{{ toolbarErrorMessage }}
{{ section.description }}
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.
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.
With an Evidencio Community account you can:
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.
| {{ (typeof row === 'object') ? row.label : row }} |
| {{ column }} | |
|---|---|
| {{ row.label }} | {{ value }} |
{{ error }}
{{ error }}
Veuillez saisir un mot de passe
Un mot de passe doit comporter au moins 8 caractères
Un mot de passe ne peut pas être plus long que 64 caractères.
Choisissez un mot de passe avec au moins une lettre majuscule
Choisissez un mot de passe avec au moins un caractère spécial (@$!%*#?&)
Veuillez accepter les conditions générales et la clause de non-responsabilité
Veuillez fournir votre adresse e-mail et nous vous enverrons un lien pour réinitialiser votre mot de passe.
Adresse email
Veuillez entrer un email valide
Si un compte a été enregistré avec cette adresse e-mail, vous recevrez un lien de récupération dans votre courrier
Veuillez utiliser le lien de réinitialisation du mot de passe contenu dans le mail pour définir votre nouveau mot de passe
Vous n'avez pas encore reçu l'e-mail ? Veuillez vérifier votre dossier spam, ou renvoyer l'email