HAPI: An efficient Hybrid Feature Engineering-based Approach for Propaganda Identification in social media.

Social media platforms serve as communication tools where users freely share information regardless of its accuracy. Propaganda on these platforms refers to the dissemination of biased or deceptive information aimed at influencing public opinion, encompassing various forms such as political campaigns, fake news, and conspiracy theories. This study introduces a Hybrid Feature Engineering Approach for Propaganda Identification (HAPI), designed to detect propaganda in text-based content like news articles and social media posts. HAPI combines conventional feature engineering methods with machine learning techniques to achieve high accuracy in propaganda detection. This study is conducted on data collected from Twitter via its API, and an annotation scheme is proposed to categorize tweets into binary classes (propaganda and non-propaganda). Hybrid feature engineering entails the amalgamation of various features, including Term Frequency-Inverse Document Frequency (TF-IDF), Bag of Words (BoW), Sentimental features, and tweet length, among others. Multiple Machine Learning classifiers undergo training and evaluation utilizing the proposed methodology, leveraging a selection of 40 pertinent features identified through the hybrid feature selection technique. All the selected algorithms including Multinomial Naive Bayes (MNB), Support Vector Machine (SVM), Decision Tree (DT), and Logistic Regression (LR) achieved promising results. The SVM-based HaPi (SVM-HaPi) exhibits superior performance among traditional algorithms, achieving precision, recall, F-Measure, and overall accuracy of 0.69, 0.69, 0.69, and 69.2%, respectively. Furthermore, the proposed approach is compared to well-known existing approaches where it overperformed most of the studies on several evaluation metrics. This research contributes to the development of a comprehensive system tailored for propaganda identification in textual content. Nonetheless, the purview of propaganda detection transcends textual data alone. Deep learning algorithms like Artificial Neural Networks (ANN) offer the capability to manage multimodal data, incorporating text, images, audio, and video, thereby considering not only the content itself but also its presentation and contextual nuances during dissemination.

Tamsulosin 0.8 mg daily dose in management of BPH patients with failed tamsulosin 0.4 mg monotherapy and unfit for surgical intervention.

AIM: This study aims to evaluate the effectiveness and safety of administering double-dose tamsulosin (0.8 mg) for treating patients with benign prostatic hyperplasia (BPH) who have not responded to the standard single dose of tamsulosin (0.4 mg) and are deemed unsuitable for transurethral resection (TUR) intervention. MATERIALS AND METHODS: Between November 2022 and July 2023, we prospectively analyzed 111 patients who were experiencing severe BPH symptoms. These patients received a double dose of tamsulosin for one month. We collected baseline characteristics such as age, body mass index, and underlying medical conditions. Various parameters including the International Prostate Symptom Score (IPSS), prostate-specific antigen (PSA) levels, prostate volume, peak urinary flow rate (Qmax), voided volume, and post-void residual volume were evaluated before and after treatment. RESULTS: All 111 patients completed the study. The mean age, PSA level, and prostate volume were 63.12 ± 4.83 years, 3.42 ± 0.93 ng/ml, and 50.37 ± 19.23 ml, respectively. Of these patients, 93 showed improvement in Qmax, post-void residual volume, and IPSS score (p-value = 0.001). The total IPSS score and total Qmax improved from 24.03 ± 2.49 and 7.72 ± 1.64 ml/sec to 16.41 ± 3.84 and 12.08 ± 2.37 ml/sec, respectively. CONCLUSION: Double-dose 0.8mg tamsulosin as an alpha-blocker therapy appears to be a viable temporary management option for BPH patients who have not responded to the standard single dose 0.4mg tamsulosin and are not suitable candidates for TUR intervention.

AuCFSR: Authentication and Color Face Self-Recovery Using Novel 2D Hyperchaotic System and Deep Learning Models.

