Oncology/Hematology

An omics-based tumor microenvironment approach and its prospects.

Multi-omics approaches are revolutionizing cancer research and treatment by integrating single-modality omics methods, such as the transcriptome, genome, epigenome, epi-transcriptome, proteome, metabolome, and developing omics (single-cell omics). These technologies enable a deeper understanding of cancer and provide personalized treatment strategies. However, challenges such as standardization an...

Hybrid deep learning-based skin cancer classification with RPO-SegNet for skin lesion segmentation.

Skin melanin lesions are typically identified as tiny patches on the skin, which are impacted by melanocyte cell overgrowth. The number of people with skin cancer is increasing worldwide. Accurate and timely skin cancer identification is critical to reduce the mortality rates. An incorrect diagnosis can be fatal to the patient. To tackle these issues, this article proposes the Recurrent Prototypic...

Deep Learning for Automated Segmentation of Basal Cell Carcinoma on Mohs Micrographic Surgery Frozen Section Slides.

BACKGROUND: Deep learning has been used to classify basal cell carcinoma (BCC) on histopathologic images. Segmentation models, required for localizati...

Detection of basal cell carcinoma by machine learning-assisted ex vivo confocal laser scanning microscopy.

BACKGROUND: Ex vivo confocal laser scanning microscopy (EVCM) is an emerging imaging modality that enables near real-time histology of whole tissue sa...

Explainable machine learning identifies a polygenic risk score as a key predictor of pancreatic cancer risk in the UK Biobank.

BACKGROUND: Predicting the risk of developing pancreatic ductal adenocarcinoma (PDAC) is of paramount importance, given its high mortality rate. Curre...

Breast radiotherapy planning: A decision-making framework using deep learning.

BACKGROUND: Effective breast cancer treatment planning requires balancing tumor control while minimizing radiation exposure to healthy tissues. Choosi...

Comparative Analysis of Nomogram and Machine Learning Models for Predicting Axillary Lymph Node Metastasis in Early-Stage Breast Cancer: A Study on Clinically and Ultrasound-Negative Axillary Cases Across Two Centers.

OBJECTIVE: Early and accurate prediction of axillary lymph node metastasis (ALNM) is crucial in determining appropriate treatment strategies for patie...

Artificial Intelligence-Driven Patient Selection for Preoperative Portal Vein Embolization for Patients with Colorectal Cancer Liver Metastases.

PURPOSE: To develop a machine learning algorithm to improve hepatic resection selection for patients with metastatic colorectal cancer (CRC) by predic...

Machine learning based on multiplatform tests assists in subtype classification of mature B-cell neoplasms.

Mature B-cell neoplasms (MBNs) are clonal proliferative diseases encompassing over 40 subtypes. The WHO classification (morphology, immunology, cytoge...

MIRD Pamphlet No. 31: MIRDcell V4-Artificial Intelligence Tools to Formulate Optimized Radiopharmaceutical Cocktails for Therapy.

Radiopharmaceutical cocktails have been developed over the years to treat cancer. Cocktails of agents are attractive because 1 radiopharmaceutical is ...

Exploring vision transformers and XGBoost as deep learning ensembles for transforming carcinoma recognition.

Early detection of colorectal carcinoma (CRC), one of the most prevalent forms of cancer worldwide, significantly enhances the prognosis of patients. ...

Exploring an novel diagnostic gene of trastuzumab-induced cardiotoxicity based on bioinformatics and machine learning.

Trastuzumab (Tra)-induced cardiotoxicity (TIC) is a serious side effect of cancer chemotherapy, which can seriously harm the health of cancer patients...

Application of machine learning in breast cancer survival prediction using a multimethod approach.

Breast cancer is one of the most prevalent cancers with an increasing trend in both incidence and mortality rates in Iran. Survival analysis is a pivo...

Liver tumor segmentation method combining multi-axis attention and conditional generative adversarial networks.

In modern medical imaging-assisted therapies, manual annotation is commonly employed for liver and tumor segmentation in abdominal CT images. However,...

Integrating omics data and machine learning techniques for precision detection of oral squamous cell carcinoma: evaluating single biomarkers.

INTRODUCTION: Early detection of oral squamous cell carcinoma (OSCC) is critical for improving clinical outcomes. Precision diagnostics integrating me...

Predicting chemotherapy responsiveness in gastric cancer through machine learning analysis of genome, immune, and neutrophil signatures.

BACKGROUND: Gastric cancer is a major oncological challenge, ranking highly among causes of cancer-related mortality worldwide. This study was initiat...

State-of-the-art performance of deep learning methods for pre-operative radiologic staging of colorectal cancer lymph node metastasis: a scoping review.

OBJECTIVES: To assess the current state-of-the-art in deep learning methods applied to pre-operative radiologic staging of colorectal cancer lymph nod...

Accurate prediction of colorectal cancer diagnosis using machine learning based on immunohistochemistry pathological images.

Colorectal cancer (CRC) ranks as the third most prevalent tumor and the second leading cause of mortality. Early and accurate diagnosis holds signific...

Identification of diagnostic biomarkers and molecular subtype analysis associated with m6A in Tuberculosis immunopathology using machine learning.

Tuberculosis (TB), ranking just below COVID-19 in global mortality, is a highly complex infectious disease involving intricate immunological molecules...

Machine learning to predict lymph node metastasis in T1 esophageal squamous cell carcinoma: a multicenter study.

BACKGROUND: Existing models do poorly when it comes to quantifying the risk of lymph node metastases (LNM). This study aimed to develop a machine-lear...