to PET image segmentation were evaluated within PETASset for the purpose of testing and demonstrating the capabilities of the software as a benchmark platform. Results A selection of clinical, physical and simulated phantom data, including “best estimates” reference contours from macroscopic specimens, simulation template, and CT scans was built into the PETASset application database. Specific metrics such as Dice Similarity Coefficient (DSC), Positive Predictive Value (PPV), and Sensitivity (S), were included to allow the user to compare the results of any given PET-AS algorithm to the reference contours. In addition, a tool to generate structured reports on the evaluation of the performance of PET-AS algorithms against the reference contours was built. The variation of the metric agreement values with the reference contours across the PET-AS methods evaluated for demonstration were between 0.51 and 0.83, 0.44 and 0.86, and 0.61 and 1.00 for DSC, PPV, and the S metric, respectively. Examples of agreement limits were provided to show how the software could be used to evaluate a new algorithm against the existing state-of-the art. Conclusions PETASset provides a platform that allows standardizing the evaluation and comparison of different PET-AS methods on a wide range of PET datasets. The developed platform will be available to users willing to evaluate their PET-AS methods and contribute more evaluation datasets.

Purpose: Radiomics is the high-throughput analysis of medical images for treatment individualisation. It conventionally relies on the quantification of different characteristics of a region of interest (ROI) delineated in the image, such as the mean intensity, volume and textural heterogeneity. The lack of standardisation of image features is one of the major limitations for reproducing and validating radiomic studies, and thus a major hurdle for further developments in the field and for clinical translation. To overcome this challenge, a large international collaboration of 19 teams from 8 countries was initiated to establish an image feature ontology, and to provide definitions of commonly used features, benchmarks for testing feature extraction and image processing software, and reporting guidelines. Methods: The initiative consisted of two phases. In phase 1, 351 commonly used features were specified and benchmarked against a simple digital phantom, without any requirement for image pre-processing steps. The feature set consisted of commonly used radiomic features and encompasses statistical, morphological and texture characteristics of the ROI, both slice-by-slice (2D) and as a volume (3D). In phase 2, image pre-processing steps were introduced, and features were benchmarked by evaluating five pre-processing configurations on a lung cancer patient CT image. The configurations differ in treatment of the image stack (2D: A-B; 3D: C-E), the interpolation method (none: A; bi/trilinear: B-D, tricubic: E) and the grey-level discretisation method (fixed bin size: A, C; fixed number of bins: B, D-E). Both phases were iterative, and participants had the opportunity to compare results and update their workflow implementation. We set the most frequently contributed value of each feature as its benchmark value, and subsequently determined its reliability based on the number of contributing groups and the consensus level. Results: 19 different software implementations were tested. In both phases, only a small number of features were found to be reliable initially. The number of reliable features increased over time as problems were identified and resolved, see Figure 1 and Table 1. Remaining features for which no agreement was reached were not commonly implemented (< 3 agreeing teams), and could therefore not be reliably assessed. Conclusion: We addressed the lack of standardised feature definitions, implementation and image pre-processing steps for radiomics by providing reliable benchmark values for commonly used features. During the initiative, the 19 teams demonstrated large initial differences, yet nevertheless managed to converge to common reference values by increasing adherence to standardised definitions. Therefore, the use of our standardised definitions and benchmarks to test and update radiomics software is imperative to increase reproducibility of future radiomics studies.

Between April 1988 and December 1991 42 patients underwent a bilateral lower eyelid blepharoplasty. Depending on the etiology either a skin-muscle-flap or a skin-flap technique was used. When the lid was lax, it was shortened by a wedge-resection to prevent postoperative ectropion. The results were satisfactory with either technique.

From the anatomical and physiological point of view th eyelid may be divided into three layers: a deep layer, comprising the tarsi and the canthal tendons with the levator aponeurosis and the lower lid retractors, the orbicularis muscle layer, and the skin. The surgical particularities of each of these layers are outlined. In the management of eyelid injuries the meticulous reconstruction of the medial canthal tendon and canthus is of paramount importance. Only after this are the lids closed from medially to laterally and from deep to superficial tissues.