Color face images are often transmitted over public channels, where they are vulnerable to tampering attacks. To address this problem, the present paper introduces a novel scheme called Authentication and Color Face Self-Recovery (AuCFSR) for ensuring the authenticity of color face images and recovering the tampered areas in these images. AuCFSR uses a new two-dimensional hyperchaotic system called two-dimensional modular sine-cosine map (2D MSCM) to embed authentication and recovery data into the least significant bits of color image pixels. This produces high-quality output images with high security level. When tampered color face image is detected, AuCFSR executes two deep learning models: the CodeFormer model to enhance the visual quality of the recovered color face image and the DeOldify model to improve the colorization of this image. Experimental results demonstrate that AuCFSR outperforms recent similar schemes in tamper detection accuracy, security level, and visual quality of the recovered images.

Internet of Medical Things and Healthcare 4.0: Trends, Requirements, Challenges, and Research Directions.

Healthcare 4.0 is a recent e-health paradigm associated with the concept of Industry 4.0. It provides approaches to achieving precision medicine that delivers healthcare services based on the patient's characteristics. Moreover, Healthcare 4.0 enables telemedicine, including telesurgery, early predictions, and diagnosis of diseases. This represents an important paradigm for modern societies, especially with the current situation of pandemics. The release of the fifth-generation cellular system (5G), the current advances in wearable device manufacturing, and the recent technologies, e.g., artificial intelligence (AI), edge computing, and the Internet of Things (IoT), are the main drivers of evolutions of Healthcare 4.0 systems. To this end, this work considers introducing recent advances, trends, and requirements of the Internet of Medical Things (IoMT) and Healthcare 4.0 systems. The ultimate requirements of such networks in the era of 5G and next-generation networks are discussed. Moreover, the design challenges and current research directions of these networks. The key enabling technologies of such systems, including AI and distributed edge computing, are discussed.

Efficient Brain Tumor Detection with Lightweight End-to-End Deep Learning Model.

In the field of medical imaging, deep learning has made considerable strides, particularly in the diagnosis of brain tumors. The Internet of Medical Things (IoMT) has made it possible to combine these deep learning models into advanced medical devices for more accurate and efficient diagnosis. Convolutional neural networks (CNNs) are a popular deep learning technique for brain tumor detection because they can be trained on vast medical imaging datasets to recognize cancers in new images. Despite its benefits, which include greater accuracy and efficiency, deep learning has disadvantages, such as high computing costs and the possibility of skewed findings due to inadequate training data. Further study is needed to fully understand the potential and limitations of deep learning in brain tumor detection in the IoMT and to overcome the obstacles associated with real-world implementation. In this study, we propose a new CNN-based deep learning model for brain tumor detection. The suggested model is an end-to-end model, which reduces the system's complexity in comparison to earlier deep learning models. In addition, our model is lightweight, as it is built from a small number of layers compared to other previous models, which makes the model suitable for real-time applications. The optimistic findings of a rapid increase in accuracy (99.48% for binary class and 96.86% for multi-class) demonstrate that the new framework model has excelled in the competition. This study demonstrates that the suggested deep model outperforms other CNNs for detecting brain tumors. Additionally, the study provides a framework for secure data transfer of medical lab results with security recommendations to ensure security in the IoMT.

Intelligent computational methods for multi-unmanned aerial vehicle-enabled autonomous mobile edge computing systems.

This paper proposes a multi-unmanned aerial vehicle (UAV)-enabled autonomous mobile edge computing (MEC) system, in which several UAVs are deployed to provide services to user devices (UDs). The aim is to reduce/minimize the overall energy consumption of the autonomous system via designing the optimal trajectories of multiple UAVs. The problem is very complicated to be solved by traditional methods, as one has to take into account the deployment updation of stop points (SPs), the association of SPs with UDs and UAVs, and the optimal trajectories designing of UAVs. To tackle this problem, we propose a variable-length trajectory planning algorithm (VLTPA) consisting of three phases. In the first phase, the deployment of SPs is updated via presenting a genetic algorithm (GA) having variable-length individuals. Accordingly, the association between UDs and SPs is addressed by using a close rule. Finally, a multi-chrome GA is proposed to jointly handle the association of SPs with UAVs and their order for UAVs. The proposed VLTPA is tested via performing extensive experiments on eight instances ranging from 60 to 200 UDs, which reveal that the proposed VLTPA outperforms other compared state-of-the-art algorithms.

