Lung Cancer

Effectiveness of AI for Enhancing Computed Tomography Image Quality and Radiation Protection in Radiology: Systematic Review and Meta-Analysis.

BACKGROUND: Artificial intelligence (AI) presents a promising approach to balancing high image quality with reduced radiation exposure in computed tomography (CT) imaging.

Evaluation of MRI anatomy in machine learning predictive models to assess hydrogel spacer benefit for prostate cancer patients.

INTRODUCTION: Hydrogel spacers (HS) are designed to minimise the radiation doses to the rectum in prostate cancer radiation therapy (RT) by creating a physical gap between the rectum and the target treatment volume inclusive of the prostate and seminal vesicles (SV). This study aims to determine the feasibility of incorporating diagnostic MRI (dMRI) information in statistical machine learning (SML...

[MP-MRI in the evaluation of non-operative treatment response, for residual and recurrent tumor detection in head and neck cancer].

As non-surgical therapies gain acceptance in head and neck tumors, the importance of imaging has increased. New therapeutic methods (in radiation ther...

AI for image quality and patient safety in CT and MRI.

Substantial endeavors have been recently dedicated to developing artificial intelligence (AI) solutions, especially deep learning-based, tailored to e...

Development and external validation of a machine learning-based model to predict postoperative recurrence in patients with duodenal adenocarcinoma: a multicenter, retrospective cohort study.

BACKGROUND: Duodenal adenocarcinoma (DA) has a high recurrence rate, making the prediction of recurrence after surgery critically important.

Biomarkers, omics and artificial intelligence for early detection of pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed in its late stages when treatment options are limited. Unlike other common cancers, th...

Soft-tissue metastasis in esophageal cancer managed by dose escalation radiation therapy: a clinical case and review of literature.

Soft tissue metastasis in esophageal cancer is a very rare entity. A 76-year-old man was referred for a week's history of dysphagia. Upper gastrointes...

Long-term solar radiation forecasting in India using EMD, EEMD, and advanced machine learning algorithms.

Solar radiation plays a critical role in the carbon sequestration processes of terrestrial ecosystems, making it a key factor in environmental sustain...

Comparative performance of PD-L1 scoring by pathologists and AI algorithms.

AIM: This study evaluates the comparative effectiveness of pathologists versus artificial intelligence (AI) algorithms in scoring PD-L1 expression in ...

Radiation oncology at crossroads: Rise of AI and managing the unexpected.

Integrating artificial intelligence (AI) into radiation oncology has revolutionized clinical workflows, enhancing efficiency, safety, and quality. How...

A recursive embedding and clustering technique for unraveling asymptomatic kidney disease using laboratory data and machine learning.

Traditional methods for diagnosing chronic kidney disease (CKD) via laboratory data may not be capable of identifying early kidney disease. Kidney bio...

An interpretable ensemble model combining handcrafted radiomics and deep learning for predicting the overall survival of hepatocellular carcinoma patients after stereotactic body radiation therapy.

PURPOSE: Hepatocellular carcinoma (HCC) remains a global health concern, marked by increasing incidence rates and poor outcomes. This study seeks to d...

MLAR-UNet: LDCT image denoising based on U-Net with multiple lightweight attention-based modules and residual reinforcement.

Computed tomography (CT) is a crucial medical imaging technique which uses x-ray radiation to identify cancer tissues. Since radiation poses a signifi...

Unveiling the crucial role of glycosylation modification in lung adenocarcinoma metastasis through artificial neural network-based spatial multi-omics single-cell analysis and Mendelian randomization.

BACKGROUND: Investigations into the intricacies of glycosylation modifications, a prevalent post-translational alteration observed in neoplasms, espec...

Does Deep Learning Reconstruction Improve Ureteral Stone Detection and Subjective Image Quality in the CT Images of Patients with Metal Hardware?

Diagnosing ureteral stones with low-dose CT in patients with metal hardware can be challenging because of image noise. The purpose of this study was ...

Deep Learning Radiomics for Survival Prediction in Non-Small-Cell Lung Cancer Patients from CT Images.

This study aims to apply a multi-modal approach of the deep learning method for survival prediction in patients with non-small-cell lung cancer (NSCLC...

A promising AI based super resolution image reconstruction technique for early diagnosis of skin cancer.

Skin cancer can be prevalent in people of any age group who are exposed to ultraviolet (UV) radiation. Among all other types, melanoma is a notable se...

Radiomics integration based on intratumoral and peritumoral computed tomography improves the diagnostic efficiency of invasiveness in patients with pure ground-glass nodules: a machine learning, cross-sectional, bicentric study.

BACKGROUND: Radiomics has shown promise in the diagnosis and prognosis of lung cancer. Here, we investigated the performance of computed tomography-ba...

Identifying invasiveness to aid lung adenocarcinoma diagnosis using deep learning and pathomics.

Most classification efforts for primary subtypes of lung adenocarcinoma (LUAD) have not yet been integrated into clinical practice. This study explore...

CT-Less Whole-Body Bone Segmentation of PET Images Using a Multimodal Deep Learning Network.

In bone cancer imaging, positron emission tomography (PET) is ideal for the diagnosis and staging of bone cancers due to its high sensitivity to malig...