Oncology/Hematology

Improvement of phoswich detector-based β+/γ-ray discrimination algorithm with deep learning.

BACKGROUND: Positron probes can accurately localize malignant tumors by directly detecting positrons emitted from positron-emitting radiopharmaceuticals that accumulate in malignant tumors. In the conventional method for direct positron detection, multilayer scintillator detection and pulse shape discrimination techniques are used. However, some γ-rays cannot be distinguished by conventional metho...

Deep learning model for predicting the presence of stromal invasion of breast cancer on digital breast tomosynthesis.

To develop a deep learning (DL)-based algorithm to predict the presence of stromal invasion in breast cancer using digital breast tomosynthesis (DBT). Our institutional review board approved this retrospective study and waived the requirement for informed consent from the patients. Initially, 499 patients (mean age 50.5 years, age range, 29-90 years) who were referred to our hospital under the sus...

Effectiveness of deep learning classifiers in histopathological diagnosis of oral squamous cell carcinoma by pathologists.

The study aims to identify histological classifiers from histopathological images of oral squamous cell carcinoma using convolutional neural network (...

Evaluating staging laparoscopy indications for pancreatic cancer based on resectability classification and treatment strategies for patients with positive peritoneal washing cytology.

INTRODUCTION: The prognosis of pancreatic ductal adenocarcinoma (PDAC) in patients with positive peritoneal washing cytology (CY1) is poor. We aimed t...

Deep learning exploration of single-cell and spatially resolved cancer transcriptomics to unravel tumour heterogeneity.

Tumour heterogeneity is one of the critical confounding aspects in decoding tumour growth. Malignant cells display variations in their gene transcript...

A Review of the Systemic Manifestations of Hepatitis B Virus Infection, Hepatitis D Virus, Hepatocellular Carcinoma, and Emerging Therapies.

Chronic hepatitis B virus (HBV) infection affects about 262 million people worldwide, leading to over 820,000 deaths each year primarily due to cirrho...

Identifying key factors in cell fate decisions by machine learning interpretable strategies.

Cell fate decisions and transitions are common in almost all developmental processes. Therefore, it is important to identify the decision-making mecha...

Auto-Segmentation and Classification of Glioma Tumors with the Goals of Treatment Response Assessment Using Deep Learning Based on Magnetic Resonance Imaging.

Glioma is the most common primary intracranial neoplasm in adults. Radiotherapy is a treatment approach in glioma patients, and Magnetic Resonance Ima...

A deep learning model using hyperspectral image for EUS-FNA cytology diagnosis in pancreatic ductal adenocarcinoma.

BACKGROUND AND AIMS: Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) is considered to be a first-line procedure for the pa...

Clinical relevance of deep learning models in predicting the onset timing of cancer pain exacerbation.

Cancer pain is a challenging clinical problem that is encountered in the management of cancer pain. We aimed to investigate the clinical relevance of ...

Machine learning applications for early detection of esophageal cancer: a systematic review.

INTRODUCTION: Esophageal cancer (EC) is a significant global health problem, with an estimated 7th highest incidence and 6th highest mortality rate. T...

Deep learning for histopathological segmentation of smooth muscle in the urinary bladder.

BACKGROUND: Histological assessment of smooth muscle is a critical step particularly in staging malignant tumors in various internal organs including ...

An Acoustical and Lexical Machine-Learning Pipeline to Identify .

Developing scalable methods for conversation analytics is essential for health care communication science and quality improvement. To assess the fea...

Automated Segmentation of Sacral Chordoma and Surrounding Muscles Using Deep Learning Ensemble.

PURPOSE: The manual segmentation of organ structures in radiation oncology treatment planning is a time-consuming and highly skilled task, particularl...

Development of a novel combined nomogram integrating deep-learning-assisted CT texture and clinical-radiological features to predict the invasiveness of clinical stage IA part-solid lung adenocarcinoma: a multicentre study.

AIM: To develop a novel combined nomogram based on deep-learning-assisted computed tomography (CT) texture (DL-TA) and clinical-radiological features ...

Cell recognition based on atomic force microscopy and modified residual neural network.

Cell recognition methods are in high demand in cell biology and medicine, and the method based on atomic force microscopy (AFM) shows a great value in...

Deep Learning-Based Computational Cytopathologic Diagnosis of Metastatic Breast Carcinoma in Pleural Fluid.

A Pleural effusion cytology is vital for treating metastatic breast cancer; however, concerns have arisen regarding the low accuracy and inter-observe...

Immunodiagnosis - the promise of personalized immunotherapy.

Immunotherapy showed remarkable efficacy in several cancer types. However, the majority of patients do not benefit from immunotherapy. Evaluating tumo...

Prognostic impact of deep learning-based quantification in clinical stage 0-I lung adenocarcinoma.

OBJECTIVES: To evaluate the performance of automatic deep learning (DL) algorithm for size, mass, and volume measurements in predicting prognosis of l...

A novel approach to quantify calcifications of thyroid nodules in US images based on deep learning: predicting the risk of cervical lymph node metastasis in papillary thyroid cancer patients.

OBJECTIVE: Based on ultrasound (US) images, this study aimed to detect and quantify calcifications of thyroid nodules, which are regarded as one of th...