AIMC Topic: Neoplasm Recurrence, Local

Clear Filters Showing 341 to 350 of 456 articles

Machine Learning-Based Prediction of Distant Recurrence Risk and Ribociclib Treatment Effect in HR+/HER2- Early Breast Cancer Using Real-World and NATALEE Data.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: Despite current standard-of-care endocrine therapy, distant recurrence remains a concern for patients with hormone receptor-positive (HR+)/HER2- early breast cancer (EBC). Understanding individual recurrence risk would aid in clinical decisi...

A machine learning approach to risk-stratification of gastric cancer based on tumour-infiltrating immune cell profiles.

Annals of medicine
BACKGROUND: Gastric cancer (GC) is a highly heterogeneous disease, and the response of patients to clinical treatment varies substantially. There is no satisfactory strategy for predicting curative effects to date. We aimed to explore a new method fo...

Multimodal Approach Predicts Relapse upon Cessation of Immune Checkpoint Inhibitors in Advanced Melanoma.

Clinical cancer research : an official journal of the American Association for Cancer Research
PURPOSE: Treatment with immune checkpoint inhibitors (ICI) in advanced melanoma can result in durable responses, yet an algorithm to decide which patients can safely discontinue ICI is still lacking.

A preliminary exploration of surgical strategies for solitary papillary thyroid carcinoma on the isthmus.

Oral oncology
BACKGROUND: For solitary papillary thyroid carcinoma on the isthmic (SPTCI), there are currently no specific guidelines for the extent of resection and lymph node dissection. This study aims to explore the surgical strategies suitable for patients wi...

Assessment of outcomes and machine Learning-based models to predict local failure risk following stereotactic radiosurgery for small brain metastases.

Journal of neuro-oncology
INTRODUCTION: We assessed the outcomes of stereotactic radiosurgery (SRS) for small intact brain metastases (SBM) (≤ 2 cm) and developed machine learning (ML) algorithms to predict the probability of local failure (LF).

Interpretable machine learning models based on body composition and inflammatory nutritional index (BCINI) to predict early postoperative recurrence of colorectal cancer: Multi-center study.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Colorectal cancer (CRC) ranks among the most prevalent cancers worldwide, with early postoperative recurrence remaining a major cause of mortality. Body composition and inflammatory-nutritional indices (BCINI) have demonstra...

Developing risk stratification strategies and biomarkers for recurrent hepatocellular carcinoma.

Clinical and translational medicine
Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality, with high rates of post-resection recurrence posing significant clinical challenges. Early recurrence is largely driven by aggressive tumor biology, while late recurr...

Current Advances in Classification, Prediction and Management of Microvascular Invasion in Hepatocellular Carcinoma.

Journal of cellular and molecular medicine
Liver resection remains the mainstay curative treatment for hepatocellular carcinoma (HCC); however, the recurrence rate is reported to exceed 70% within 5 years after surgery. Microvascular invasion (MVI) has attracted great research interest in the...

Automated three-dimensional body composition analysis identifies visceral adipose tissue radiodensity as a predictor of mortality and recurrence in colorectal cancer.

Clinical nutrition (Edinburgh, Scotland)
BACKGROUND: Artificial intelligence enables automated three-dimensional (3D) volumetric body composition (BC) analysis from computed tomography (CT), opposed to single third lumbar vertebra (L3) slices alone. This study aimed to identify relationship...