Oncology/Hematology

Cancer survivorship care and general practice: A qualitative study of roles of general practice team members in Australia.

Primary care providers, including general practice teams (GPTs), are well positioned within the community to integrate cancer survivorship care into ongoing health management. However, roles of GPT members in delivery of cancer survivorship care have not been explored. The purpose of this study is to explore these roles from the perspectives of General Practitioners (GPs), Practice Nurses (PNs) an...

Method for Diagnosis of Acute Lymphoblastic Leukemia Based on ViT-CNN Ensemble Model.

Acute lymphocytic leukemia (ALL) is a deadly cancer that not only affects adults but also accounts for about 25% of childhood cancers. Timely and accurate diagnosis of the cancer is an important premise for effective treatment to improve survival rate. Since the image of leukemic B-lymphoblast cells (cancer cells) under the microscope is very similar in morphology to that of normal B-lymphoid prec...

A Molecular Hepatocellular Carcinoma Prognostic Score System Precisely Predicts Overall Survival of Hepatocellular Carcinoma Patients.

BACKGROUND AND AIMS: With high rates of recurrence post-treatment, hepatocellular carcinoma (HCC) is one of the most common types of cancer worldwide ...

Assessment of Axillary Lymph Nodes for Metastasis on Ultrasound Using Artificial Intelligence.

The purpose of this study was to evaluate an artificial intelligence (AI) system for the classification of axillary lymph nodes on ultrasound compared...

Estimating the conditional probability of developing human papilloma virus related oropharyngeal cancer by combining machine learning and inverse Bayesian modelling.

The epidemic increase in the incidence of Human Papilloma Virus (HPV) related Oropharyngeal Squamous Cell Carcinomas (OPSCCs) in several countries wor...

Predicting and characterizing a cancer dependency map of tumors with deep learning.

Genome-wide loss-of-function screens have revealed genes essential for cancer cell proliferation, called cancer dependencies. It remains challenging t...

A machine learning approach to identify predictive molecular markers for cisplatin chemosensitivity following surgical resection in ovarian cancer.

Ovarian cancer is associated with poor prognosis. Platinum resistance contributes significantly to the high rate of tumour recurrence. We aimed to ide...

Prediction models applying machine learning to oral cavity cancer outcomes: A systematic review.

OBJECTIVES: Machine learning platforms are now being introduced into modern oncological practice for classification and prediction of patient outcomes...

Cancer-associated fibroblasts are associated with poor prognosis in solid type of lung adenocarcinoma in a machine learning analysis.

Cancer-associated fibroblasts (CAFs) participate in critical processes in the tumor microenvironment, such as extracellular matrix remodeling, recipro...

A Tri-Stage Wrapper-Filter Feature Selection Framework for Disease Classification.

In machine learning and data science, feature selection is considered as a crucial step of data preprocessing. When we directly apply the raw data for...

Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study.

BACKGROUND: Response to immunotherapy in gastric cancer is associated with microsatellite instability (or mismatch repair deficiency) and Epstein-Barr...

Evaluation of the Effectiveness of Artificial Intelligence Chest CT Lung Nodule Detection Based on Deep Learning.

Lung cancer is one of the most malignant tumors. If it can be detected early and treated actively, it can effectively improve a patient's survival rat...

Identification of biomarkers for acute leukemia via machine learning-based stemness index.

Traditional methods to understand leukemia stem cell (LSC)'s biological characteristics include constructing LSC-like cells and mouse models by transg...

Automatic classification and detection of oral cancer in photographic images using deep learning algorithms.

BACKGROUND: Oral cancer is a deadly disease among the most common malignant tumors worldwide, and it has become an increasingly important public healt...

Deep learning-based predictive biomarker of pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer.

BACKGROUND: Pathological complete response (pCR) is considered a surrogate endpoint for favorable survival in breast cancer patients treated with neoa...

Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences.

Chromatin interactions play important roles in regulating gene expression. However, the availability of genome-wide chromatin interaction data is limi...

Comparative analysis of machine learning approaches to classify tumor mutation burden in lung adenocarcinoma using histopathology images.

Both histologic subtypes and tumor mutation burden (TMB) represent important biomarkers in lung cancer, with implications for patient prognosis and tr...

Inter-database validation of a deep learning approach for automatic sleep scoring.

STUDY OBJECTIVES: Development of inter-database generalizable sleep staging algorithms represents a challenge due to increased data variability across...

Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems.

Prostate cancer (PCa) is one of the most commonly diagnosed cancer and one of the leading causes of death among men, with almost 1.41 million new case...

Feature-fusion improves MRI single-shot deep learning detection of small brain metastases.

BACKGROUND AND PURPOSE: To examine whether feature-fusion (FF) method improves single-shot detector's (SSD's) detection of small brain metastases on c...