Lung Cancer

Preoperative Prediction of STAS Risk in Primary Lung Adenocarcinoma Using Machine Learning: An Interpretable Model with SHAP Analysis.

BACKGROUND: Accurate preoperative prediction of spread through air spaces (STAS) in primary lung adenocarcinoma (LUAD) is critical for optimizing surgical strategies and improving patient outcomes.

Optimizing imaging modalities for sarcoma subtypes in radiation therapy: State of the art.

The choice of imaging modalities is essential in sarcoma management, as different techniques provide complementary information depending on tumor subtype and anatomical location. This narrative review examines the role of imaging in sarcoma characterization and treatment planning, particularly in the context of radiation therapy (RT). Magnetic resonance imaging (MRI) provides superior soft tissue ...

Precision Oncology in Non-small Cell Lung Cancer: A Comparative Study of Contextualized ChatGPT Models.

OBJECTIVES: The growing adoption of Large Language Models (LLMs) in medicine has raised important questions about their potential utility for clinical...

A tumor-infiltrating B lymphocytes -related index based on machine-learning predicts prognosis and immunotherapy response in lung adenocarcinoma.

INTRODUCTION: Tumor-infiltrating B lymphocytes (TILBs) play a pivotal role in shaping the immune microenvironment of tumors (TIME) and in the progress...

Automated classification of tertiary lymphoid structures in colorectal cancer using TLS-PAT artificial intelligence tool.

Colorectal cancer (CRC) ranks as the third most common and second deadliest cancer worldwide. The immune system, particularly tertiary lymphoid struct...

Development of PDAC diagnosis and prognosis evaluation models based on machine learning.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is difficult to detect early and highly aggressive, often leading to poor patient prognosis. Exist...

Nanomaterial-Based Molecular Imaging in Cancer: Advances in Simulation and AI Integration.

Nanomaterials represent an innovation in cancer imaging by offering enhanced contrast, improved targeting capabilities, and multifunctional imaging mo...

American College of Veterinary Radiology and European College of Veterinary Diagnostic Imaging position statement on artificial intelligence.

The American College of Veterinary Radiology (ACVR) and the European College of Veterinary Diagnostic Imaging (ECVDI) recognize the transformative pot...

Stroma and lymphocytes identified by deep learning are independent predictors for survival in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers known to humans. However, not all patients fare equally poor survival, and a...

A semi-supervised convolutional neural network for diagnosis of pancreatic ductal adenocarcinoma based on EUS-FNA cytological images.

BACKGROUND: The cytological diagnostic process of EUS-FNA smears is time-consuming and manpower-intensive, and the conclusion could be subjective and ...

DPI-MoCo: Deep Prior Image Constrained Motion Compensation Reconstruction for 4D CBCT.

4D cone-beam computed tomography (CBCT) plays a critical role in adaptive radiation therapy for lung cancer. However, extremely sparse sampling projec...

A multimodal framework for assessing the link between pathomics, transcriptomics, and pancreatic cancer mutations.

In Pancreatic Ductal Adenocarcinoma (PDAC), predicting genetic mutations directly from histopathological images using Deep Learning can provide valuab...

Machine learning prediction of overall survival in prostate adenocarcinoma using ensemble techniques.

Prostate adenocarcinoma (PAC) is a complex and common cancer in males and is one of the leading causes of cancer-related death globally. PAC is a mult...

Deep learning radiomics for the prediction of epidermal growth factor receptor mutation status based on MRI in brain metastasis from lung adenocarcinoma patients.

BACKGROUND: Early and accurate identification of epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patient...

Robust vs. Non-robust radiomic features: the quest for optimal machine learning models using phantom and clinical studies.

PURPOSE: This study aimed to select robust features against lung motion in a phantom study and use them as input to feature selection algorithms and m...

Low-dose CT reconstruction using cross-domain deep learning with domain transfer module.

. X-ray computed tomography employing low-dose x-ray source is actively researched to reduce radiation exposure. However, the reduced photon count in ...

Introduction of a hybrid approach based on statistical shape model and Adaptive Neural Fuzzy Inference System (ANFIS) to assess dosimetry uncertainty: A Monte Carlo study.

The increasing use of ionizing radiation has raised concerns about adverse and long-term health risks for individuals. Therefore, to evaluate the rang...

Enhancing pancreatic cancer diagnostics: Ensemble-based model for automated urine biomarker classification.

This research addresses the critical challenge of early detection in pancreatic ductal adenocarcinoma (PDAC) by exploring urinary biomarkers and integ...

Direct-to-Treatment Adaptive Radiation Therapy: Live Planning of Spine Metastases Using Novel Cone Beam Computed Tomography.

PURPOSE: Cone beam computed tomography (CBCT)-based online adaptive radiation therapy is carried out using a synthetic CT (sCT) created through deform...

Prediction of tumor spread through air spaces with an automatic segmentation deep learning model in peripheral stage I lung adenocarcinoma.

BACKGROUND: To evaluate the clinical applicability of deep learning (DL) models based on automatic segmentation in preoperatively predicting tumor spr...