Oncology/Hematology

Automated tumour budding quantification by machine learning augments TNM staging in muscle-invasive bladder cancer prognosis.

Tumour budding has been described as an independent prognostic feature in several tumour types. We report for the first time the relationship between tumour budding and survival evaluated in patients with muscle invasive bladder cancer. A machine learning-based methodology was applied to accurately quantify tumour buds across immunofluorescence labelled whole slide images from 100 muscle invasive ...

Can We Accurately Identify Peritoneal Metastases Based on Their Appearance? An Assessment of the Current Practice of Intraoperative Gastrointestinal Cancer Staging.

BACKGROUND: Peritoneal lesions are common findings during operative abdominal cancer staging. The decision to perform biopsy is made subjectively by the surgeon, a practice the authors hypothesized to be imprecise. This study aimed to describe optical characteristics differentiating benign peritoneal lesions from peritoneal metastases.

A Remotely Controlled Transformable Soft Robot Based on Engineered Cardiac Tissue Construct.

Many living organisms undergo conspicuous or abrupt changes in body structure, which is often accompanied by a behavioral change. Inspired by the natu...

Application of a machine learning method to whole brain white matter injury after radiotherapy for nasopharyngeal carcinoma.

BACKGROUND: The purpose/aim of this study was to 1) use magnetic resonance diffusion tensor imaging (DTI), fibre bundle/tract-based spatial statistics...

Early experience with Watson for oncology in Korean patients with colorectal cancer.

BACKGROUND: Watson for oncology (WFO) is a cognitive computing system providing decision support. We evaluated the concordance rates between the treat...

A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis.

This study reviews the technique of convolutional neural network (CNN) applied in a specific field of mammographic breast cancer diagnosis (MBCD). It ...

A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning.

In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of organs-at-risk for radiation therapy planning of ...

LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features.

BACKGROUND: Lung adenocarcinoma is the most common type of lung cancers. Whole-genome sequencing studies disclosed the genomic landscape of lung adeno...

Swarm-Fuzzy Rule-Based Targeted Nano Delivery Using Bioinspired Nanomachines.

Cooperative navigation and swarm decision making take center stage in a broad range of distributed applications with high-environmental and measuremen...

CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation.

Accurate segmentation of the prostate and organs at risk (e.g., bladder and rectum) in CT images is a crucial step for radiation therapy in the treatm...

Glioma Tumor Grade Identification Using Artificial Intelligent Techniques.

Computer aided diagnosis using artificial intelligent techniques made tremendous improvement in medical applications especially for easy detection of ...

Group Lasso Regularized Deep Learning for Cancer Prognosis from Multi-Omics and Clinical Features.

Accurate prognosis of patients with cancer is important for the stratification of patients, the optimization of treatment strategies, and the design o...

Combination of Peri- and Intratumoral Radiomic Features on Baseline CT Scans Predicts Response to Chemotherapy in Lung Adenocarcinoma.

PURPOSE: To identify the role of radiomics texture features both within and outside the nodule in predicting time to progression (TTP) and overall su...

Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network.

PURPOSE: We aimed to use deep learning with convolutional neural network (CNN) to discriminate between benign and malignant breast mass images from ul...

Comparison of long-term outcomes of laparoscopic and robot-assisted laparoscopic partial nephrectomy.

In this study, we compared the long-term oncological and functional outcomes of laparoscopic partial nephrectomy (LPN) and robot-assisted laparoscopic...

Predicting Ewing Sarcoma Treatment Outcome Using Infrared Spectroscopy and Machine Learning.

BACKGROUND: Improved outcome prediction is vital for the delivery of risk-adjusted, appropriate and effective care to paediatric patients with Ewing s...

Gene Expression Classification of Lung Adenocarcinoma into Molecular Subtypes.

As one of the most common malignancies in the world, lung adenocarcinoma (LUAD) is currently difficult to cure. However, the advent of precision medic...

A novel end-to-end brain tumor segmentation method using improved fully convolutional networks.

Accurate brain magnetic resonance imaging (MRI) tumor segmentation continues to be an active research topic in medical image analysis since it provide...

3D radiotherapy dose prediction on head and neck cancer patients with a hierarchically densely connected U-net deep learning architecture.

The treatment planning process for patients with head and neck (H&N) cancer is regarded as one of the most complicated due to large target volume, mul...

Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma.

OBJECTIVES: To develop and validate an algorithm to predict occult nodal metastasis in clinically node negative oral cavity squamous cell carcinoma (O...