Oncology/Hematology

Development and Validation of Machine Learning Model Platelet Index-based Predictor for Colorectal Cancer Stage.

INTRODUCTION: Colorectal cancer (CRC) staging is essential for effective treatment planning and prognosis. While platelet indices have shown promise in indicating CRC aggressiveness, a platelet index-based predictor for CRC staging has not been established in Indonesia. This study aimed to explore the relationship between platelet indices and CRC stage and to develop a predictive model and applica...

Computed tomography-based fully automated artificial intelligence model to predict extrapancreatic perineural invasion in pancreatic ductal adenocarcinoma.

BACKGROUND: Extrapancreatic perineural invasion (EPNI) increases the risk of postoperative recurrence in pancreatic ductal adenocarcinoma (PDAC). This study aimed to develop and validate a computed tomography (CT)-based, fully automated preoperative artificial intelligence (AI) model to predict EPNI in patients with PDAC.

A novel AI-based score for assessing the prognostic value of intra-epithelial lymphocytes in oral epithelial dysplasia.

BACKGROUND: Oral epithelial dysplasia (OED) poses a significant clinical challenge due to its potential for malignant transformation and the lack of r...

Machine learning analysis of the effects of COVID-19 on migration patterns.

This study investigates the impact of the COVID-19 pandemic on European tourist mobility patterns from 2019 to 2021 by conceptualizing countries as mo...

anti-cancer activity of siddha metallo-mineral formulation from Thanga uram with Hela cell lines.

The second most common malignant tumour in women worldwide, cervical cancer seriously jeopardizes their health. It has been established that a high-ri...

Impact of deep learning reconstruction on radiation dose reduction and cancer risk in CT examinations: a real-world clinical analysis.

PURPOSE: The purpose of this study is to estimate the extent to which the implementation of deep learning reconstruction (DLR) may reduce the risk of ...

"Artificial histology" in colonic Neoplasia: A critical approach.

BACKGROUND: The histological assessment of colorectal precancer and cancer lesions is challenging and primarily impacts the clinical strategies of sec...

Data - Knowledge driven machine learning model for cancer pain medication decisions.

BACKGROUND: Cancer pain is one of the most common symptoms in cancer patients, and drug decision-making in cancer pain management remains challenges. ...

Assessment of clinical feasibility:offline adaptive radiotherapy for lung cancer utilizing kV iCBCT and UNet++ based deep learning model.

BACKGROUND: Lung cancer poses a significant global health challenge. Adaptive radiotherapy (ART) addresses uncertainties due to lung tumor dynamics. W...

Integrated multi-omics and machine learning reveal a gefitinib resistance signature for prognosis and treatment response in lung adenocarcinoma.

Gefitinib resistance (GR) presents a significant challenge in treating lung adenocarcinoma (LUAD), highlighting the need for alternative therapies. Th...

Machine learning based on alcohol drinking-gut microbiota-liver axis in predicting the occurrence of early-stage hepatocellular carcinoma.

BACKGROUND: Alcohol drinking and gut microbiota are related to hepatocellular carcinoma (HCC), but the specific relationship between them remains uncl...

Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning.

Integrating diverse types of biological data is essential for a holistic understanding of cancer biology, yet it remains challenging due to data heter...

Prediction model for ocular metastasis of breast cancer: machine learning model development and interpretation study.

BACKGROUND: Breast cancer (BC) is caused by the uncontrolled proliferation of breast epithelial cells followed by malignant transformation, and it has...

Development and validation of an individualized nomogram for predicting distant metastases in gastric cancer using a CT radiomics-clinical model.

PURPOSE: This study aimed to develop and validate a model for accurately assessing the risk of distant metastases in patients with gastric cancer (GC)...

Multiparametric MRI-based Machine Learning Radiomics for Predicting Treatment Response to Transarterial Chemoembolization Combined with Targeted and Immunotherapy in Unresectable Hepatocellular Carcinoma: A Multicenter Study.

RATIONALE AND OBJECTIVES: To develop and validate multiple machine learning predictive models incorporating clinical features and pretreatment multipa...

Using supervised machine learning algorithms to predict bovine leukemia virus seropositivity in dairy cattle in Florida: A 10-year retrospective study.

Supervised machine-learning (SML) algorithms are potentially powerful tools that may be used for screening cows for infectious diseases such as bovine...

Multiple-Instance Learning for thyroid gland disease classification: A hands-on experience.

The morphological diagnosis of thyroid gland neoplasms presents a dual challenge: it requires the expertise of highly trained specialists and consider...

The role of artificial intelligence in immune checkpoint inhibitor research: A bibliometric analysis.

Immune checkpoint inhibitors (ICIs) are revolutionizing cancer treatment, and Artificial Intelligence (AI) is a key player in this field. A comprehens...

Analysis of four long non-coding RNAs for hepatocellular carcinoma screening and prognosis by the aid of machine learning techniques.

Hepatocellular carcinoma (HCC) represents a significant health burden in Egypt, largely attributable to the endemic prevalence of hepatitis B and C vi...

Cell quantification at the osteochondral interface from synchrotron radiation phase contrast micro-computed tomography images using a deep learning approach.

Osteochondral interface consists of two tissues: the calcified cartilage (CC) containing chondrocytes, and subchondral bone (SCB) containing osteocyte...