Oncology/Hematology

Prediction of visceral pleural invasion of clinical stage I lung adenocarcinoma using thoracoscopic images and deep learning.

PURPOSE: To develop deep learning models using thoracoscopic images to identify visceral pleural invasion (VPI) in patients with clinical stage I lung adenocarcinoma, and to verify if these models can be applied clinically.

Prognostication of lung adenocarcinomas using CT-based deep learning of morphological and histopathological features: a retrospective dual-institutional study.

OBJECTIVES: To develop and validate CT-based deep learning (DL) models that learn morphological and histopathological features for lung adenocarcinoma prognostication, and to compare them with a previously developed DL discrete-time survival model.

Radiomics-based Machine Learning to Predict the Recurrence of Hepatocellular Carcinoma: A Systematic Review and Meta-analysis.

RATIONALE AND OBJECTIVES: Recurrence of hepatocellular carcinoma (HCC) is a major concern in its management. Accurately predicting the risk of recurre...

Enlightening the path to NSCLC biomarkers: Utilizing the power of XAI-guided deep learning.

BACKGROUND AND OBJECTIVE: The early diagnosis of Non-small cell lung cancer (NSCLC) is of prime importance to improve the patient's survivability and ...

Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide images.

Multiple instance learning (MIL) models have achieved remarkable success in analyzing whole slide images (WSIs) for disease classification problems. H...

Machine Learning in Cardio-Oncology: New Insights from an Emerging Discipline.

A growing body of evidence on a wide spectrum of adverse cardiac events following oncologic therapies has led to the emergence of cardio-oncology as a...

Multiparametric MRI-based radiomics model for predicting human papillomavirus status in oropharyngeal squamous cell carcinoma: optimization using oversampling and machine learning techniques.

OBJECTIVES: To develop and validate a multiparametric MRI-based radiomics model with optimal oversampling and machine learning techniques for predicti...

Deep learning to predict breast cancer sentinel lymph node status on INSEMA histological images.

BACKGROUND: Sentinel lymph node (SLN) status is a clinically important prognostic biomarker in breast cancer and is used to guide therapy, especially ...

Augmented Decision-Making in wound Care: Evaluating the clinical utility of a Deep-Learning model for pressure injury staging.

BACKGROUND: Precise categorization of pressure injury (PI) stages is critical in determining the appropriate treatment for wound care. However, the ex...

Structure-based identification of new orally bioavailable BRD9-PROTACs for treating acute myelocytic leukemia.

BRD9 is essential in regulating gene transcription and chromatin remodeling, and blocking BRD9 profoundly affects the survival of AML cells. However, ...

A Spitzoid Tumor dataset with clinical metadata and Whole Slide Images for Deep Learning models.

Spitzoid tumors (ST) are a group of melanocytic tumors of high diagnostic complexity. Since 1948, when Sophie Spitz first described them, the diagnost...

Artificial Intelligence-Based Organ Delineation for Radiation Treatment Planning of Prostate Cancer on Computed Tomography.

PURPOSE: Meticulous manual delineations of the prostate and the surrounding organs at risk are necessary for prostate cancer radiation therapy to avoi...

Current Status and Emerging Trends in Colorectal Cancer Screening and Diagnostics.

Colorectal cancer (CRC) is a prevalent and potentially fatal disease categorized based on its high incidences and mortality rates, which raised the ne...

Predictive function of tumor burden-incorporated machine-learning algorithms for overall survival and their value in guiding management decisions in patients with locally advanced nasopharyngeal carcinoma.

OBJECTIVE: Accurate prognostic predictions and personalized decision-making on induction chemotherapy (IC) for individuals with locally advanced nasop...

Classification of Brain Tumor Images Using CNN.

A brain tumor is a serious malignant condition caused by unregulated as well as aberrant cell partitioning. Recent advances in deep learning have aide...

Editorial for "Magnetic Resonance Deep Learning Radiomic Model Based on Distinct Metastatic Vascular Patterns for Evaluating Recurrence-Free Survival in Hepatocellular Carcinoma".

Identifying primary tumor site of origin for liver metastases via a combination of handcrafted and deep learning features.

Liver is one of the most common sites for metastases, which can occur on account of primary tumors from multiple sites of origin. Identifying the prim...

One label is all you need: Interpretable AI-enhanced histopathology for oncology.

Artificial Intelligence (AI)-enhanced histopathology presents unprecedented opportunities to benefit oncology through interpretable methods that requi...

Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images.

Current diagnosis of glioma types requires combining both histological features and molecular characteristics, which is an expensive and time-consumin...

Advance of microfluidic flow cytometry enabling high-throughput characterization of single-cell electrical and structural properties.

This paper reported a micro flow cytometer capable of high-throughput characterization of single-cell electrical and structural features based on cons...