Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Dosage Regimen Designs: Nomograms and Tabulations

Dosage Regimen Designs: Nomograms and Tabulations

Nomograms and tabulations are vital tools used by clinicians to design accurate and individualized dosage regimens. These instruments provide a straightforward method for adjusting dosages based on individual patient characteristics, including age, weight, and physiological condition. The foundation of a drug's nomogram is population pharmacokinetic data collected and analyzed using specific models. This data simplifies complex equations, presenting them diagrammatically or tabularly for easy...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same authorSame journal

Salvage Neck Dissection in Recurrent Head and Neck Squamous Cell Carcinoma After Primary Radiotherapy (RESCUE): An IReC Multicentre Consecutive Cohort Study.

Head & neck·2026

Same author

Toward a Better Paradigm for Head and Neck Cancer Treatment Applying AI (HNC-TACTIC): Protocol for an International Cohort Study of Electronic Health Records.

JMIR research protocols·2026

Same author

The development of a Patient Decision Aid and Patient Concerns Inventory for people diagnosed with recurrent head and neck cancer: a mixed-methods study protocol.

BMJ open·2026

Same author

Standardization of objectives and response criteria for neoadjuvant immunotherapy in resectable squamous cell carcinoma of the head and neck.

Cancer treatment reviews·2026

Same author

Correction: Challenges in Developing a Patient-Reported Symptom-Based Risk Stratification System for Suspected Head and Neck Cancer: Protocol for a Qualitative Case Study.

JMIR research protocols·2026

Same author

Response to "Concerns Regarding Model Robustness and Clinical Generalizability in the Proposed Nomogram for Osteoradionecrosis After Fibula Free Flap Reconstruction".

Head & neck·2026

Search research articles

Related Experiment Video

Updated: Jan 18, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

Predictive Nomogram for Risk Stratification After Transoral Surgery in HPV-Related Oropharyngeal Cancer.

Andrea Costantino¹, Vinidh Paleri^2,3,4, F Christopher Holsinger⁵

¹Department of Otolaryngology-Head and Neck Surgery, AdventHealth Orlando, Orlando, Florida, USA.

|September 12, 2025

Summary

A new model predicts survival for HPV-related oropharyngeal squamous cell carcinoma (OPSCC) patients after surgery. Adjuvant treatment benefits intermediate and high-risk groups, but not low-risk patients.

Keywords:

adjuvant chemotherapy adjuvant radiotherapy squamous cell carcinoma of the head and neck transoral laser microsurgery transoral robotic surgery

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Related Experiment Videos

Last Updated: Jan 18, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Area of Science:

Oncology
Surgical Oncology
Cancer Prognostics

Background:

HPV-related oropharyngeal squamous cell carcinoma (OPSCC) is a significant clinical challenge.
Stratifying patients by survival outcomes is crucial for personalized treatment planning.
Adjuvant treatment decisions post-transoral surgery (TOS) require refined predictive tools.

Purpose of the Study:

To develop and validate a predictive model for stratifying OPSCC patients based on survival.
To assess the impact of adjuvant treatment on survival outcomes in different risk strata.
To guide personalized treatment strategies following TOS for OPSCC.

Main Methods:

Utilized data from the National Cancer Database for patients undergoing primary TOS.
Constructed a multivariable Cox regression model to identify key predictive variables.
Developed a nomogram incorporating demographic, socioeconomic, and clinical factors for risk stratification.

Main Results:

A total of 5569 OPSCC patients were analyzed.
The predictive model stratified patients into low-risk (57%), intermediate-risk (35.5%), and high-risk (7.5%) groups with distinct survival differences.
Adjuvant treatment showed no survival benefit in the low-risk group but was associated with improved survival in intermediate and high-risk cohorts.

Conclusions:

The developed predictive model effectively stratifies OPSCC patients by risk and identifies differential survival outcomes.
Findings suggest that adjuvant treatment decisions should be risk-stratified.
Further external validation is recommended to confirm the model's generalizability.