Oncology/Hematology

Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning.

PURPOSE: To identify optimal classification methods for computed tomography (CT) radiomics-based preoperative prediction of clear cell renal cell carcinoma (ccRCC) grade.

Real world study for the concordance between IBM Watson for Oncology and clinical practice in advanced non-small cell lung cancer patients at a lung cancer center in China.

BACKGROUND: IBM Watson for Oncology (WFO) provides physicians with evidence-based treatment options. This study was designed to explore the concordance of the suggested therapeutic regimen for advanced non-small cell lung (NSCLC) cancer patients between the updated version of WFO and physicians in our department, in order to reflect the differences of cancer treatment between China and the United ...

Real-time tumor localization with single x-ray projection at arbitrary gantry angles using a convolutional neural network (CNN).

For tumor tracking therapy, precise knowledge of tumor position in real-time is very important. A technique using single x-ray projection based on a c...

SAEROF: an ensemble approach for large-scale drug-disease association prediction by incorporating rotation forest and sparse autoencoder deep neural network.

Drug-disease association is an important piece of information which participates in all stages of drug repositioning. Although the number of drug-dise...

Hierarchical Analysis of Factors Associated with T Staging of Gastric Cancer by Endoscopic Ultrasound.

BACKGROUND: Size, ulcer, differentiation, and location are known to be factors affecting the T stage accuracy of EUS in gastric cancer. However, wheth...

Computer-aided Detection of Brain Metastases in T1-weighted MRI for Stereotactic Radiosurgery Using Deep Learning Single-Shot Detectors.

Background Brain metastases are manually identified during stereotactic radiosurgery (SRS) treatment planning, which is time consuming and potentially...

Identifying Lung Cancer Risk Factors in the Elderly Using Deep Neural Networks: Quantitative Analysis of Web-Based Survey Data.

BACKGROUND: Lung cancer is one of the most dangerous malignant tumors, with the fastest-growing morbidity and mortality, especially in the elderly. Wi...

A convolutional neural network-based system to classify patients using FDG PET/CT examinations.

BACKGROUND: As the number of PET/CT scanners increases and FDG PET/CT becomes a common imaging modality for oncology, the demands for automated detect...

Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data.

Cell Population Data (CPD) provides various blood cell parameters that can be used for differential diagnosis. Data analytics using Machine Learning (...

Machine Learning Analysis of Individual Tumor Lesions in Four Metastatic Colorectal Cancer Clinical Studies: Linking Tumor Heterogeneity to Overall Survival.

Total tumor size (TS) metrics used in TS models in oncology do not consider tumor heterogeneity, which could help to better predict drug efficacy. We ...

Deep-Learning Detection of Cancer Metastases to the Brain on MRI.

BACKGROUND: Approximately one-fourth of all cancer metastases are found in the brain. MRI is the primary technique for detection of brain metastasis, ...

Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning.

Non-small cell lung cancer (NSCLC) is one of the most common lung cancers worldwide. Accurate prognostic stratification of NSCLC can become an importa...

Skin Lesion Segmentation from Dermoscopic Images Using Convolutional Neural Network.

Clinical treatment of skin lesion is primarily dependent on timely detection and delimitation of lesion boundaries for accurate cancerous region local...

Prediction of tumor location in prostate cancer tissue using a machine learning system on gene expression data.

BACKGROUND: Finding the tumor location in the prostate is an essential pathological step for prostate cancer diagnosis and treatment. The location of ...

Machine Learning techniques in breast cancer prognosis prediction: A primary evaluation.

More than 750 000 women in Italy are surviving a diagnosis of breast cancer. A large body of literature tells us which characteristics impact the most...

Improving breast mass classification by shared data with domain transformation using a generative adversarial network.

Training of a convolutional neural network (CNN) generally requires a large dataset. However, it is not easy to collect a large medical image dataset....

Identification of a novel gene signature for the prediction of recurrence in HCC patients by machine learning of genome-wide databases.

Hepatocellular carcinoma (HCC) is a common malignant tumor in China. In the present study, we aimed to construct and verify a prediction model of recu...

Statistical Modeling of Longitudinal Data with Non-ignorable Non-monotone Missingness with Semiparametric Bayesian and Machine Learning Components.

In longitudinal studies, outcomes are measured repeatedly over time and it is common that not all the patients will be measured throughout the study. ...

Can a machine-learning model improve the prediction of nodal stage after a positive sentinel lymph node biopsy in breast cancer?

The current standard for evaluating axillary nodal burden in clinically node negative breast cancer is sentinel lymph node biopsy (SLNB). However, th...

Data-driven translational prostate cancer research: from biomarker discovery to clinical decision.

Prostate cancer (PCa) is a common malignant tumor with increasing incidence and high heterogeneity among males worldwide. In the era of big data and a...