Location and Time Aware Multitask Allocation in Mobile Crowd-Sensing Based on Genetic Algorithm.

Mobile crowd-sensing (MCS) is a well-known paradigm used for obtaining sensed data by using sensors found in smart devices. With the rise of more sensing tasks and workers in the MCS system, it is now essential to design an efficient approach for task allocation. Moreover, to ensure the completion of the tasks, it is necessary to incentivise the workers by rewarding them for participating in performing the sensing tasks. In this paper, we aim to assist workers in selecting multiple tasks while considering the time constraint of the worker and the requirements of the task. Furthermore, a pricing mechanism is adopted to determine each task budget, which is then used to determine the payment for the workers based on their willingness factor. This paper proves that the task-allocation is a non-deterministic polynomial (NP)-complete problem, which is difficult to solve by conventional optimization techniques. A worker multitask allocation-genetic algorithm (WMTA-GA) is proposed to solve this problem to maximize the workers welfare. Finally, theoretical analysis demonstrates the effectiveness of the proposed WMTA-GA. We observed that it performs better than the state-of-the-art algorithms in terms of average performance, workers welfare, and the number of assigned tasks.

Efficient multimodal deep-learning-based COVID-19 diagnostic system for noisy and corrupted images.

INTRODUCTION: In humanity's ongoing fight against its common enemy of COVID-19, researchers have been relentless in finding efficient technologies to support mitigation, diagnosis, management, contact tracing, and ultimately vaccination. OBJECTIVES: Engineers and computer scientists have deployed the potent properties of deep learning models (DLMs) in COVID-19 detection and diagnosis. However, publicly available datasets are often adulterated during collation, transmission, or storage. Meanwhile, inadequate, and corrupted data are known to impact the learnability and efficiency of DLMs. METHODS: This study focuses on enhancing previous efforts via two multimodal diagnostic systems to extract required features for COVID-19 detection using adulterated chest X-ray images. Our proposed DLM consists of a hierarchy of convolutional and pooling layers that are combined to support efficient COVID-19 detection using chest X-ray images. Additionally, a batch normalization layer is used to curtail overfitting that usually arises from the convolution and pooling (CP) layers. RESULTS: In addition to matching the performance of standard techniques reported in the literature, our proposed diagnostic systems attain an average accuracy of 98% in the detection of normal, COVID-19, and viral pneumonia cases using corrupted and noisy images. CONCLUSIONS: Such robustness is crucial for real-world applications where data is usually unavailable, corrupted, or adulterated.

Multimedia Cryptosystem for IoT Applications Based on a Novel Chaotic System around a Predefined Manifold.

Multimedia data play an important role in our daily lives. The evolution of internet technologies means that multimedia data can easily participate amongst various users for specific purposes, in which multimedia data confidentiality and integrity have serious security issues. Chaos models play an important role in designing robust multimedia data cryptosystems. In this paper, a novel chaotic oscillator is presented. The oscillator has a particular property in which the chaotic dynamics are around pre-located manifolds. Various dynamics of the oscillator are studied. After analyzing the complex dynamics of the oscillator, it is applied to designing a new image cryptosystem, in which the results of the presented cryptosystem are tested from various viewpoints such as randomness, time encryption, correlation, plain image sensitivity, key-space, key sensitivity, histogram, entropy, resistance to classical types of attacks, and data loss analyses. The goal of the paper is proposing an applicable encryption method based on a novel chaotic oscillator with an attractor around a pre-located manifold. All the investigations confirm the reliability of using the presented cryptosystem for various IoT applications from image capture to use it.

