PET/CT-based 3D multi-class semantic segmentation of ovarian cancer and the stability of the extracted radiomics features.

Accurate segmentation of ovarian cancer (OC) lesions in PET/CT images is essential for effective disease management, yet manual segmentation for radiomics analysis is labor-intensive and time-consuming. This study introduces the application of a 3D U-Net deep learning model, leveraging advanced 3D networks, for multi-class semantic segmentation of OC in PET/CT images and assesses the stability of the extracted radiomics features. Utilizing a dataset of 3120 PET/CT images from 39 OC patients, the dataset was divided into training (70%), validation (15%), and test (15%) subsets to optimize and evaluate the model's performance.

Assessing Heterogeneity Effects on Points A, B, and Organs at Risk Doses in High-dose-Rate Brachytherapy for Cervical Cancer - A Comparison of Ir and Co Sources Using Monte Carlo N-Particle 5.

Purpose: The present article deals with investigating the effects of tissue heterogeneity consideration on the dose distribution of Ir and Co sources in high-dose-rate brachytherapy (HDR-BT).

Materials And Methods: A Monte Carlo N-Particle 5 (MCNP5) code was developed for the simulation of the dose distribution in homogeneous and heterogeneous phantoms for cervical cancer patients. The phantoms represented water-equivalent and human body-equivalent tissues.

Readiness for advance care planning and related factors in the general population: a cross sectional study in Iran.

Context: Advance Care Planning (ACP), as a process for expressing and recording patients' preferences about end-of-life care, has received increasing attention in recent years. However, implementing ACP has been challenging in Iran.

Objectives: To assess the readiness for advance care planning and related factors in the general population of Iran.

Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities.

Ovarian cancer poses a major worldwide health issue, marked by high death rates and a deficiency in reliable diagnostic methods. The precise and prompt detection of ovarian cancer holds great importance in advancing patient outcomes and determining suitable treatment plans. Medical imaging techniques are vital in diagnosing ovarian cancer, but achieving accurate diagnoses remains challenging.

The OCDA-Net: a 3D convolutional neural network-based system for classification and staging of ovarian cancer patients using [F]FDG PET/CT examinations.

Objective: To create the 3D convolutional neural network (CNN)-based system that can use whole-body [F]FDG PET for recurrence/post-therapy surveillance in ovarian cancer (OC).

Methods: In this study, 1224 image sets from OC patients who underwent whole-body [F]FDG PET/CT at Kowsar Hospital between April 2019 and May 2022 were investigated. For recurrence/post-therapy surveillance, diagnostic classification as cancerous, and non-cancerous and staging as stage III, and stage IV were determined by pathological diagnosis and specialists' interpretation.

Relationship between blood clots and COVID-19 vaccines: A literature review.

SARS-CoV-2 pandemic is one of the most critical pandemics during human civilization. Several therapeutic strategies for COVID-19 management have been offered; nonetheless, none of them seems to be sufficiently beneficial. In effect, vaccines have been proffered as a viable option.

The influence of tool path strategies on cutting force and surface texture during ball end milling of low curvature convex surfaces.

Advancement in machining technology of curved surfaces for various engineering applications is increasing. Various methodologies and computer tools have been developed by the manufacturers to improve efficiency of freeform surface machining. Selection of the right sets of cutter path strategies and appropriate cutting conditions is extremely important in ensuring high productivity rate, meeting the better quality level, and lower cutting forces.