Oncology/Hematology

Using machine learning for predicting cancer-specific mortality in bladder cancer patients undergoing radical cystectomy: a SEER-based study.

BACKGROUND: Accurately assessing the prognosis of bladder cancer patients after radical cystectomy has important clinical and research implications. Current models, based on traditional statistical approaches and complex variables, have limited performance. We aimed to develop a machine learning (ML)-based prognostic model to predict 5-year cancer-specific mortality (CSM) in bladder cancer patient...

A CT-based deep learning-driven tool for automatic liver tumor detection and delineation in patients with cancer.

Liver tumors, whether primary or metastatic, significantly impact the outcomes of patients with cancer. Accurate identification and quantification are crucial for effective patient management, including precise diagnosis, prognosis, and therapy evaluation. We present SALSA (system for automatic liver tumor segmentation and detection), a fully automated tool for liver tumor detection and delineatio...

Artificial intelligence to enhance the diagnosis of ocular surface squamous neoplasia.

To provide an artificial intelligence (AI) method using in vivo confocal microscopy (IVCM) to differentiate ocular surface squamous neoplasia (OSSN) f...

Deciphering the dark cancer phosphoproteome using machine-learned co-regulation of phosphosites.

Mass spectrometry-based phosphoproteomics offers a comprehensive view of protein phosphorylation, yet our limited knowledge about the regulation and f...

A fusion model to predict the survival of colorectal cancer based on histopathological image and gene mutation.

Colorectal cancer (CRC) is a prevalent gastrointestinal tumor worldwide with high morbidity and mortality. Predicting the survival of CRC patients not...

Development of PDAC diagnosis and prognosis evaluation models based on machine learning.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is difficult to detect early and highly aggressive, often leading to poor patient prognosis. Exist...

Nanomaterial-Based Molecular Imaging in Cancer: Advances in Simulation and AI Integration.

Nanomaterials represent an innovation in cancer imaging by offering enhanced contrast, improved targeting capabilities, and multifunctional imaging mo...

American College of Veterinary Radiology and European College of Veterinary Diagnostic Imaging position statement on artificial intelligence.

The American College of Veterinary Radiology (ACVR) and the European College of Veterinary Diagnostic Imaging (ECVDI) recognize the transformative pot...

Compressed Representation of Extreme Learning Machine with Self-Diffusion Graph Denoising Applied for Dissecting Molecular Heterogeneity.

Molecular heterogeneity exists in many biological systems, such as major malignancies or diverse cell populations. Clustering of gene expression profi...

Artificial Intelligence and Convolutional Neural Networks-Driven Detection of Micro and Macro Metastasis of Cutaneous Melanoma to the Lymph Nodes.

BACKGROUND: Lymph node (LN) assessment is a critical component in the staging and management of cutaneous melanoma. Traditional histopathological eval...

Stroma and lymphocytes identified by deep learning are independent predictors for survival in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers known to humans. However, not all patients fare equally poor survival, and a...

Ensemble-based multiclass lung cancer classification using hybrid CNN-SVD feature extraction and selection method.

Lung cancer (LC) is a leading cause of cancer-related fatalities worldwide, underscoring the urgency of early detection for improved patient outcomes....

Multimodal treatment of colorectal liver metastases: Where are we? Current strategies and future perspectives.

Despite the continued high prevalence of colorectal cancer in the Western world, recent years have witnessed a decline in its mortality rate, largely ...

Enhanced Artificial Intelligence in Bladder Cancer Management: A Comparative Analysis and Optimization Study of Multiple Large Language Models.

With the rapid advancement of artificial intelligence in health care, large language models (LLMs) demonstrate increasing potential in medical applic...

Deep learning based on intratumoral heterogeneity predicts histopathologic grade of hepatocellular carcinoma.

OBJECTIVES: The potential of medical imaging to non-invasively assess intratumoral heterogeneity (ITH) is increasingly being recognized. This study ai...

A semi-supervised convolutional neural network for diagnosis of pancreatic ductal adenocarcinoma based on EUS-FNA cytological images.

BACKGROUND: The cytological diagnostic process of EUS-FNA smears is time-consuming and manpower-intensive, and the conclusion could be subjective and ...

Denoised recurrence label-based deep learning for prediction of postoperative recurrence risk and sorafenib response in HCC.

BACKGROUND: Pathological images of hepatocellular carcinoma (HCC) contain abundant tumor information that can be used to stratify patients. However, t...

Identification of prognostic subtypes and the role of FXYD6 in ovarian cancer through multi-omics clustering.

BACKGROUND: Ovarian cancer (OC), as a malignant tumor that seriously endangers the lives and health of women, is renowned for its complex tumor hetero...

Revealing gut microbiota biomarkers associated with melanoma immunotherapy response and key bacteria-fungi interaction relationships: evidence from metagenomics, machine learning, and SHAP methodology.

INTRODUCTION: The gut microbiota is associated with the response to immunotherapy in cutaneous melanoma (CM). However, gut fungal biomarkers and bacte...

Multimodal diagnostic models and subtype analysis for neoadjuvant therapy in breast cancer.

BACKGROUND: Breast cancer, a heterogeneous malignancy, comprises multiple subtypes and poses a substantial threat to women's health globally. Neoadjuv...