Technology in cancer research & treatment
39668711
INTRODUCTION: Since the response of patients with rectal cancer (RC) to neoadjuvant therapy is highly variable, there is an urgent need to develop accurate methods to predict the post-treatment T (pT) stage. The purpose of this study was to evaluate ...
INTRODUCTION: Patient body composition (BC) has been shown to help predict clinical outcomes in rectal cancer patients. Artificial intelligence algorithms have allowed for easier acquisition of BC measurements, creating a comprehensive BC profile in ...
This study explores the potential use of ChatGPT, an AI-based language model, in assessing herbal-drug interactions (HDi) to enhance clinical decision-making. HDi can pose significant health risks by reducing drug efficacy or causing unwanted side ef...
BACKGROUND: Accurate prediction of pathological complete response (pCR) and disease-free survival (DFS) in locally advanced rectal cancer (LARC) patients undergoing neoadjuvant chemoradiotherapy (NCRT) is essential for formulating effective treatment...
BACKGROUND: The preservation of the pelvic autonomic nervous system in total mesorectal excision remains challenging to date. The application of laparoscopy has enabled visualization of fine anatomical structures; however, the rate of urogenital dysf...
MAIN OBJECTIVES: We aimed at comparing intratumoral and peritumoral deep learning, radiomics, and fusion models in predicting KRAS mutations in rectal cancer using endorectal ultrasound imaging.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
39675574
BACKGROUND & PURPOSE: Deep learning (DL) based auto-segmentation has shown to be beneficial for online adaptive radiotherapy (OART). However, auto-segmentation of clinical target volumes (CTV) is complex, as clinical interpretations are crucial in th...
RATIONALE AND OBJECTIVES: To develop interpretable machine learning models that utilize deep learning (DL) and radiomics based on multiparametric Magnetic resonance imaging (MRI) to predict preoperative lymph node (LN) metastasis in rectal cancer.
International journal of colorectal disease
39833443
PURPOSE: This systematic review examines the utility of deep learning algorithms in predicting pathological complete response (pCR) in rectal cancer patients undergoing neoadjuvant chemoradiotherapy (nCRT). The primary goal is to evaluate the perform...
BACKGROUND: Specific pelvic bone dimensions have been identified as predictors of total mesorectal excision (TME) difficulty and outcomes. However, manual measurement of these dimensions (pelvimetry) is labor intensive and thus, anatomic criteria are...