Oncology/Hematology

Measurement and immunophenotyping of pleural fluid EpCAM-positive cells and clusters for the management of non-small cell lung cancer patients.

OBJECTIVES: A malignant pleural effusion (MPE) is a common complication in non-small cell lung cancer (NSCLC) with important staging and prognostic information. Patients with MPEs are often candidates for advanced therapies, however, the current gold standard, cytological analysis of pleural fluid samples, has limited sensitivity. We aimed to demonstrate the feasibility of non-invasive enumeration...

Repeat Ultrasonography in the First Years after Therapy with Radioiodine Is Not Necessary in Most Patients with Papillary Thyroid Carcinoma when Postoperative Ultrasonography Is Negative: A Reduction of Costs and False-Positives.

BACKGROUND: Periodic ultrasonography (US) examination is recommended in many patients with papillary thyroid carcinoma (PTC) after treatment with radioactive iodine (RAI), but recurrences are confirmed in few cases. This study determined whether the indication of US in the first years after treatment with RAI can be selective when postoperative US and posttherapy whole-body scanning (RxWBS) ruled ...

Preliminary study on alginate/NIPAM hydrogel-based soft microrobot for controlled drug delivery using electromagnetic actuation and near-infrared stimulus.

Currently, microrobots are receiving attention because of their small size and motility, which can be applied to minimal invasive therapy. Additionall...

Robot-assisted percutaneous placement of K-wires during minimally invasive interventions of the spine.

To assess the accuracy and time requirements of image-guided percutaneous K-wire insertion in the spine using an advanced robot assistance device for...

Machine-learning-derived sleep-wake staging from around-the-ear electroencephalogram outperforms manual scoring and actigraphy.

Quantification of sleep is important for the diagnosis of sleep disorders and sleep research. However, the only widely accepted method to obtain sleep...

Automated assessment of breast cancer margin in optical coherence tomography images via pretrained convolutional neural network.

The benchmark method for the evaluation of breast cancers involves microscopic testing of a hematoxylin and eosin (H&E)-stained tissue biopsy. Resurge...

Prediction of early metastatic disease in experimental breast cancer bone metastasis by combining PET/CT and MRI parameters to a Model-Averaged Neural Network.

Macrometastases in bone are preceded by bone marrow invasion of disseminated tumor cells. This study combined functional imaging parameters from FDG-P...

Radiomic versus Convolutional Neural Networks Analysis for Classification of Contrast-enhancing Lesions at Multiparametric Breast MRI.

Purpose To compare the diagnostic performance of radiomic analysis (RA) and a convolutional neural network (CNN) to radiologists for classification of...

A machine learning approach for distinguishing uterine sarcoma from leiomyomas based on perfusion weighted MRI parameters.

PURPOSE: To propose a computer-assisted method for distinguishing uterine sarcoma from leiomyomas based on perfusion weighted magnetic resonance imagi...

Predicting chemo-brain in breast cancer survivors using multiple MRI features and machine-learning.

PURPOSE: Breast cancer (BC) is the most common cancer in women worldwide. There exist various advanced chemotherapy drugs for BC; however, chemotherap...

[Interest of robotic stereotactic radiosurgery in the management of brain metastases: Results of a retrospective, single center analysis].

PURPOSE: The management of malignant brain metastases becomes a main issue for the treatment of patients, because of the survival extension related to...

Temporal separation of Cerenkov radiation and scintillation using a clinical LINAC and artificial intelligence.

Convolutional neural network (CNN) type artificial intelligences were trained to estimate the Cerenkov radiation present in the temporal response of a...

Combined Low Dose Rate Brachytherapy and External Beam Radiation Therapy for Intermediate-Risk Prostate Cancer.

INTRODUCTION: This is a retrospective study conducted to report the tumor control and late toxicity outcomes of patients with intermediate-risk prosta...

A machine learning texture model for classifying lung cancer subtypes using preliminary bronchoscopic findings.

PURPOSE: Bronchoscopy is useful in lung cancer detection, but cannot be used to differentiate cancer types. A computer-aided diagnosis (CAD) system wa...

Multiregion segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks.

PURPOSE: Precise segmentation of bladder walls and tumor regions is an essential step toward noninvasive identification of tumor stage and grade, whic...

Fully tuned RBF neural network controller for ultrasound hyperthermia cancer tumour therapy.

Thermal dose is an important clinical efficacy index for hyperthermia cancer treatment. This paper presents a new direct radial basis function (RBF) n...

Space-time trends of PM constituents in the conterminous United States estimated by a machine learning approach, 2005-2015.

Particulate matter with aerodynamic diameter less than 2.5 μm (PM) is a complex mixture of chemical constituents emitted from various emission sources...

Auto-delineation of oropharyngeal clinical target volumes using 3D convolutional neural networks.

Accurate clinical target volume (CTV) delineation is essential to ensure proper tumor coverage in radiation therapy. This is a particularly difficult ...

A Lightweight Multi-Section CNN for Lung Nodule Classification and Malignancy Estimation.

The size and shape of a nodule are the essential indicators of malignancy in lung cancer diagnosis. However, effectively capturing the nodule's struct...

Machine learning predicts individual cancer patient responses to therapeutic drugs with high accuracy.

Precision or personalized cancer medicine is a clinical approach that strives to customize therapies based upon the genomic profiles of individual pat...