Jove
Visualize
Contact Us

Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

455
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
455
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
  1. Home
  3. Survival Outcomes And Prognostic Factors In Spindle Cell Variants Of Squamous Cell Carcinoma: A Machine Learning Analysis Of 1086 Patients From The Seer Database.
  1. Home
  3. Survival Outcomes And Prognostic Factors In Spindle Cell Variants Of Squamous Cell Carcinoma: A Machine Learning Analysis Of 1086 Patients From The Seer Database.

Related Experiment Video

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

194

Survival outcomes and prognostic factors in spindle cell variants of squamous cell carcinoma: a machine learning

Akef Obeidat1, Tarek Ziad Arabi1, Belal Nedal Sabbah1

  • 1College of Medicine, Alfaisal University, Riyadh, Saudi Arabia.

Annals of Medicine and Surgery (2012)
|August 11, 2025

View abstract on PubMed

Summary
This summary is machine-generated.
Keywords:
SEER databasemachine learningprognosisspindle cell carcinomasquamous cell carcinomasurvival analysis

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
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

376

Related Experiment 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

194
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
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

376

Spindle cell squamous cell carcinoma outcomes are influenced by tumor site and stage, not current treatments. Machine learning, specifically random survival forests, shows promise for improving risk stratification in clinical practice.

Area of Science:

  • Oncology
  • Data Science
  • Biostatistics

Background:

  • Spindle cell squamous cell carcinoma (SCSC) is a rare, aggressive cancer with unclear prognostic factors.
  • This study analyzes prognostic factors and treatment outcomes for SCSC using a large dataset.

Purpose of the Study:

  • To identify key prognostic factors for SCSC.
  • To compare the effectiveness of traditional statistical methods with machine learning models for predicting patient survival.
  • To evaluate the potential of machine learning in clinical risk stratification for SCSC.

Main Methods:

  • Retrospective analysis of 1086 SCSC patients from the Surveillance, Epidemiology, and End Results (SEER) database (1992-2021).
  • Utilized Cox regression, random survival forests (RSF), gradient boosted survival (GBSurv), and DeepSurv models.
  • Model performance assessed using concordance indices and decision curve analysis.

    • Main Results:

    • Anatomical site (kidney/renal pelvis, urinary bladder, lung/bronchus) and disease stage were significant prognostic factors.
    • Treatment modalities like radiation and chemotherapy showed limited impact on outcomes.
    • The RSF model achieved superior discriminative ability (C-index: 0.733) compared to GBSurv and DeepSurv.

    Conclusions:

    • Anatomical site and disease stage are critical determinants of survival in SCSC.
    • Current treatment modalities have a limited effect on SCSC outcomes.
    • Machine learning, particularly RSF, offers a valuable tool for enhancing risk stratification in SCSC patient management.