Other Cancers

CEMRI-Based Quantification of Intratumoral Heterogeneity for Predicting Aggressive Characteristics of Hepatocellular Carcinoma Using Habitat Analysis: Comparison and Combination of Deep Learning.

RATIONALE AND OBJECTIVES: To explore both an intratumoral heterogeneity (ITH) model based on habitat analysis and a deep learning (DL) model based on contrast-enhanced magnetic resonance imaging (CEMRI) and validate its efficiency for predicting microvascular invasion (MVI) and pathological differentiation in hepatocellular carcinoma (HCC).

Deep learning radiomics of multimodal ultrasound for classifying metastatic cervical lymphadenopathy into primary cancer sites: a feasibility study.

PURPOSE: To investigate the feasibility of deep learning radiomics (DLR) based on multimodal ultrasound to differentiate the primary cancer sites of metastatic cervical lymphadenopathy (CLA).

The effect of deep learning-based lesion segmentation on failure load calculations of metastatic femurs using finite element analysis.

Bone ranks as the third most frequent tissue affected by cancer metastases, following the lung and liver. Bone metastases are often painful and may re...

Deep learning-enabled breast cancer endocrine response determination from H&E staining based on ESR1 signaling activity.

Estrogen receptor (ER) positivity by immunohistochemistry has long been a main selection criterium for breast cancer patients to be treated with endoc...

Prognosis Forecast of Re-Irradiation for Recurrent Nasopharyngeal Carcinoma Based on Deep Learning Multi-Modal Information Fusion.

Radiation therapy is the primary treatment for recurrent nasopharyngeal carcinoma. However, it may induce necrosis of the nasopharynx, leading to seve...

The changing face of circulating tumor DNA (ctDNA) profiling: Factors that shape the landscape of methodologies, technologies, and commercialization.

Liquid biopsies, in particular the profiling of circulating tumor DNA (ctDNA), have long held promise as transformative tools in cancer precision medi...

Enhanced sleep staging with artificial intelligence: a validation study of new software for sleep scoring.

Manual sleep staging (MSS) using polysomnography is a time-consuming task, requires significant training, and can lead to significant variability amon...

A self-assembled three-dimensional hierarchical nanoflower: an efficient enzyme-mimetic material for cancer cell detection that improves ROS generation for therapy.

Three-dimensional (3D) nanomaterials with high functional properties are emerging as the most promising artificial enzymes for overcoming the signific...

Deep UV-excited fluorescence microscopy installed with CycleGAN-assisted image translation enhances precise detection of lymph node metastasis towards rapid intraoperative diagnosis.

Rapid and precise intraoperative diagnosing systems are required for improving surgical outcomes and patient prognosis. Because of the poor quality an...

Deep learning nomogram for preoperative distinction between Xanthogranulomatous cholecystitis and gallbladder carcinoma: A novel approach for surgical decision.

The distinction between Xanthogranulomatous Cholecystitis (XGC) and Gallbladder Carcinoma (GBC) is challenging due to their similar imaging features. ...

Deep learning models for automatic tumor segmentation and total tumor volume assessment in patients with colorectal liver metastases.

BACKGROUND: We developed models for tumor segmentation to automate the assessment of total tumor volume (TTV) in patients with colorectal liver metast...

Effects of Transcranial Direct Current Stimulation on Cognitive Deficits in Depression: A Systematic Review.

BACKGROUND: Major depressive disorder is the leading cause of mental health-related burden globally and up to one-third of major depressive disorder p...

A deep learning model predicts the presence of diverse cancer types using circulating tumor cells.

Circulating tumor cells (CTCs) are cancer cells that detach from the primary tumor and intravasate into the bloodstream. Thus, non-invasive liquid bio...

Identifying Explainable Machine Learning Models and a Novel SFRP2 Fibroblast Signature as Predictors for Precision Medicine in Ovarian Cancer.

Ovarian cancer (OC) is a type of malignant tumor with a consistently high mortality rate. The diagnosis of early-stage OC and identification of functi...

Quantitative Modeling of Stemness in Single-Cell RNA Sequencing Data: A Nonlinear One-Class Support Vector Machine Method.

Intratumoral heterogeneity and the presence of cancer stem cells are challenging issues in cancer therapy. An appropriate quantification of the stemne...

Artificial intelligence in breast imaging: potentials and challenges.

Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, i...

Endorobotic submucosal dissection of rectal lesions using the single port robot DaVinci-SP: initial experience of the first 10 cases.

BACKGROUND: Endoluminal surgery is increasingly recognized as a mode of treatment for colorectal neoplasms with the latest robotic single port platfor...

Broadening the horizon: potential applications of CAR-T cells beyond current indications.

Engineering immune cells to treat hematological malignancies has been a major focus of research since the first resounding successes of CAR-T-cell the...

Implications of ultrasound-based deep learning model for preoperatively differentiating combined hepatocellular-cholangiocarcinoma from hepatocellular carcinoma and intrahepatic cholangiocarcinoma.

OBJECTIVES: The current study developed an ultrasound-based deep learning model to make preoperative differentiation among hepatocellular carcinoma (H...

A review on brain tumor segmentation based on deep learning methods with federated learning techniques.

Brain tumors have become a severe medical complication in recent years due to their high fatality rate. Radiologists segment the tumor manually, which...