Oncology/Hematology

Adjustable spot wide-field Raman spectroscopy combined with machine learning for accurate classification of breast cancer cells.

Cell heterogeneity presents significant challenges for the accurate diagnosis and classification of breast cancer at the single-cell level using Raman spectroscopy. Traditional Raman spectroscopy systems are limited by their small laser spot sizes, which restrict them to capturing localized biochemical information within cells. To address this limitation, we propose a wide-field Raman spectroscopy...

Cutting-edge nanobiosensors: Revolutionizing cancer diagnosis and enabling precision medicine.

Integrating nanotechnology and artificial intelligence (AI) revolutionizes cancer diagnostics, propelling precision medicine into a transformative era. This review examines state-of-the-art nanobiosensors capable of detecting critical biomarkers-circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and microRNAs (miRNAs), with unprecedented sensitivity and specificity. We comprehensively ...

Decoding the gut microbiota metabolite-matrix metalloproteinase-3 axis in breast cancer: a multi-omics and network pharmacology study.

Breast cancer is a malignant tumor originating from the breast epithelium, and emerging evidence suggests that the gut microbiota influences its devel...

Machine learning models for predicting recurrence and malignant transformation of oral leukoplakia.

OBJECTIVE: This study aimed to evaluate machine learning models for predicting the recurrence and malignant transformation of oral leukoplakia (OL). M...

Elucidating anti-triple-negative breast cancer mechanisms and mitigating toxicity of Fritillaria thunbergii Miq.: A multi-omics and network pharmacology approach.

ETHNOPHARMACOLOGICAL RELEVANCE: Fritillaria thunbergii Miq. (Zhebeimu, ZBM) is traditionally recognized in Chinese medicine for its effects of clearin...

Multimodal deep learning method based on multiple spectra for lung cancer early diagnosis.

Lung cancer remains one of the most lethal malignancies worldwide, and the early and accurate diagnostic is critical. Traditional diagnostic technique...

Evaluation of the Detectability of Oral Potentially Malignant Diseases with a Deep Learning Approach: A Retrospective Pilot Study.

Oral potentially malignant diseases (OPMD) may arise during the malignant transformation of the oral mucosa, with cellular changes in these lesions in...

Artificial intelligence in gastric cancer: a systematic review of machine learning and deep learning applications.

BACKGROUND: Gastric cancer (GC) remains a major global health concern, ranking as the fifth most prevalent malignancy and the fourth leading cause of ...

Application of Convolutional Neural Network Image Analysis and Machine Learning to Basic Blood Tests for Intelligent Diagnostic Assistance.

BACKGROUND AND OBJECTIVES: We developed an automated morphological image recognition deep learning system (image recognition DLS) of peripheral blood ...

An economic scenario analysis of implementing artificial intelligence in BreastScreen Norway-Impact on radiologist person-years, costs and effects.

ObjectiveTo study the implications of implementing artificial intelligence (AI) as a decision support tool in the Norwegian breast cancer screening pr...

Integration of machine learning and SERS technology for precise classification and diagnosis of colon cancer from plasma samples.

Surface-Enhanced Raman Spectroscopy (SERS) combined with machine learning offers a transformative label-free approach for colorectal cancer detection,...

Leveraging Machine Learning for Rapid and Accurate Diagnosis of Acute Leukemia.

CONTEXT: Early detection of acute leukemia (AL) is crucial for timely intervention and improved outcomes. Machine learning (ML) models provide a promi...

Prognostic Utility of a Deep Learning Radiomics Nomogram Integrating Ultrasound and Multi-Sequence MRI in Triple-Negative Breast Cancer Treated With Neoadjuvant Chemotherapy.

OBJECTIVE: The aim of this study is to evaluate the prognostic performance of a nomogram integrating clinical parameters with deep learning radiomics ...

Diagnosis of leukemia using microarray analysis based on Hidden Markov Model and Random Convolutional Kernel Transform.

INTRODUCTION: Leukemia is one of the most prevalent cancers worldwide, and early detection is critical for effective treatment. Microarray data is a k...

A Causal Machine Learning Framework for Estimating the Impact of Cancer Diagnosis on Receipt of Advance Care Planning.

OBJECTIVE: Develop a causal machine learning (causal ML) framework for estimating how a diagnosis (cancer in this study) affects the likelihood of rec...

Harnessing biomarkers to guide immunotherapy in esophageal cancer: toward precision oncology.

Esophageal cancer (EC) is one of the most serious health issues around the world, ranking seventh among the most lethal types of cancer and eleventh a...

Artificial intelligence-assisted assessment of metabolic response to tebentafusp in metastatic uveal melanoma: a long axial field-of-view [18F]FDG PET/CT study.

PURPOSE: Tebentafusp has emerged as the first systemic therapy to significantly prolong survival in treatment-naïve HLA-A*02:01 + patients with unrese...

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 ...

MRI-based deep learning radiomics in predicting histological differentiation of oropharyngeal cancer: a multicenter cohort study.

BACKGROUND: The primary aim of this research was to create and rigorously assess a deep learning radiomics (DLR) framework utilizing magnetic resonanc...

Residual disease in NPM1-mutated acute myeloid leukemia.

Acute myeloid leukemia (AML) represents a genetically heterogeneous malignancy, with mutations in the nucleophosmin-1 (NPM1) gene identified as the mo...