Latest AI and machine learning research in oncology/hematology for healthcare professionals.
This study investigated healthcare utilization patterns prior to prostate cancer diagnoses, aiming to develop machine learning models for early prediction of cancer diagnosis. Data from the All of Us Research Program was used, focusing on adult patients diagnosed with prostate cancer between 2010 and 2019. Key variables were derived from procedure, measurements, and condition records, including PS...
This paper introduces a novel approach for predicting symptom escalation in chemotherapy patients by leveraging Convolutional Neural Networks (CNNs). Accurate forecasting of symptom escalation is crucial in cancer care, as it enables timely interventions and enhances symptom management, ultimately improving patients' quality of life during treatment. The analytical dataset consists of daily self-r...
Chronic lymphocytic leukemia (CLL) exhibits a heterogeneous clinical course. Prognostic markers that impact patient outcomes have been identified, inc...
Non-specific clinical biomarkers have been shown to help identify prognostic risks in cancer patients. However, the accuracy of prognostic biomarkers ...
Cancer remains a leading global health challenge and a major cause of mortality. This study leverages machine learning (ML) to predict the survivabi...
DNA methylation is an epigenetic mechanism that regulates gene expression by adding methyl groups to DNA. Abnormal methylation patterns can disrupt ...
Mohs micrographic surgery (MMS) is the gold standard technique for removing high risk nonmelanoma skin cancer however, intraoperative histopathologi...
Robust localization of lymph nodes (LNs) in multiparametric MRI (mpMRI) is critical for the assessment of lymphadenopathy. Radiologists routinely me...
BACKGROUND: The prognosis of gastric cancer (GC) patients is poor, and an accurate prognostic staging system would help assess patients' prognostic st...
BACKGROUND: Early detection of esophageal squamous neoplasms (ESN) is essential for improving patient prognosis. Optical diagnosis of ESN remains chal...
Accurate brain tumor diagnosis relies on the assessment of multiple Magnetic Resonance Imaging (MRI) sequences. However, in clinical practice, the a...
Recently, vision transformers were shown to be capable of outperforming convolutional neural networks when pretrained on sufficiently large datasets...
This study explores the application of supervised and unsupervised autoencoders (AEs) to automate nuclei classification in clear cell renal cell car...
Composite lymphoma (CL) is rare. We conducted an analysis of 53 329 cases of diffuse large B-cell lymphoma (DLBCL), 17,916 cases of Hodgkin lymphoma (...
BACKGROUND: Recent advances in large language models (LLM) have enabled human-like qualities of natural language competency. Applied to oncology, LLMs...
Leukemia is 10th most frequently diagnosed cancer and one of the leading causes of cancer related deaths worldwide. Realistic analysis of Leukemia r...
The causal relationships between biomarkers are essential for disease diagnosis and medical treatment planning. One notable application is Alzheimer...
BACKGROUND AND PURPOSE: We aimed to develop a physics-informed deep learning model for beam dose prediction in intensity-modulated radiation therapy (...
BACKGROUND: At present, breast cancer has become the cancer with the highest incidence rate in the world. Breast intervention robot is an important bi...
Given that more than 20% of patients with cT1 solid NSCLC showed nodal or extrathoracic metastasis, early detection of metastasis is crucial and urgen...