Oncology/Hematology

Development and validation of CT-based fusion model for preoperative prediction of invasion and lymph node metastasis in adenocarcinoma of esophagogastric junction.

PURPOSE: In the context of precision medicine, radiomics has become a key technology in solving medical problems. For adenocarcinoma of esophagogastric junction (AEG), developing a preoperative CT-based prediction model for AEG invasion and lymph node metastasis is crucial.

Development and validation of a machine learning model for central compartmental lymph node metastasis in solitary papillary thyroid microcarcinoma via ultrasound imaging features and clinical parameters.

BACKGROUND: Papillary thyroid microcarcinoma (PTMC) is the most common malignant subtype of thyroid cancer. Preoperative assessment of the risk of central compartment lymph node metastasis (CCLNM) can provide scientific support for personalized treatment decisions prior to microwave ablation of thyroid nodules. The objective of this study was to develop a predictive model for CCLNM in patients wit...

Computed tomography-based radiomics predicts prognostic and treatment-related levels of immune infiltration in the immune microenvironment of clear cell renal cell carcinoma.

OBJECTIVES: The composition of the tumour microenvironment is very complex, and measuring the extent of immune cell infiltration can provide an import...

Deep learning-based lung cancer classification of CT images.

Lung cancer remains a leading cause of cancer-related deaths worldwide, with accurate classification of lung nodules being critical for early diagnosi...

The value of machine learning based on spectral CT quantitative parameters in the distinguishing benign from malignant thyroid micro-nodules.

BACKGROUND AND AIMS: More cases of thyroid micro-nodules have been diagnosed annually in recent years because of advancements in diagnostic technologi...

Can mutation abundance assess the biological behavior of BRAF-positive papillary thyroid carcinoma?

BACKGROUND: BRAF mutation is the most common genetic change in papillary thyroid carcinoma (PTC). Nevertheless, the association between BRAF mutation ...

Development and clinical validation of deep learning-based immunohistochemistry prediction models for subtyping and staging of gastrointestinal cancers.

BACKGROUND: Immunohistochemistry (IHC) is a critical tool for tumor diagnosis and treatment, but it is time and tissue consuming, and highly dependent...

Evaluation of a fusion model combining deep learning models based on enhanced CT images with radiological and clinical features in distinguishing lipid-poor adrenal adenoma from metastatic lesions.

OBJECTIVE: To evaluate the diagnostic performance of a machine learning model combining deep learning models based on enhanced CT images with radiolog...

2.5D deep learning radiomics and clinical data for predicting occult lymph node metastasis in lung adenocarcinoma.

BACKGROUND: Occult lymph node metastasis (OLNM) refers to lymph node involvement that remains undetectable by conventional imaging techniques, posing ...

Perilesional dominance: radiomics of multiparametric MRI enhances differentiation of IgG4-Related ophthalmic disease and orbital MALT lymphoma.

BACKGROUND: To develop and validate a diagnostic framework integrating intralesional (ILN) and perilesional (PLN) radiomics derived from multiparametr...

MRI radiomics model for predicting tumor immune microenvironment types and efficacy of anti-PD-1/PD-L1 therapy in hepatocellular carcinoma.

BACKGROUND: To improve the prediction of immune checkpoint inhibitors (ICIs) efficacy in hepatocellular carcinoma (HCC), this study categorized the tu...

Development and validation of a small-sample machine learning model to predict 5-year overall survival in patients with hepatocellular carcinoma.

BACKGROUND: Early-onset hepatocellular carcinoma (HCC) is insidious, with characteristics of easy metastasis, high recurrence rate, and significant mo...

Deep learning for automated segmentation of radiation-induced changes in cerebral arteriovenous malformations following radiosurgery.

BACKGROUND: Despite the widespread use of stereotactic radiosurgery (SRS) to treat cerebral arteriovenous malformations (AVMs), this procedure can lea...

Deep learning radiomics and mediastinal adipose tissue-based nomogram for preoperative prediction of postoperative‌ brain metastasis risk in non-small cell lung cancer.

Risk factors and prediction of distant metastasis (DM) of colon adenocarcinoma: a logistic regression and machine learning study based on surveillance, epidemiology, and end results (SEER) database.

An interpretable machine learning model for predicting early liver metastasis after pancreatic cancer surgery.

BACKGROUND: Liver metastasis is the most frequent site of distant metastasis in pancreatic ductal adenocarcinoma (PDAC), significantly contributing to...

Burnout protective patterns among oncology nurses: a cross-sectional study using machine learning analysis.

BACKGROUND: Oncology nurses face unique and intense demands due to the nature of their work, caring for patients with life-threatening illnesses. The ...

An MOF-Enhanced Anti-Fouling Immunoprobe Platform for Efficient Direct Screening of Pancreatic Cancer.

Monitoring biomarkers offers insights for early disease (e.g., cancer, chronic diseases) screening, treatment guidance and response evaluation. To tac...

Artificial Intelligence in Palliative Care: A Scoping Review of Current Applications, Challenges, and Future Directions.

BackgroundArtificial Intelligence (AI) is increasingly integrated into healthcare systems, presenting opportunities to improve clinical outcomes. In t...

Taking the Guess Work Out of Endoscopic Polyp Measurement: From Traditional Methods to AI.

Colonoscopy is a crucial tool for evaluating lower gastrointestinal disease, monitoring high-risk patients for colorectal neoplasia, and screening for...