Oncology/Hematology

Deep Learning: a Promising Method for Histological Class Prediction of Breast Tumors in Mammography.

The objective of the study was to determine if the pathology depicted on a mammogram is either benign or malignant (ductal or non-ductal carcinoma) using deep learning and artificial intelligence techniques. A total of 559 patients underwent breast ultrasound, mammography, and ultrasound-guided breast biopsy. Based on the histopathological results, the patients were divided into three categories: ...

Predicting drug sensitivity of cancer cells based on DNA methylation levels.

Cancer cell lines, which are cell cultures derived from tumor samples, represent one of the least expensive and most studied preclinical models for drug development. Accurately predicting drug responses for a given cell line based on molecular features may help to optimize drug-development pipelines and explain mechanisms behind treatment responses. In this study, we focus on DNA methylation profi...

Evaluation of auto-segmentation accuracy of cloud-based artificial intelligence and atlas-based models.

BACKGROUND: Contour delineation, a crucial process in radiation oncology, is time-consuming and inaccurate due to inter-observer variation has been a ...

Artificial intelligence for automatic diagnosis of biliary stricture malignancy status in single-operator cholangioscopy: a pilot study.

BACKGROUND AND AIMS: The diagnosis and characterization of biliary strictures (BSs) is challenging. The introduction of digital single-operator cholan...

Deep learning detects acute myeloid leukemia and predicts NPM1 mutation status from bone marrow smears.

The evaluation of bone marrow morphology by experienced hematopathologists is essential in the diagnosis of acute myeloid leukemia (AML); however, it ...

Selection, Visualization, and Interpretation of Deep Features in Lung Adenocarcinoma and Squamous Cell Carcinoma.

Although deep learning networks applied to digital images have shown impressive results for many pathology-related tasks, their black-box approach and...

Deep learning models for benign and malign ocular tumor growth estimation.

Relatively abundant availability of medical imaging data has provided significant support in the development and testing of Neural Network based image...

Deep Learning Predicts Interval and Screening-detected Cancer from Screening Mammograms: A Case-Case-Control Study in 6369 Women.

Background The ability of deep learning (DL) models to classify women as at risk for either screening mammography-detected or interval cancer (not det...

Multitask Deep Learning for Segmentation and Classification of Primary Bone Tumors on Radiographs.

Background An artificial intelligence model that assesses primary bone tumors on radiographs may assist in the diagnostic workflow. Purpose To develop...

Predicting late symptoms of head and neck cancer treatment using LSTM and patient reported outcomes.

Patient-Reported Outcome (PRO) surveys are used to monitor patients' symptoms during and after cancer treatment. Acute symptoms refer to those experie...

Influential Usage of Big Data and Artificial Intelligence in Healthcare.

Artificial intelligence (AI) is making computer systems capable of executing human brain tasks in many fields in all aspects of daily life. The enhanc...

Artificial intelligence in radiation oncology: A review of its current status and potential application for the radiotherapy workforce.

OBJECTIVE: Radiation oncology is a continually evolving speciality. With the development of new imaging modalities and advanced imaging processing tec...

Automatic identification of suspicious bone metastatic lesions in bone scintigraphy using convolutional neural network.

BACKGROUND: We aimed to construct an artificial intelligence (AI) guided identification of suspicious bone metastatic lesions from the whole-body bone...

DeepWL: Robust EPID based Winston-Lutz analysis using deep learning, synthetic image generation and optical path-tracing.

Radiation therapy requires clinical linear accelerators to be mechanically and dosimetrically calibrated to a high standard. One important quality ass...

Accurate and Feasible Deep Learning Based Semi-Automatic Segmentation in CT for Radiomics Analysis in Pancreatic Neuroendocrine Neoplasms.

Current clinical practice or radiomics studies of pancreatic neuroendocrine neoplasms (pNENs) require manual delineation of the lesions in computed to...

Method of Tumor Pathological Micronecrosis Quantification Via Deep Learning From Label Fuzzy Proportions.

The presence of necrosis is associated with tumor progression and patient outcomes in many cancers, but existing analyses rarely adopt quantitative me...

Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy.

PURPOSE: The aim of this study is to improve the performance of machine learning (ML) models in predicting response of non-small cell lung cancer (NSC...

Disease type detection in lung and colon cancer images using the complement approach of inefficient sets.

Lung and colon cancers are deadly diseases that can develop simultaneously in organs and adversely affect human life in some special cases. Although t...

Artificial intelligence for MRI diagnosis of joints: a scoping review of the current state-of-the-art of deep learning-based approaches.

Deep learning-based MRI diagnosis of internal joint derangement is an emerging field of artificial intelligence, which offers many exciting possibilit...

Leveraging advances in immunopathology and artificial intelligence to analyze in vitro tumor models in composition and space.

Cancer is the leading cause of death worldwide. Unfortunately, efforts to understand this disease are confounded by the complex, heterogenous tumor mi...