Oncology/Hematology

MFmap: A semi-supervised generative model matching cell lines to tumours and cancer subtypes.

Translating in vitro results from experiments with cancer cell lines to clinical applications requires the selection of appropriate cell line models. Here we present MFmap (model fidelity map), a machine learning model to simultaneously predict the cancer subtype of a cell line and its similarity to an individual tumour sample. The MFmap is a semi-supervised generative model, which compresses high...

Optimizing the transfer-learning with pretrained deep convolutional neural networks for first stage breast tumor diagnosis using breast ultrasound visual images.

Female accounts for approximately 50% of the total population worldwide and many of them had breast cancer. Computer-aided diagnosis frameworks could reduce the number of needless biopsies and the workload of radiologists. This research aims to detect benign and malignant tumors automatically using breast ultrasound (BUS) images. Accordingly, two pretrained deep convolutional neural network (CNN) ...

Artificial intelligence-aided clinical annotation of a large multi-cancer genomic dataset.

To accelerate cancer research that correlates biomarkers with clinical endpoints, methods are needed to ascertain outcomes from electronic health reco...

Cytotoxic effect of sea anemone pore-forming toxin on K562 chronic myeloid leukemia cells.

Chronic myelogenous leukemia (CML) is one of prevalent cancer worldwide. In spite of various designed drugs, chemoresistance remains the main obstacle...

Pharmacological, Technological, and Digital Innovative Aspects in Rhinology.

Innovation refers to the introduction of a product, a process, a service or a solution resulting in something new or significantly improved compared t...

Robotic Versus Laparoscopic Surgery for Rectal Cancer: A Comprehensive Review of Oncological Outcomes.

The treatment of rectal cancer is complex and involves specialized multidisciplinary care, although the tenet is still rooted in a high-quality total ...

Deep learning data augmentation for Raman spectroscopy cancer tissue classification.

Recently, Raman Spectroscopy (RS) was demonstrated to be a non-destructive way of cancer diagnosis, due to the uniqueness of RS measurements in reveal...

Using a convolutional neural network for classification of squamous and non-squamous non-small cell lung cancer based on diagnostic histopathology HES images.

Histological stratification in metastatic non-small cell lung cancer (NSCLC) is essential to properly guide therapy. Morphological evaluation remains ...

DNN-Boost: Somatic mutation identification of tumor-only whole-exome sequencing data using deep neural network and XGBoost.

Detection of somatic mutation in whole-exome sequencing data can help elucidate the mechanism of tumor progression. Most computational approaches requ...

Artificial Intelligence and Deep Learning for Upper Gastrointestinal Neoplasia.

Upper gastrointestinal (GI) neoplasia account for 35% of GI cancers and 1.5 million cancer-related deaths every year. Despite its efficacy in preventi...

Molecular Biology in Treatment Decision Processes-Neuro-Oncology Edition.

Computational approaches including machine learning, deep learning, and artificial intelligence are growing in importance in all medical specialties a...

A Pan-Cancer Analysis of Predictive Methylation Signatures of Response to Cancer Immunotherapy.

Recently, tumor immunotherapy based on immune checkpoint inhibitors (ICI) has been introduced and widely adopted for various tumor types. Nevertheless...

Deep-Learning Approach to Predict Survival Outcomes Using Wearable Actigraphy Device Among End-Stage Cancer Patients.

Survival prediction is highly valued in end-of-life care clinical practice, and patient performance status evaluation stands as a predominant componen...

ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides.

Although advancing the therapeutic alternatives for treating deadly cancers has gained much attention globally, still the primary methods such as chem...

Lesion Segmentation in Ultrasound Using Semi-Pixel-Wise Cycle Generative Adversarial Nets.

Breast cancer is the most common invasive cancer with the highest cancer occurrence in females. Handheld ultrasound is one of the most efficient ways ...

Unsupervised Learning Framework With Multidimensional Scaling in Predicting Epithelial-Mesenchymal Transitions.

Clustering tumor metastasis samples from gene expression data at the whole genome level remains an arduous challenge, in particular, when the number o...

WMLRR: A Weighted Multi-View Low Rank Representation to Identify Cancer Subtypes From Multiple Types of Omics Data.

The identification of cancer subtypes is of great importance for understanding the heterogeneity of tumors and providing patients with more accurate d...

Predicting distant metastases in soft-tissue sarcomas from PET-CT scans using constrained hierarchical multi-modality feature learning.

Positron emission tomography-computed tomography (PET-CT) is regarded as the imaging modality of choice for the management of soft-tissue sarcomas (ST...

A hybrid approach based on deep learning and level set formulation for liver segmentation in CT images.

Accurate liver segmentation is essential for radiation therapy planning of hepatocellular carcinoma and absorbed dose calculation. However, liver segm...

The identification of gene signatures in patients with extranodal NK/T-cell lymphoma from a pair of twins.

BACKGROUND: There is no unified treatment standard for patients with extranodal NK/T-cell lymphoma (ENKTL). Cancer neoantigens are the result of somat...