Oncology/Hematology

Toward automatic prediction of EGFR mutation status in pulmonary adenocarcinoma with 3D deep learning.

To develop a deep learning system based on 3D convolutional neural networks (CNNs), and to automatically predict EGFR-mutant pulmonary adenocarcinoma in CT images. A dataset of 579 nodules with EGFR mutation status labels of mutant (Mut) or wild-type (WT) was retrospectively analyzed. A deep learning system, namely 3D DenseNets, was developed to process 3D patches of nodules from CT data, and lear...

Deep convolutional neural networks for tongue squamous cell carcinoma classification using Raman spectroscopy.

With deep convolutional neural networks and fiber optic Raman spectroscopy, this study presents a novel classification method that discriminates tongue squamous cell carcinoma (TSCC) from non-tumorous tissue. To achieve this purpose, 24 tissues spectral data were first collected from 12 patients who had undergone a surgical resection due to the tongue squamous cell carcinomas. Then 6 blocks with e...

Classification for invasion depth of esophageal squamous cell carcinoma using a deep neural network compared with experienced endoscopists.

BACKGROUND AND AIMS: Cancer invasion depth is a critical factor affecting the choice of treatment in patients with superficial squamous cell carcinoma...

High-Resolution Raman Microscopic Detection of Follicular Thyroid Cancer Cells with Unsupervised Machine Learning.

We use Raman microscopic images with high spatial and spectral resolution to investigate differences between human follicular thyroid (Nthy-ori 3-1) a...

Predicting breast cancer metastasis by using serum biomarkers and clinicopathological data with machine learning technologies.

BACKGROUND: Approximately 10%-15% of patients with breast cancer die of cancer metastasis or recurrence, and early diagnosis of it can improve prognos...

A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction.

Background Mammographic density improves the accuracy of breast cancer risk models. However, the use of breast density is limited by subjective assess...

Deep learning-based survival prediction of oral cancer patients.

The Cox proportional hazards model commonly used to evaluate prognostic variables in survival of cancer patients may be too simplistic to properly pre...

Prediction of irinotecan toxicity in metastatic colorectal cancer patients based on machine learning models with pharmacokinetic parameters.

Irinotecan (CPT-11) is a drug used against a wide variety of tumors, which can cause severe toxicity, possibly leading to the delay or suspension of t...

Machine learning for prediction of chemoradiation therapy response in rectal cancer using pre-treatment and mid-radiation multi-parametric MRI.

PURPOSE: To predict the neoadjuvant chemoradiation therapy (CRT) response in patients with locally advanced rectal cancer (LARC) using radiomics and d...

Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm.

Recent analysis identified distinct genomic subtypes of lower-grade glioma tumors which are associated with shape features. In this study, we propose ...

Diagnostic ability of artificial intelligence using deep learning analysis of cyst fluid in differentiating malignant from benign pancreatic cystic lesions.

The diagnosis of pancreatic cystic lesions remains challenging. This study aimed to investigate the diagnostic ability of carcinoembryonic antigen (CE...

Development and Validation of Machine Learning Models in Prediction of Remission in Patients With Moderate to Severe Crohn Disease.

IMPORTANCE: Biological therapies have revolutionized inflammatory bowel disease management, but many patients do not respond to biological monotherapy...

Machine-learned target volume delineation of F-FDG PET images after one cycle of induction chemotherapy.

Biological tumour volume (GTV) delineation on F-FDG PET acquired during induction chemotherapy (ICT) is challenging due to the reduced metabolic uptak...

Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI.

BACKGROUND: Detecting and segmenting brain metastases is a tedious and time-consuming task for many radiologists, particularly with the growing use of...

Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms.

Small nucleolar RNAs (snoRNAs) are a new type of functional small RNAs involved in the chemical modifications of rRNAs, tRNAs, and small nuclear RNAs....

Potential Role of Machine Learning in Oncology.

Machine learning (ML) is the ability of computers to learn from data autonomously. It is a core branch of artificial intelligence (AI), which is defin...

Liver tissue segmentation in multiphase CT scans using cascaded convolutional neural networks.

PURPOSE: We address the automatic segmentation of healthy and cancerous liver tissues (parenchyma, active and necrotic parts of hepatocellular carcino...

Effectiveness of ozonated saline in the treatment of VX2 tumors in rabbits.

To investigate the efficacy, safety, and associated mechanisms of injected ozonated saline in the treatment of VX2 tumors. A total of 90 rabbits bea...

Deep learning model integrating features and novel classifiers fusion for brain tumor segmentation.

Automatic and precise segmentation and classification of tumor area in medical images is still a challenging task in medical research. Most of the con...

A Novel Method for Classifying Liver and Brain Tumors Using Convolutional Neural Networks, Discrete Wavelet Transform and Long Short-Term Memory Networks.

Rapid classification of tumors that are detected in the medical images is of great importance in the early diagnosis of the disease. In this paper, a ...