Nomogram predicting No-ER rates for intermediate‐ to high‐risk DTC pati - Evidencio
Nomogram predicting No-ER rates for intermediate‐ to high‐risk DTC patients receiving RAT.
In recent years, nomograms have emerged as powerful predictive tools in oncology research. By integrating multiple prognostic factors, nomograms provide a visual representation of individualized risk assessments, allowing clinicians to estimate the probability of specific outcomes for patients. By leveraging a large cohort of DTC patients who underwent surgical resection and received RAT, we aim to construct and validate a nomogram that incorporates relevant prognostic factors. The findings from this study hold significant potential for improving clinical management and decision-making in DTC patients. The development of a nomogram will enhance our ability to predict the clinical response to surgery and RAT, leading to more tailored treatment strategies and optimized patient outcomes.
Research authors: Lu Lu
Version: 1.7
  • Public
  • Nuclear medicine
  • {{ modelType }}
  • Details
  • Validate algorithm
  • Save input
  • Load input
Display
Units

{{ section.title }}

{{ section.description }}

Calculate the result

Set more parameters to perform the calculation

Predicted No-ER risk

{{ resultSubheader }}
{{ $t('download_result_availability') }}
{{ chart.title }}
Result interval {{ additionalResult.min }} to {{ additionalResult.max }}

Conditional information

Based on the risk factors from multivariate logistic regression analysis, we constructed a nomogram for predicting the No-ER rate. Each variable was assigned a score on a scale. By adding scores for each of the selected variables, a total score was obtained. Then a vertical line was dropped down from the total points row to estimate the risk of No-ER. The nomogram showed that Tg level, TgAb level, and CLNM were the top 3 contributors to the No-ER, followed by ATA  risk and bilateral foci

{{ file.classification }}
PRO
Note
Notes are only visible in the result download and will not be saved by Evidencio

This algorithm is provided for educational, training and information purposes. It must not be used to support medical decision making, or to provide medical or diagnostic services. Read our full disclaimer.

Underlying algorithms Part of
Comments
Comment
Please enter a comment
Comments are visible to anyone

Algorithm feedback

No feedback yet 1 Comment {{ model.comments.length }} Comments
On {{ comment.created_at }} {{ comment.user.username }} a no longer registered author wrote:
{{ comment.content }}
logo

Please sign in to enable Evidencio print features

In order to use the Evidencio print features, you need to be logged in.
If you don't have an Evidencio Community Account you can create your free personal account at:

https://www.evidencio.com/registration

Printed results - Examples {{ new Date().toLocaleString() }}


Evidencio Community Account Benefits


With an Evidencio Community account you can:

  • Create and publish your own prediction algorithms.
  • Share your prediction algorithms with your colleagues, research group, organization or the world.
  • Review and provide feedback on algorithms that have been shared with you.
  • Validate your algorithms and validate algorithms from other users.
  • Find algorithms based on Title, Keyword, Author, Institute, or MeSH classification.
  • Use and save prediction algorithms and their data.
  • Use patient specific protocols and guidelines based on sequential algorithms and decision trees.
  • Stay up-to-date with new algorithms in your field as they are published.
  • Create your own lists of favorite algorithms and topics.

A personal Evidencio account is free, with no strings attached!
Join us and help create clarity, transparency, and efficiency in the creation, validation, and use of medical prediction algorithms.


Disclaimer: Calculations alone should never dictate patient care, and are no substitute for professional judgement.
Evidencio v3.38 © 2015 - 2025 Evidencio. All Rights Reserved