Other Cancers

Using machine learning to develop a stacking ensemble learning model for the CT radiomics classification of brain metastases.

The objective of this study was to explore the potential of machine-learning techniques in the automatic identification and classification of brain metastases from a radiomic perspective, aiming to improve the accuracy of tumor volume assessment for radiotherapy. By using various machine-learning algorithms, including random forest, support vector machine, gradient boosting machine, XGBoost, decis...

Convolutional neural network for oral cancer detection combined with improved tunicate swarm algorithm to detect oral cancer.

Early Diagnosis of oral cancer is very important and can save you from some oral malignancies. However, while this approach aids in the rapid healing of patients and the preservation of their lives, there are several causes for poor and wrong diagnosis of oral cancer. In recent years, the use of computer-aided design diagnosis tools as an auxiliary tool alongside clinicians has greatly benefited i...

Explainable Machine Learning Models Using Robust Cancer Biomarkers Identification from Paired Differential Gene Expression.

In oncology, there is a critical need for robust biomarkers that can be easily translated into the clinic. We introduce a novel approach using paired ...

Model Based on Ultrasound Radiomics and Machine Learning to Preoperative Differentiation of Follicular Thyroid Neoplasm.

OBJECTIVES: To evaluate the value of radiomics based on ultrasonography in differentiating follicular thyroid carcinoma (FTC) and follicular thyroid a...

Deep learning based analysis of dynamic video ultrasonography for predicting cervical lymph node metastasis in papillary thyroid carcinoma.

BACKGROUND: Cervical lymph node metastasis (CLNM) is the most common form of thyroid cancer metastasis. Accurate preoperative CLNM diagnosis is of mor...

Diagnosis and typing of leukemia using a single peripheral blood cell through deep learning.

Leukemia is highly heterogeneous, meaning that different types of leukemia require different treatments and have different prognoses. Current clinical...

A combined model integrating radiomics and deep learning based on multiparametric magnetic resonance imaging for classification of brain metastases.

BACKGROUND: Radiomics and deep learning (DL) can individually and efficiently identify the pathological type of brain metastases (BMs).

Prior information guided deep-learning model for tumor bed segmentation in breast cancer radiotherapy.

BACKGROUND AND PURPOSE: Tumor bed (TB) is the residual cavity of resected tumor after surgery. Delineating TB from CT is crucial in generating clinica...

The study on ultrasound image classification using a dual-branch model based on Resnet50 guided by U-net segmentation results.

In recent years, the incidence of nodular thyroid diseases has been increasing annually. Ultrasonography has become a routine diagnostic tool for thyr...

In Vivo Time-Resolved Fluorescence Detection of Liver Cancer Supported by Machine Learning.

OBJECTIVES: One of the widely used optical biopsy methods for monitoring cellular and tissue metabolism is time-resolved fluorescence. The use of this...

Generalizable Magnetic Resonance Imaging-based Nasopharyngeal Carcinoma Delineation: Bridging Gaps Across Multiple Centers and Raters With Active Learning.

PURPOSE: To develop a deep learning method exploiting active learning and source-free domain adaptation for gross tumor volume delineation in nasophar...

Automatic TNM staging of colorectal cancer radiology reports using pre-trained language models.

BACKGROUND AND OBJECTIVE: Colorectal cancer is one of the major causes of cancer death worldwide. Essential for prognosis and treatment planning, TNM ...

An artificial intelligence-based recognition model of colorectal liver metastases in intraoperative ultrasonography with improved accuracy through algorithm integration.

BACKGROUND/PURPOSE: Contrast-enhanced intraoperative ultrasonography (CE-IOUS) is crucial for detecting colorectal liver metastases (CLM) during surge...

Non-invasive multiple cancer screening using trained detection canines and artificial intelligence: a prospective double-blind study.

The specificity and sensitivity of a simple non-invasive multi-cancer screening method in detecting breast, lung, prostate, and colorectal cancer in b...

A model for integrating palliative care into Eastern Mediterranean health systems with a primary care approach.

BACKGROUND AND AIMS: Palliative care in the Eastern Mediterranean Region (EMR) faces challenges despite the high number of patients in need. To provid...

AFSleepNet: Attention-Based Multi-View Feature Fusion Framework for Pediatric Sleep Staging.

The widespread prevalence of sleep problems in children highlights the importance of timely and accurate sleep staging in the diagnosis and treatment ...

Machine learning-driven estimation of mutational burden highlights DNAH5 as a prognostic marker in colorectal cancer.

BACKGROUND: Tumor Mutational Burden (TMB) have emerged as pivotal predictive biomarkers in determining prognosis and response to immunotherapy in colo...

Lymph Node Metastasis Prediction From In Situ Lung Squamous Cell Carcinoma Histopathology Images Using Deep Learning.

Lung squamous cell carcinoma (LUSC), a subtype of non-small cell lung cancer, represents a significant portion of lung cancer cases with distinct hist...

Comparison of MRI artificial intelligence-guided cognitive fusion-targeted biopsy versus routine cognitive fusion-targeted prostate biopsy in prostate cancer diagnosis: a randomized controlled trial.

BACKGROUND: Cognitive fusion MRI-guided targeted biopsy (cTB) has been widely used in the diagnosis of prostate cancer (PCa). However, cTB relies heav...

Brain tumor diagnosis in MRI scans images using Residual/Shuffle Network optimized by augmented Falcon Finch optimization.

Brain tumor diagnosis is an important task in prognosing and treatment planning of the patients with brain cancer. in the meantime, using the Magnetic...