The eye is an effective and frequently used means of communication. Innate ocular signals express basic feelings and are identical all over the world. Acquired signals depend on cultural status, intelligence, and education. Eye diseases and orbital anomalies can provide misleading impressions, because they can be confused with true eye signals or because they can trigger innate reaction mechanisms. Surgically, the eye can often be improved as an emittor of signals; the message, however, has to be composed by the patient.

Twenty patients underwent intubation of the lacrimal system with silicone stents. The preliminary results are favourable. New indications for intubation in lacrimal surgery are discussed.

Three unusual cases of posttraumatic upper eyelid retraction are described. They were observed following orbital trauma without apparent involvement of the superior rectus muscle after latency periods ranging from several months to years. As other reasons for the lid retraction were ruled out the question arises as to whether peripheral neurogenic or muscular upper eyelid retraction must be assumed to be a consequence of trauma, although this has never been described before.

The technique of intubation of the lacrimal system is described. A silicone tube with stainless steel probes on either end is used. Special emphasis is placed on management of difficulties in finding the probe in the nose and guiding it with a lacrimal groove director.

In a 55-year-old woman patient with congenital Horner's syndrome the ptosis and miosis disappeared completely for a short time after a bout of coughing. Pharmacologically the defect was localized in the second neuron. No etiology or pathogenetic mechanism of the phenomenon could be identified.

Purpose\ud \ud The purpose of this educational report is to provide an overview of the present state-of-the-art PET auto-segmentation (PET-AS) algorithms and their respective validation, with an emphasis on providing the user with help in understanding the challenges and pitfalls associated with selecting and implementing a PET-AS algorithm for a particular application.\ud \ud \ud Approach\ud \ud A brief description of the different types of PET-AS algorithms is provided using a classification based on method complexity and type. The advantages and the limitations of the current PET-AS algorithms are highlighted based on current publications and existing comparison studies. A review of the available image datasets and contour evaluation metrics in terms of their applicability for establishing a standardized evaluation of PET-AS algorithms is provided. The performance requirements for the algorithms and their dependence on the application, the radiotracer used and the evaluation criteria are described and discussed. Finally, a procedure for algorithm acceptance and implementation, as well as the complementary role of manual and auto-segmentation are addressed.\ud \ud Findings\ud \ud A large number of PET-AS algorithms have been developed within the last 20 years. Many of the proposed algorithms are based on either fixed or adaptively selected thresholds. More recently, numerous papers have proposed the use of more advanced image analysis paradigms to perform semi-automated delineation of the PET images. However, the level of algorithm validation is variable and for most published algorithms is either insufficient or inconsistent which prevents recommending a single algorithm. This is compounded by the fact that realistic image configurations with low signal-to-noise ratios (SNR) and heterogeneous tracer distributions have rarely been used. Large variations in the evaluation methods used in the literature point to the need for a standardized evaluation protocol.\ud \ud \ud Conclusions\ud \ud Available comparison studies suggest that PET-AS algorithms relying on advanced image analysis paradigms provide generally more accurate segmentation than approaches based on PET activity thresholds, particularly for realistic configurations. However, this may not be the case for simple shape lesions in situations with a narrower range of parameters, where simpler methods may also perform well. Recent algorithms which employ some type of consensus or automatic selection between several PET-AS methods have potential to overcome the limitations of the individual methods when appropriately trained. In either case, accuracy evaluation is required for each different PET scanner and scanning and image reconstruction protocol. For the simpler, less robust approaches, adaptation to scanning conditions, tumor type, and tumor location by optimization of parameters is necessary. The results from the method evaluation stage can be used to estimate the contouring uncertainty. All PET-AS contours should be critically verified by a physician. A standard test, i.e., a benchmark dedicated to evaluating both existing and future PET-AS algorithms needs to be designed, to aid clinicians in evaluating and selecting PET-AS algorithms and to establish performance limits for their acceptance for clinical use. The initial steps toward designing and building such a standard are undertaken by the task group members.