Oncology/Hematology

An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm.

Among the currently proposed brain segmentation methods, brain tumor segmentation methods based on traditional image processing and machine learning are not ideal enough. Therefore, deep learning-based brain segmentation methods are widely used. In the brain tumor segmentation method based on deep learning, the convolutional network model has a good brain segmentation effect. The deep convolutiona...

Machine Learning Based Radiomic HPV Phenotyping of Oropharyngeal SCC: A Feasibility Study Using MRI.

OBJECTIVES: To investigate whether a radiomic MRI feature-based prediction model can differentiate oropharyngeal squamous cell carcinoma (SCC) according to the human papillomavirus (HPV) status.

Primary Central Nervous System Lymphoma: Clinical Evaluation of Automated Segmentation on Multiparametric MRI Using Deep Learning.

BACKGROUND: Precise volumetric assessment of brain tumors is relevant for treatment planning and monitoring. However, manual segmentations are time-co...

Robotic multivisceral pelvic resection: experience from an exenteration unit.

BACKGROUND: Pelvic exenteration remains a viable and effective treatment option for the management of locally advanced or recurrent pelvic malignancy....

Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: A systematic review.

This systematic review analyses and describes the application and diagnostic accuracy of Artificial Intelligence (AI) methods used for detection and g...

Gut microbiome, big data and machine learning to promote precision medicine for cancer.

The gut microbiome has been implicated in cancer in several ways, as specific microbial signatures are known to promote cancer development and influen...

Radiomics in radiation oncology-basics, methods, and limitations.

Over the past years, the quantity and complexity of imaging data available for the clinical management of patients with solid tumors has increased sub...

Fully multi-target segmentation for breast ultrasound image based on fully convolutional network.

Ultrasound image segmentation plays an important role in computer-aided diagnosis of breast cancer. Existing approaches focused on extracting the tumo...

Molecular docking and machine learning analysis of Abemaciclib in colon cancer.

BACKGROUND: The main challenge in cancer research is the identification of different omic variables that present a prognostic value and personalised d...

Image based cellular contractile force evaluation with small-world network inspired CNN: SW-UNet.

We propose an image based cellular contractile force evaluation method using a machine learning technique. We use a special substrate that exhibits wr...

Radiomics can predict tumour response in patients treated with Nivolumab for a metastatic renal cell carcinoma: an artificial intelligence concept.

Improving the accuracy of gastrointestinal neuroendocrine tumor grading with deep learning.

The Ki-67 index is an established prognostic factor in gastrointestinal neuroendocrine tumors (GI-NETs) and defines tumor grade. It is currently estim...

Automated spheroid generation, drug application and efficacy screening using a deep learning classification: a feasibility study.

The last two decades saw the establishment of three-dimensional (3D) cell cultures as an acknowledged tool to investigate cell behaviour in a tissue-l...

A deep learning MR-based radiomic nomogram may predict survival for nasopharyngeal carcinoma patients with stage T3N1M0.

PURPOSE: To estimate the prognostic value of deep learning (DL) magnetic resonance (MR)-based radiomics for stage T3N1M0 nasopharyngeal carcinoma (NPC...

Accuracy and efficiency of an artificial intelligence tool when counting breast mitoses.

BACKGROUND: The mitotic count in breast carcinoma is an important prognostic marker. Unfortunately substantial inter- and intra-laboratory variation e...

Machine learning of diffraction image patterns for accurate classification of cells modeled with different nuclear sizes.

Measurement of nuclear-to-cytoplasm (N:C) ratios plays an important role in detection of atypical and tumor cells. Yet, current clinical methods rely ...

Radiomics in liver diseases: Current progress and future opportunities.

Liver diseases, a wide spectrum of pathologies from inflammation to neoplasm, have become an increasingly significant health problem worldwide. Noninv...

A priori prediction of tumour response to neoadjuvant chemotherapy in breast cancer patients using quantitative CT and machine learning.

Response to Neoadjuvant chemotherapy (NAC) has demonstrated a high correlation to survival in locally advanced breast cancer (LABC) patients. An early...

Histological Subtypes Classification of Lung Cancers on CT Images Using 3D Deep Learning and Radiomics.

RATIONALE AND OBJECTIVES: Histological subtypes of lung cancers are critical for clinical treatment decision. In this study, we attempt to use 3D deep...

Development of a Deep Learning Model to Identify Lymph Node Metastasis on Magnetic Resonance Imaging in Patients With Cervical Cancer.

IMPORTANCE: Accurate identification of lymph node metastasis preoperatively and noninvasively in patients with cervical cancer can avoid unnecessary s...