Oncology/Hematology

Artificial intelligence in outcomes research: a systematic scoping review.

: Despite the number of systematic reviews of how artificial intelligence is being used in different areas of medicine, there is no study on the scope of artificial intelligence methods used in outcomes research, the cornerstone of health technology assessment (HTA). This systematic scoping review aims to systematically capture the scope of artificial intelligence methods used in outcomes research...

Simple Python Module for Conversions Between DICOM Images and Radiation Therapy Structures, Masks, and Prediction Arrays.

Deep learning is becoming increasingly popular and available to new users, particularly in the medical field. Deep learning image segmentation, outcome analysis, and generators rely on presentation of Digital Imaging and Communications in Medicine (DICOM) images and often radiation therapy (RT) structures as masks. Although the technology to convert DICOM images and RT structures into other data t...

Predicting benign, preinvasive, and invasive lung nodules on computed tomography scans using machine learning.

OBJECTIVE: The study objective was to investigate if machine learning algorithms can predict whether a lung nodule is benign, adenocarcinoma, or its p...

Comparison of diagnostic performance between convolutional neural networks and human endoscopists for diagnosis of colorectal polyp: A systematic review and meta-analysis.

Prospective randomized trials and observational studies have revealed that early detection, classification, and removal of neoplastic colorectal polyp...

Artificial intelligence, machine learning, and drug repurposing in cancer.

: Drug repurposing provides a cost-effective strategy to re-use approved drugs for new medical indications. Several machine learning (ML) and artifici...

A deep learning model (ALNet) for the diagnosis of acute leukaemia lineage using peripheral blood cell images.

BACKGROUND AND OBJECTIVES: Morphological differentiation among blasts circulating in blood in acute leukaemia is challenging. Artificial intelligence ...

TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations.

Drug combinations have demonstrated great potential in cancer treatments. They alleviate drug resistance and improve therapeutic efficacy. The fast-gr...

Robotic Right Adrenalectomy for Metastatic Sarcoma.

Robotic chemotherapy compounding: A multicenter productivity approach.

INTRODUCTION: The aim of this study is to compare productivity of the KIRO Oncology compounding robot in three hospital pharmacy departments and ident...

Full robotic multivisceral resections: the Modena experience and literature review.

The robotic platform is becoming a multidisciplinary tool, versatile, and suitable for multiple procedures. Combined multivisceral resections may repr...

Information retrieval on oncology knowledge base using recursive paraphrase lattice.

For annotation in cancer genomic medicine, oncologists have to refer to various knowledge bases worldwide and retrieve all information (e.g., drugs, c...

Discrimination of malignant from benign thyroid lesions through neural networks using FTIR signals obtained from tissues.

The current gold standard in cancer diagnosis-the microscopic examination of hematoxylin and eosin (H&E)-stained biopsies-is prone to bias since it gr...

Deep learning-based auto-delineation of gross tumour volumes and involved nodes in PET/CT images of head and neck cancer patients.

PURPOSE: Identification and delineation of the gross tumour and malignant nodal volume (GTV) in medical images are vital in radiotherapy. We assessed ...

Systematic characterization of mutations altering protein degradation in human cancers.

The ubiquitin-proteasome system (UPS) is the primary route for selective protein degradation in human cells. The UPS is an attractive target for novel...

Deep learning-augmented radiotherapy visualization with a cylindrical radioluminescence system.

This study aims to demonstrate a low-cost camera-based radioluminescence imaging system (CRIS) for high-quality beam visualization that encourages acc...

Forecasting influenza activity using machine-learned mobility map.

Human mobility is a primary driver of infectious disease spread. However, existing data is limited in availability, coverage, granularity, and timelin...

Automated diagnostic support system with deep learning algorithms for distinction of Philadelphia chromosome-negative myeloproliferative neoplasms using peripheral blood specimen.

Philadelphia chromosome-negative myeloproliferative neoplasms (Ph-negative MPNs) such as polycythemia vera (PV), essential thrombocythemia (ET), and p...

Deep learning-based differentiation of invasive adenocarcinomas from preinvasive or minimally invasive lesions among pulmonary subsolid nodules.

OBJECTIVES: To evaluate a deep learning-based model using model-generated segmentation masks to differentiate invasive pulmonary adenocarcinoma (IPA) ...

Deep Learning With 3D Convolutional Neural Network for Noninvasive Prediction of Microvascular Invasion in Hepatocellular Carcinoma.

BACKGROUND: Microvascular invasion (MVI) is a critical prognostic factor of hepatocellular carcinoma (HCC). However, it could only be obtained by post...

A deep-learning semantic segmentation approach to fully automated MRI-based left-ventricular deformation analysis in cardiotoxicity.

Left-ventricular (LV) strain measurements with the Displacement Encoding with Stimulated Echoes (DENSE) MRI sequence provide accurate estimates of car...