Lung Cancer

Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma.

Early cancer detection greatly increases the chances for successful treatment, but available diagnostics for some tumours, including lung adenocarcinoma (LA), are limited. An ideal early-stage diagnosis of LA for large-scale clinical use must address quick detection, low invasiveness, and high performance. Here, we conduct machine learning of serum metabolic patterns to detect early-stage LA. We e...

Predicting respiratory failure after pulmonary lobectomy using machine learning techniques.

BACKGROUND: When pulmonary complications occur, postlobectomy patients have a higher mortality rate, increased length of stay, and higher readmission rates. Because of a lack of high-quality consolidated clinical data, it is challenging to assess and recognize at-risk thoracic patients to avoid respiratory failure and standardize outcome measures.

Radiomics in radiation oncology-basics, methods, and limitations.

Over the past years, the quantity and complexity of imaging data available for the clinical management of patients with solid tumors has increased sub...

Molecular docking and machine learning analysis of Abemaciclib in colon cancer.

BACKGROUND: The main challenge in cancer research is the identification of different omic variables that present a prognostic value and personalised d...

Histological Subtypes Classification of Lung Cancers on CT Images Using 3D Deep Learning and Radiomics.

RATIONALE AND OBJECTIVES: Histological subtypes of lung cancers are critical for clinical treatment decision. In this study, we attempt to use 3D deep...

Artificial intelligence for the detection of esophageal and esophagogastric junctional adenocarcinoma.

BACKGROUND AND AIM: Conventional endoscopy for the early detection of esophageal and esophagogastric junctional adenocarcinoma (E/J cancer) is limited...

Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas.

OBJECTIVES: To evaluate the differential diagnostic performance of a computed tomography (CT)-based deep learning nomogram (DLN) in identifying tuberc...

Concise Polygenic Models for Cancer-Specific Identification of Drug-Sensitive Tumors from Their Multi-Omics Profiles.

In silico models to predict which tumors will respond to a given drug are necessary for Precision Oncology. However, predictive models are only availa...

Deep learning combined with radiomics may optimize the prediction in differentiating high-grade lung adenocarcinomas in ground glass opacity lesions on CT scans.

PURPOSE: Adenocarcinoma (ADC) is the most common histological subtype of lung cancers in non-small cell lung cancer (NSCLC) in which ground glass opac...

A machine learning-based prognostic predictor for stage III colon cancer.

Limited biomarkers have been identified as prognostic predictors for stage III colon cancer. To combat this shortfall, we developed a computer-aided a...

Artificial intelligence-based collaborative filtering method with ensemble learning for personalized lung cancer medicine without genetic sequencing.

In personalized medicine, many factors influence the choice of compounds. Hence, the selection of suitable medicine for patients with non-small-cell l...

Survival prediction for oral tongue cancer patients via probabilistic genetic algorithm optimized neural network models.

OBJECTIVES: High throughput pre-treatment imaging features may predict radiation treatment outcome and guide individualized treatment in radiotherapy ...

First robot-assisted radical prostatectomy in a client-owned Bernese mountain dog with prostatic adenocarcinoma.

OBJECTIVE: To describe robot-assisted radical prostatectomy (RARP) and report the short-term outcome of a dog with prostatic cancer treated with RARP.

Facial expression monitoring system for predicting patient's sudden movement during radiotherapy using deep learning.

PURPOSE: Imaging, breath-holding/gating, and fixation devices have been developed to minimize setup errors so that the prescribed dose can be exactly ...

Deep learning for identification of critical regions associated with toxicities after liver stereotactic body radiation therapy.

PURPOSE: Radiation therapy (RT) is prescribed for curative and palliative treatment for around 50% of patients with solid tumors. Radiation-induced to...

Machine-learning based MRI radiomics models for early detection of radiation-induced brain injury in nasopharyngeal carcinoma.

BACKGROUND: Early radiation-induced temporal lobe injury (RTLI) diagnosis in nasopharyngeal carcinoma (NPC) is clinically challenging, and prediction ...

Development and Validation of a Deep Learning Model for Non-Small Cell Lung Cancer Survival.

IMPORTANCE: There is a lack of studies exploring the performance of a deep learning survival neural network in non-small cell lung cancer (NSCLC).

Lower Incidence of Postoperative Acute Kidney Injury in Robot-Assisted Partial Nephrectomy Than in Open Partial Nephrectomy: A Propensity Score-Matched Study.

Acute kidney injury (AKI) after partial nephrectomy is attributed to parenchymal reduction and ischemia, but the extent of its effect remains unclear...

Artificial intelligence models versus empirical equations for modeling monthly reference evapotranspiration.

Accurate estimation of reference evapotranspiration (ET) is profoundly crucial in crop modeling, sustainable management, hydrological water simulation...

Agalsidase beta treatment slows estimated glomerular filtration rate loss in classic Fabry disease patients: results from an individual patient data meta-analysis.

BACKGROUND: Fabry disease is a rare, X-linked genetic disorder that, if untreated in patients with the Classic phenotype, often progresses to end-stag...