{{ 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.
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.
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 }}
Please enter a password
A password has to be at least 8 characters.
A password cannot be longer then 64 characters.
Choose a password with at least one capital letter.
Choose a password with at least one special character (@$!%*#?&)
Please agree to the Terms & Conditions and the Disclaimer
Please provide your e-mail address and we'll send you a link to reset your password.
Email Address
Please enter a valid email
If an account was registered with this email address you will receive a recovery link in your mail.
Please use the reset password link in it to set your new password.
Didn't receive the email yet? Please check your spam folder, or resend the email.