Oncology/Hematology

State-of-the-art surgery for pancreatic cancer.

BACKGROUND: The d evelopment of surgical techniques and specialization and specifically complication management in pancreatic surgery have improved surgical outcomes as well as oncological results in pancreatic surgery in recent decades. Historical morbidity and especially mortality rates of up to 80% have decreased to below 5% today. This review summarizes the current state of the art in pancreat...

Concomitant robot-assisted laparoscopic surgeries for upper and lower urinary tract malignancies: a comprehensive literature review.

Worldwide, we have witnessed an expansion of robot-assisted laparoscopic surgery (RALS) and thanks to the global adoption of high-resolution diagnostic imaging technologies, an increased incidence of newly diagnosed prostatic, renal and bladder cancers has been recorded with concurrent second primary urological cancer diagnoses increasing by 1.5%. Diverse authors have reported their findings conce...

A deep learning method for counting white blood cells in bone marrow images.

BACKGROUND: Differentiating and counting various types of white blood cells (WBC) in bone marrow smears allows the detection of infection, anemia, and...

Machine learning random forest for predicting oncosomatic variant NGS analysis.

Since 2017, we have used IonTorrent NGS platform in our hospital to diagnose and treat cancer. Analyzing variants at each run requires considerable ti...

Integrated Cells and Collagen Fibers Spatial Image Analysis.

Modern technologies designed for tissue structure visualization like brightfield microscopy, fluorescent microscopy, mass cytometry imaging (MCI) and ...

Multimodal MRI Analysis of Cervical Cancer on the Basis of Artificial Intelligence Algorithm.

The purpose of this study is to explore the application value of artificial intelligence algorithm in multimodal MRI image diagnosis of cervical cance...

Development of a Machine Learning Classifier for Brain Tumors Diagnosis Based on DNA Methylation Profile.

More than 150 types of brain tumors have been documented. Accurate diagnosis is important for making appropriate therapeutic decisions in treating th...

Adaptive fuzzy deformable fusion and optimized CNN with ensemble classification for automated brain tumor diagnosis.

Automatic classification of brain tumor plays a vital role to speed up the treatment procedure, plan and boost the survival rate of patients. Nowadays...

Deep learning predicts epidermal growth factor receptor mutation subtypes in lung adenocarcinoma.

PURPOSE: This study aimed to explore the predictive ability of deep learning (DL) for the common epidermal growth factor receptor (EGFR) mutation subt...

Selective identification and localization of indolent and aggressive prostate cancers via CorrSigNIA: an MRI-pathology correlation and deep learning framework.

Automated methods for detecting prostate cancer and distinguishing indolent from aggressive disease on Magnetic Resonance Imaging (MRI) could assist i...

Evaluating the presence of deregulated tumoral onco-microRNAs in serum-derived exosomes of gastric cancer patients as noninvasive diagnostic biomarkers.

Exosomal microRNAs (miRNAs) are emerging diagnostic biomarkers for different types of cancers. We aim to detect gastric cancer (GC)-specific miRNAs i...

The regulatory and modulatory roles of TRP family channels in malignant tumors and relevant therapeutic strategies.

Transient receptor potential (TRP) channels are one primary type of calcium (Ca) permeable channels, and those relevant transmembrane and intracellula...

Improving diagnosing performance for malignant parotid gland tumors using machine learning with multifeatures based on diffusion-weighted magnetic resonance imaging.

In this study, the performance of machine learning in classifying parotid gland tumors based on diffusion-related features obtained from the parotid g...

The viral expression and immune status in human cancers and insights into novel biomarkers of immunotherapy.

BACKGROUND: Viral infections are prevalent in human cancers and they have great diagnostic and theranostic values in clinical practice. Recently, thei...

Identification of Uncontrolled Symptoms in Cancer Patients Using Natural Language Processing.

CONTEXT: For patients with cancer, uncontrolled pain and other symptoms are the leading cause of unplanned hospitalizations. Early access to specialty...

Accurate classification of plasma cell dyscrasias is achieved by combining artificial intelligence and flow cytometry.

Monoclonal gammopathy of unknown significance (MGUS), smouldering multiple myeloma (SMM), and multiple myeloma (MM) are very common neoplasms. However...

Building a predictive model to assist in the diagnosis of cervical cancer.

Cervical cancer is still one of the most common gynecologic cancers in the world. Since cervical cancer is a potentially preventive cancer, earlier d...

A priori prediction of local failure in brain metastasis after hypo-fractionated stereotactic radiotherapy using quantitative MRI and machine learning.

This study investigated the effectiveness of pre-treatment quantitative MRI and clinical features along with machine learning techniques to predict lo...

Application potential of biogenically synthesized silver nanoparticles using L. extracts as pharmaceuticals and catalysts for organic pollutant degradation.

This study was designed to evaluate the optimal conditions for the eco-friendly synthesis of silver nanoparticles (AgNPs) using L. (Lythraceae) aqueo...

Identification of Benign and Malignant Lung Nodules in CT Images Based on Ensemble Learning Method.

BACKGROUND AND OBJECTIVE: Under the background of urgent need for computer-aided technology to provide physicians with objective decision support, aim...