Probability of axillary lymph node metastasis in early breast cancer patien - Evidencio
Probability of axillary lymph node metastasis in early breast cancer patients with positive axillary ultrasound
This model includes ultrasound parameters to predict the probability of ALN metastasis in early breast cancer patients.
The model needs to be further validated before applying into clinical practice.
Les auteurs de la recherche: Si-Qi Qiu, Huan-Cheng Zeng, Fan Zhang, Cong Chen, Wen-He Huang, Rick G Pleijhuis, Jun-Dong Wu, Gooitzen M van Dam, Guo-Jun Zhang.
Version: 1.59
  • Public
  • Oncologie
  • {{ modelType }}
  • Algorithme
  • Détails
  • Valider
  • Sauvegarder l'entrée
  • Entrée de la charge
  • {{ toolbarErrorMessage }}

Affichage
Unités

{{ section.title }}

{{ section.description }}

Calculer le résultat

Définir d'autres paramètres pour effectuer le calcul

Predicted probability of axillary metastasis:

{{ resultSubheader }}
{{ $t('download_result_availability') }}
slide-left-right {{ $t('scroll_chart') }}
{{ chart.title }}
Intervalle de résultats {{ additionalResult.min }} à {{ additionalResult.max }}

Informations conditionnelles

Votre ou vos saisies ont été modifiées. Veuillez mettre à jour votre résultat. Mettre à jour

Model performance
In the modeling group (N=322), lymph node metastasis was detected in 163 (50.6%) patients. The model was well calibrated in the modeling group. Especially in the low predictive-probability subgroups, the model was found to provide a promising predictive value in early breast cancer patients. Hosmer-Lemeshow goodness-of-fit test indicated a good overall fit of the model (P-value: 0.18).

Validation:
The model developers performed an external validation on a separate cohort consisting of 234 patients. The calculated area under the ROC curve (c-index) for the validation group was 0.864, indicating good discriminative power of the model.

Predictors included in the model:
Tumor size and histological grade have been reported to be risk factors for ALN metastasis in many other studies (references included in original research paper). The current study confirmed these results. The predictive value of ER and PR status was uncertain in previous studies, with some studies showing no predictive value for ER and PR status and others reporting that lower risk of ALN metastasis was found in tumors with negative expression of either ER or PR. In the current study, ER overexpression was found to be associated with higher probability of ALN metastasis. This finding may be counterintuitive, but it was similar to the findings from Bevilacqua et al (2007). Although the reason of this phenomenon is unknown, it is hypothesized that ER negative tumors prefer hematogenous metastasis rather than lymphatic metastasis.

Model limitations:
Although the model showed good stability in the underlying study, it needs to be validated in more external validation groups to further evaluate its predictive ability.
Second, risk factors like clinical tumor size, cortical thickness and transverse diameter of lymph node may differ when measured by different doctors.

Source:
Qiu SQ, Zeng HC, Zhang F, et al. A nomogram to predict the probability of axillary lymph node metastasis in early breast cancer patients with positive axillary ultrasound. Sci Rep. 2016;6:21196.

{{ file.classification }}
PRO
Note
Les notes ne sont visibles que dans le téléchargement des résultats et ne sont pas sauvegardées par Evidencio.

Ce algorithme est fourni à des fins d'éducation, de formation et d'information. Il ne doit pas être utilisé pour aider à la prise de décision médicale ou pour fournir des services médicaux ou de diagnostic. Lire l'intégralité de notre disclaimer.

Algorithmes sous-jacents Une partie de
Commentaires
Commentaire
Veuillez saisir un commentaire
Les commentaires sont visibles par tous

Retour d'information sur le algorithme

Pas encore de retour d'information 1 Commentaire {{ model.comments.length }} Commentaires
Sur {{ comment.created_at }} {{ comment.user.username }} un auteur qui n'est plus enregistré a écrit :
{{ comment.content }}
logo

Veuillez vous connecter pour activer les fonctions d'impression d'Evidencio

Pour utiliser les fonctions d'impression d'Evidencio, vous devez être connecté.
Si vous n'avez pas de compte communautaire Evidencio, vous pouvez créer un compte personnel gratuit à:

https://www.evidencio.com/registration

Résultats imprimés - Exemples 2026-11-15 01:44


Avantages du compte communautaire Evidencio


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.


Clause de non-responsabilité : les calculs ne doivent jamais dicter les soins aux patients et ne remplacent pas le jugement d'un professionnel.