Oncology/Hematology

Development and validation of a deep learning model for morphological assessment of myeloproliferative neoplasms using clinical data and digital pathology.

The subjectivity of morphological assessment and the overlapping pathological features of different subtypes of myeloproliferative neoplasms (MPNs) make accurate diagnosis challenging. To improve the pathological assessment of MPNs, we developed a diagnosis model (fusion model) based on the combination of bone marrow whole-slide images (deep learning [DL] model) and clinical parameters (clinical m...

MultiSCCHisto-Net-KD: A deep network for multi-organ explainable squamous cell carcinoma diagnosis with knowledge distillation.

Squamous cell carcinoma is a prevalent cancer type that affects various organs in the human body. Manual analysis for detecting squamous cell carcinoma in histopathological images is time-consuming and may be subjective. Squamous cell carcinoma diagnosis is typically based on the differences in the architectural arrangement of squamous epithelial layers and the presence of keratinization. However,...

Classification of acute myeloid leukemia by pre-trained deep neural networks: A comparison with different activation functions.

Acute Myeloid Leukemia(AML) is a rapidly progressing cancer affecting blood and bone marrow, marked by the swift proliferation of abnormal myeloid cel...

Integrated Proteomics and Machine Learning Approach Reveals PYCR1 as a Novel Biomarker to Predict Prognosis of Sinonasal Squamous Cell Carcinoma.

Sinonasal squamous cell carcinoma (SNSCC) is a rare tumor with a high 5-year mortality rate. However, proteomic technologies have not yet been utilize...

DMOIT: denoised multi-omics integration approach based on transformer multi-head self-attention mechanism.

Multi-omics data integration has become increasingly crucial for a deeper understanding of the complexity of biological systems. However, effectively ...

Integrating machine learning, bioinformatics and experimental verification to identify a novel prognostic marker associated with tumor immune microenvironment in head and neck squamous carcinoma.

Head and neck squamous carcinoma (HNSC), characterized by a high degree of malignancy, develops in close association with the tumor immune microenviro...

Advancing brain tumor classification: A robust framework using EfficientNetV2 transfer learning and statistical analysis.

Brain tumors are a significant health risk threatening humanity, and they seem to be unique challenges due to their critical location and the complexi...

AI potential in PET/CT cancer imaging.

Positron emission tomography/computed tomography (PET/CT) is a hybrid medical imaging technique that combines PET and CT to provide detailed images of...

Integrating necroptosis into pan-cancer immunotherapy: a new era of personalized treatment.

INTRODUCTION: Necroptosis has emerged as a promising biomarker for predicting immunotherapy responses across various cancer types. Its role in modulat...

The Value of Machine Learning-based Radiomics Model Characterized by PET Imaging with Ga-FAPI in Assessing Microvascular Invasion of Hepatocellular Carcinoma.

RATIONALE AND OBJECTIVES: This study aimed to develop a radiomics model characterized by Ga-fibroblast activation protein inhibitors (FAPI) positron e...

VaxOptiML: leveraging machine learning for accurate prediction of MHC-I and II epitopes for optimized cancer immunotherapy.

Cancer immunotherapy hinges on accurate epitope prediction for advancing vaccine development. VaxOptiML (available at https://vaxoptiml.streamlit.app/...

Tunable and real-time automatic interventional x-ray collimation from semi-supervised deep feature extraction.

BACKGROUND: The use of endovascular procedures is becoming increasingly popular across multiple clinical domains. These procedures are generally perfo...

Federated learning for enhanced dose-volume parameter prediction with decentralized data.

BACKGROUND: The widespread adoption of knowledge-based planning in radiation oncology clinics is hindered by the lack of data and the difficulty assoc...

MDMNI-DGD: A novel graph neural network approach for druggable gene discovery based on the integration of multi-omics data and the multi-view network.

Accurately predicting druggable genes is of paramount importance for enhancing the efficacy of targeted therapies, reducing drug-related toxicities an...

Diagnostic modalities in the mediastinum and the role of bronchoscopy in mediastinal assessment: a narrative review.

BACKGROUND AND OBJECTIVE: Diagnosis of pathology in the mediastinum has proven quite challenging, given the wide variability of both benign and malign...

A prognostic and predictive model based on deep learning to identify optimal candidates for intensity-modulated radiotherapy alone in patients with stage II nasopharyngeal carcinoma: A retrospective multicenter study.

PURPOSE: To develop and validate a prognostic and predictive model integrating deep learning MRI features and clinical information in patients with st...

Development and validation of a machine-learning model to predict lymph node metastasis of intrahepatic cholangiocarcinoma: A retrospective cohort study.

Lymph node metastasis in intrahepatic cholangiocarcinoma significantly impacts overall survival, emphasizing the need for a predictive model. This stu...

Machine learning approach identifies inflammatory gene signature for predicting survival outcomes in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide, often linked to chronic inflammation. Our study aime...

A multi-view prognostic model for diffuse large B-cell lymphoma based on kernel canonical correlation analysis and support vector machine.

BACKGROUND AND OBJECTIVE: Positron emission tomography/computed tomography (PET/CT) is recommended as the standard imaging modality for diffuse large ...

Weakly Supervised Classification for Nasopharyngeal Carcinoma With Transformer in Whole Slide Images.

Pathological examination of nasopharyngeal carcinoma (NPC) is an indispensable factor for diagnosis, guiding clinical treatment and judging prognosis....