Other Cancers

BrainNet: a fusion assisted novel optimal framework of residual blocks and stacked autoencoders for multimodal brain tumor classification.

A significant issue in computer-aided diagnosis (CAD) for medical applications is brain tumor classi...

Deep Learning-Based Spermatogenic Staging in Tissue Sections of Cynomolgus Macaque Testes.

The indirect assessment of adverse effects on fertility in cynomolgus monkeys requires that tissue s...

MRI-Based Machine Learning Radiomics for Preoperative Assessment of Human Epidermal Growth Factor Receptor 2 Status in Urothelial Bladder Carcinoma.

BACKGROUND: The human epidermal growth factor receptor 2 (HER2) has recently emerged as hotspot in t...

Artificial Intelligence-assisted colonoscopy and colorectal cancer screening: Where are we going?

Colorectal cancer is a significant global health concern, necessitating effective screening strategi...

Improving the detection of hypo-vascular liver metastases in multiphase contrast-enhanced CT with slice thickness less than 5 mm using DenseNet.

INTRODUCTION: Thinner slices are more susceptible in detecting small lesions but suffer from higher ...

Prediction of immunotherapy response in idiopathic membranous nephropathy using deep learning-pathological and clinical factors.

BACKGROUND: Owing to individual heterogeneity, patients with idiopathic membranous nephropathy (IMN)...

Development and external validation of a machine learning model for prediction of survival in extremity leiomyosarcoma.

PURPOSE: Machine learning (ML) models have been used to predict cancer survival in several sarcoma s...

Application of deep-learning to the automatic segmentation and classification of lateral lymph nodes on ultrasound images of papillary thyroid carcinoma.

PURPOSE: It is crucial to preoperatively diagnose lateral cervical lymph node (LN) metastases (LNMs)...

An integrative machine learning model for the identification of tumor T-cell antigens.

The escalating global incidence of cancer poses significant health challenges, underscoring the need...

DEBCM: Deep Learning-Based Enhanced Breast Invasive Ductal Carcinoma Classification Model in IoMT Healthcare Systems.

Accurate breast cancer (BC) diagnosis is a difficult task that is critical for the proper treatment ...

A deep learning model of tumor cell architecture elucidates response and resistance to CDK4/6 inhibitors.

Cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6is) have revolutionized breast cancer therapy. How...

Diagnostic Performance of Artificial Intelligence in Detection of Hepatocellular Carcinoma: A Meta-analysis.

Due to the increasing interest in the use of artificial intelligence (AI) algorithms in hepatocellul...

Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single-institutional study.

PURPOSE: Predicting recurrence following stereotactic body radiotherapy (SBRT) for non-small cell lu...

Multitask deep learning for prediction of microvascular invasion and recurrence-free survival in hepatocellular carcinoma based on MRI images.

BACKGROUND AND AIMS: Accurate preoperative prediction of microvascular invasion (MVI) and recurrence...

Classification of multi-feature fusion ultrasound images of breast tumor within category 4 using convolutional neural networks.

BACKGROUND: Breast tumor is a fatal threat to the health of women. Ultrasound (US) is a common and e...

Advanced hybrid attention-based deep learning network with heuristic algorithm for adaptive CT and PET image fusion in lung cancer detection.

Lung cancer is one of the most deadly diseases in the world. Lung cancer detection can save the pati...

A Preliminary Study Comparing the Performance of Thyroid Molecular Tests to a Deep Learning Algorithm in Predicting Malignancy in Indeterminate Thyroid Fine Needle Aspiration Biopsies.

Critical evaluation of artificial intelligence as a digital twin of pathologists for prostate cancer pathology.

Prostate cancer pathology plays a crucial role in clinical management but is time-consuming. Artific...

Predicting microvascular invasion in hepatocellular carcinoma with a CT- and MRI-based multimodal deep learning model.

PURPOSE: To investigate the value of a multimodal deep learning (MDL) model based on computed tomogr...

Deep Learning for Automatic Gross Tumor Volumes Contouring in Esophageal Cancer Based on Contrast-Enhanced Computed Tomography Images: A Multi-Institutional Study.

PURPOSE: To develop and externally validate an automatic artificial intelligence (AI) tool for delin...

Applications of artificial intelligence in biliary tract cancers.

Biliary tract cancers are malignant neoplasms arising from bile duct epithelial cells. They include ...