Lung Cancer

Classification of non-small cell lung cancer by histologic subtype using deep learning in public and private data sets of computed tomography images.

OBJECTIVE: To develop a deep learning system to classify non-small cell lung cancer (NSCLC) by histologic subtype-adenocarcinoma or squamous cell carcinoma (SCC)-from computed tomography (CT) images in which the tumor regions were segmented, comparing our results with those of similar studies conducted in other countries and evaluating the accuracy of automated classification by using data from th...

Non-invasive CT based multiregional radiomics for predicting pathologic complete response to preoperative neoadjuvant chemoimmunotherapy in non-small cell lung cancer.

PURPOSE: This study aims to develop and validate a multiregional radiomics model to predict pathological complete response (pCR) to neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC), and further evaluate the performance of the model in different specific subgroups (N2 stage and anti-PD-1/PD-L1).

Non-orthogonal kV imaging guided patient position verification in non-coplanar radiation therapy with dataset-free implicit neural representation.

BACKGROUND: Cone-beam CT (CBCT) is crucial for patient alignment and target verification in radiation therapy (RT). However, for non-coplanar beams, p...

Study on the relationship between vaginal dose and radiation-induced vaginal injury following cervical cancer radiotherapy, and model development.

OBJECTIVE: This study investigates the relationship between vaginal radiation dose and radiation-induced vaginal injury in cervical cancer patients, w...

Radiomics of Dynamic Contrast-Enhanced MRI for Predicting Radiation-Induced Hepatic Toxicity After Intensity Modulated Radiotherapy for Hepatocellular Carcinoma: A Machine Learning Predictive Model Based on the SHAP Methodology.

OBJECTIVE: To develop an interpretable machine learning (ML) model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomic data,...

Emerging research themes in ferroptosis research for non-small cell lung cancer: a bibliometric and visualized analysis.

BACKGROUND: Ferroptosis, an iron-dependent form of regulated cell death, has garnered significant attention as a potential therapeutic target in oncol...

2.5D Multi-view Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-count PET Reconstruction with CT-less Attenuation Correction.

Positron Emission Tomography (PET) is an important clinical imaging tool but inevitably introduces radiation exposure to patients and healthcare provi...

A novel model for predicting immunotherapy response and prognosis in NSCLC patients.

BACKGROUND: How to screen beneficiary populations has always been a clinical challenge in the treatment of non-small-cell lung cancer (NSCLC) with imm...

External Validation of a CT-Based Radiogenomics Model for the Detection of EGFR Mutation in NSCLC and the Impact of Prevalence in Model Building by Using Synthetic Minority Over Sampling (SMOTE): Lessons Learned.

RATIONALE AND OBJECTIVES: Radiogenomics holds promise in identifying molecular alterations in nonsmall cell lung cancer (NSCLC) using imaging features...

Assembling ″Balloon-String″ Colorimetric Probes through Click Chemistry and Hybridization Chain Reaction for Dual-Signal Amplification in Aptamer-Based Lateral Flow Assays.

As a simple and visually assessable point-of-care testing method, lateral flow assays (LFAs) are widely used for detecting procymidone in vegetable sa...

An integrated machine learning-based prognostic model in head and neck cancer using the systemic inflammatory response index and correlations with patient reported financial toxicity.

OBJECTIVE: To investigate the prognostic utility of systemic inflammatory response index (SIRI) as a biological readout of stress associated immune mo...

Machine learning models for predicting survival in lung cancer patients undergoing microwave ablation.

OBJECTIVE: To develop and validate predictive models assessing survival outcomes in patients with non-small cell lung cancer (NSCLC) treated with micr...

A biorefinery approach for the simultaneous obtention of essential oils, organic acids and polyphenols from citrus peels: Phytochemical characterization and bioactive potential.

This investigation evaluates the valorization of citrus peels (lemon, tangerine, and orange) to recover both on-polar and polar fractions simultaneous...

Thorax-encompassing multi-modality PET/CT deep learning model for resected lung cancer prognostication: A retrospective, multicenter study.

BACKGROUND: Patients with early-stage non-small cell lung cancer (NSCLC) typically receive surgery as their primary form of treatment. However, studie...

GLIO-Select: Machine Learning-Based Feature Selection and Weighting of Tissue and Serum Proteomic and Metabolomic Data Uncovers Sex Differences in Glioblastoma.

Glioblastoma (GBM) is a fatal brain cancer known for its rapid and aggressive growth, with some studies indicating that females may have better surviv...

Detecting the left atrial appendage in CT localizers using deep learning.

Patients with cardioembolic stroke often undergo CT of the left atrial appendage (LAA), for example, to determine whether thrombi are present in the L...

Predicting Gene Comutation of EGFR and TP53 by Radiomics and Deep Learning in Patients With Lung Adenocarcinomas.

PURPOSE: This study was designed to construct progressive binary classification models based on radiomics and deep learning to predict the presence of...

Machine learning in prediction of epidermal growth factor receptor status in non-small cell lung cancer brain metastases: a systematic review and meta-analysis.

BACKGROUND: Epidermal growth factor receptor (EGFR) mutations are present in 10-60% of all non-small cell lung cancer (NSCLC) patients and are associa...

Deep learning-based classification of coronary arteries and left ventricle using multimodal data for autonomous protocol selection or adjustment in angiography.

Optimal selection of X-ray imaging parameters is crucial in coronary angiography and structural cardiac procedures to ensure optimal image quality and...

Evaluation of deliverable artificial intelligence-based automated volumetric arc radiation therapy planning for whole pelvic radiation in gynecologic cancer.

This study aimed to develop a deep learning (DL)-based deliverable whole pelvic volumetric arc radiation therapy (VMAT) for patients with gynecologic ...