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Introduction Formation of intestinal stoma is a widely performed surgical procedure worldwide. It is associated with variable complications which can impact physical and mental health of the patient. The purpose of this study is to study the aspects (indications, complications, change in quality-of-life etc) and make necessary recommendations for quality improvement and to make patient experience better. Method study included 150 patients with stoma formation. Data categorised according to indications, complications, based on their response to change to quality of life. Results A total of 150 patients were included in this study. There were 89 male (59.3%), female (40.6%). 63 patients (42%) had loop ileostomy;38 patients (25.3%) had transverse colostomy. 77 patients (51.3%) had bowel malignancy, diverticular perforation in 19.3%. 21 patients (14%) mentioned change into their self-esteem following the surgery, 14% patients reported decline in their sexual life. Conclusions Pre & postoperative assessments should be carried out in detail with the patient to help them understand the implications of having a “stoma”. Stoma nurse involvement helps the patient to accept the changes to their life after the surgery. Psychological &sexual consultation helps in improving patient’s response to the challenge it may impose.

Purpose: Integrative analysis combining diagnostic imaging and genomic information can uncover biological insights into lesions that are visible on radiologic images. We investigate techniques for interrogating a deep neural network trained to predict quantitative image (radiomic) features and histology from gene expression in non-small cell lung cancer (NSCLC). Approach: Using 262 training and 89 testing cases from two public datasets, deep feedforward neural networks were trained to predict the values of 101 computed tomography (CT) radiomic features and histology. A model interrogation method called gene masking was used to derive the learned associations between subsets of genes and a radiomic feature or histology class [adenocarcinoma (ADC), squamous cell, and other]. Results: Overall, neural networks outperformed other classifiers. In testing, neural networks classified histology with area under the receiver operating characteristic curves (AUCs) of 0.86 (ADC), 0.91 (squamous cell), and 0.71 (other). Classification performance of radiomics features ranged from 0.42 to 0.89 AUC. Gene masking analysis revealed new and previously reported associations. For example, hypoxia genes predicted histology ( >0.90 AUC ). Previously published gene signatures for classifying histology were also predictive in our model ( >0.80 AUC ). Gene sets related to the immune or cardiac systems and cell development processes were predictive ( >0.70 AUC ) of several different radiomic features. AKT signaling, tumor necrosis factor, and Rho gene sets were each predictive of tumor textures. Conclusions: This work demonstrates neural networks' ability to map gene expressions to radiomic features and histology types in NSCLC and to interpret the models to identify predictive genes associated with each feature or type.

PurposeTo investigate the use of deep neural networks to learn associations between gene expression and radiomics or histology in non-small cell lung cancer (NSCLC).Materials and MethodsDeep feedforward neural networks were used for radio-genomic mapping, where 21,766 gene expressions were inputs to individually predict histology and 101 CT radiomic features. Models were compared against logistic regression, support vector machines, random forests, and gradient boosted trees on 262 training and 89 testing patients. Neural networks were interpreted using gene masking to derive the learned associations between subsets of gene expressions to a radiomic feature or histology type.ResultsNeural networks outperformed other classifiers except in five radiomic features, where training differences were = 0.70 AUC. Gene masking of the models showed new and previously reported histology-gene or radiogenomic associations. For example, hypoxia genes could predict histology with >0.90 test AUC and published gene signatures for histology prediction were also predictive in our models (>0.80 test AUC). Gene sets related to the immune or cardiac systems and cell development processes were predictive (>0.70 test AUC) of several different radiomic features while AKT signaling, TNF, and Rho gene sets were each predictive of tumor textures.ConclusionWe demonstrate the ability of neural networks to map gene expressions to radiomic features and histology in NSCLC and interpret the models to identify predictive genes associated with each feature or type.Author SummaryNon-small-cell lung cancer (NSCLC) patients can have different presentations as seen in the CT scans, tumor gene expressions, or histology types. To improve the understanding of these complementary data types, this study attempts to map tumor gene expressions associated with a patient’s CT radiomic features or a histology type. We explore a deep neural network approach to learn gene-radiomic associations (i.e., the subsets of co-expressed genes that are predictive of a value of an individual radiomic feature) and gene-histology associations in two separate public cohorts. Our modeling approach is capable of learning relevant information by showing the model can predict histology and that the learned relationships are consistent with prior works. The study provides evidence for coherent patterns between gene expressions and radiomic features and suggests such integrated associations could improve patient stratification.

