Oncology/Hematology

Prognostic value of FDG-PET radiomics with machine learning in pancreatic cancer.

Patients with pancreatic cancer have a poor prognosis, therefore identifying particular tumor characteristics associated with prognosis is important. This study aims to investigate the utility of radiomics with machine learning using F-fluorodeoxyglucose (FDG)-PET in patients with pancreatic cancer. We enrolled 161 patients with pancreatic cancer underwent pretreatment FDG-PET/CT. The area of the ...

Deep neural network based artificial intelligence assisted diagnosis of bone scintigraphy for cancer bone metastasis.

Bone scintigraphy (BS) is one of the most frequently utilized diagnostic techniques in detecting cancer bone metastasis, and it occupies an enormous workload for nuclear medicine physicians. So, we aimed to architecture an automatic image interpreting system to assist physicians for diagnosis. We developed an artificial intelligence (AI) model based on a deep neural network with 12,222 cases of Tc...

Application of Computed Tomography Imaging in Diagnosis of Endocrine Nerve of Gastric Cancer and Nursing Intervention Effect.

In this article, some parameters and characteristics of computed tomography (CT) images in patients with gastric cancer are analyzed and the applicati...

Aerobic Fitness is a Predictor of Body Composition in Women With Breast Cancer at Diagnosis.

BACKGROUND: The objective of this study was to investigate the relationship of aerobic fitness (AF) at diagnosis, before treatment and its relationshi...

CUP-AI-Dx: A tool for inferring cancer tissue of origin and molecular subtype using RNA gene-expression data and artificial intelligence.

BACKGROUND: Cancer of unknown primary (CUP), representing approximately 3-5% of all malignancies, is defined as metastatic cancer where a primary site...

Randomness in appendage coordination facilitates strenuous ground self-righting.

Randomness is common in biological and artificial systems, resulting either from stochasticity of the environment or noise in organisms or devices the...

PAIP 2019: Liver cancer segmentation challenge.

Pathology Artificial Intelligence Platform (PAIP) is a free research platform in support of pathological artificial intelligence (AI). The main goal o...

MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer.

BACKGROUND: To use clinical and MRI radiomic features coupled with machine learning to assess HER2 expression level and predict pathologic response (p...

Applications of Computational Methods in Biomedical Breast Cancer Imaging Diagnostics: A Review.

With the exponential increase in new cases coupled with an increased mortality rate, cancer has ranked as the second most prevalent cause of death in ...

Utilisation of artificial intelligence for the development of an EUS-convolutional neural network model trained to enhance the diagnosis of autoimmune pancreatitis.

OBJECTIVE: The diagnosis of autoimmune pancreatitis (AIP) is challenging. Sonographic and cross-sectional imaging findings of AIP closely mimic pancre...

Study of morphological and textural features for classification of oral squamous cell carcinoma by traditional machine learning techniques.

BACKGROUND: Oral squamous cell carcinoma (OSCC) is the most prevalent form of oral cancer. Very few researches have been carried out for the automatic...

A Machine Learning-Assisted Nanoparticle-Printed Biochip for Real-Time Single Cancer Cell Analysis.

Cancers are a complex conglomerate of heterogeneous cell populations with varying genotypes and phenotypes. The intercellular heterogeneity within the...

Application of BERT to Enable Gene Classification Based on Clinical Evidence.

The identification of profiled cancer-related genes plays an essential role in cancer diagnosis and treatment. Based on literature research, the class...

Machine learning in predicting early remission in patients after surgical treatment of acromegaly: a multicenter study.

PURPOSE: Accurate prediction of postoperative remission is beneficial for effective patient-physician communication in acromegalic patients. This stud...

Machine learning-based FDG PET-CT radiomics for outcome prediction in larynx and hypopharynx squamous cell carcinoma.

AIM: To determine whether machine learning-based radiomic feature analysis of baseline integrated 2-[F]-fluoro-2-deoxy-d-glucose (FDG) positron-emissi...

Ovoid Foramen Magnum Shape is Associated with Increased Complications and Decreased Extent of Resection for Anterolateral Foramen Magnum Meningiomas.

 Antero-laterally located meningiomas of the foramen magnum (FM) pose significant surgical resection challenges. The effect of FM shape on surgical r...

Estimating 3-dimensional liver motion using deep learning and 2-dimensional ultrasound images.

PURPOSE: The main purpose of this study is to construct a system to track the tumor position during radiofrequency ablation (RFA) treatment. Existing ...

Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A.

BACKGROUND: The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of docto...

Optimization of an automated tumor-infiltrating lymphocyte algorithm for improved prognostication in primary melanoma.

Tumor-infiltrating lymphocytes (TIL) have potential prognostic value in melanoma and have been considered for inclusion in the American Joint Committe...

Effectiveness of Radiofrequency Ablation in the Treatment of Painful Osseous Metastases: A Correlation Meta-Analysis with Machine Learning Cluster Identification.

A systematic review and meta-analysis of pain response after radiofrequency (RF) ablation over time for osseous metastases was conducted in 2019. Anal...