Efficient deep learning approach for augmented detection of Coronavirus disease.

The new Coronavirus disease 2019 (COVID-19) is rapidly affecting the world population with statistics quickly falling out of date. Due to the limited availability of annotated Coronavirus X-ray and CT images, the detection of COVID-19 remains the biggest challenge in diagnosing this disease. This paper provides a promising solution by proposing a COVID-19 detection system based on deep learning. The proposed deep learning modalities are based on convolutional neural network (CNN) and convolutional long short-term memory (ConvLSTM). Two different datasets are adopted for the simulation of the proposed modalities. The first dataset includes a set of CT images, while the second dataset includes a set of X-ray images. Both of these datasets consist of two categories: COVID-19 and normal. In addition, COVID-19 and pneumonia image categories are classified in order to validate the proposed modalities. The proposed deep learning modalities are tested on both X-ray and CT images as well as a combined dataset that includes both types of images. They achieved an accuracy of 100% and an F1 score of 100% in some cases. The simulation results reveal that the proposed deep learning modalities can be considered and adopted for quick COVID-19 screening.

A Smart Surveillance System for Uncooperative Gait Recognition Using Cycle Consistent Generative Adversarial Networks (CCGANs).

Surveillance remains an important research area, and it has many applications. Smart surveillance requires a high level of accuracy even when persons are uncooperative. Gait Recognition is the study of recognizing people by the way they walk even when they are unwilling to cooperate. It is another form of a behavioral biometric system in which unique attributes of an individual's gait are analyzed to determine their identity. On the other hand, one of the big limitations of the gait recognition system is uncooperative environments in which both gallery and probe sets are made under different and unknown walking conditions. In order to tackle this problem, we propose a deep learning-based method that is trained on individuals with the normal walking condition, and to deal with an uncooperative environment and recognize the individual with any dynamic walking conditions, a cycle consistent generative adversarial network is used. This method translates a GEI disturbed from different covariate factors to a normal GEI. It works like unsupervised learning, and during its training, a GEI disrupts from different covariate factors of each individual and acts as a source domain while the normal walking conditions of individuals are our target domain to which translation is required. The cycle consistent GANs automatically find an individual pair with the help of the Cycle Loss function and generate the required GEI, which is tested by the CNN model to predict the person ID. The proposed system is evaluated over a publicly available data set named CASIA-B, and it achieved excellent results. Moreover, this system can be implemented in sensitive areas, like banks, seminar halls (events), airports, embassies, shopping malls, police stations, military areas, and other public service areas for security purposes.

An Integrated Approach for Cancer Survival Prediction Using Data Mining Techniques.

Ovarian cancer is the third most common gynecologic cancers worldwide. Advanced ovarian cancer patients bear a significant mortality rate. Survival estimation is essential for clinicians and patients to understand better and tolerate future outcomes. The present study intends to investigate different survival predictors available for cancer prognosis using data mining techniques. Dataset of 140 advanced ovarian cancer patients containing data from different data profiles (clinical, treatment, and overall life quality) has been collected and used to foresee cancer patients' survival. Attributes from each data profile have been processed accordingly. Clinical data has been prepared corresponding to missing values and outliers. Treatment data including varying time periods were created using sequence mining techniques to identify the treatments given to the patients. And lastly, different comorbidities were combined into a single factor by computing Charlson Comorbidity Index for each patient. After appropriate preprocessing, the integrated dataset is classified using appropriate machine learning algorithms. The proposed integrated model approach gave the highest accuracy of 76.4% using ensemble technique with sequential pattern mining including time intervals of 2 months between treatments. Thus, the treatment sequences and, most importantly, life quality attributes significantly contribute to the survival prediction of cancer patients.

Percutaneous nephrostomy versus JJ ureteric stent as the initial drainage method in kidney stone patients presenting with acute kidney injury: A prospective randomized study.