Motivation Cancer heterogeneity is observed at multiple biological levels. To improve our understanding of these differences and their relevance in medicine, approaches to link organ- and tissue-level information from diagnostic images and cellular-level information from genomics are needed. However, these ‘radiogenomic’ studies often use linear or shallow models, depend on feature selection, or consider one gene at a time to map images to genes. Moreover, no study has systematically attempted to understand the molecular basis of imaging traits based on the interpretation of what the neural network has learned. These studies are thus limited in their ability to understand the transcriptomic drivers of imaging traits, which could provide additional context for determining clinical outcomes. Results We present a neural network-based approach that takes high-dimensional gene expression data as input and performs non-linear mapping to an imaging trait. To interpret the models, we propose gene masking and gene saliency to extract learned relationships from radiogenomic neural networks. In glioblastoma patients, our models outperformed comparable classifiers (>0.10 AUC) and our interpretation methods were validated using a similar model to identify known relationships between genes and molecular subtypes. We found that tumor imaging traits had specific transcription patterns, e.g. edema and genes related to cellular invasion, and 10 radiogenomic traits were significantly predictive of survival. We demonstrate that neural networks can model transcriptomic heterogeneity to reflect differences in imaging and can be used to derive radiogenomic traits with clinical value. Availability and implementation https://github.com/novasmedley/deepRadiogenomics. Contact whsu@mednet.ucla.edu Supplementary information Supplementary data are available at Bioinformatics online.

The growing amount of longitudinal data for a large population of patients has necessitated the application of algorithms that can discover patterns to inform patient management. This study demonstrates how temporal patterns generated from a combination of clinical and imaging measurements improve residual survival prediction in glioblastoma patients. Temporal patterns were identified with sequential pattern mining using data from 304 patients. Along with patient covariates, the patterns were incorporated as features in logistic regression models to predict 2-, 6-, or 9-month residual survival at each visit. The modeling approach that included temporal patterns achieved test performances of 0.820, 0.785, and 0.783 area under the receiver operating characteristic curve for predicting 2-, 6-, and 9-month residual survival, respectively. This approach significantly outperformed models that used tumor volume alone (p 3/day, a decrease in KPS across multiple visits, moderate neurologic symptoms, and worsening overall neurologic function suggested lower residual survival. These patterns are readily interpretable and found to be consistent with known prognostic indicators, suggesting they can provide early indicators to clinicians of changes in patient state and inform management decisions.

Radiogenomic studies have suggested that biological heterogeneity of tumors is reflected radiographically through visible features on magnetic resonance (MR) images. We apply deep learning techniques to map between tumor gene expression profiles and tumor morphology in pre-operative MR studies of glioblastoma patients. A deep autoencoder was trained on 528 patients, each with 12,042 gene expressions. Then, the autoencoder's weights were used to initialize a supervised deep neural network. The supervised model was trained using a subset of 109 patients with both gene and MR data. For each patient, 20 morphological image features were extracted from contrast-enhancing and peritumoral edema regions. We found that neural network pre-trained with an autoencoder and dropout had lower errors than linear regression in predicting tumor morphology features by an average of 16.98% mean absolute percent error and 0.0114 mean absolute error, where several features were significantly different (adjusted p-value < 0.05). These results indicate neural networks, which can incorporate nonlinear, hierarchical relationships between gene expressions, may have the representational power to find more predictive radiogenomic associations than pairwise or linear methods.

Objective It is crucial for clinicians to stay up to date on current literature in order to apply recent evidence to clinical decision making. Automatic summarization systems can help clinicians quickly view an aggregated summary of literature on a topic. Casama, a representation and summarization system based on “contextualized semantic maps,” captures the findings of biomedical studies as well as the contexts associated with patient population and study design. This paper presents a user-oriented evaluation of Casama in comparison to a context-free representation, SemRep. Materials and methods The effectiveness of the representation was evaluated by presenting users with manually annotated Casama and SemRep summaries of ten articles on driver mutations in cancer. Automatic annotations were evaluated on a collection of articles on EGFR mutation in lung cancer. Seven users completed a questionnaire rating the summarization quality for various topics and applications. Results Casama had higher median scores than SemRep for the majority of the topics (p ≤ 0.00032), all of the applications (p ≤ 0.00089), and in overall summarization quality (p ≤ 1.5e-05). Casama's manual annotations outperformed Casama's automatic annotations (p = 0.00061). Discussion Casama performed particularly well in the representation of strength of evidence, which was highly rated both quantitatively and qualitatively. Users noted that Casama's less granular, more targeted representation improved usability compared to SemRep. Conclusion This evaluation demonstrated the benefits of a contextualized representation for summarizing biomedical literature on cancer. Iteration on specific areas of Casama's representation, further development of its algorithms, and a clinically-oriented evaluation are warranted.

