New Associations with the HIV Predisposing and Protective Alleles of the Human Leukocyte Antigen System in a Peruvian Population.

The accurate determination of an individual's unique human leukocyte antigen (HLA) allele holds important significance in evaluating the risk associated with autoimmune and infectious diseases, such as human immunodeficiency virus (HIV) infection. Several allelic variants within the HLA system have been linked to either increased protection or susceptibility in the context of infectious and autoimmune diseases. This study aimed to determine the frequency and association of HLA alleles between people living with HIV (PLHIV) as the case group and Peruvian individuals without HIV with high-risk behaviors of sexually transmitted diseases as the control group.

Defining the potential for sexual structures-sparing for prostate cancer external beam radiotherapy: A dosimetric study.

Purpose: The purpose of the study was to evaluate the dosimetric impact of sexual-sparing radiotherapy for prostate cancer, with magnetic resonance-only treatment planning.

Material And Methods: Fifteen consecutive patients receiving prostate cancer radiotherapy were selected. A synthetic CT was generated with a deep learning method from each T2-weighted MRI performed at the time of treatment planning.

Variants in the -acetyltranferase 2 gene, acetylator phenotypes and their association with tuberculosis: Findings in Peruvian patients.

Background: Tuberculosis (TB) is a highly prevalent chronic infectious disease in developing countries, with Peru being one of the most affected countries in the world. The variants of the -acetyltransferase 2 () gene are related to xenobiotic metabolism and have potential usefulness in TB studies.

Aim: To determine whether gene variants and acetylator phenotypes are associated with active TB in Peruvian patients.

Road traffic noise prediction model based on artificial neural networks.

This paper proposes a model based on machine learning for the prediction of road traffic noise for the city of Bogota-Colombia. The input variables of the model were: vehicle capacity, speed, type of flow and number of lanes. The input data were obtained through measurement campaigns in which audio and video recordings were made.

3D Unsupervised deep learning method for magnetic resonance imaging-to-computed tomography synthesis in prostate radiotherapy.

Background And Purpose: Magnetic resonance imaging (MRI)-to-computed tomography (CT) synthesis is essential in MRI-only radiotherapy workflows, particularly through deep learning techniques known for their accuracy. However, current supervised methods are limited to specific center's learnings and depend on registration precision. The aim of this study was to evaluate the accuracy of unsupervised and supervised approaches in the context of prostate MRI-to-CT generation for radiotherapy dose calculation.

A deep learning model to generate synthetic CT for prostate MR-only radiotherapy dose planning: a multicenter study.

Introduction: For radiotherapy based solely on magnetic resonance imaging (MRI), generating synthetic computed tomography scans (sCT) from MRI is essential for dose calculation. The use of deep learning (DL) methods to generate sCT from MRI has shown encouraging results if the MRI images used for training the deep learning network and the MRI images for sCT generation come from the same MRI device. The objective of this study was to create and evaluate a generic DL model capable of generating sCTs from various MRI devices for prostate radiotherapy.

Computed tomography synthesis from magnetic resonance imaging using cycle Generative Adversarial Networks with multicenter learning.

Addressing the need for accurate dose calculation in MRI-only radiotherapy, the generation of synthetic Computed Tomography (sCT) from MRI has emerged. Deep learning (DL) techniques, have shown promising results in achieving high sCT accuracies. However, existing sCT synthesis methods are often center-specific, posing a challenge to their generalizability.

Clinical Application of Digital and Computational Pathology in Renal Cell Carcinoma: A Systematic Review.

Context: Computational pathology is a new interdisciplinary field that combines traditional pathology with modern technologies such as digital imaging and machine learning to better understand the diagnosis, prognosis, and natural history of many diseases.

Objective: To provide an overview of digital and computational pathology and its current and potential applications in renal cell carcinoma (RCC).

Evidence Acquisition: A systematic review of the English-language literature was conducted using the PubMed, Web of Science, and Scopus databases in December 2022 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PROSPERO ID: CRD42023389282).

Determination of acceptable Hounsfield units uncertainties via a sensitivity analysis for an accurate dose calculation in the context of prostate MRI-only radiotherapy.

Radiation therapy is moving from CT based to MRI guided planning, particularly for soft tissue anatomy. An important requirement of this new workflow is the generation of synthetic-CT (sCT) from MRI to enable treatment dose calculations. Automatic methods to determine the acceptable range of CT Hounsfield Unit (HU) uncertainties to avoid dose distribution errors is thus a key step toward safe MRI-only radiotherapy.

Selective sparing of bladder and rectum sub-regions in radiotherapy of prostate cancer combining knowledge-based automatic planning and multicriteria optimization.

Background And Purpose: The association between dose to selected bladder and rectum symptom-related sub-regions (SRS) and late toxicity after prostate cancer radiotherapy has been evidenced by voxel-wise analyses. The aim of the current study was to explore the feasibility of combining knowledge-based (KB) and multi-criteria optimization (MCO) to spare SRSs without compromising planning target volume (PTV) dose delivery, including pelvic-node irradiation.

Materials And Methods: Forty-five previously treated patients (74.

Voxel-based analysis: Roadmap for clinical translation.