OBJECTIVE: To compare percutaneous nephrostomy tube versus JJ stent as an initial urinary drainage procedure in kidney stone patients presenting with acute kidney injury. METHODS: Between January 2017 and January 2019, 143 patients with acute kidney injury secondary to obstructive kidney stone were prospectively randomized into the percutaneous nephrostomy tube group (71 patients) and JJ stent group (72 patients) at Beni-Suef University Hospital, Beni-Suef, Egypt. Exclusion criteria included candidates for acute dialysis, fever (>38°C), pyonephrosis, pregnancy and uncontrolled coagulopathy. The period required for serum creatinine normalization, failure of insertion, operative and fluoroscopy time were recorded. Definitive stone management for proximal ureteral stones >1.5 cm consisted of percutaneous nephrolithotomy for the percutaneous nephrostomy group and ureteroscopic laser lithotripsy for the JJ stent group. For stone size <1.5 cm, ureteroscopy or shockwave lithotripsy was carried out for both groups. Percutaneous nephrolithotomy was carried out for renal stones >2 cm, and shockwave lithotripsy for stones <2 cm. Distal and mid ureteral stones were treated by ureteroscopy. RESULTS: The percutaneous nephrostomy group had shorter operative time (P = 0.001). There was no significant difference in the recovery period for normalization of serum creatinine between both groups (P = 0.120). Procedural failure, ureteric mucosal injury and perforations increased in the case of male sex, stone size >1.5 cm and upper ureteric stones in the JJ stent group. Procedural failure, pelvic perforations and intraoperative bleeding increased in case of male sex, mild hydronephrosis and stone size >2.5 cm in the percutaneous nephrostomy group. Suprapubic pain, urethral pain and lower urinary tract symptoms were significant in the JJ stent group. The presence of a JJ stent directed us toward ureteroscopy (P = 0.002) and the presence of a percutaneous nephrostomy directed us toward percutaneous nephrolithotomy (P = 0.001). CONCLUSIONS: Percutaneous nephrostomy facilitates subsequent percutaneous nephrolithotomy, especially when carried out by a urologist, and it has a higher insertion success rate, a shorter operative time and a lesser incidence of postoperative urinary tract infection than a JJ stent. A JJ stent facilitates subsequent ureteroscopy, but operative complications can increase in the case of proximal ureteral stones >1.5 cm.

Deploying Machine and Deep Learning Models for Efficient Data-Augmented Detection of COVID-19 Infections.

This generation faces existential threats because of the global assault of the novel Corona virus 2019 (i.e., COVID-19). With more than thirteen million infected and nearly 600000 fatalities in 188 countries/regions, COVID-19 is the worst calamity since the World War II. These misfortunes are traced to various reasons, including late detection of latent or asymptomatic carriers, migration, and inadequate isolation of infected people. This makes detection, containment, and mitigation global priorities to contain exposure via quarantine, lockdowns, work/stay at home, and social distancing that are focused on "flattening the curve". While medical and healthcare givers are at the frontline in the battle against COVID-19, it is a crusade for all of humanity. Meanwhile, machine and deep learning models have been revolutionary across numerous domains and applications whose potency have been exploited to birth numerous state-of-the-art technologies utilised in disease detection, diagnoses, and treatment. Despite these potentials, machine and, particularly, deep learning models are data sensitive, because their effectiveness depends on availability and reliability of data. The unavailability of such data hinders efforts of engineers and computer scientists to fully contribute to the ongoing assault against COVID-19. Faced with a calamity on one side and absence of reliable data on the other, this study presents two data-augmentation models to enhance learnability of the Convolutional Neural Network (CNN) and the Convolutional Long Short-Term Memory (ConvLSTM)-based deep learning models (DADLMs) and, by doing so, boost the accuracy of COVID-19 detection. Experimental results reveal improvement in terms of accuracy of detection, logarithmic loss, and testing time relative to DLMs devoid of such data augmentation. Furthermore, average increases of 4% to 11% in COVID-19 detection accuracy are reported in favour of the proposed data-augmented deep learning models relative to the machine learning techniques. Therefore, the proposed algorithm is effective in performing a rapid and consistent Corona virus diagnosis that is primarily aimed at assisting clinicians in making accurate identification of the virus.

