Roach formula (Lymph Node involvement)
A simple equation for estimating the risk of positive lymph nodes was empirically derived from a nomogram published by Partin et al. demonstrating the value of combining the pre-treatment prostate specific antigen and Gleason Score in predicting the risk of lymph node metastasis for patients with clinically localized prostate cancer. The risk of positive nodes (NS) was calculated using the equation; N+ = (2/3*PSA) + (GS - 6) X 10, where PSA and GS are the pre-treatment prostate specific antigen and Gleason Score respectively, and the calculated risk is constrained between O-65% for a PSA < 40 ng/ml (as in the nomogram).
Research authors: Mack Roach III, Carol Marquez, Hae-Sook Yuo, Perinchery Narayan, Lorie Coleman, Unyime O. Nseyo, Zarrin Navvar, Peter R. Carrol
Details Formula Study characteristics Files & References
★★
Model author
Model ID
1144
Version
1.7
Revision date
2018-05-07
Specialty
MeSH terms
No MeSH classifications added
Model type
Custom model (Calculation)
Status
public
Rating
Share
Formula

Additional information

No additional information available

Study Population

Total population size: 212

Additional characteristics

No additional characteristics defined

Related files

No related files available

Calculated risk for Lymph Node Involvement is:
...
%

{{ resultSubheader }}

{{ model.survival.PITTitle }}

{{ model.survival.YNETitle }}

Result
Note
Notes are only visible in the result download and will not be saved by Evidencio

Calculated risk for Lymph Node Involvement is: %

{{ resultSubheader }}
{{ chart.title }}

Outcome stratification

Result interval {{ additionalResult.min }} to {{ additionalResult.max }}

Conditional information

Result interpretation

The predicted risk on pelvic lymph node invasion supports decision making regarding the performance of a pelvic lymph node dissection in patients with prostate cancer patients. Patients at low risk of lymph node metastases can be spared from surgical removal of the lymph nodes. 

{{ file.classification }}

Calculations alone should never dictate patient care, and are no substitute for professional judgement. See our full disclaimer.

Comments
Rating
Comment
Please enter a comment of rating
Comments are visible to anyone

Model feedback

No feedback yet 1 Comment {{ model.comments.length }} Comments
Not rated | On {{ comment.created_at }} {{ comment.user.username }} a no longer registered author wrote:
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 models.
  • Share your prediction models with your colleagues, research group, organization or the world.
  • Review and provide feedback on models that have been shared with you.
  • Validate your models and validate models from other users.
  • Find models based on Title, Keyword, Author, Institute, or MeSH classification.
  • Use and save prediction models and their data.
  • Use patient specific protocols and guidelines based on sequential models and decision trees.
  • Stay up-to-date with new models in your field as they are published.
  • Create your own lists of favorite models 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 models.

Disclaimer: Calculations alone should never dictate patient care, and are no substitute for professional judgement.