Oncology/Hematology

Machine learning algorithms develop a tumor-educated platelets-related gene signature to predict colorectal cancer prognosis and therapy response.

Tumor-educated platelets (TEPs) have recently emerged as an important component of liquid biopsy, yet the clinical relevance in colorectal cancer (CRC) remains unclear. Here, we employed 10 machine learning algorithms to develop a stable, accurate TEP-related gene signature (TEPGS) to explore its links to tumor-associated macrophages (TAMs) and spatial platelet abundance. TEPGS correlated strongly...

Integrating artificial intelligence into cancers of unknown primary diagnosis and treatment.

Cancers of unknown primary (CUP) refer to a highly heterogeneous group of metastatic tumors whose primary site remains undetectable despite comprehensive conventional evaluations. Characterized by obscure primary origins and extremely poor prognosis, the pathogenesis of CUP remains incompletely elucidated. Empirical chemotherapy, the traditional mainstay of treatment, yields limited efficacy, whil...

Basket and umbrella trials in pediatric precision medicine: a systematic review of designs, opportunities, and challenges.

BACKGROUND: Personalized medicine, driven by genomic insights, has catalyzed the emergence of innovative clinical trial designs such as basket and umb...

Automated classification of clinical T staging pulmonary oncology from chest CT reports: a natural language processing framework for real-world data utilization.

BACKGROUND: Clinical Tumor, Node, and Metastasis (cTNM) classification is vital for predicting treatment efficacy and prognosis in patients with cance...

Lung cancer multimodal auxiliary diagnosis based on entropy weight decision fusion.

BACKGROUND: Lung cancer ranks among the most lethal malignancies globally, and its traditional diagnosis suffers from strong subjectivity, high misdia...

Intelligent three-dimensional imaging in pathology: Applications and developments.

Conventional two-dimensional (2D) pathology relies on a limited number of tissue sections and therefore provides information from isolated planes, whi...

Predicting PPOI secondary to laparoscopic radical resection of rectal cancer combined with lobaplatin hyperthermic intraperitoneal chemotherapy: a multi-center study.

PURPOSE: PPOI is one of the common complications of intraperitoneal hyperthermic chemotherapy during laparoscopic radical resection of rectal cancer, ...

Postoperative Inflammatory and Nutritional Biomarkers for Predicting Anastomotic Leakage After Ovarian Cancer Surgery: A Multicenter Study.

BackgroundUltra-radical cytoreductive surgery is frequently performed for patients with advanced ovarian cancer (OC). However, anastomotic leakage (AL...

Plasma proteomics improves thrombosis prediction in patients with cancer and identifies targetable IL-17-driven endothelial activation.

Thrombosis remains a major cause of morbidity and mortality in patients with cancer. Existing risk models fail to reliably predict venous thromboembol...

Visceral adipose tissue inflammation in endometrial cancer.

BACKGROUND: Obesity is the largest risk factor for endometrial cancer. Body Mass Index (BMI) does not fully capture obesity's metabolic and inflammato...

Deep learning based time series analysis for breathing phase prediction in phase-gated proton therapy.

OBJECTIVE: Phase gating is a critical technique to mitigate tumor motion during radiotherapy, particularly in spot-scanned particle therapy (SSPT) whe...

Uncertainty-aware 3D tumor segmentation with deep ensembles: an evaluation of temporal versus data diversity.

Artificial intelligence (AI) is transforming segmentation tasks in radiotherapy, but model reliability remains a critical concern, particularly for tu...

Single-cell transcriptomic analysis reveals the T lymphocyte microenvironment and identifies HOPX as a hub gene in membranous nephropathy.

BACKGROUND: Membranous nephropathy (MN) is an autoimmune disease characterized by immune complex deposition and progressive renal function impairment....

Fatty acid synthesis therapy-induced senescence (FASTIS) in cancer cells.

Therapy-induced senescence (TIS) in cancer cells can be triggered by radiotherapy, chemotherapy, and certain targeted therapeutics. Here, we demonstra...

Repeatability of AI-quantified incidental findings on lung cancer screening CT scans in the NELSON trial.

OBJECTIVES: Computed tomography (CT) scans for lung cancer screening provide the opportunity of quantifying incidental findings. We evaluated the repe...

A multimodal MRI radiomics model for distinguishing borderline from malignant ovarian epithelial tumors.

BACKGROUND: To develop and validate a multimodal MRI radiomics machine learning model for differentiating borderline epithelial ovarian tumors (BEOTs)...

Predicting early-stage breast cancer disease-free survival and adjuvant therapy benefit from multimodal information using deep learning.

Accurate prediction of disease-free survival (DFS) is essential for tailoring adjuvant regimens and improving clinical outcomes in early-stage breast ...

Radiomics using contrast-enhanced T1-weighted imaging and clinical features for predicting response to EGFR-TKIs in EGFR-mutated non-small cell lung cancer patients with brain metastases.

OBJECTIVES: To construct and validate a model based on clinical characteristics and magnetic resonance imaging (MRI) radiomics to predict 1-year effic...

Radiomics-based unsupervised clustering and deep learning nomogram for response prediction after neoadjuvant chemotherapy in locally advanced laryngeal cancer.

OBJECTIVES: To develop a nomogram model for individualized prediction of neoadjuvant chemotherapy (NAC) response in locally advanced laryngeal cancer ...

Machine learning model based on clinical and imaging features for predicting fungal infections in children with leukemia.

BACKGROUND: Due to immunosuppression, mucosal barrier injury, and prolonged neutropenia resulting from both the disease and chemotherapy, along with t...