Exploring the efficacy of multi-flavored feature extraction with radiomics and deep features for prostate cancer grading on mpMRI.

Background: The purpose of this study is to investigate the use of radiomics and deep features obtained from multiparametric magnetic resonance imaging (mpMRI) for grading prostate cancer. We propose a novel approach called multi-flavored feature extraction or tensor, which combines four mpMRI images using eight different fusion techniques to create 52 images or datasets for each patient. We evaluate the effectiveness of this approach in grading prostate cancer and compare it to traditional methods.

Auto-segmentation of head and neck tumors in positron emission tomography images using non-local means and morphological frameworks.

Purpose: Accurately segmenting head and neck cancer (HNC) tumors in medical images is crucial for effective treatment planning. However, current methods for HNC segmentation are limited in their accuracy and efficiency. The present study aimed to design a model for segmenting HNC tumors in three-dimensional (3D) positron emission tomography (PET) images using Non-Local Means (NLM) and morphological operations.

Thyroid function following radiation therapy in breast cancer patients: risk of radiation-induced hypothyroidism.

Background: Radiation exposure to the thyroid gland seems unavoidable in breast cancer (BC) patients receiving radiation therapy (RT) to the supraclavicular (SC) region. Hence, this study aimed to evaluate the effects of SC region RT on thyroid function and the prevalence of radiation-induced hypothyroidism (RIHT) in BC patients at regular intervals post-treatment.

Materials And Methods: Twenty-one patients with BC were enrolled in this analytical cross-sectional study by simple and convenient sampling, from March 2019 to March 2020.

Machine learning approach for automated predicting of COVID-19 severity based on clinical and paraclinical characteristics: Serum levels of zinc, calcium, and vitamin D.

Background & Aims: Considering that no standard therapy has yet been found for the novel coronavirus disease (COVID-19), identifying severe cases as early as possible, and such that treatment procedures can be escalated seems necessary. Hence, the present study aimed to develop a machine learning (ML) approach for automated severity assessment of COVID-19 based on clinical and paraclinical characteristics like serum levels of zinc, calcium, and vitamin D.

Methods: In this analytical cross-sectional study which was conducted from May 2020 to May 2021, clinical and paraclinical data sets of COVID-19-positive patients with known outcomes were investigated by combining statistical comparison and correlation methods with ML algorithms, including Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM).

Breast cancer prediction with transcriptome profiling using feature selection and machine learning methods.

Background: We used a hybrid machine learning systems (HMLS) strategy that includes the extensive search for the discovery of the most optimal HMLSs, including feature selection algorithms, a feature extraction algorithm, and classifiers for diagnosing breast cancer. Hence, this study aims to obtain a high-importance transcriptome profile linked with classification procedures that can facilitate the early detection of breast cancer.

Methods: In the present study, 762 breast cancer patients and 138 solid tissue normal subjects were included.

Investigation of how stimulation intensity of rTMS affects magneto permeabilization of the Blood-Brain Barrier (BBB).

The present study aimed to assess the effect of the rTMS (repetitive Transcranial Magnetic Stimulation) intensity on the permeability of the BBB for brain-targeted drug delivery. For this purpose, different rTMS intensities including 70%, 100%, and 130% of Resting Motor Threshold (RMT) assessed in three groups of rats (three groups of 5 rats). Stimulation applied over the right hemisphere of the animals.

Pulsed high magnetic field-induced reversible blood-brain barrier permeability to enhance brain-targeted drug delivery.

The present study aimed to select an effective Pulsed High Magnetic Field (PHMF) stimulation protocol that would induce the Blood-Brain Barrier's (BBB) reversible permeability to enhance brain-targeted drug delivery. PHMF was applied to the skull over the right hemisphere of 60 Wistar rats. The sham group contained other 10 rats that did not receive PHMF stimulation.

Comparative study of the effects of venlafaxine and duloxetine on chemotherapy-induced peripheral neuropathy.

Objective: One of the complications of chemotherapy is peripheral neuropathy. Various studies have shown that potent norepinephrine and serotonin reuptake inhibitors such as gabapentin, venlafaxine and duloxetine have therapeutic effects on neuropathy. The aim of this study was to compare the effects of venlafaxine vs.

Influence of the contrast agents on treatment planning dose calculations of prostate and rectal cancers.

Aim: The aim of the present study is to quantify differences in dose calculations caused by using CA and determine if the resulting differences are clinically significant.

Background: The influence of contrast agents (CA) on radiation dose calculations must be taken into account in treatment planning.

Materials And Methods: Eleven patients with pelvic cancers were included in this study and two sets of CTs were taken for each patient (without and with CA) in the same position and coordinates.