Results 211 to 220 of about 206,195 (300)

A sample size analysis of a mathematical model of longitudinal tumor volume and progression‐free survival for Bayesian individual dynamic predictions in recurrent high‐grade glioma

open access: yesCPT: Pharmacometrics &Systems Pharmacology, Volume 14, Issue 3, Page 495-509, March 2025.
Abstract Patients with recurrent high‐grade glioma (rHGG) have a poor prognosis with median progression‐free survival (PFS) of <7 months. Responses to treatment are heterogenous, suggesting a clinical need for prognostic models. Bayesian data analysis can exploit individual patient follow‐up imaging studies to adaptively predict the risk of progression.
Daniel J. Glazar   +5 more
wiley   +1 more source

Radiation-Induced Fibrosis (RIF) in Head and Neck Squamous Cell Carcinoma (HNSCC): A Review. [PDF]

open access: yesCells
Muehlebach ME   +6 more
europepmc   +1 more source

Mechanistic Learning for Predicting Survival Outcomes in Head and Neck Squamous Cell Carcinoma

open access: yesCPT: Pharmacometrics &Systems Pharmacology, Volume 14, Issue 3, Page 540-550, March 2025.
ABSTRACT We employed a mechanistic learning approach, integrating on‐treatment tumor kinetics (TK) modeling with various machine learning (ML) models to address the challenge of predicting post‐progression survival (PPS)—the duration from the time of documented disease progression to death—and overall survival (OS) in Head and Neck Squamous Cell ...
Kevin Atsou   +4 more
wiley   +1 more source

Tumour‐informed workflow to use ctDNA as a biomarker for risk of recurrence in head and neck cancer patients post‐treatment

open access: yesVIEW, EarlyView.
Our study presents a tumour‐informed circulating tumour DNA (ctDNA) workflow designed to enhance the detection of recurrence in head and neck cancer patients, addressing key challenges such as low ctDNA tumour fractions and tumour heterogeneity. Abstract Circulating tumour DNA (ctDNA) is a promising minimally invasive biomarker for monitoring treatment
Xiaomin Huang   +8 more
wiley   +1 more source

Development and validation of a multimodal model based on histopathological and computed tomography imaging for predicting survival outcomes in stage III colorectal cancer

open access: yesVIEW, EarlyView.
Patients with stage III colorectal cancer exhibit substantial heterogeneity in clinical outcomes despite receiving standardized treatments, which complicates accurate risk stratification. In this study, we integrated features derived from routine histopathological slides and preoperative computed tomography scans using machine learning to develop a ...
Danyi Li   +20 more
wiley   +1 more source

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