Lung Cancer

Deep-Learning Algorithm and Concomitant Biomarker Identification for NSCLC Prediction Using Multi-Omics Data Integration.

Early diagnosis of lung cancer to increase the survival rate, which is currently at a low range of mid-30%, remains a critical need. Despite this, multi-omics data have rarely been applied to non-small-cell lung cancer (NSCLC) diagnosis. We developed a multi-omics data-affinitive artificial intelligence algorithm based on the graph convolutional network that integrates mRNA expression, DNA methyla...

Radiation therapist perceptions on how artificial intelligence may affect their role and practice.

INTRODUCTION: The use of artificial intelligence (AI) has increased in medical radiation science, with advanced computing and modelling. Considering radiation therapists (RTs) perceptions of how this may affect their role is imperative, as this will contribute to increasing the efficiency of implementation and improve service delivery.

Feasibility of a lung airway navigation system using fiber-Bragg shape sensing and artificial intelligence for early diagnosis of lung cancer.

Currently early diagnosis of malignant lesions at the periphery of lung parenchyma requires guidance of the biopsy needle catheter from the bronchosco...

A machine learning method for improving the accuracy of radiation biodosimetry by combining data from the dicentric chromosomes and micronucleus assays.

A large-scale malicious or accidental radiological event can expose vast numbers of people to ionizing radiation. The dicentric chromosome (DCA) and c...

Compact Wideband Double-Slot Microstrip Feed Engraved TEM Horn Strip Antennas on a Multilayer Substrate Board for in Bed Resting Body Positions Determination Based on Artificial Intelligence.

In this paper, a horn-shaped strip antenna exponentially tapered carved on a multilayer dielectric substrate for an indoor body position tracking syst...

Effect of da Vinci robot-assisted versus traditional thoracoscopic bronchial sleeve lobectomy.

OBJECTIVE: To analyze the short-term effect of Da Vinci robot-assisted thoracoscopic (RATS) bronchial sleeve lobectomy, so as to summarize its safety ...

X-ray dose profiles using artificial neural networks.

This paper introduces a novel computational method to simulate and predict radiation dose profiles in a water phantom irradiated by X-rays of 6 and 15...

Computed Tomography of the Spine : Systematic Review on Acquisition and Reconstruction Techniques to Reduce Radiation Dose.

The introduction of the first whole-body CT scanner in 1974 marked the beginning of cross-sectional spine imaging. In the last decades, the technologi...

Short-term outcomes of robot-assisted versus conventional laparoscopic surgery for mid and low rectal cancer after neoadjuvant chemoradiotherapy: a propensity score-matched analysis.

The benefits of robot-assisted laparoscopic surgery (RALS) for rectal cancer remain controversial. Only a few studies have evaluated the safety and fe...

A multidomain fusion model of radiomics and deep learning to discriminate between PDAC and AIP based on F-FDG PET/CT images.

PURPOSE: To explore a multidomain fusion model of radiomics and deep learning features based on F-fluorodeoxyglucose positron emission tomography/comp...

Radiation pneumonitis prediction after stereotactic body radiation therapy based on 3D dose distribution: dosiomics and/or deep learning-based radiomics features.

BACKGROUND: This study was designed to establish radiation pneumonitis (RP) prediction models using dosiomics and/or deep learning-based radiomics (DL...

Deep learning for predicting major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: A multicentre study.

BACKGROUND: This study, based on multicentre cohorts, aims to utilize computed tomography (CT) images to construct a deep learning model for predictin...

Radiation Dosimetry, Artificial Intelligence and Digital Twins: Old Dog, New Tricks.

Developments in artificial intelligence, particularly convolutional neural networks and deep learning, have the potential for problem solving that has...

Deep reinforcement learning and its applications in medical imaging and radiation therapy: a survey.

Reinforcement learning takes sequential decision-making approaches by learning the policy through trial and error based on interaction with the enviro...

Patient-specific transfer learning for auto-segmentation in adaptive 0.35 T MRgRT of prostate cancer: a bi-centric evaluation.

BACKGROUND: Online adaptive radiation therapy (RT) using hybrid magnetic resonance linear accelerators (MR-Linacs) can administer a tailored radiation...

Deep learning to estimate durable clinical benefit and prognosis from patients with non-small cell lung cancer treated with PD-1/PD-L1 blockade.

Different biomarkers based on genomics variants have been used to predict the response of patients treated with PD-1/programmed death receptor 1 ligan...

Uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology.

A model's ability to express its own predictive uncertainty is an essential attribute for maintaining clinical user confidence as computational biomar...

Revealing low-temperature plasma efficacy through a dose-rate assessment by DNA damage detection combined with machine learning models.

Low-temperature plasmas have quickly emerged as alternative and unconventional types of radiation that offer great promise for various clinical modali...

Emergence of MXene and MXene-Polymer Hybrid Membranes as Future- Environmental Remediation Strategies.

The continuous deterioration of the environment due to extensive industrialization and urbanization has raised the requirement to devise high-performa...

An Unsupervised Deep Learning-Based Model Using Multiomics Data to Predict Prognosis of Patients with Stomach Adenocarcinoma.

METHODS: Patients (363 in total) with stomach adenocarcinoma from The Cancer Genome Atlas (TCGA) cohort were included. An autoencoder was constructed ...