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.
Research authors: 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.
Details Custom formula Study characteristics Files & References
★★★★
Model author
Model ID
170
Version
1.57
Revision date
2017-01-15
Specialty
MeSH terms
  • Breast Cancer
  • Lymph Nodes
  • Metastasis
  • Ultrasound Imaging
  • Nomograms
  • Model type
    Logistic regression (Calculation)
    Status
    public
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    Formula
    No Formula defined yet
    Condition Formula

    Additional information

    Study population: 
    This study enrolled a consecutive series of 322 patients with primary invasive early breast carcinoma treated at the Breast Center, Cancer Hospital of Shantou University Medical College from November 2009 to April 2014 for developing the predictive model.

    Inclusion criteria: The inclusion criteria were female patients with early invasive breast cancer (TNM stage according to the 7th edition of the AJCC Cancer Staging Manual: T1-3 and N0-1), having positive axillary ultrasound (defined as at least one lymph node was visible by ultrasound), and receiving a successful SLNB or ALND.

    Exclusion criteria:
    Patients with local advanced disease (TNM stage according to the 7th edition of the AJCC Cancer Staging Manual: T4 or N2-3), neo-adjuvant treatment, or bilateral breast cancer were excluded.

    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.

    Study Population

    Total population size: 322
    Males: {{ model.numberOfMales }}
    Females: {{ model.numberOfFemales }}

    Continuous characteristics

    Name LL Q1 Median Q3 UL Unit
    Tumor size 23 30 40 mm
    Transverse diameter 10 13 17 mm
    Longitudinal diameter 5 7 9 mm
    Cortical thickness 3 4 6 mm

    Categorical characteristics

    Name Subset / Group Nr. of patients
    Patient age ≤35 years 25
    >35 years 297
    Menopausal status Premenopausal 182
    Postmenopausal 140
    Clinical tumor size by T-class T1 74
    T2 223
    T3 22
    Unknown 3
    Tumor locations Upper outer quadrant 152
    Lower outer quadrant 42
    Upper inner quadrant 51
    Lower inner quardant 15
    Central 62
    Histological grade Grade I 49
    Grade II 104
    Grade III 154
    Unknown 15
    Histological type Ductal 294
    Lobular 10
    Other 18
    Estrogen receptor status Negative 119
    1+ 22
    2+ 57
    3+ 124
    Progesteron receptor status Negative 132
    1+ 38
    2+ 63
    3+ 89
    HER2neu receptor Negative 223
    Positive 99
    Ki-67 ≤14 51
    >14 268
    Unknown 3
    P53 Negative 117
    Positive 197
    Unknown 8
    VEGF-C Negative 75
    Positive 222
    Unknown 25
    Molecular subtype Luminal A 173
    Luminal B 44
    HER-2 enriched 51
    Triple negative 54
    Lymph node detected by ultrasound 1 123
    ≥2 199
    Absence of medulla Yes 87
    No 235
    Absence of hilum Yes 126
    No 196
    Lymph node metastases Yes 163
    No 159

    Related files

    Predicted probability of axillary metastasis:
    ...

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    Result
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    Predicted probability of axillary metastasis:

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    Outcome stratification

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    Conditional information

    Result interpretation

    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.

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

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