Lung Cancer

Proteomic Profiling of Plasma Extracellular Vesicles Combined with Multivariate Modeling Identified Potential Biomarkers for Distinguishing Benign Pulmonary Nodules from Early-Stage Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer and is difficult to distinguish from benign pulmonary nodules (BPNs), particularly at early stages. Extracellular vesicles (EVs) represent a promising source of biomarkers for the diagnosis of malignant pulmonary nodules. This study aimed to identify robust and clinically relevant EV-based protein biomarkers via isolation with EX...

Ensemble learning-based radiomics model for predicting radiation-induced temporal lobe injury in nasopharyngeal carcinoma.

PURPOSE: To develop and validate a multimodal ensemble machine learning model integrating multi-sequence magnetic resonance imaging (MRI) radiomics, clinical characteristics, and hematological biomarkers for early prediction of Radiation-induced temporal lobe injury (RTLI) in Nasopharyngeal carcinoma (NPC) patients before radiotherapy. METHODS: A total of 161 NPC patients treated with intensity-mo...

Computed Tomography-Pathology Deep Learning Model for the Precise Prediction of Recurrence in Pathological Stage IA Lung Adenocarcinoma.

BACKGROUND: The postoperative prognosis of pathological stage IA lung adenocarcinoma (LUAD) exhibits significant heterogeneity. While the tumor node m...

Combining the prognostic values of entropy-based heterogeneity features from 18 F-fluorodeoxyglucose PET and transmission computed tomography using machine learning in patients with lung adenocarcinoma undergoing curative surgery.

OBJECTIVE: The objective of this study is to evaluate the combined prognostic values of 18 F-fluorodeoxyglucose ( 18 F-FDG) PET and computed tomograph...

A multicenter multimodel habitat radiomics model for predicting immunotherapy response in advanced NSCLC.

A robust predictive biomarker is critical for identifying patients with NSCLC who may benefit from immunotherapy. This study developed a CT-based habi...

Automated Lymph Node and Extranodal Extension Assessment Improves Risk Stratification in Oropharyngeal Carcinoma.

PURPOSE: Extranodal extension (ENE) is a biomarker in oropharyngeal carcinoma (OPC) but can only be diagnosed via surgical pathology. We applied an au...

Smooth Muscle Disruptive Feature Defines Molecular Subtyping, Risk Stratification, and Metastatic Potential in Prostate Cancer.

BACKGROUND: Smooth muscle (SM) invasion represents a critical feature of prostate cancer (PCa) progression and metastasis. This study aimed to develop...

Relationship between cardiometabolic index and worsening renal function in T2DM: Insights from machine learning, growth mixture modelling, and development of a web-based risk prediction tool.

AIM: Worsening renal function (WRF) is a common and serious complication of type 2 diabetes mellitus (T2DM), contributing to adverse clinical outcomes...

The actual performance of ML/AI models in predicting radiation-induced toxicity in head and neck cancer: a systematic review and meta-analysis.

An increasing number of Artificial intelligence (AI) and machine learning (ML) models are being developed to predict radiation-induced toxicities (RIT...

Bayesian selection of lipidome dynamics features for a K-nearest neighbors (KNN) classifier model of tumor response in patients with advanced gastrointestinal adenocarcinoma undergoing systemic treatment.

BACKGROUND: Dysregulated lipid metabolism is common in patients with gastrointestinal (GI) cancer. This study investigated the ability of plasma lipid...

Amentoflavone from Ginkgo biloba inhibits EMT-driven lung cancer metastasis by targeting TGFBR2: Integrative network pharmacology, machine learning, and experimental validation.

ETHNOPHARMACOLOGICAL RELEVANCE: In non-small cell lung cancer (NSCLC), fatal outcomes predominantly result from metastatic dissemination, highlighting...

Artificial intelligence takes on the multidisciplinary committee: A single-center study for rectal cancer management.

BACKGROUND: As rectal cancer management evolves, the multidisciplinary committee becomes increasingly important in integrating expertise to optimize p...

Interpretable fusion deep learning on super-resolution MRI for perineural invasion prediction in pancreatic ductal adenocarcinoma: a multicenter study.

PURPOSE: To develop an interpretable fusion deep learning model based on super-resolution (SR) MRI for predicting preoperative perineural invasion (PN...

Clinical practice, barriers to implementation, and priorities for equitable access of Stereotactic Body Radiation Therapy: An analysis of the global status by the ESTRO SBRT Focus Group.

BACKGROUND: Stereotactic Body Radiation Therapy (SBRT) has become an established treatment for several primary and metastatic malignancies; however, c...

Multimodality Artificial Intelligence for Involved-Site Radiation Therapy: Clinical Target Volume Delineation in High-Risk Pediatric Hodgkin Lymphoma.

PURPOSE: Clinical target volume (CTV) delineation for involved-site radiation therapy (ISRT) in Hodgkin lymphoma (HL) is time-consuming because of the...

FRET-SAM: SAM_Med2D-based automatic FRET two-hybrid analysis.

BACKGROUND AND OBJECTIVE: The fluorescence resonance energy transfer (FRET) two-hybrid assay enables quantification of the stoichiometry and binding a...

Circadian rhythm: An emerging force for colorectal cancer treatment.

The relationship between circadian rhythm disorder and the occurrence and progression of colorectal cancer (CRC) has received attention. The circadian...

Emerging nanoparticle-based therapies for pancreatic cancer: Current clinical landscape.

Pancreatic cancer, particularly ductal adenocarcinoma (PDAC) is one of the most aggressive and lethal subtypes due to late diagnosis, the absence of e...

Establishment of CT diagnostic reference levels (DRLs) in Tokyo.

OBJECTIVE: Diagnostic reference levels (DRLs) are essential for optimizing radiation dose in CT examinations. However, current DRLs may not reflect th...

Attention-Guided Deep Learning of Chemical Exchange Saturation Transfer Magnetic Resonance Imaging to Differentiate Between Tumor Progression and Radiation Necrosis in Brain Metastasis.

PURPOSE: Stereotactic radiosurgery (SRS) is a standard treatment for brain metastases; however, it may lead to radiation necrosis (RN). RN can be virt...