Other Cancers

Implementing a deep learning model for automatic tongue tumour segmentation in ex-vivo 3-dimensional ultrasound volumes.

Three-dimensional (3D) ultrasound can assess the margins of resected tongue carcinoma during surgery. Manual segmentation (MS) is time-consuming, labour-intensive, and subject to operator variability. This study aims to investigate use of a 3D deep learning model for fast intraoperative segmentation of tongue carcinoma in 3D ultrasound volumes. Additionally, it investigates the clinical effect of ...

Artificial intelligence in the detection of skin cancer: State of the art.

The incidence of melanoma is increasing rapidly. This cancer has a good prognosis if detected early. For this reason, various systems of skin lesion image analysis, which support imaging diagnostics of this neoplasm, are developing very dynamically. To detect and recognize neoplastic lesions, such systems use various artificial intelligence (AI) algorithms. This area of computer science applicatio...

Leveraging technology-driven strategies to untangle omics big data: circumventing roadblocks in clinical facets of oral cancer.

Oral cancer is one of the 19most rapidly progressing cancers associated with significant mortality, owing to its extreme degree of invasiveness and ag...

Deep learning prediction of esophageal squamous cell carcinoma invasion depth from arterial phase enhanced CT images: a binary classification approach.

BACKGROUND: Precise prediction of esophageal squamous cell carcinoma (ESCC) invasion depth is crucial not only for optimizing treatment plans but also...

Enhancing brain metastasis prediction in non-small cell lung cancer: a deep learning-based segmentation and CT radiomics-based ensemble learning model.

BACKGROUND: Brain metastasis (BM) is most common in non-small cell lung cancer (NSCLC) patients. This study aims to enhance BM risk prediction within ...

Artificial intelligence for oral squamous cell carcinoma detection based on oral photographs: A comprehensive literature review.

INTRODUCTION: Oral squamous cell carcinoma (OSCC) presents a significant global health challenge. The integration of artificial intelligence (AI) and ...

Diagnostic value of artificial intelligence based on computed tomography (CT) density in benign and malignant pulmonary nodules: a retrospective investigation.

OBJECTIVE: To evaluate the diagnostic value of artificial intelligence (AI) in the detection and management of benign and malignant pulmonary nodules ...

iTTCA-MVL: A multi-view learning model based on physicochemical information and sequence statistical information for tumor T cell antigens identification.

Immunotherapy is an emerging treatment method aimed at activating the human immune system and relying on its own immune function to kill cancer cells ...

Combining a deep learning model with clinical data better predicts hepatocellular carcinoma behavior following surgery.

Hepatocellular carcinoma (HCC) is among the most common cancers worldwide, and tumor recurrence following liver resection or transplantation is one of...

Machine learning based on SEER database to predict distant metastasis of thyroid cancer.

OBJECTIVE: Distant metastasis of thyroid cancer often indicates poor prognosis, and it is important to identify patients who have developed distant me...

Discrimination of benign and malignant breast lesions on dynamic contrast-enhanced magnetic resonance imaging using deep learning.

PURPOSE: To evaluate the capability of deep transfer learning (DTL) and fine-tuning methods in differentiating malignant from benign lesions in breast...

Machine learning based on magnetic resonance imaging and clinical parameters helps predict mesenchymal-epithelial transition factor expression in oral tongue squamous cell carcinoma: a pilot study.

OBJECTIVES: This study aimed to develop machine learning models to predict phosphorylated mesenchymal-epithelial transition factor (p-MET) expression ...

A computational tumor growth model experience based on molecular dynamics point of view using deep cellular automata.

Cancer, as identified by the World Health Organization, stands as the second leading cause of death globally. Its intricate nature makes it challengin...

A deep learning and radiomics fusion model based on contrast-enhanced computer tomography improves preoperative identification of cervical lymph node metastasis of oral squamous cell carcinoma.

OBJECTIVES: In this study, we constructed and validated models based on deep learning and radiomics to facilitate preoperative diagnosis of cervical l...

Detection and classification of brain tumor using hybrid deep learning models.

Accurately classifying brain tumor types is critical for timely diagnosis and potentially saving lives. Magnetic Resonance Imaging (MRI) is a widely u...

SkinViT: A transformer based method for Melanoma and Nonmelanoma classification.

Over the past few decades, skin cancer has emerged as a major global health concern. The efficacy of skin cancer treatment greatly depends upon early ...

Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer.

Irrespective of men and women, colorectal cancer (CRC), is the third most common cancer in the population with more than 1.85 million cases annually. ...

Treatment prediction with machine learning in prostate cancer patients.

There are various treatment modalities for prostate cancer, which has a high incidence. In this study, it is aimed to make predictions with machine le...

Identification of patients at high risk for recurrence in carcinoma of the ampulla of Vater: Analysis in 460 patients.

AIM: Carcinoma of the ampulla of Vater (CAV) shows a favorable prognosis compared to that with the other periampullary tumors, while some cases have a...

Pembrolizumab alters the tumor immune landscape in a patient with dMMR glioblastoma.

Congenital DNA mismatch repair defects (dMMR), such as Lynch Syndrome, predispose patients to a variety of cancers and account for approximately 1% of...