Lung Cancer

Improved prognostication of overall survival after radiotherapy in lung cancer patients by an interpretable machine learning model integrating lung and tumor radiomics and clinical parameters.

BACKGROUND: Accurate prognostication of overall survival (OS) for non-small cell lung cancer (NSCLC) patients receiving definitive radiotherapy (RT) is crucial for developing personalized treatment strategies. This study aims to construct an interpretable prognostic model that combines radiomic features extracted from normal lung and from primary tumor with clinical parameters. Our model aimed to ...

Understanding and modeling human-AI interaction of artificial intelligence tool in radiation oncology clinic using deep neural network: a feasibility study using three year prospective data.

Artificial intelligence (AI) based treatment planning tools are being implemented in clinic. However, human interactions with such AI tools are rarely analyzed. This study aims to comprehend human planner's interaction with the AI planning tool and incorporate the analysis to improve the existing AI tool.An in-house AI tool for whole breast radiation therapy planning was deployed in our institutio...

Artificial intelligence neural network and fuzzy modelling of unsteady Sisko trihybrid nanofluids for cancer therapy with entropy insights.

The main objective of the current endeavor is to monitor hypothetical processes utilizing a Sisko tri-hybrid fluid over a rotating disk with entropy g...

Feasibility of Ultra-low Radiation and Contrast Medium Dosage in Aortic CTA Using Deep Learning Reconstruction at 60 kVp: An Image Quality Assessment.

OBJECTIVE: To assess the viability of using ultra-low radiation and contrast medium (CM) dosage in aortic computed tomography angiography (CTA) throug...

Machine learning model for early prediction of survival in gallbladder adenocarcinoma: A comparison study.

The prognosis for gallbladder adenocarcinoma (GBAC), a highly malignant cancer, is not good. In order to facilitate individualized risk stratification...

Exploring the spatial effects influencing the EGFR/ERK pathway dynamics with machine learning surrogate models.

The fate of cells is regulated by biochemical reactions taking place inside of them, known as intracellular pathways. Cells display a variety of chara...

Weakly Supervised Multiple Instance Learning Model With Generalization Ability for Clinical Adenocarcinoma Screening on Serous Cavity Effusion Pathology.

Accurate and rapid screening of adenocarcinoma cells in serous cavity effusion is vital in diagnosing the stage of metastatic tumors and providing pro...

Clinical commissioning and introduction of an in-house artificial intelligence (AI) platform for automated head and neck intensity modulated radiation therapy (IMRT) treatment planning.

BACKGROUND AND PURPOSE: To describe the clinical commissioning of an in-house artificial intelligence (AI) treatment planning platform for head-and-ne...

Automated real-world data integration improves cancer outcome prediction.

The digitization of health records and growing availability of tumour DNA sequencing provide an opportunity to study the determinants of cancer outcom...

Automatic delineation of cervical cancer target volumes in small samples based on multi-decoder and semi-supervised learning and clinical application.

Radiotherapy has been demonstrated to be one of the most significant treatments for cervical cancer, during which accurate and efficient delineation o...

Non-small cell lung cancer detection through knowledge distillation approach with teaching assistant.

Non-small cell lung cancer (NSCLC) exhibits a comparatively slower rate of metastasis in contrast to small cell lung cancer, contributing to approxima...

Association of artificial intelligence-based immunoscore with the efficacy of chemoimmunotherapy in patients with advanced non-squamous non-small cell lung cancer: a multicentre retrospective study.

PURPOSE: Currently, chemoimmunotherapy is effective only in a subset of patients with advanced non-squamous non-small cell lung cancer. Robust biomark...

Evaluating the Efficacy of Deep Learning Reconstruction in Reducing Radiation Dose for Computer-Aided Volumetry for Liver Tumor: A Phantom Study.

OBJECTIVE: The purpose of this study was to compare radiation dose reduction capability for accurate liver tumor measurements of a computer-aided volu...

Human-Artificial Intelligence Symbiotic Reporting for Theranostic Cancer Care.

Reporting of diagnostic nuclear images in clinical cancer management is generally qualitative. Theranostic treatment with Lu radioligands for prostate...

Enhanced NSCLC subtyping and staging through attention-augmented multi-task deep learning: A novel diagnostic tool.

OBJECTIVES: The objective of this study is to develop a novel multi-task learning approach with attention encoders for classifying histologic subtypes...

Machine learning for enhancing prediction of biogas production and building a VFA/ALK soft sensor in full-scale dry anaerobic digestion of kitchen food waste.

Based on operational data collected over 1.5 years from four full-scale dry anaerobic digesters used for kitchen food waste treatment, this study adop...

Convolutional neural network-based classification of craniosynostosis and suture lines from multi-view cranial X-rays.

Early and precise diagnosis of craniosynostosis (CSO), which involves premature fusion of cranial sutures in infants, is crucial for effective treatme...

Predicting radiation pneumonitis in lung cancer using machine learning and multimodal features: a systematic review and meta-analysis of diagnostic accuracy.

OBJECTIVES: To evaluate the diagnostic accuracy of machine learning models incorporating multimodal features for predicting radiation pneumonitis in l...

Integrated machine learning to predict the prognosis of lung adenocarcinoma patients based on SARS-COV-2 and lung adenocarcinoma crosstalk genes.

Viruses are widely recognized to be intricately associated with both solid and hematological malignancies in humans. The primary goal of this research...

Deep Learning and Automatic Differentiation of Pancreatic Lesions in Endoscopic Ultrasound: A Transatlantic Study.

INTRODUCTION: Endoscopic ultrasound (EUS) allows for characterization and biopsy of pancreatic lesions. Pancreatic cystic neoplasms (PCN) include muci...