A Robust Quasi-Quantum Walks-Based Steganography Protocol for Secure Transmission of Images on Cloud-Based E-healthcare Platforms.

Traditionally, tamper-proof steganography involves using efficient protocols to encrypt the stego cover image and/or hidden message prior to embedding it into the carrier object. However, as the inevitable transition to the quantum computing paradigm beckons, its immense computing power will be exploited to violate even the best non-quantum, i.e., classical, stego protocol. On its part, quantum walks can be tailored to utilise their astounding 'quantumness' to propagate nonlinear chaotic behaviours as well as its sufficient sensitivity to alterations in primary key parameters both important properties for efficient information security. Our study explores using a classical (i.e., quantum-inspired) rendition of the controlled alternate quantum walks (i.e., CAQWs) model to fabricate a robust image steganography protocol for cloud-based E-healthcare platforms by locating content that overlays the secret (or hidden) bits. The design employed in our technique precludes the need for pre and/or post encryption of the carrier and secret images. Furthermore, our design simplifies the process to extract the confidential (hidden) information since only the stego image and primary states to run the CAQWs are required. We validate our proposed protocol on a dataset of medical images, which exhibited remarkable outcomes in terms of their security, good visual quality, high resistance to data loss attacks, high embedding capacity, etc., making the proposed scheme a veritable strategy for efficient medical image steganography.

Quantum-inspired cascaded discrete-time quantum walks with induced chaotic dynamics and cryptographic applications.

Designing efficient and secure cryptosystems has been a preoccupation for many scientists and engineers for a long time wherein they use chaotic systems to design new cryptosystems. While one dimensional (1-D) chaotic maps possess powerful properties compared to higher dimension ones, they are vulnerable to various attacks due to their small key space, chaotic discontinuous ranges, and degradation in chaotic dynamical behaviours. Moreover, when simulated on a computer, every such chaotic system produces a periodic cycle. Meanwhile, quantum random walks exhibit the potential for deployment in efficient cryptosystem design, which makes it an excellent solution for this problem. In this context, we present a new method for constructing substitution boxes (S-boxes) based on cascaded quantum-inspired quantum walks and chaos inducement. The performance of the proposed S-box scheme is investigated via established S-box evaluation criterion and outcomes suggest that the constructed S-box has significant qualities for viable applications information security. Further, we present an efficient scheme for pseudo-random numbers generation (PRNG) whose sustainability over long periods remedies the periodicity problem associated with traditional cryptographic applications. Furthermore, by combining the two mechanisms, an atypical image encryption scheme is introduced. Simulation results and analysis validate that the proposed image encryption algorithm will offer gains in many cryptographic applications.

A Multidimensional Hyperjerk Oscillator: Dynamics Analysis, Analogue and Embedded Systems Implementation, and Its Application as a Cryptosystem.

A lightweight image encryption algorithm is presented based on chaos induction via a 5-dimensional hyperjerk oscillator (5DHO) network. First, the dynamics of our 5DHO network is investigated and shown to exhibit up to five coexisting hidden attractors in the state space that depend exclusively on the system's initial values. Further, a simple implementation of the circuit was used to validate its ability to exhibit chaotic dynamical properties. Second, an Arduino UNO platform is used to confirm the usability of our oscillator in embedded system implementation. Finally, an efficient image encryption application is executed using the proposed chaotic networks based on the use of permutation-substitution sequences. The superior qualities of the proposed strategy are traced to the dynamic set of keys used in the substitution process which heralds the generation of the final ciphered image. Based on the average results obtained from the entropy analysis (7.9976), NPCR values (99.62), UACI tests (33.69) and encryption execution time for 512 × 512 images (0.1141 s), the proposed algorithm is adjudged to be fast and robust to differential and statistical attacks relative to similar approaches.

