Oncology/Hematology

Artificial intelligence in endoscopy: Present and future perspectives.

Artificial intelligence (AI) has been attracting considerable attention as an important scientific topic in the field of medicine. Deep-leaning (DL) technologies have been applied more dominantly than other traditional machine-learning methods. They have demonstrated excellent capability to retract visual features of objectives, even unnoticeable ones for humans, and analyze huge amounts of inform...

Thin-Slice Pituitary MRI with Deep Learning-based Reconstruction: Diagnostic Performance in a Postoperative Setting.

Background Achieving high-spatial-resolution pituitary MRI is challenging because of the trade-off between image noise and spatial resolution. Deep learning-based MRI reconstruction enables image denoising with sharp edges and reduced artifacts, which improves the image quality of thin-slice MRI. Purpose To assess the diagnostic performance of 1-mm slice thickness MRI with deep learning-based reco...

Obtaining PET/CT images from non-attenuation corrected PET images in a single PET system using Wasserstein generative adversarial networks.

Positron emission tomography (PET) imaging plays an indispensable role in early disease detection and postoperative patient staging diagnosis. However...

Prediction of cancer dependencies from expression data using deep learning.

Detecting cancer dependencies is key to disease treatment. Recent efforts have mapped gene dependencies and drug sensitivities in hundreds of cancer c...

Evaluation of a delineation software for cardiac atlas-based autosegmentation: An example of the use of artificial intelligence in modern radiotherapy.

PURPOSE: The primary objective of this work was to implement and evaluate a cardiac atlas-based autosegmentation technique based on the "Workflow Box"...

Single-Cell Techniques and Deep Learning in Predicting Drug Response.

Rapidly developing single-cell sequencing analyses produce more comprehensive profiles of the genomic, transcriptomic, and epigenomic heterogeneity of...

Review of Laser Raman Spectroscopy for Surgical Breast Cancer Detection: Stochastic Backpropagation Neural Networks.

Laser Raman spectroscopy (LRS) is a highly specific biomolecular technique which has been shown to have the ability to distinguish malignant and norma...

Machine Learning-Guided Adjuvant Treatment of Head and Neck Cancer.

IMPORTANCE: Postoperative chemoradiation is the standard of care for cancers with positive margins or extracapsular extension, but the benefit of chem...

The proliferative history shapes the DNA methylome of B-cell tumors and predicts clinical outcome.

We report a systematic analysis of the DNA methylation variability in 1,595 samples of normal cell subpopulations and 14 tumor subtypes spanning the e...

Use of Artificial Intelligence Deep Learning to Determine the Malignant Potential of Pancreatic Cystic Neoplasms With Preoperative Computed Tomography Imaging.

BACKGROUND: Society consensus guidelines are commonly used to guide management of pancreatic cystic neoplasms (PCNs). However, downsides of these guid...

Prediction of Cranial Radiotherapy Treatment in Pediatric Acute Lymphoblastic Leukemia Patients Using Machine Learning: A Case Study at MAHAK Hospital.

BACKGROUND: Acute Lymphoblastic Leukemia (ALL) is the most common blood disease in children and is responsible for the most deaths amongst children. D...

Prediction of breast cancer distant recurrence using natural language processing and knowledge-guided convolutional neural network.

Distant recurrence of breast cancer results in high lifetime risks and low 5-year survival rates. Early prediction of distant recurrent breast cancer ...

A novel extended Kalman filter with support vector machine based method for the automatic diagnosis and segmentation of brain tumors.

BACKGROUND: Brain tumors are life-threatening, and their early detection is crucial for improving survival rates. Conventionally, brain tumors are det...

Detecting Vasodilation as Potential Diagnostic Biomarker in Breast Cancer Using Deep Learning-Driven Thermomics.

Breast cancer is the most common cancer in women. Early diagnosis improves outcome and survival, which is the cornerstone of breast cancer treatment. ...

Intraprostatic Tumor Segmentation on PSMA PET Images in Patients with Primary Prostate Cancer with a Convolutional Neural Network.

Accurate delineation of the intraprostatic gross tumor volume (GTV) is a prerequisite for treatment approaches in patients with primary prostate cance...

Robot-Assisted Radical Cystectomy for Nonmetastatic Urothelial Carcinoma of Urinary Bladder: A Comparison Between Intracorporeal Versus Extracorporeal Orthotopic Ileal Neobladder.

To compare surgical, oncologic, functional outcomes and complication rate between intracorporeal neobladder (ICNB) and extracorporeal neobladder (ECN...

Synchronous Robot-Assisted Pulmonary and Urologic Resections for Cancer.

Synchronous cancers should be first evaluated at high-volume referral oncological centers. Multidisciplinary evaluation, as the first step of multimod...

Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients.

Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, cur...

Academics as leaders in the cancer artificial intelligence revolution.

The successful translation of artificial intelligence (AI) applications into clinical cancer care practice requires guidance by academic cancer resear...

Using Convolutional Neural Network with Cheat Sheet and Data Augmentation to Detect Breast Cancer in Mammograms.

The American Cancer Society expected to diagnose 276,480 new cases of invasive breast cancer in the USA and 48,530 new cases of noninvasive breast can...