Oncology/Hematology

Toward a cure for multiple myeloma within a decade.

Multiple myeloma (MM), long viewed as incurable, is entering a transformative era marked by deeper remissions, prolonged survival, and the realistic prospect of cure. The greatest opportunity lies at diagnosis, before genomic heterogeneity and immune escape limit therapeutic impact. High-risk smoldering MM provides a preclinical window where early intervention with targeted antibodies or combinati...

Label-Free Raman Spectral Identification of NK and NKT Cells in Immune Heterogeneous Cell Populations.

NK and NKT cells act as major effector subsets across multiple immune heterogeneous cell populations (HCPs), and their precise identification is essential for assessing product quality and predicting clinical efficacy. Here, we present a label-free identification strategy that couples Raman spectroscopy with a convolutional neural network (CNN) to achieve rapid and accurate classification of NKT a...

Integrative spatial profiling pipeline for determining TME architectures in archival clinical specimens using CmTSA superplex technology.

The tumor microenvironment (TME) comprises diverse cellular components that spatially interact to form distinct functional niches (FNs). Profiling the...

A meta learning and task adaptive approach for drug target affinity prediction.

Accurate and robust prediction of drug-target affinity (DTA) plays a critical role in drug discovery. While deep learning has advanced DTA prediction,...

A lightweight CNN for enhanced non-small cell lung cancer classification using CT scan image.

Lung cancer is a leading cause of cancer-related mortality worldwide, and its early and accurate detection is critical for improving patient outcomes....

Diagnostic, predictive, and therapeutic approaches for impacted canines: a systematic review and meta-analysis.

OBJECTIVE: Maxillary canine impaction affects approximately 1-3% of the population and presents diagnostic, prognostic, and therapeutic challenges. Th...

Leveraging proteomics and machine learning for mechanism and biomarker discovery on glioma progression and transformation: from LGG to GBM.

BACKGROUND: Glioma was the most common malignant tumor of the central nervous system in adults. Low-grade gliomas (LGGs) have a potential of grade pro...

Automatic segmentation of clinical target volume for radiation therapy in breast-conserving patients and exploration of clinical factors influential to its performance.

OBJECTIVES: To develop and validate a deep learning model for whole breast clinical target volume (CTV) contouring and evaluate clinical features affe...

Intratumoral and peritumoral radiomics-based machine learning models for the postoperative survival prediction in esophageal squamous cell carcinoma.

OBJECTIVES: This study aimed to develop and validate machine learning (ML) models to predict survival following oesophagectomy in oesophageal squamous...

Deep learning with limited data: a transfer learning approach for transcriptomic survival prediction.

BACKGROUND: Deep neural networks (DNNs) are promising for analyzing high-dimensional transcriptomic data in cancer research but are limited by data sc...

Assessment of quantum chemical predictors for anti-colorectal cancer agents using QSAR modeling.

Optimizing the prediction of anti-colorectal cancer agent activity is essential aspect in the identification and creation of medications. Machine lear...

Radiomics-Based machine learning for preoperative prediction of Large-Number central lymph node metastasis in papillary thyroid carcinoma.

Machine learning-based prognostic prediction for cutaneous malignant melanoma patient survival.

BACKGROUND: Cutaneous malignant melanoma (CMM) is a highly malignant tumor that necessitates early diagnosis and precise survival prediction. The deve...

Pediatric SleepNet: A Deep Learning Network for Reliable Pediatric Sleep Staging Across Developmental Stages.

STUDY OBJECTIVES: Manual sleep staging in pediatric populations is challenging due to developmental variability and limited scoring consistency, espec...

Clinical Validation of AI-assisted Evaluation of Indeterminate Biliary Strictures in Digital-Single Operator Cholangioscopy: a Transcontinental Multicentric Study.

INTRODUCTION: Biliary strictures (BS) are a significant challenge, with malignant strictures frequently diagnosed at advanced stages, limiting curativ...

Integrated and Ultrafast Multiomics Sample Preparation Workflow for Screening Biomarker Panel of Platelet Biomolecules for Early Diagnosis of HCC.

Early detection of hepatocellular carcinoma (HCC) remains a persistent worldwide challenge. Owing to its minimal invasiveness, liquid biopsy has emerg...

AI might be the next stage of our natural evolution: A cancer neuroscience perspective.

Cancer neuroscience is an emerging field at the intersection of oncology, neuroscience, and immunology in which the interactions between cancer cells ...

Deciphering lncRNA-disease associations based on multi-representation fusion and boosting with Gaussian process.

LncRNA-disease association (LDA) identification can provide valuable insights for understanding disease pathogenesis. Existing most deep learning-base...

Machine learning and logistic regression in estimating survival in patients with high-malignant deep-seated soft tissue sarcomas: development and analysis based on a population-based retrospective cohort.

BACKGROUND AND PURPOSE:  Soft tissue sarcomas are a heterogeneous group of malignant tumors with a high risk of metastasis, primarily to the lungs, ma...

[Digital transformation in endocrinology/diabetology, pneumology, and hematology/oncology : Where do we stand today-and what lies ahead?].

As in many areas of medicine, increasing digitalisation is also having an impact on everyday practice in internal medicine, entailing both enormous po...