Lung Cancer

PanMB2-Net: a deep learning framework for preoperative MB2 canal risk assessment on panoramic radiographs.

OBJECTIVES: This study aimed to develop and validate a clinically motivated artificial intelligence framework for preoperative risk assessment of the second mesiobuccal (MB2) canal in maxillary first molars using panoramic radiographs. MATERIALS AND METHODS: A total of 388 panoramic radiographs were retrospectively collected. Stage 1 used YOLOv5 to localize maxillary first molars and crop tooth-le...

Radiation-induced erectile dysfunction in prostate cancer: A systematic review of pathophysiology, clinical radiobiology, and predictive modeling.

BACKGROUND: Radiation-induced erectile dysfunction (RIED) remains one of the most prevalent and distressing late toxicities among prostate cancer survivors. Despite advances in radiation therapy techniques, its underlying mechanisms, dose-response relationships, and predictive modeling strategies are not yet fully understood. METHODS: We searched PubMed/MEDLINE, Web of Science, Scopus, Embase and ...

Integrating CT Radiomics and Machine Learning for Preoperative Assessment of STAS Grading and Prognostic Stratification in Lung Adenocarcinoma.

RATIONALE AND OBJECTIVES: The distance of spread through air spaces (STAS) dissemination is associated with prognosis in lung adenocarcinoma (LUAD). T...

Glycosylation-related gene risk model and functional validation of OSTC and TUBA1C in lung adenocarcinoma.

INTRODUCTION: OSTC and TUBA1C drive the malignant progression of lung adenocarcinoma (LUAD) through the modulation of N-glycosylation and the PI3K/AKT...

Evaluating Risk Factors Associating With Late Recurrence After Low-Dose-Rate Brachytherapy for Prostate Cancer Using Machine Learning.

BACKGROUND: The Phoenix definition is widely used to define biochemical recurrence (BCR) after radiation therapy for prostate cancer; however, a defin...

Next-generation precision oncology in microsatellite stable colorectal cancer: integrated targeting beyond RAS and RAF.

The therapeutic landscape of colorectal cancer (CRC) has evolved with the identification of molecular subtypes, including mismatch repair-deficient/mi...

MoCaPS: a machine learning model for stratification of cancer-associated cachexia based on blood biomarkers.

Identification of minimally invasive biomarkers of cancer-associated cachexia may help to recognize high risk patients for progression to more severe ...

BiCervi: Pap smear microscopy image dataset for cervical cancer detection and classification.

Cervical cancer is still a major public-health challenge, especially in low and middle-income countries where there are limited numbers of specialists...

A prospective development and evaluation of a 2D convolutional neural network-based auto-segmentation model for cervical cancer radiotherapy.

BACKGROUND: Accurate delineation of target volumes and organs at risk (OAR) is essential in radiotherapy planning for cervical cancer. Deep learning (...

Image optimization for low-dose 18F-FDG breast PET/MRI using deep learning: a pilot study.

BACKGROUND: Positron emission tomography with magnetic resonance imaging (PET/MRI) provides noninvasive molecular characterization of breast cancer an...

Clinical Study on the Prevention of High-Risk Pulmonary Nodule Progression With Yifei Sanjie Pill: Protocol for a Multicenter Randomized Controlled Trial.

BACKGROUND: High-risk subsolid pulmonary nodules, especially mixed ground-glass nodules, can represent precancerous or early-stage lung adenocarcinoma...

An explainable multimodal 2.5D deep learning-radiomics model for predicting extranodal extension in lung adenocarcinoma using preoperative CT: a multicenter retrospective cohort study.

OBJECTIVE: This study aimed to develop the Radiomics-Assembled ENE system (RAIEm), a multimodal preoperative computed tomography (CT) radiomics model,...

MRI-based radiomics interpretable model predicts microsatellite instability status in endometrioid adenocarcinoma.

OBJECTIVE: To develop and validate a multiparametric MRI-based radiomics model for noninvasive preoperative prediction of microsatellite instability (...

An Interactive Virtual Nephrologist to Engage Patients With Advanced CKD About Transplantation.

RATIONALE & OBJECTIVE: Despite advantages in survival and quality of life with kidney transplantation (KT) compared to other treatments for kidney fai...

Explainable Machine-learning Model Based on Multimodal Ultrasound for Non-invasive Detection of Early Renal Fibrosis: A Multicenter Study.

RATIONALE AND OBJECTIVES: To develop and validate an interpretable, multimodal, ultrasound-based machine-learning (ML) model for the non-invasive iden...

Multi-omics machine learning-driven investigation of super-enhancers signatures and prognostic biomarkers in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common histological subtype of malignant lung tumors, characterized by high incidence and mortality rates. Supe...

Machine learning-based prognostic model and single-cell transcriptomic integration for identifying brain metastasis-associated malignant subpopulations and potential therapeutic targets in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is a highly prevalent and lethal form of lung cancer. Brain metastasis (BrM) is a major cause of mortality in p...

Label-Free Prediction of EGFR Mutation Status Using Fluorescence Lifetime Imaging and Deep Learning in Lung Adenocarcinoma.

UNLABELLED: Accurate prediction of epidermal growth factor receptor (EGFR) mutations is essential for guiding targeted therapy in non-small cell lung ...

ProphDR: An Interpretable Deep Learning Model for Predicting Cancer Drug Response via Multi-Omics and Cross-Attention Mechanisms.

Predicting cancer drug responses (CDRs) accurately remains a significant challenge due to the complexity of tumor biology and the limitations of exist...

Explainable hourly global solar radiation forecasting using a CNN-BiLSTM model with temperature scaled softmax attention.

Accurate global solar radiation (GSR) forecasting is vital for smart grids and resilient energy systems. However, the nonlinear and non-stationary nat...