AI Medical Compendium Journal:
Cancer medicine

Showing 1 to 10 of 86 articles

Prior Clinico-Radiological Features Informed Multi-Modal MR Images Convolution Neural Network: A novel deep learning framework for prediction of lymphovascular invasion in breast cancer.

Cancer medicine
BACKGROUND: Current methods utilizing preoperative magnetic resonance imaging (MRI)-based radiomics for assessing lymphovascular invasion (LVI) in patients with early-stage breast cancer lack precision, limiting the options for surgical planning.

A computed tomography urography-based machine learning model for predicting preoperative pathological grade of upper urinary tract urothelial carcinoma.

Cancer medicine
OBJECTIVES: Development and validation of a computed tomography urography (CTU)-based machine learning (ML) model for prediction of preoperative pathology grade of upper urinary tract urothelial carcinoma (UTUC).

Artificial intelligence for oral squamous cell carcinoma detection based on oral photographs: A comprehensive literature review.

Cancer medicine
INTRODUCTION: Oral squamous cell carcinoma (OSCC) presents a significant global health challenge. The integration of artificial intelligence (AI) and computer vision holds promise for the early detection of OSCC through the analysis of digitized oral...

Prediction of early-onset colorectal cancer mortality rates in the United States using machine learning.

Cancer medicine
INTRODUCTION: The current study, focusing on a significant US (United States) colorectal cancer (CRC) burden, employs machine learning for predicting future rates among young population.

Cuproptosis gene-related, neural network-based prognosis prediction and drug-target prediction for KIRC.

Cancer medicine
BACKGROUND: Kidney renal clear cell carcinoma (KIRC), as a common case in renal cell carcinoma (RCC), has the risk of postoperative recurrence, thus its prognosis is poor and its prognostic markers are usually based on imaging methods, which have the...

Wavelet scattering networks in deep learning for discovering protein markers in a cohort of Swedish rectal cancer patients.

Cancer medicine
BACKGROUND: Cancer biomarkers play a pivotal role in the diagnosis, prognosis, and treatment response prediction of the disease. In this study, we analyzed the expression levels of RhoB and DNp73 proteins in rectal cancer, as captured in immunohistoc...

A contemporary analysis of disease upstaging of Gleason 3 + 3 prostate cancer patients after robot-assisted laparoscopic prostatectomy.

Cancer medicine
BACKGROUND: Risk of biochemical recurrence (BCR) in localised prostate cancer can be stratified using the 5-tier Cambridge Prognostic Group (CPG) or 3-tier European Association of Urology (EAU) model. Active surveillance is the current recommendation...

An interpretable deep learning framework for predicting liver metastases in postoperative colorectal cancer patients using natural language processing and clinical data integration.

Cancer medicine
BACKGROUND: The significance of liver metastasis (LM) in increasing the risk of death for postoperative colorectal cancer (CRC) patients necessitates innovative approaches to predict LM.

Personalising monitoring for chemotherapy patients through predicting deterioration in renal and hepatic function.

Cancer medicine
BACKGROUND: In those receiving chemotherapy, renal and hepatic dysfunction can increase the risk of toxicity and should therefore be monitored. We aimed to develop a machine learning model to identify those patients that need closer monitoring, enabl...

A deep learning-based interpretable decision tool for predicting high risk of chemotherapy-induced nausea and vomiting in cancer patients prescribed highly emetogenic chemotherapy.

Cancer medicine
OBJECTIVE: This study aims to develop a risk prediction model for chemotherapy-induced nausea and vomiting (CINV) in cancer patients receiving highly emetogenic chemotherapy (HEC) and identify the variables that have the most significant impact on pr...