Other Cancers

Advancing miRNA cancer research through artificial intelligence: from biomarker discovery to therapeutic targeting.

MicroRNAs (miRNAs), a class of small non-coding RNAs, play a vital role in regulating gene expression at the post-transcriptional level. Their discovery has profoundly impacted therapeutic strategies, particularly in cancer treatment, where RNA therapeutics, including miRNA-based targeted therapies, have gained prominence. Advances in RNA sequencing technologies have facilitated a comprehensive ex...

Application of machine learning for mass spectrometry-based multi-omics in thyroid diseases.

Thyroid diseases, including functional and neoplastic diseases, bring a huge burden to people's health. Therefore, a timely and accurate diagnosis is necessary. Mass spectrometry (MS) based multi-omics has become an effective strategy to reveal the complex biological mechanisms of thyroid diseases. The exponential growth of biomedical data has promoted the applications of machine learning (ML) tec...

The establishment of PD-1 inhibitor treatment prognosis model based on dynamic changes of peripheral blood indexes in patients with advanced lung squamous cell carcinoma.

BACKGROUND: Unlike patients with lung adenocarcinoma, patients with lung squamous cell carcinoma (LUSC) do not derive significant benefits from target...

Targeting liver cancer stem cells: the prognostic significance of MRPL17 in immunotherapy response.

BACKGROUND: Liver hepatocellular carcinoma (LIHC) ranks as the foremost cause of cancer-related deaths worldwide, and its early detection poses consid...

Machine learning-based selection of immune cell markers in osteosarcoma: prognostic determination and validation of CLK1 in disease progression.

INTRODUCTION: Osteosarcoma (OS) is a malignancy of the bone that mainly afflicts younger individuals. Despite existing treatment approaches, patients ...

Machine Learning-Engineered Nanozyme System for Synergistic Anti-Tumor Ferroptosis/Apoptosis Therapy.

Nanozymes with multienzyme-like activity have sparked significant interest in anti-tumor therapy via responding to the tumor microenvironment (TME). H...

Dual biomarkers CT-based deep learning model incorporating intrathoracic fat for discriminating benign and malignant pulmonary nodules in multi-center cohorts.

BACKGROUND: Recent studies in the field of lung cancer have emphasized the important role of body composition, particularly fatty tissue, as a prognos...

DFASGCNS: A prognostic model for ovarian cancer prediction based on dual fusion channels and stacked graph convolution.

Ovarian cancer is a malignant tumor with different clinicopathological and molecular characteristics. Due to its nonspecific early symptoms, the major...

Machine learning and multi-omics characterization of SLC2A1 as a prognostic factor in hepatocellular carcinoma: SLC2A1 is a prognostic factor in HCC.

Hepatocellular carcinoma (HCC) is characterized by high incidence, significant mortality, and marked heterogeneity, making accurate molecular subtypin...

XGBoost-based nomogram for predicting lymph node metastasis in endometrial carcinoma.

This study aims to construct and optimize risk prediction models for lymph node metastasis (LNM) in endometrial carcinoma (EC) patients, thus improvin...

Advancing precision and personalized breast cancer treatment through multi-omics technologies.

Breast cancer is the most common malignant tumour in women, with more than 685,000 women dying of breast cancer each year. The heterogeneity of breast...

Comparison between two artificial intelligence models to discriminate cancerous cell nuclei based on confocal fluorescence imaging in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) exhibits an exceptional intratumoral heterogeneity that might influence diagnosis and outcome. Advances in ...

Exploratory study of extracellular matrix biomarkers for non-invasive liver fibrosis staging: A machine learning approach with XGBoost and explainable AI.

BACKGROUND: Novel circulating markers for the non-invasive staging of chronic liver disease (CLD) are in high demand. Although underutilized, extracel...

Predicting axillary lymph node metastasis in breast cancer using a multimodal radiomics and deep learning model.

OBJECTIVE: To explore the value of combined radiomics and deep learning models using different machine learning algorithms based on mammography (MG) a...

Predicting distant metastasis of bladder cancer using multiple machine learning models: a study based on the SEER database with external validation.

BACKGROUND AND PURPOSE: Distant metastasis in bladder cancer is linked to poor prognosis and significant mortality. Machine learning (ML), a key area ...

Development of a centrosome amplification-associated signature in kidney renal clear cell carcinoma based on multiple machine learning models.

BACKGROUND: Centrosome amplification (CA) has been shown to be capable of initiating tumorigenesis with metastatic potential and enhancing cell invasi...

Genotype-negative multiple endocrine neoplasia type 1 with prolactinoma, hyperparathyroidism, and subclinical Cushing's syndrome accompanied by hyperglycemia: a case report.

BACKGROUND: Multiple endocrine neoplasia type 1 (MEN1) is a rare autosomal dominant disorder, accompanied by multiple endocrine neoplasms of the parat...

Artificial intelligence in surgical pathology - Where do we stand, where do we go?

Surgical and neuropathologists continuously search for new and disease-specific features, such as independent predictors of tumor prognosis or determi...

Deep Learning for Detecting and Subtyping Renal Cell Carcinoma on Contrast-Enhanced CT Scans Using 2D Neural Network with Feature Consistency Techniques.

 The aim of this study was to explore an innovative approach for developing deep learning (DL) algorithm for renal cell carcinoma (RCC) detection and...

Breath-hold diffusion-weighted MR imaging (DWI) using deep learning reconstruction: Comparison with navigator triggered DWI in patients with malignant liver tumors.

INTRODUCTION: This study investigated the feasibility of single breath-hold (BH) diffusion-weighted MR imaging (DWI) using deep learning reconstructio...