Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Phase 2 trial of avelumab in combination with gemcitabine in advanced leiomyosarcoma as a second-line treatment (EAGLES, Korean Cancer Study Group UN18-09).

Cancer·2024
Same author

Human Immunodeficiency Virus-Associated Gastrointestinal Kaposi's Sarcoma: A Case Report.

Taehan Yongsang Uihakhoe chi·2022
Same author

Longitudinal changes in skeletal muscle mass in patients with advanced squamous cell lung cancer.

Thoracic cancer·2021
Same author

Opportunistic use of chest CT for screening osteoporosis and predicting the risk of incidental fracture in breast cancer patients: A retrospective longitudinal study.

PloS one·2020
Same author

Oncologic outcomes of adjuvant chemotherapy alone after radical surgery for stage IB-IIA cervical cancer patients.

Journal of gynecologic oncology·2017
Same author

Prognostic significance of cachexia score assessed by CT in male patients with small cell lung cancer.

European journal of cancer care·2017

Related Experiment Video

Updated: Mar 27, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

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

Published on: September 27, 2024

1.0K

A Prognostic Nomogram Combining Radiologic Emphysema and Clinical Parameters in Small Cell Lung Cancer.

Young Saing Kim1, Eung Chang Lee2, Hee Young Lee3

  • 1Division of Oncology, Department of Internal Medicine, Gil Medical Center, Gachon University College of Medicine, Incheon, Republic of Korea.

Cancer Management and Research
|March 26, 2026
PubMed
Summary
This summary is machine-generated.

A new nomogram accurately predicts survival for small cell lung cancer (SCLC) patients using age, emphysema, and treatment. This tool aids clinicians in personalized prognostication and treatment decisions for SCLC.

Keywords:
calibrationcox proportional hazards modelsdecision support techniquesrisk assessmentsurvival analysis

More Related Videos

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

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

847
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

11.0K

Related Experiment Videos

Last Updated: Mar 27, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

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

Published on: September 27, 2024

1.0K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

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

847
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

11.0K

Area of Science:

  • Oncology
  • Pulmonology
  • Medical Imaging

Background:

  • Small cell lung cancer (SCLC) is aggressive with poor survival rates.
  • Current prognostic models lack precision for individual risk stratification.
  • Accessible clinical and imaging data are underutilized in SCLC prognostication.

Purpose of the Study:

  • To develop and validate a prognostic nomogram for SCLC.
  • To incorporate easily obtainable clinical and imaging data for improved risk stratification.
  • To enhance individualized prognostication in SCLC patients.

Main Methods:

  • Retrospective analysis of 149 SCLC patients (pre-immunotherapy era).
  • Quantification of emphysema burden using an AI-based tool on CT scans.
  • Multivariate Cox regression and nomogram construction, validated with bootstrap resampling and external data split.

Main Results:

  • Age, emphysema, and treatment modality independently predicted overall survival (OS).
  • The nomogram showed excellent discrimination (C-index=0.807) and good calibration for 1- and 3-year survival.
  • The model demonstrated robust internal and external validity, significantly differentiating survival across risk quartiles.

Conclusions:

  • A validated nomogram using age, emphysema, and treatment offers accurate SCLC survival predictions.
  • This tool can assist clinicians in refining treatment strategies and shared decision-making.
  • External validation is recommended to further confirm the nomogram's utility.