Oncology/Hematology

Machine Learning Identifies Metabolic Signatures that Predict the Risk of Recurrent Angina in Remitted Patients after Percutaneous Coronary Intervention: A Multicenter Prospective Cohort Study.

Recurrent angina (RA) after percutaneous coronary intervention (PCI) has few known risk factors, hampering the identification of high-risk populations. In this multicenter study, plasma samples are collected from patients with stable angina after PCI, and these patients are followed-up for 9 months for angina recurrence. Broad-spectrum metabolomic profiling with LC-MS/MS followed by multiple machi...

CT based automatic clinical target volume delineation using a dense-fully connected convolution network for cervical Cancer radiation therapy.

BACKGROUND: It is very important to accurately delineate the CTV on the patient's three-dimensional CT image in the radiotherapy process. Limited to the scarcity of clinical samples and the difficulty of automatic delineation, the research of automatic delineation of cervical cancer CTV based on CT images for new patients is slow. This study aimed to assess the value of Dense-Fully Connected Convo...

MRI-based clinical-radiomics model predicts tumor response before treatment in locally advanced rectal cancer.

Neoadjuvant chemo-radiotherapy (CRT) followed by total mesorectal excision (TME) represents the standard treatment for patients with locally advanced ...

Micro-morphological feature visualization, auto-classification, and evolution quantitative analysis of tumors by using SR-PCT.

Tissue micro-morphological abnormalities and interrelated quantitative data can provide immediate evidences for tumorigenesis and metastasis in microe...

A deep learning approach for mitosis detection: Application in tumor proliferation prediction from whole slide images.

The tumor proliferation, which is correlated with tumor grade, is a crucial biomarker indicative of breast cancer patients' prognosis. The most common...

A novel lightweight deep convolutional neural network for early detection of oral cancer.

OBJECTIVES: To develop a lightweight deep convolutional neural network (CNN) for binary classification of oral lesions into benign and malignant or po...

Towards radiologist-level cancer risk assessment in CT lung screening using deep learning.

PURPOSE: Lung cancer is the leading cause of cancer mortality in the US, responsible for more deaths than breast, prostate, colon and pancreas cancer ...

Prediction of the histology of colorectal neoplasm in white light colonoscopic images using deep learning algorithms.

The treatment plan of colorectal neoplasm differs based on histology. Although new endoscopic imaging systems have been developed, there are clear dia...

Is Anti-Müllerian Hormone a Marker of Ovarian Reserve in Young Breast Cancer Patients Receiving a GnRH Analog during Chemotherapy?

INTRODUCTION: Anti-Müllerian hormone (AMH) is the most reliable biomarker of ovarian reserve; however, its role in predicting ovarian recovery after c...

Therapeutic Potentials of Selected Antihypertensive Agents and Their Fixed-Dose Combinations Against Trastuzumab-Mediated Cardiotoxicity.

Trastuzumab () is useful in the clinical management of HER2-positive metastatic breast, gastric, and colorectal carcinoma but has been limited by its ...

Whole-slide margin control through deep learning in Mohs micrographic surgery for basal cell carcinoma.

BACKGROUND: Basal cell carcinoma (BCC) is the most common type of skin cancer with incidence rates rising each year. Mohs micrographic surgery (MMS) i...

Artificial Intelligence Techniques That May Be Applied to Primary Care Data to Facilitate Earlier Diagnosis of Cancer: Systematic Review.

BACKGROUND: More than 17 million people worldwide, including 360,000 people in the United Kingdom, were diagnosed with cancer in 2018. Cancer prognosi...

Accurate surface ultraviolet radiation forecasting for clinical applications with deep neural network.

Exposure to appropriate doses of UV radiation provides enormously health and medical treatment benefits including psoriasis. Typical hospital-based ph...

Deep learning systems detect dysplasia with human-like accuracy using histopathology and probe-based confocal laser endomicroscopy.

Probe-based confocal laser endomicroscopy (pCLE) allows for real-time diagnosis of dysplasia and cancer in Barrett's esophagus (BE) but is limited by ...

Artificial intelligence prediction model for overall survival of clear cell renal cell carcinoma based on a 21-gene molecular prognostic score system.

We developed and validated a new prognostic model for predicting the overall survival in clear cell renal cell carcinoma (ccRCC) patients. In this stu...

Cytotoxicity of targeted PLGA nanoparticles: a systematic review.

Recent advances in nanotechnology have contributed tremendously to the development and revolutionizing of drug delivery systems in the field of nanome...

Adequacy and Effectiveness of Watson For Oncology in the Treatment of Thyroid Carcinoma.

BACKGROUND: IBM's Watson for Oncology (WFO) is an artificial intelligence tool that trains by acquiring data from the Memorial Sloan Kettering Cancer ...

Generation of Dual functional Nanobody-Nanoluciferase Fusion and its potential in Bioluminescence Enzyme Immunoassay for trace Glypican-3 in Serum.

Glypican-3 (GPC3) is a serological biomarker for the diagnosis of Hepatocellular carcinoma (HCC), but it is a challenging task to develop a bioassay f...

A machine learning-based gene signature of response to the novel alkylating agent LP-184 distinguishes its potential tumor indications.

BACKGROUND: Non-targeted cytotoxics with anticancer activity are often developed through preclinical stages using response criteria observed in cell l...

Machine learning-based prediction model using clinico-pathologic factors for papillary thyroid carcinoma recurrence.

This study analyzed the prognostic significance of clinico-pathologic factors, including the number of metastatic lymph nodes (LNs) and lymph node rat...