The sensitivity of initial transurethral resection or biopsy of bladder tumor(s) for detecting bladder cancer variants on radical cystectomy.

PURPOSE: We determined the ability of bladder biopsy and transurethral resection of the bladder to accurately predict bladder cancer variants on radical cystectomy since certain variants may affect prognosis and treatment. MATERIALS AND METHODS: We retrospectively evaluated the records of 302 patients who underwent biopsy and/or transurethral resection of the bladder followed by radical cystectomy from 2008 to 2010. The frequency of variant morphology and the sensitivity of the precystectomy material was determined using pathological findings at radical cystectomy as the final result. RESULTS: Bladder cancer variants were identified in 159 patients (53%) on initial biopsy/transurethral resection and/or final pathological evaluation at radical cystectomy. The most common variant was urothelial carcinoma with squamous differentiation in 72 of 159 patients (45%), followed by micropapillary urothelial carcinoma in 41 (26%). In 9 patients (6%) variant morphology was identified only on biopsy/transurethral resection bladder and not on final radical cystectomy pathological assessment. The remaining 150 patients (94%) showed variant morphology on radical cystectomy with (79 or 53%) or without (71 or 47%) variant morphology on the preceding biopsy/transurethral resection. The sensitivity of variant detection showed a broad range by variant subtype. Overall, initial biopsy/transurethral resection sensitivity was 39% for predicting variant morphology on radical cystectomy. CONCLUSIONS: Overall sensitivity for predicting bladder cancer variants from biopsy/transurethral resection of the bladder sampling is relatively low. This is likely due to sampling and tumor heterogeneity rather than to an inaccurate pathological diagnosis. Additional predictive markers of variant morphology may be useful to determine which tumors contain aggressive variants that may alter outcomes or therapy.

Validation of new AJCC exclusion criteria for subepithelial prostatic stromal invasion from pT4a bladder urothelial carcinoma.

PURPOSE: In 2010 the AJCC (American Joint Committee on Cancer) excluded urothelial carcinoma with subepithelial prostatic stromal invasion from the pT4a bladder cancer staging class, which is otherwise defined by direct prostatic invasion transmurally from the bladder. We determined if the new guidelines were reflective of differences in survival between subepithelial prostatic stromal invasion and transmural pT4a disease. MATERIALS AND METHODS: A retrospective, multi-institutional cohort of cystectomy cases with subepithelial prostatic stromal invasion from the University of Chicago and Cleveland Clinic were compared to a cohort with transmural pT4a disease. All pathological specimens were rereviewed at the respective institutions. Patients were excluded from the final cohort if variant bladder cancer histology, pT3 bladder disease or extraprostatic extension of urothelial carcinoma were identified. The primary end points were cancer specific and overall survival. RESULTS: Our study sample consisted of 48 patients with subepithelial prostatic stromal invasion and 49 patients with transmural pT4a disease. Median followup was 12.8 months (IQR 4.9 to 31.4). Patients with subepithelial prostatic stromal invasion had lower rates of lymph node involvement than those with transmural pT4a disease (14.6% vs 61.2%, p <0.001) and lower rates of positive surgical margins (18.7% vs 61.2%, p <0.001). Rates of perioperative chemotherapy were similar in both groups. When comparing subepithelial prostatic stromal invasion and transmural pT4a groups, overall survival was 64.0 vs 9.8 months and median cancer specific survival was not achieved vs 16.5 months, respectively (p <0.001). CONCLUSIONS: Subepithelial prostatic stromal invasion from urothelial carcinoma has more favorable outcomes compared to transmural pT4a disease. Our results support the exclusion of subepithelial prostatic stromal invasion from the pT4a bladder urothelial carcinoma staging class.