Other Cancers

A deep learning-clinical nomogram hybrid for predicting sentinel lymph node metastasis in melanoma.

BACKGROUND: Melanoma is a life-threatening skin malignancy, with sentinel lymph node metastasis (SLNM) serving as a critical prognostic factor. While machine and deep learning models using histopathology have focused on melanoma diagnosis, limited efforts have aimed to predict SLNM. OBJECTIVES: This study aimed to develop a collaborative machine and deep learning model that integrates histopatholo...

Fusion of Deep Transfer Learning and Radiomics in MRI-Based Prediction of Post-Surgical Recurrence in Soft Tissue Sarcoma.

Soft  tissue sarcomas (STS) are heterogeneous malignancies with high recurrence rates (33-39%) post-surgery, necessitating improved prognostic tools. This study proposes a fusion model integrating deep transfer learning and radiomics from MRI to predict postoperative STS recurrence. Axial T2-weighted fat-suppressed imaging (T2WI) of 803 STS patients from two institutions was retrospectively collec...

Super-Resolution MR Spectroscopic Imaging via Diffusion Models for Tumor Metabolism Mapping.

High-resolution magnetic resonance spectroscopic imaging (MRSI) plays a crucial role in characterizing tumor metabolism and guiding clinical decisions...

Application of artificial intelligence in oral potentially malignant disorders: current opinions and future barriers.

Oral potentially malignant disorders (OPMDs) refer to oral mucosal disorders with an increased risk of malignancy, primarily oral squamous cell carcin...

Identification of uridine phosphatase 1 as a potential therapeutic target in gastric cancer by integrated bioinformatics analysis and experimental validation.

Gastric cancer remains a major global health challenge, and its early diagnosis and prognosis prediction pose significant challenges to the current cl...

Utilizing a combined approach of machine learning and structure-based drug design principles to identify potential hits targeting SphK1.

Sphingosine kinase (SphK1) is acrucial enzyme that aids in the processing of sphingolipids by adding a phosphate group to sphingosine, converting it i...

LYnet: Computational identification of tumor T cell antigens using convolutional and recurrent neural networks.

BACKGROUND: Immunotherapy represents a paradigm shift in oncology, offering advantages in efficacy and specificity over traditional therapies. Key to ...

Artificial Intelligence for Quantifying Endoscopic Mucosal Ulceration in Crohn's Disease.

BACKGROUND & AIMS: Endoscopic scoring of Crohn's disease (CD) is challenging, as mucosal disease is patchy with highly variable morphology, size, and ...

Automating the Referral of Bone Metastases Patients With and Without the Use of Large Language Models.

BACKGROUND AND OBJECTIVES: Bone metastases, affecting more than 4.8% of patients with cancer annually, and particularly spinal metastases require urge...

CT-Based radiomics and deep learning for the preoperative prediction of peritoneal metastasis in ovarian cancers.

OBJECTIVES: To develop a CT-based deep learning radiomics nomogram (DLRN) for the preoperative prediction of peritoneal metastasis (PM) in patients wi...

Determining the Survival Impact and Cost-Effectiveness of Multi-Gene Panel Sequencing in Metastatic Colorectal Cancer With Super Learning Approaches.

OBJECTIVE: To determine the effectiveness and cost-effectiveness of multi-gene panel sequencing compared to single-gene KRAS testing for metastatic co...

Comparative analysis of tumor and mesorectum radiomics in predicting neoadjuvant chemoradiotherapy response in locally advanced rectal cancer.

PURPOSE: Neoadjuvant chemoradiotherapy (CRT) is known to increase sphincter preservation rates and decrease the risk of postoperative recurrence in pa...

Real-time Use of Computer-aided Diagnosis in the Optical Diagnosis of Gastric Neoplasia: A Multicenter Randomized Controlled Trial.

BACKGROUND & AIMS: The multicenter, randomized, control trial was conducted to evaluate whether computer-aided diagnosis (CADx) improves the optical d...

Efficacy of artificial intelligence for adenoma detection in water exchange colonoscopy: a two-center randomized controlled trial.

Water exchange and artificial intelligence-based computer-aided detection (CADe) separately improve the adenoma detection rate (ADR) and number of ade...

Development and validation of a transformer-based deep learning model for predicting distant metastasis in non-small cell lung cancer using 18FDG PET/CT images.

BACKGROUND: This study aimed to develop and validate a hybrid deep learning (DL) model that integrates convolutional neural network (CNN) and vision t...

Portrayal of Cancer Patients in the Era of AI: A Content Analysis of Images Produced by Generative AI Tools.

This study sought to characterize images of cancer patients generated by Artificial Intelligence (AI) text-to-image tools, and assess whether images d...

Cancer vaccines: Discovery, development, and challenges for clinical translation.

Cancer vaccines stimulate antitumor immunity by delivering tumor antigens and, in recent years, have emerged as a promising therapeutic strategy again...

Liquid biopsy in breast cancer management: From circulating biomarkers to clinical breakthroughs.

Breast cancer continues to be a major global health concern, particularly for women, despite improvements in early detection and treatment strategies....

Repeatability and Reproducibility of Artificial Imaging-based Digital Pathology for the Evaluation of Liver Fibrosis.

BACKGROUND & AIMS: Fibrosis stage is a key determinant of outcomes in metabolic dysfunction-associated steatohepatitis (MASH). Assessment of fibrosis ...

Intraductal Papillary Mucinous Neoplasm and Pancreatic Cancer: Opportunity Knocks Twice.

Pancreatic cystic lesions are widely recognized as harbingers of pancreatic cancer. Intraductal papillary mucinous neoplasm (IPMN) is the most common ...