Oncology/Hematology

Exploring artificial intelligence in point-of-care and standard breast ultrasound: a paired reader study.

BackgroundBreast cancer diagnoses are limited in low- and middle-income settings due to lack of medical resources. In these settings, point-of-care ultrasound (POCUS) combined with artificial intelligence (AI)-based interpretation could be a suitable approach.PurposeTo compare the performance of an AI-based breast cancer classification algorithm with radiologists and assess whether POCUS performs ...

Prediction of early death in peripheral T-cell lymphoma-NOS patients based on machine learning.

Peripheral T-cell lymphoma-not otherwise specified (PTCL-NOS) is a highly aggressive and heterogeneous lymphoma subtype with a poor prognosis. This study aims to develop a machine learning-based model to predict early death (within 3 months of diagnosis) in PTCL-NOS patients using data from the SEER database (2016-2021). A total of 1,156 patients were included and randomly divided into training (n...

Identifying the relationship between exosome genes and breast cancer risk using bioinformatics and machine learning methods.

BACKGROUND: Breast cancer (BC) is the most prevalent cancer among women globally, with a high mortality rate. The treatment and prevention of this dis...

Machine learning for predicting liver metastasis in colorectal cancer.

AIM: To evaluate the performance of machine learning models in predicting liver metastasis in colorectal cancer (CRC) patients using the SEER database...

Identifying PLAU as a shared pathogenic gene in type 2 diabetes and bladder urothelial carcinoma through integrated transcriptomic analysis and machine learning for diagnostic and therapeutic value.

Type 2 diabetes mellitus (T2DM) and bladder urothelial carcinoma (BLCA) are two kinds of diseases that seriously threaten human health. Their pathogen...

Development and validation of a machine learning-based model for predicting delirium risk in postoperative brain tumor patients in the Intensive Care Unit.

OBJECTIVE: This study aimed to clarify the incidence and influencing factors of delirium in ICU patients after brain tumor surgery, construct and vali...

Performance evaluation of deep learning based YOLOv5 and YOLOv8 models for real time breast cancer detection in mammographic images.

Breast cancer detection remains a significant challenge in medical diagnostics. Traditional diagnostic methods are time-consuming, unable to detect co...

Prompt-directed ambient artificial intelligence for automated multidisciplinary tumor board documentation.

BACKGROUND: Tumor board documentation is challenging due to complexity of multidisciplinary input. Ambient artificial intelligence (AI) for medical di...

Quantification of Histotripsy Dosage Using Machine Learning-Enhanced Ultrasound Imaging Analysis: Correlation with Histological Outcomes.

RATIONALE AND OBJECTIVES: Histotripsy is a noninvasive ultrasound therapy that mechanically disrupts target tissue through controlled acoustic cavitat...

A CLIP-based framework for multiclass lung histopathology classification with prompt engineering and class-imbalance-aware focal optimization.

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, and accurate histopathological classification is essential for ti...

Differentiating malignancy from liver parenchyma in Ex-Vivo OCT images using anomaly detection.

Primary liver cancer and colorectal liver metastases (CRLM) pose significant challenges, because of limited early diagnosis and the reliance on time-c...

AI-assisted extraction of opportunistic findings from oncologic CT reports using large language models.

Opportunistic findings at imaging (iOFs), such as osteoporosis, liver steatosis, or coronary artery calcifications, are clinically relevant abnormalit...

Bulk and single-cell transcriptomics reveal prognostic signatures of phosphoinositide metabolism in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is one of the most severe malignant tumors. Phosphoinositides metabolism (PIM) plays an important role in maintaining the n...

AI-assisted improvement of endoscopic diagnosis of ampullary lesions.

Ampullary neoplasms are often challenging to detect during forward-viewing esophagogastroduodenoscopy. This study aimed to evaluate the impact of arti...

Development and External Validation of a Transcriptome-Based Multivariable Prediction Model for Treatment-Free Remission in Chronic Myeloid Leukemia.

PURPOSE: Treatment-free remission (TFR) is a major therapeutic objective in chronic myeloid leukemia (CML). However, nearly 50% of patients relapse af...

LLMs as emerging tools for understanding and managing bone metastasis and cancer-induced bone pain.

Artificial intelligence (AI) chatbots powered by large language models (LLMs) show promise in human-like responses, yet their utility for complex biom...

Deep-learning-based artificial intelligence approaches for grading and progression prediction of clear cell renal cell carcinoma.

The grading classification of clear cell renal cell carcinoma (CCRCC) is a crucial prognostic factor. However, manual observation can lead to subjecti...

Dynamic urinary proteomics integrates single-cell and spatial transcriptomics to reveal tumour microenvironment and predict immunotherapy response in biliary tract cancer.

BACKGROUND: Most patients with biliary tract cancer (BTC) do not derive durable clinical benefit (DCB) from immune checkpoint inhibitors (ICIs), under...

Targeting colorectal cancer stem-like cells: Overcoming therapeutic resistance and reprogramming the immunosuppressive niche.

Colorectal cancer stem-like cells (CRC-SCs) represent a tumor subpopulation with enhanced tumor-initiating capacity and have been implicated in metast...

Deciphering the potential molecular links between ochratoxin A and colorectal cancer: an integrated computational toxicological and single-cell transcriptomic study.

BACKGROUND: Ochratoxin A (OTA), a common foodborne mycotoxin, is classified as a potential human carcinogen. However, the specific molecular mechanism...