Background Postoperative oral nutritional supplementation has been shown to be of clinical benefit. This study examined the clinical effects and cost of administration of oral supplements both before and after surgery. Methods This was a randomized clinical trial conducted in three centres. Patients undergoing lower gastrointestinal tract surgery were randomized to one of four groups: group CC received no nutritional supplements, group SS took supplements both before and after surgery, group CS received postoperative supplements only, and group SC were given supplements only before surgery. Preoperative supplements were given from the time it was decided to operate to 1 day before surgery. Postoperative supplements were started when the patient was able to take free fluids and continued for 4 weeks after discharge from hospital. Data collected included weight change, complications, length of stay, nutritional intake, anthropometrics, quality of life and detailed costings covering all aspects of care. Results Some 179 patients were randomized, of whom 27 were withdrawn and 152 analysed (CC 44, SS 32, CS 35, SC 41). Dietary intake was similar in all four groups throughout the study. Mean energy intake from preoperative supplements was 536 and 542 kcal/day in the SS and SC groups respectively; that 2 weeks after discharge from hospital was 274 and 361 kcal/day in the SS and CS groups respectively. There was significantly less postoperative weight loss in the SS group than in the CC and CS groups (P < 0·050), and significantly fewer minor complications in the SS and CS groups than the CC group (P < 0·050). There were no differences in the rate of major complications, anthropometrics and quality of life. Mean overall costs were greatest in the CC group, although differences between groups were not significant. Conclusion Perioperative oral nutritional supplementation started before hospital admission for lower gastrointestinal tract surgery significantly diminished the degree of weight loss and incidence of minor complications, and was cost-effective.

s / International Journal of Surgery 23 (2015) S15eS134 S104 concise review of current assessment, diagnosis and management of each disease. Conclusion: The Gordon Museum and similar collections present unique opportunities to learn about fundamental surgical disease. We hope that the study presented here offers a novel insight into the wealth of educational material available. 0747: HOW WELL SURGEONS ARE AWARE OF RISKS ASSOCIATED WITH ROUTINE DIAGNOSTIC IMAGING? R. Hafeez , A. Ahad , N. Obeid , F. Smedley . King’s college Hospital, UK; Darent valley Hospital, UK; 3 Lewisham and Greenwich NHS Trust, UK Aim: To assess the level of awareness among surgeons of the nature and risks associated with routine diagnostic imaging. Methods: 114 clinicians from different surgical specialties were asked to fill a questionnaire which include total 32 questions about nature of radiations used in routine imaging, effective radiation dose, natural background radiation exposure, dose of single chest X-ray, estimated dose of other radiological investigation in comparisons to CXR, common side effects and organs aff5cted by the exposure. Participants were also asked to select the level of cancer risk association among different age groups and whether they consider alternative investigations. Results: 54(47.37%) participants have misunderstanding that routine radiological imaging involve non ionizing radiations. 47 (41.2%) knew correct effective radiation dose unit (mSv) while just 30% were aware of natural background radiation exposure. 73% overestimated the dose of CXR but 42% underestimated the cancer risk associated with CT abdomen. Although only 27% identified common side effects and organs aff5cted by exposure however majority (80%) do consider patient’s age and alternative investigations before requesting one. Conclusion: Surgeons need to be educated about the linear relation between radiation exposure and cancer development in order to minimize unnecessary exposure of patients. 0752: DEVELOPING THE LEADERSHIP SKILLS OF JUNIOR SURGICAL TRAINEES THROUGH A HANDS ON APPROACH A.A. Abdullah, J.M. Norris, N. Noor, L. Thorley. Bedford Hospital, UK Aim: The expanding role of doctors continues to evolve within the healthcare setting, in particular the need for leadership and managerial (L&M) skills. This case series provides a platform for junior doctors to develop their L&M skills through running structured courses Methods: A surgical course is designed based on a tested infrastructure to meet the needs of junior doctors, following an audit. The course is carried out with the support of previous course directors (CD). Effectiveness of the course and it's delivery are ascertained through a reaudit process. Participants are then encouraged to repeat the course using the same infrastructure with the help of their CD, ensuring the cycle continues. The CD are evaluated using a questionnaire and subsequent projects they run. Results: 3 CD in 3 different trusts ran 3 courses with 50 participants. A reaudit showed 100% of participants had benefitted from the course with 60% implementing their new skills in a clinical context. 67% of the CD have since lead other projects. 100% of the CD had learnt new skills, gained confidence from the process and would recommend it to others. Conclusion: This process offers junior doctors hands-on experience in developing L&M skills under supervision and support 0754: SIMULATION OF TENDON REPAIR USING MICROFOAM TAPE A. Elfaki, N. Abreo, M. Wilmot, S. Murphy, P. Gillespie. Addenbrookes

Introduction : Pilonidal sinus is common in sacroiliac region and occasionally occurs in the interdigital web space.Method : We describe an unusual case of an acquired implantation pilonidal sinus involving the perineal wound which occurred after an anteroposterior resection of the rectum.Result/Discussion : This patient was successfully treated by excision of the sinuses. Recognition and treatment of this condition may cure the patient with a persistent post operative perineal sinus.