Voxel-based analysis (VBA) allows the full, 3-dimensional, dose distribution to be considered in radiotherapy outcome analysis. This provides new insights into anatomical variability of pathophysiology and radiosensitivity by removing the need for a priori definition of organs assumed to drive the dose response associated with patient outcomes. This approach may offer powerful biological insights demonstrating the heterogeneity of the radiobiology across tissues and potential associations of the radiotherapy dose with further factors.

Deep Learning Methodologies Applied to Digital Pathology in Prostate Cancer: A Systematic Review.

Deep learning (DL), often called artificial intelligence (AI), has been increasingly used in Pathology thanks to the use of scanners to digitize slides which allow us to visualize them on monitors and process them with AI algorithms. Many articles have focused on DL applied to prostate cancer (PCa). This systematic review explains the DL applications and their performances for PCa in digital pathology.

Promoter hypermethylation of RARB and GSTP1 genes in plasma cell-free DNA as breast cancer biomarkers in Peruvian women.

Background: Promoter hypermethylation is one of the enabling mechanisms of hallmarks of cancer. Tumor suppressor genes like RARB and GSTP1 have been reported as hypermethylated in breast cancer tumors compared with normal tissues in several populations. This case-control study aimed to determine the association between the promoter methylation ratio (PMR) of RARB and GSTP1 genes (separately and as a group) with breast cancer and its clinical-pathological variables in Peruvian patients, using a liquid biopsy approach.

Voxel-wise analysis: A powerful tool to predict radio-induced toxicity and potentially perform personalised planning in radiotherapy.

Dose - volume histograms have been historically used to study the relationship between the planned radiation dose and healthy tissue damage. However, this approach considers neither spatial information nor heterogenous radiosensitivity within organs at risk, depending on the tissue. Recently, voxel-wise analyses have emerged in the literature as powerful tools to fully exploit three-dimensional information from the planned dose distribution.

Artificial Intelligence-Based Opportunities in Liver Pathology-A Systematic Review.

Background: Artificial Intelligence (AI)-based Deep Neural Networks (DNNs) can handle a wide range of applications in image analysis, ranging from automated segmentation to diagnostic and prediction. As such, they have revolutionized healthcare, including in the liver pathology field.

Objective: The present study aims to provide a systematic review of applications and performances provided by DNN algorithms in liver pathology throughout the Pubmed and Embase databases up to December 2022, for tumoral, metabolic and inflammatory fields.

Deep learning-based segmentation of prostatic urethra on computed tomography scans for treatment planning.

Background And Purpose: The intraprostatic urethra is an organ at risk in prostate cancer radiotherapy, but its segmentation in computed tomography (CT) is challenging. This work sought to: i) propose an automatic pipeline for intraprostatic urethra segmentation in CT, ii) analyze the dose to the urethra, iii) compare the predictions to magnetic resonance (MR) contours.

Materials And Methods: First, we trained Deep Learning networks to segment the rectum, bladder, prostate, and seminal vesicles.

Multi T1-weighted contrast imaging and T1 mapping with compressed sensing FLAWS at 3 T.

Objective: The Fluid And White matter Suppression (FLAWS) MRI sequence provides multiple T1-weighted contrasts of the brain in a single acquisition. However, the FLAWS acquisition time is approximately 8 min with a standard GRAPPA 3 acceleration factor at 3 T. This study aims at reducing the FLAWS acquisition time by providing a new sequence optimization based on a Cartesian phyllotaxis k-space undersampling and a compressed sensing (CS) reconstruction.

Deep Learning-Based Segmentation of Head and Neck Organs-at-Risk with Clinical Partially Labeled Data.

Radiotherapy is one of the main treatments for localized head and neck (HN) cancer. To design a personalized treatment with reduced radio-induced toxicity, accurate delineation of organs at risk (OAR) is a crucial step. Manual delineation is time- and labor-consuming, as well as observer-dependent.

Chip-based digital Polymerase Chain Reaction as quantitative technique for the detection of mutations in breast cancer patients.

Background: is a gene frequently mutated in breast cancer. With the FDA approval of alpelisib, the evaluation of for activating mutations is becoming routinely. Novel platforms for gene analysis as digital PCR (dPCR) are emerging as a potential replacement for the traditional Sanger sequencing.

Quality assurance for MRI-only radiation therapy: A voxel-wise population-based methodology for image and dose assessment of synthetic CT generation methods.

The quality assurance of synthetic CT (sCT) is crucial for safe clinical transfer to an MRI-only radiotherapy planning workflow. The aim of this work is to propose a population-based process assessing local errors in the generation of sCTs and their impact on dose distribution. For the analysis to be anatomically meaningful, a customized interpatient registration method brought the population data to the same coordinate system.

A high-performance method of deep learning for prostate MR-only radiotherapy planning using an optimized Pix2Pix architecture.

Purpose: The first aim was to generate and compare synthetic-CT (sCT) images using a conditional generative adversarial network (cGAN) method (Pix2Pix) for MRI-only prostate radiotherapy planning by testing several generators, loss functions, and hyper-parameters. The second aim was to compare the optimized Pix2Pix model with five other architectures (bulk-density, atlas-based, patch-based, U-Net, and GAN).

Methods: For 39 patients treated by VMAT for prostate cancer, T2-weighted MRI images were acquired in addition to CT images for treatment planning.