RATIONALE AND OBJECTIVES: To develop and validate a nomogram that combines contrast-enhanced spectral mammography (CESM) deep learning with clinical-pathological features to predict neoadjuvant chemotherapy (NAC) response (either low Miller Payne (MP...
BACKGROUND AND AIMS: The complications of liver cirrhosis occur after long asymptomatic stages of progressive fibrosis and are generally diagnosed late. We aimed to develop a plasma metabolomic-based score tool to predict these events.
BACKGROUND: Establishment of a reliable prognostic model and identification of novel biomarkers are urgently needed to develop precise therapy strategies for clear cell renal cell carcinoma (ccRCC). Stress response stated T cells (Tstr) are a new T-c...
BACKGROUND: The timely identification and management of ovarian cancer are critical determinants of patient prognosis. In this study, we developed and validated a deep learning radiomics nomogram (DLR_Nomogram) based on ultrasound (US) imaging to acc...
Thyroid nodules are a common occurrence, and although most are non-cancerous, some can be malignant. The American College of Radiology has developed the Thyroid Imaging Reporting and Data System (TI-RADS) to standardize the interpretation and reporti...
Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
Apr 2, 2024
OBJECTIVES: Comprehensive cross-interaction of multiple programmed cell death (PCD) patterns in the patients with lung adenocarcinoma (LUAD) have not yet been thoroughly investigated.
Accurately predicting the prognosis of Gastrointestinal stromal tumor (GIST) patients is an important task. The goal of this study was to create and assess models for GIST patients' survival patients using the Surveillance, Epidemiology, and End Resu...
PURPOSE: To explore the value of deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram in predicting the Ki-67 expression in rectal cancer.