Lung Cancer

Multimodal data deep learning method for predicting symptomatic pneumonitis caused by lung cancer radiotherapy combined with immunotherapy.

OBJECTIVES: The pairing of immunotherapy and radiotherapy in the treatment of locally advanced nonsmall cell lung cancer (NSCLC) has shown promise. By combining radiotherapy with immunotherapy, the synergistic effects of these modalities not only bolster antitumor efficacy but also exacerbate lung injury. Consequently, developing a model capable of accurately predicting radiotherapy- and immunothe...

Integration of single-cell transcriptomics and bulk transcriptomics to explore prognostic and immunotherapeutic characteristics of nucleotide metabolism in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is a highly aggressive tumor with one of the highest morbidity and mortality rates in the world. Nucleotide metabolic processes are critical for cancer development, progression, and alteration of the tumor microenvironment. However, the effect of nucleotide metabolism on LUAD remains to be thoroughly investigated.

Artificial Intelligence, Machine Learning and Big Data in Radiation Oncology.

This review explores the applications of artificial intelligence and machine learning (AI/ML) in radiation oncology, focusing on computer vision (CV) ...

Partial-Label Contrastive Representation Learning for Fine-Grained Biomarkers Prediction From Histopathology Whole Slide Images.

In the domain of histopathology analysis, existing representation learning methods for biomarkers prediction from whole slide images (WSIs) face chall...

Editorial: Modelling esophageal adenocarcinoma.

Feasibility of reconstructingpatient 3D dose distributions from 2D EPID image data using convolutional neural networks.

. The primary purpose of this work is to demonstrate the feasibility of a deep convolutional neural network (dCNN) based algorithm that uses two-dimen...

Interaction of human gut microbiota and local immune system in progression of colorectal adenoma (MIMICA-1): a protocol for a prospective, observational cohort study.

INTRODUCTION: The current understanding of colorectal carcinogenesis is based on the adenoma-carcinoma sequence, where genetics, intestinal microbiota...

Anti-ceramide antibody and sphingosine-1-phosphate as potential biomarkers of unresectable non-small cell lung cancer.

OBJECTIVES: Spingosine-1-phosphate (S1P) and ceramides are bioactive sphingolipids that influence cancer cell fate. Anti-ceramide antibodies might inh...

Explainable PET-Based Habitat and Peritumoral Machine Learning Model for Predicting Progression-free Survival in Clinical Stage IA Pure-Solid Non-small Cell Lung Cancer: A Two-center Study.

RATIONALE AND OBJECTIVES: This study aimed to develop and validate machine learning (ML) models utilizing positron emission tomography (PET)-habitat o...

Integrating artificial intelligence with endoscopic ultrasound in the early detection of bilio-pancreatic lesions: Current advances and future prospects.

The integration of Artificial Intelligence (AI) in endoscopic ultrasound (EUS) represents a transformative advancement in the early detection and mana...

Explainable Machine Learning to Predict Treatment Response in Advanced Non-Small Cell Lung Cancer.

PURPOSE: Immune checkpoint inhibitors (ICIs) have demonstrated promise in the treatment of various cancers. Single-drug ICI therapy (immuno-oncology [...

A GPU-accelerated fuzzy method for real-time CT volume filtering.

During acquisition and reconstruction, medical images may become noisy and lose diagnostic quality. In the case of CT scans, obtaining less noisy imag...

Identification of key gene signatures for predicting chemo-immunotherapy efficacy in extensive-stage small-cell lung cancer using machine learning.

OBJECTIVES: The lack of definitive biomarkers presents a significant challenge for chemo-immunotherapy in extensive-stage small-cell lung cancer (ES-S...

Exploring machine learning tools in a retrospective case-study of patients with metastatic non-small cell lung cancer treated with first-line immunotherapy: A feasibility single-centre experience.

BACKGROUND: Artificial intelligence (AI) models are emerging as promising tools to identify predictive features among data coming from health records....

Identification of Factors Influencing Donor-Derived Cell-Free DNA Levels up to One Year After Kidney Transplant.

Donor-derived cell-free DNA (dd-cfDNA) in the peripheral blood of allograft recipients has shown to early identify allograft injury. In this study, w...

AI-Driven insights in pancreatic cancer imaging: from pre-diagnostic detection to prognostication.

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related deaths in the United States, largely due to its poor five-year su...

Analysis of diagnostic genes and molecular mechanisms of Crohn's disease and colon cancer based on machine learning algorithms.

Crohn's disease (CD) is a chronic inflammatory bowel condition, and colon adenocarcinoma (COAD), as one of the most prevalent malignant tumors of the ...

Radiomics for differentiating adenocarcinoma and squamous cell carcinoma in non-small cell lung cancer beyond nodule morphology in chest CT.

Distinguishing between primary adenocarcinoma (AC) and squamous cell carcinoma (SCC) within non-small cell lung cancer (NSCLC) tumours holds significa...

Assessing the value of deep neural networks for postoperative complication prediction in pancreaticoduodenectomy patients.

INTRODUCTION: Pancreaticoduodenectomy (PD) for patients with pancreatic ductal adenocarcinoma (PDAC) is associated with a high risk of postoperative c...

Multimodal Deep Learning Fusing Clinical and Radiomics Scores for Prediction of Early-Stage Lung Adenocarcinoma Lymph Node Metastasis.

RATIONALE AND OBJECTIVES: To develop and validate a multimodal deep learning (DL) model based on computed tomography (CT) images and clinical knowledg...