Oncology/Hematology

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 appro...

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 ...

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 h...

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. ...

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 mali...

An Acoustical and Lexical Machine-Learning Pipeline to Identify .

Developing scalable methods for conversation analytics is essential for health care communication s...

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 t...

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) t...

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 at...

Predicting venous thromboembolism (VTE) risk in cancer patients using machine learning.

BACKGROUND: The association between cancer and venous thromboembolism (VTE) is well-established with...

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 a...

Response to "Large language model artificial intelligence: the current state and future of ChatGPT in neuro-oncology publishing".

Immunodiagnosis - the promise of personalized immunotherapy.

Immunotherapy showed remarkable efficacy in several cancer types. However, the majority of patients ...

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 o...

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, an...

Pectoralis muscle predicts distant metastases in breast cancer by deep learning radiomics.

BACKGROUND: Sarcopenia is associated with a poor prognosis in patients with breast cancer (BC). Curr...

Short-term outcomes of robot-assisted versus video-assisted thoracoscopic surgery for non-small cell lung cancer patients with neoadjuvant immunochemotherapy: a single-center retrospective study.

BACKGROUND: Neoadjuvant immunochemotherapy has been increasingly applied to treat non-small cell lun...

Modeling Epidemiology Data with Machine Learning Technique to Detect Risk Factors for Gastric Cancer.

PURPOSE: Gastric cancer (GC) ranks as the 7th most common cancer worldwide and a leading cause of ca...

Explainable artificial intelligence to predict and identify prostate cancer tissue by gene expression.

BACKGROUND AND OBJECTIVE: Prostate cancer is one of the most prevalent forms of cancer in men worldw...

Deep learning model for predicting the survival of patients with primary gastrointestinal lymphoma based on the SEER database and a multicentre external validation cohort.

PURPOSE: Due to the rarity of primary gastrointestinal lymphoma (PGIL), the prognostic factors and o...

A patient-specific deep learning framework for 3D motion estimation and volumetric imaging during lung cancer radiotherapy.

. Respiration introduces a constant source of irregular motion that poses a significant challenge fo...