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Patients with high-risk and advanced prostate cancer require safe and efficacious therapies likely to offer a survival advantage while minimizing the treatment-related toxicities. Improvements in the surgical technology, diagnostic modalities, radiological staging, and risk stratification have made surgery for high-risk and advanced prostate cancer a safe and feasible option. In this review, we outline the role of radical prostatectomy in high-risk localized, locally advanced, and metastatic prostate cancer. We overview available data evaluating the use of surgery in the context of a multi-modal approach and highlight ongoing trials in this area. Furthermore, the role of surgery as a non-systemic modality for metastasis-directed therapy (MDT) is also described. Emerging imaging modalities enabling more accurate staging and longer follow-up of clinical trials for prognostic endpoints are anticipated to help identify patient cohorts and treatment strategies, where the use of surgical treatments is likely to provide oncological benefits and acceptable toxicity. Cite this article as: Roy CSD, Sachdeva A, Kandaswamy GV, Rai BP. The role of surgery in high risk and advanced prostate cancer: A narrative review. Turk J Urol 2020; 47(Supp. 1): S56-S64.

In the last two decades, forensic pathology and crime scene investigations have seen a rapid increase in examination tools due to the implementation of several imaging techniques, e.g., CT and MR scanning, surface scanning and photogrammetry. These tools encompass relatively simple visualization tools to powerful instruments for performing virtual 3D crime scene reconstructions. A multi-modality and multiscale approach to a crime scene, where 3D models of victims and the crime scene are combined, offers several advantages. A permanent documentation of all evidence in a single 3D environment can be used during the investigation phases (e.g., for testing hypotheses) or during the court procedures (e.g., to visualize the scene and the victim in a more intuitive manner). Advanced computational approaches to understand what might have happened during a crime can also be applied by, e.g., performing a virtual animation of the victim in the actual context, which can provide important information about possible dynamics during the event. Here, we present an overview of the different techniques and modalities used in forensic pathology in conjunction with crime scene investigations. Based on our experiences, the advantages and challenges of an image-based multi-modality approach will be discussed, including how their use may introduce new visualization modalities in court, e.g., virtual reality (VR) and 3D printing. Finally, considerations about future directions in research will be mentioned.

Aim:To analyse the presentation, treatment strategies and outcomes of neuroendocrine carcinoma of cervix treated with multi-modality approach at our institute.Materials and methods:The data of patients diagnosed to have cervical cancer between October 2004 and November 2018 were retrieved, and 14 patients of neuroendocrine carcinoma cervix who received treatment in our institution were identified. The patients were analysed based on demographic characteristics, disease stage, pathological characteristics, treatment and follow-up. The median overall survival and disease-free survival were calculated.Results:Median follow-up period was 8 months (range 1–52 months). Six patients died within 4 months of completion of treatment due to disease progression. Median overall survival was 12 months and median disease-free interval was 5·5 months. Four of the patients who underwent combined modality treatment consisting of neoadjuvant chemotherapy, concurrent chemoradiation therapy and brachytherapy are still on regular follow-up and are disease-free.Conclusion:Neuroendocrine carcinoma of the cervix is a rare but aggressive histological subtype. Combined modality approach with judicious use of systemic chemotherapy along with surgery and radiation therapy is essential for optimal outcomes.

Light Detection and Ranging (LiDAR), which applies light in the formation of a pulsed laser to estimate the distance between the LiDAR sensor and objects, is an effective remote sensing technology. Many applications use LiDAR including autonomous vehicles, robotics, and virtual and augmented reality (VR/AR). The 3D point cloud classification is now a hot research topic with the evolution of LiDAR technology. This research aims to provide a high performance and compatible real-world data method for 3D point cloud classification. More specifically, we introduce a novel framework for 3D point cloud classification, namely, GSV-NET, which uses Gaussian Supervector and enhancing region representation. GSV-NET extracts and combines both global and regional features of the 3D point cloud to further enhance the information of the point cloud features for the 3D point cloud classification. Firstly, we input the Gaussian Supervector description into a 3D wide-inception convolution neural network (CNN) structure to define the global feature. Secondly, we convert the regions of the 3D point cloud into color representation and capture region features with a 2D wide-inception network. These extracted features are inputs of a 1D CNN architecture. We evaluate the proposed framework on the point cloud dataset: ModelNet and the LiDAR dataset: Sydney. The ModelNet dataset was developed by Princeton University (New Jersey, United States), while the Sydney dataset was created by the University of Sydney (Sydney, Australia). Based on our numerical results, our framework achieves more accuracy than the state-of-the-art approaches.

Dada la necesidad de comprender las formas de comunicación de personas con discapacidad intelectual para favorecer su participación social, este estudio trata la narración como una instancia discursiva autogestionada más allá del lenguaje. El objetivo es sistematizar una metodología que permita explorar su discurso narrativo usando una perspectiva multimodal. La descripción corresponde a las narraciones de quince escolares chilenos con discapacidad intelectual y escaso desarrollo de la lengua oral, pero con intención narrativa.La metodología resulta apropiada para dar cuenta de los recursos narrativos usados por estos estudiantes en los primeros años escolares y legitima sus estrategias narrativas, lo cual contribuye al desarrollo de la disciplina educación especial y al estudio de la comunicación desde una mirada inclusiva.

Significant growth has been made with multi-modal data as its entrance in the field of deep learning; whereas, Convolutional Neural Network (CNN) provides sufficient training data to develop a representative encrusted image. Yet, the multi-modality approach in CNN affect the performance by slowly converge the variance along with high-dimensionality, heterogeneity and non-aconvex optimization problems. To abridge these issues, a novel Gradient Foster Shared-representation Convolutional Network (GFSCN) framework is proposed, which improve and optimize the performance interms of accuracy and dimensionality reduction. Initially, the framework incorporates a multiple scant weighted de-noising autoencoder to solve the heterogeneity problem and reduces the dimensionality of data by transforming shared feature representation. Consequently, the work integrated enhanced stochastic variance reduced ascension approach. This approach diminishes the non-convex optimization problem through integrating two gradients consuming mini-batches, which reduced the loss function thereby achieves faster convergence even with the usage of larger dataset. Thus, the proposed framework achieves better performance in terms of achieving utmost accuracy with faster convergence and reduced variance.