Oncology/Hematology

Establishment and validation of an interactive artificial intelligence platform to predict postoperative ambulatory status for patients with metastatic spinal disease: a multicenter analysis.

BACKGROUND: Identification of patients with high-risk of experiencing inability to walk after surgery is important for surgeons to make therapeutic strategies for patients with metastatic spinal disease. However, there is a lack of clinical tool to assess postoperative ambulatory status for those patients. The emergence of artificial intelligence (AI) brings a promising opportunity to develop accu...

Development and validation of a deep learning radiomics model with clinical-radiological characteristics for the identification of occult peritoneal metastases in patients with pancreatic ductal adenocarcinoma.

BACKGROUND: Occult peritoneal metastases (OPM) in patients with pancreatic ductal adenocarcinoma (PDAC) are frequently overlooked during imaging. The authors aimed to develop and validate a computed tomography (CT)-based deep learning-based radiomics (DLR) model to identify OPM in PDAC before treatment.

Deep learning-based multi-model prediction for disease-free survival status of patients with clear cell renal cell carcinoma after surgery: a multicenter cohort study.

BACKGROUND: Although separate analysis of individual factor can somewhat improve the prognostic performance, integration of multimodal information int...

Impact of F-FDG PET Intensity Normalization on Radiomic Features of Oropharyngeal Squamous Cell Carcinomas and Machine Learning-Generated Biomarkers.

We aimed to investigate the effects of F-FDG PET voxel intensity normalization on radiomic features of oropharyngeal squamous cell carcinoma (OPSCC) a...

Identifying lncRNAs and mRNAs related to survival of NSCLC based on bioinformatic analysis and machine learning.

Non-small cell lung cancer (NSCLC) is the most common histopathological type, and it is purposeful for screening potential prognostic biomarkers for N...

Enhancing breast cancer outcomes with machine learning-driven glutamine metabolic reprogramming signature.

BACKGROUND: This study aims to identify precise biomarkers for breast cancer to improve patient outcomes, addressing the limitations of traditional st...

Letter to the Editor Regarding Article "Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset".

The cited article reports on a convolutional neural network trained to predict response to neoadjuvant chemotherapy from pre-treatment breast MRI scan...

Automatic Skeleton Segmentation in CT Images Based on U-Net.

Bone metastasis, emerging oncological therapies, and osteoporosis represent some of the distinct clinical contexts which can result in morphological a...

Artificial Intelligence-Driven Platform: Unveiling Critical Hepatic Molecular Alterations in Hepatocellular Carcinoma Development.

Since most Hepatocellular Carcinoma (HCC) typically arises as a consequence of long-term liver damage, the hepatic molecular characteristics are close...

An Optimization Numerical Spiking Neural Membrane System with Adaptive Multi-Mutation Operators for Brain Tumor Segmentation.

Magnetic Resonance Imaging (MRI) is an important diagnostic technique for brain tumors due to its ability to generate images without tissue damage or ...

Machine learning models for prediction of postoperative venous thromboembolism in gynecological malignant tumor patients.

AIM: To identify risk factors that associated with the occurrence of venous thromboembolism (VTE) within 30 days after hysterectomy among gynecologica...

Leveraging radiomics and machine learning to differentiate radiation necrosis from recurrence in patients with brain metastases.

OBJECTIVE: Radiation necrosis (RN) can be difficult to radiographically discern from tumor progression after stereotactic radiosurgery (SRS). The obje...

Advanced AI-driven approach for enhanced brain tumor detection from MRI images utilizing EfficientNetB2 with equalization and homomorphic filtering.

Brain tumors pose a significant medical challenge necessitating precise detection and diagnosis, especially in Magnetic resonance imaging(MRI). Curren...

Study on the differential diagnosis of benign and malignant breast lesions using a deep learning model based on multimodal images.

OBJECTIVE: To establish a multimodal model for distinguishing benign and malignant breast lesions.

Radiation dose estimation with multiple artificial neural networks in dicentric chromosome assay.

PURPOSE: The dicentric chromosome assay (DCA), often referred to as the 'gold standard' in radiation dose estimation, exhibits significant challenges ...

Deep multiple instance learning versus conventional deep single instance learning for interpretable oral cancer detection.

The current medical standard for setting an oral cancer (OC) diagnosis is histological examination of a tissue sample taken from the oral cavity. This...

Analysis of Bladder Cancer Staging Prediction Using Deep Residual Neural Network, Radiomics, and RNA-Seq from High-Definition CT Images.

Bladder cancer has recently seen an alarming increase in global diagnoses, ascending as a predominant cause of cancer-related mortalities. Given this ...

A deep learning-based 3D Prompt-nnUnet model for automatic segmentation in brachytherapy of postoperative endometrial carcinoma.

PURPOSE: To create and evaluate a three-dimensional (3D) Prompt-nnUnet module that utilizes the prompts-based model combined with 3D nnUnet for produc...

Fully automated 3D machine learning model for HPV status characterization in oropharyngeal squamous cell carcinomas based on CT images.

BACKGROUND: Human papillomavirus (HPV) status plays a major role in predicting oropharyngeal squamous cell carcinoma (OPSCC) survival. This study asse...

Application of machine learning for high-throughput tumor marker screening.

High-throughput sequencing and multiomics technologies have allowed increasing numbers of biomarkers to be mined and used for disease diagnosis, risk ...