Abstract
Breast segmentation has a critical role for objective pre and postoperative aesthetic evaluation but challenged by limited data (privacy concerns), class imbalance, and anatomical variability. As a response to the noted obstacles, we introduce an encoder–decoder framework with a Segment Anything Model (SAM) backbone, enhanced with synthetic depth maps and a multiterm loss combining weighted crossentropy, convexity, and depth alignment constraints. Evaluated on a 120patient dataset split into 70% training, 10% validation, and 20% testing, our approach achieves a balanced test dice score of 98.75%—a 4.5% improvement over prior methods—with dice of 95.5% (breast) and 89.2% (nipple). Ablations show depth injection reduces noise and focuses on anatomical regions, yielding dice gains of 0.47% (body) and 1.04% (breast). Geometric alignment increases convexity by almost 3% up to 99.86%, enhancing geometric plausibility of the nipple masks. Lastly, crossdataset evaluation on CINDERELLA samples demonstrates robust generalization, with small performance gain primarily attributable to differences in annotation styles. © 2025 Elsevier B.V., All rights reserved.

Abstract
Background: The study aims to present and explain the development stages of a mobile app designed to improve health literacy for self-management of oncological diseases. Through the integration of gamification, the app aims to enhance patient engagement and education in an interactive manner. Methods: The methodology of Design Science in Information Systems and Software Engineering was employed, which included stages of needs identification, requirements definition, prototyping, and iterative validation of the developed artifact. A total of 132 participants, consisting of patients and healthcare professionals, were involved in the development of the PocketOnco application. The subsequent implementation of the App, PocketOnco, involved usability testing, System Usability Scale assessment, and the collection of qualitative feedback. Results: The usability testing analysis revealed excellent acceptance of PocketOnco, with the gamified elements such as quizzes and reward systems being particularly appreciated for their ability to consistently engage and motivate users. Conclusion: The various stages in the development of this resource ensure the quality of its purpose. The application proved to be a viable and attractive solution for both patients and healthcare professionals, suggesting a promising path for future digital interventions in the field of oncology.

Abstract

Abstract
Exploring a group of influential spreaders to acquire maximum influence has become an emerging area of research in complex network analysis. The main challenge of this research is to identify the group of important nodes that are scattered broadly, such that the propagation ability of information is maximum to a network. Researchers proposed many centrality-based approaches with certain limitations to identify the influential nodes (spreaders) considering different properties of the networks. To find a group of spreaders, the VoteRank (a voting mechanism) based method produces effective results with low time complexity, wherein each iteration, the node votes for its neighbors by its voting capability, and the node obtaining the maximum vote score is identified as an influential spreader. The major loophole of existing VoteRank methods is measuring the voting capability based on the degree, k-shell index, or contribution of neighbors methods, which does not efficiently identify the spreaders from the diverse regions based on their spreading ability. In this paper, we propose a novel Community-based VoteRank method (CVoteRank) to identify a group of influential spreaders from diverse network regions by which the diffusion process is enhanced. Firstly, we measure every node's spreading ability based on intra- and inter-connectivity structure in a community, which signifies the local and global importance of the node. To identify the seed nodes, we assign the spreading ability to that node's voting capability and iteratively calculate the voting score of anode based on its neighboring voting capability and its spreading ability. Then, the node acquiring the maximum voting score is identified as the influential spreader in each iteration. Finally, to solve the problem of influence overlapping, CVoteRank reduces the voting capability of the neighboring nodes of the identified spreader. The efficiency of CVoteRank is evaluated and compared with the different state-of-the-art methods on twelve real networks. Utilizing the stochastic susceptible-infected-recovered epidemic method, we calculate the infected scale, final infected scale, and the average shortest path length among the identified spreaders. The experimental results show that CVoteRank identifies the most efficient spreaders with the highest spreading ability within a short period and the maximum reachability, and the identified spreaders are situated at diverse portions of the networks.

Abstract
Breast cancer patients often face difficulties when choosing among diverse surgeries. To aid patients, this paper proposes ACID-GAN (Anatomically and Clinically Informed Deep Generative Adversarial Network), a conditional generative model for predicting post-operative breast cancer outcomes using deep learning. Built on Pix2Pix, the model incorporates clinical metadata, such as surgery type and cancer laterality, by introducing a dedicated encoder for semantic supervision. Further improvements include colour preservation and anatomically informed losses, as well as clinical supervision via segmentation and classification modules. Experiments on a private dataset demonstrate that the model produces realistic, context-aware predictions. The results demonstrate that the model presents a meaningful trade-off between generating precise, anatomically defined results and maintaining patient-specific appearance, such as skin tone and shape. © 2025 Elsevier B.V., All rights reserved.

Abstract
Objective: The aim of this systematic review and meta-analysis is to systematically collect, evaluate, and critically synthesize research findings on the effects of physical exercise on chemotherapy-induced peripheral neuropathy (CIPN). Method: The Joanna Briggs Institute (JBI) methodology for systematic reviews was adopted for this study. We searched the Medline (R), CINAHL, SportDiscus, and Scopus databases to identify relevant articles published from inception to March 2024. This review was reported in accordance with the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Results: Twelve studies met the inclusion criteria, totaling 928 participants. Interventions ranged from aerobic and resistance exercises to balance and strength training. A range of physical exercise interventions was explored, including brisk walking, endurance training, weight exercises, and resistance bands, as well as combined programs of aerobics, resistance, and balance training, all tailored to improve symptoms and quality of life in patients with chemotherapy-induced peripheral neuropathy. The meta-analysis focused on five studies that used the FACT/GOG-Ntx scale indicated a standardized mean difference of 0.50 (95% CI: 0.26, 0.74), favoring exercise, reflecting significant improvements in neuropathy symptoms. The heterogeneity among the studies was low (I 2 = 2%), suggesting consistency in the beneficial effects of exercise. Conclusions: From the results analyzed, the descriptive analysis of the 12 included studies shows promising outcomes not only related to individuals' perceptions of CIPN severity but also in terms of physical functioning, balance, ADL (Activities of Daily Living) performance, pain, and quality of life. The findings support the integration of structured exercise programs into oncological treatment plans. (c) 2024 The Authors. Published by Elsevier Inc. on behalf of American Society for Pain Management Nursing. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)