Other Cancers

Multimodal radiomics-based methods using deep learning for prediction of brain metastasis in non-small cell lung cancer withF-FDG PET/CT images.

. Approximately 57% of non-small cell lung cancer (NSCLC) patients face a 20% risk of brain metastases (BMs). The delivery of drugs to the central nervous system is challenging because of the blood-brain barrier, leading to a relatively poor prognosis for patients with BMs. Therefore, early detection and treatment of BMs are highly important for improving patient prognosis. This study aimed to inv...

Renal Cell Carcinoma Discrimination through Attenuated Total Reflection Fourier Transform Infrared Spectroscopy of Dried Human Urine and Machine Learning Techniques.

Renal cell carcinoma (RCC) is the sixth most common cancer in men and is often asymptomatic, leading to incidental detection in advanced disease stages that are associated with aggressive histology and poorer outcomes. Various cancer biomarkers are found in urine samples from patients with RCC. In this study, we propose to investigate the use of Attenuated Total Reflection-Fourier Transform Infrar...

Contrast-Enhanced Computed Tomography-Based Machine Learning Radiomics Predicts IDH1 Expression and Clinical Prognosis in Head and Neck Squamous Cell Carcinoma.

RATIONALE AND OBJECTIVES: Isocitrate dehydrogenase 1 (IDH1) is a potential therapeutic target across various tumor types. Here, we aimed to devise a r...

Applying deep learning-based ensemble model to [F]-FDG-PET-radiomic features for differentiating benign from malignant parotid gland diseases.

OBJECTIVES: To develop and identify machine learning (ML) models using pretreatment 2-deoxy-2-[F]fluoro-D-glucose ([F]-FDG)-positron emission tomograp...

Diagnostic Value of Magnetic Resonance Imaging Radiomics and Machine-learning in Grading Soft Tissue Sarcoma: A Mini-review on the Current State.

Soft tissue sarcomas (STS) are a heterogeneous group of rare malignant tumors. Tumor grade might be underestimated in biopsy due to intratumoral heter...

The transformative potential of AI-driven CRISPR-Cas9 genome editing to enhance CAR T-cell therapy.

This narrative review examines the promising potential of integrating artificial intelligence (AI) with CRISPR-Cas9 genome editing to advance CAR T-ce...

Deep Learning-Based Image Classification and Segmentation on Digital Histopathology for Oral Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis.

BACKGROUND: Artificial intelligence (AI)-based tools have shown promise in histopathology image analysis in improving the accuracy of oral squamous ce...

Artificial intelligence system for identification of overlooked lung metastasis in abdominopelvic computed tomography scans of patients with malignancy.

PURPOSE: This study aimed to evaluate whether an artificial intelligence (AI) system can identify basal lung metastatic nodules examined using abdomin...

Implementing machine learning to predict survival outcomes in patients with resected pulmonary large cell neuroendocrine carcinoma.

BACKGROUND: The post-surgical prognosis for Pulmonary Large Cell Neuroendocrine Carcinoma (PLCNEC) patients remains largely unexplored. Developing a p...

Impact of SUSAN Denoising and ComBat Harmonization on Machine Learning Model Performance for Malignant Brain Neoplasms.

BACKGROUND AND PURPOSE: Feature variability in radiomics studies due to technical and magnet strength parameters is well-known and may be addressed th...

Development of a prognostic model for NSCLC based on differential genes in tumour stem cells.

Non-small cell lung cancer (NSCLC) constitutes a significant portion of lung cancers and cytotoxic drugs (e.g. cisplatin) are currently the first-line...

A Multi-Scale Liver Tumor Segmentation Method Based on Residual and Hybrid Attention Enhanced Network with Contextual Integration.

Liver cancer is one of the malignancies with high mortality rates worldwide, and its timely detection and accurate diagnosis are crucial for improving...

Development of a novel prognostic signature derived from super-enhancer-associated gene by machine learning in head and neck squamous cell carcinoma.

Dysregulated super-enhancer (SE) results in aberrant transcription that drives cancer initiation and progression. SEs have been demonstrated as novel ...

The utility and reliability of a deep learning algorithm as a diagnosis support tool in head & neck non-melanoma skin malignancies.

OBJECTIVE: The incidence of non-melanoma skin cancers, encompassing basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC), is on the...

In silico assessments of the small molecular boron agents to pave the way for artificial intelligence-based boron neutron capture therapy.

Boron neutron capture therapy (BNCT) is a highly targeted, selective and effective technique to cure various types of cancers, with less harm to the h...

Precision meets generalization: Enhancing brain tumor classification via pretrained DenseNet with global average pooling and hyperparameter tuning.

Brain tumors pose significant global health concerns due to their high mortality rates and limited treatment options. These tumors, arising from abnor...

Evaluating the Alignment of Artificial Intelligence-Generated Recommendations With Clinical Guidelines Focused on Soft Tissue Tumors.

BACKGROUND: The integration of artificial intelligence (AI), particularly, in oncology, has significantly shifted the paradigms of medical diagnostics...

PSEENet: A Pseudo-Siamese Neural Network Incorporating Electroencephalography and Electrooculography Characteristics for Heterogeneous Sleep Staging.

Sleep staging plays a critical role in evaluating the quality of sleep. Currently, most studies are either suffering from dramatic performance drops w...

The impact of deep learning on diagnostic performance in the differentiation of benign and malignant thyroid nodules.

AIMS: This study aims to use deep learning (DL) to classify thyroid nodules as benign and malignant with ultrasonography (US). In addition, this study...

Brain tumor detection and segmentation using deep learning.

OBJECTIVES: Brain tumor detection, classification and segmentation are challenging due to the heterogeneous nature of brain tumors. Different deep lea...