Oncology/Hematology

Machine Learning for Automated Interpretation of Fluorodeoxyglucose-Positron Emission Tomography Scans in Lymphoma.

Development of a differential treatment selection model for depression on consolidated and transformed clinical trial datasets.

Major depressive disorder (MDD) is the leading cause of disability worldwide, yet treatment selection still proceeds via "trial and error". Given the varied presentation of MDD and heterogeneity of treatment response, the use of machine learning to understand complex, non-linear relationships in data may be key for treatment personalization. Well-organized, structured data from clinical trials wit...

Factors associated with the local control of brain metastases: a systematic search and machine learning application.

BACKGROUND: Enhancing Local Control (LC) of brain metastases is pivotal for improving overall survival, which makes the prediction of local treatment ...

Multi omics analysis of mitophagy subtypes and integration of machine learning for predicting immunotherapy responses in head and neck squamous cell carcinoma.

Mitophagy serves as a critical mechanism for tumor cell death, significantly impacting the progression of tumors and their treatment approaches. There...

Artificial intelligence in dermatopathology: Updates, strengths, and challenges.

Artificial intelligence (AI) has evolved to become a significant force in various domains, including medicine. We explore the role of AI in pathology,...

From Deep Learning to the Discovery of Promising VEGFR-2 Inhibitors.

Vascular endothelial growth factor receptor 2 (VEGFR-2) stands as a prominent therapeutic target in oncology, playing a critical role in angiogenesis,...

Patient-Specific, Mechanistic Models of Tumor Growth Incorporating Artificial Intelligence and Big Data.

Despite the remarkable advances in cancer diagnosis, treatment, and management over the past decade, malignant tumors remain a major public health pro...

Diagnosis to dissection: AI's role in early detection and surgical intervention for gastric cancer.

Gastric cancer remains a formidable health challenge worldwide; early detection and effective surgical intervention are critical for improving patient...

An improved approach for automated cervical cell segmentation with PointRend.

Regular screening for cervical cancer is one of the best tools to reduce cancer incidence. Automated cell segmentation in screening is an essential ta...

Automatic classification of normal and abnormal cell division using deep learning.

In recent years, there has been a surge in the development of methods for cell segmentation and tracking, with initiatives like the Cell Tracking Chal...

Explainable prediction model for the human papillomavirus status in patients with oropharyngeal squamous cell carcinoma using CNN on CT images.

Several studies have emphasised how positive and negative human papillomavirus (HPV+  and HPV-, respectively) oropharyngeal squamous cell carcinoma (O...

Robust and consistent biomarker candidates identification by a machine learning approach applied to pancreatic ductal adenocarcinoma metastasis.

BACKGROUND: Machine Learning (ML) plays a crucial role in biomedical research. Nevertheless, it still has limitations in data integration and irreprod...

Development and evaluation of machine learning models for predicting large-for-gestational-age newborns in women exposed to radiation prior to pregnancy.

INTRODUCTION: The correlation between radiation exposure before pregnancy and abnormal birth weight has been previously proven. However, for large-for...

Identifying novel circadian rhythm biomarkers for diagnosis and prognosis of melanoma by an integrated bioinformatics and machine learning approach.

Melanoma is a highly malignant skin tumor with poor prognosis. Circadian rhythm is closely related to melanoma pathogenesis. This study aimed to ident...

Comparative studies on modeling and optimization of fermentation process conditions for fungal asparaginase production using artificial intelligence and machine learning techniques.

The L-asparaginase is commercial enzyme used as chemotherapeutic agent in cancer treatment and food processing agent in backed and fried food industri...

Multi-modal segmentation with missing image data for automatic delineation of gross tumor volumes in head and neck cancers.

BACKGROUND: Head and neck (HN) gross tumor volume (GTV) auto-segmentation is challenging due to the morphological complexity and low image contrast of...

Multi-omics deep learning for radiation pneumonitis prediction in lung cancer patients underwent volumetric modulated arc therapy.

BACKGROUND AND OBJECTIVE: To evaluate the feasibility and accuracy of radiomics, dosiomics, and deep learning (DL) in predicting Radiation Pneumonitis...

Validation of a Machine Learning Algorithm, EVendo, for Predicting Esophageal Varices in Hepatocellular Carcinoma.

BACKGROUND: Treatment with atezolizumab and bevacizumab has become standard of care for advanced unresectable hepatocellular carcinoma (HCC) but carri...

Building a challenging medical dataset for comparative evaluation of classifier capabilities.

Since the 2000s, digitalization has been a crucial transformation in our lives. Nevertheless, digitalization brings a bulk of unstructured textual dat...

Empowering standardization of cancer vaccines through ontology: enhanced modeling and data analysis.

BACKGROUND: The exploration of cancer vaccines has yielded a multitude of studies, resulting in a diverse collection of information. The heterogeneity...