Oncology/Hematology

Drug repurposing in oncology: Compounds, pathways, phenotypes and computational approaches for colorectal cancer.

The strategy of using existing drugs originally developed for one disease to treat other indications has found success across medical fields. Such drug repurposing promises faster access of drugs to patients while reducing costs in the long and difficult process of drug development. However, the number of existing drugs and diseases, together with the heterogeneity of patients and diseases, notabl...

An ontology for representing hematologic malignancies: the cancer cell ontology.

BACKGROUND: Within the cancer domain, ontologies play an important role in the integration and annotation of data in order to support numerous biomedical tools and applications. This work seeks to leverage existing standards in immunophenotyping cell types found in hematologic malignancies to provide an ontological representation of them to aid in data annotation and analysis for patient data.

Cells in ExperimentaL Life Sciences (CELLS-2018): capturing the knowledge of normal and diseased cells with ontologies.

Cell cultures and cell lines are widely used in life science experiments. In conjunction with the 2018 International Conference on Biomedical Ontology...

Multimodal brain tumor image segmentation using WRN-PPNet.

Tumor segmentation is of great importance for diagnosis and prognosis of brain cancer in medical field. Because of the noise, inhomogeneous gray, dive...

Brain Tumor Segmentation Based on Improved Convolutional Neural Network in Combination with Non-quantifiable Local Texture Feature.

Accurate and reliable brain tumor segmentation is a critical component in cancer diagnosis. According to deep learning model, a novel brain tumor segm...

Machine Learning with Optical Phase Signatures for Phenotypic Profiling of Cell Lines.

Robust and reproducible profiling of cell lines is essential for phenotypic screening assays. The goals of this study were to determine robust and rep...

Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging.

PURPOSE: Tumors are continuously evolving biological systems, and medical imaging is uniquely positioned to monitor changes throughout treatment. Alth...

Attention-aware fully convolutional neural network with convolutional long short-term memory network for ultrasound-based motion tracking.

PURPOSE: One of the promising options for motion management in radiation therapy (RT) is the use of LINAC-compatible robotic-arm-mounted ultrasound im...

Comparison of orthogonal NLP methods for clinical phenotyping and assessment of bone scan utilization among prostate cancer patients.

OBJECTIVE: Clinical care guidelines recommend that newly diagnosed prostate cancer patients at high risk for metastatic spread receive a bone scan pri...

Derivation of an optimal trajectory and nonlinear adaptive controller design for drug delivery in cancerous tumor chemotherapy.

Numerous models have investigated cancer behavior by considering different factors in chemotherapy. The subject of a controller design approach for th...

Qualification of a chemotherapy-compounding robot.

KIRO® Oncology (Kiro Grifols, Spain) is a robotic system for automated compounding of sterile injectable drugs including intravenous cytotoxic treatme...

Viable and necrotic tumor assessment from whole slide images of osteosarcoma using machine-learning and deep-learning models.

Pathological estimation of tumor necrosis after chemotherapy is essential for patients with osteosarcoma. This study reports the first fully automated...

Machine Learning to Build and Validate a Model for Radiation Pneumonitis Prediction in Patients with Non-Small Cell Lung Cancer.

PURPOSE: Radiation pneumonitis is an important adverse event in patients with non-small cell lung cancer (NSCLC) receiving thoracic radiotherapy. Howe...

Quantitative Prediction of the Landscape of T Cell Epitope Immunogenicity in Sequence Space.

Immunodominant T cell epitopes preferentially targeted in multiple individuals are the critical element of successful vaccines and targeted immunother...

Rapid histology of laryngeal squamous cell carcinoma with deep-learning based stimulated Raman scattering microscopy.

Maximal resection of tumor while preserving the adjacent healthy tissue is particularly important for larynx surgery, hence precise and rapid intraope...

Head and neck squamous cell carcinoma: prediction of cervical lymph node metastasis by dual-energy CT texture analysis with machine learning.

OBJECTIVES: This study was conducted in order to evaluate a novel risk stratification model using dual-energy CT (DECT) texture analysis of head and n...

Automatic PET cervical tumor segmentation by combining deep learning and anatomic prior.

Cervical tumor segmentation on 3D FDG PET images is a challenging task because of the proximity between cervix and bladder, both of which can uptake F...

Prognostic Value of Deep Learning PET/CT-Based Radiomics: Potential Role for Future Individual Induction Chemotherapy in Advanced Nasopharyngeal Carcinoma.

PURPOSE: We aimed to evaluate the value of deep learning on positron emission tomography with computed tomography (PET/CT)-based radiomics for individ...

Capsule Network Based Modeling of Multi-omics Data for Discovery of Breast Cancer-Related Genes.

Breast cancer is one of the most common cancers all over the world, which bring about more than 450,000 deaths each year. Although this malignancy has...

Cervical cell recognition based on AGVF-Snake algorithm.

PURPOSE: In recent years, with the increasing incidence of cervical cancer, it is a tedious and time-consuming task with unsatisfying accuracy to manu...