Intrinsic and extrinsic flaws of the nomogram predicting bone-only metastasis in women with early breast cancer: An external validation study

Ouldamer, Lobna; Bendifallah, Sofiane; Chas, Marie; Boivin, Laura; Bedouet, L.; Body, G.; Ballester, Marcos; Daraı̈, Émile

doi:10.1016/j.ejca.2016.09.039

Cited by 7 publications

(6 citation statements)

References 35 publications

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“…We aim to filter out high-risk patients and take effective treatment timely, which will make it possible for clinical benefit. Although some models have been recently developed to risk prediction, the population studied in academic cancer centers with breast cancer is relatively small compared with SEER database, which limits the veracity of the model ( 22 ). Ouldamer has verified that the MDACC model failed to confirm its generalizability to predict the risk of bone metastasis in an independent stage I–III breast cancer population ( 22 ).…”

Section: Discussionmentioning

confidence: 99%

Nomogram for predicting the risk of bone metastasis in breast cancer: a SEER population-based study

Ye¹,

Suo²,

Liang³

et al. 2020

Transl Cancer Res TCR

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Background: Bone is the most common metastasis site of breast cancer. The prognosis of bone metastasis is better than other distant metastases, but patients with skeletal related events (SREs) have a poor quality of life, high healthcare costs and low survival rates. This study aimed to establish an effective nomogram for predicting risk of bone metastasis of breast cancer. Methods: The nomogram was built on 4,895 adult/female/primary invasive breast cancer patients with complete clinicopathologic information, captured by the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. Five biological factors (age, grade, histologic type, surgery of breast lesions and subtypes) were assessed with logistic regression to predict the risk of bone metastases. The predictive accuracy and discriminative ability of the nomogram were determined by the Receiver Operating Characteristic (ROC) curves and the calibration plot. Results were validated on a separate 2,093 cohort using bootstrap resampling from 2010 to 2015 as an internal group and a retrospective study on 120 patients in the First Affiliated Hospital of China Medical University from 2010 to 2014 at the same situation as an external group.Results: On multivariate logistic regression of the primary cohort, independent factors for bone metastases were age, grade, histologic type, surgery of breast lesions and subtypes, which were all selected into the nomogram. The calibration plot for probability of incidence showed good agreement between prediction by nomogram and two observations. The ROC curves presented a good statistical model for risk of bone metastasis, and the corresponding AUC value of the development group, internal validation group and external validation group were 0.678, 0.689 and 0.704 respectively. Conclusions:The proposed nomogram resulted in more-accurate prognostic prediction for breast cancer patients with bone metastases.

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Section: Discussionmentioning

confidence: 99%

Nomogram for predicting the risk of bone metastasis in breast cancer: a SEER population-based study

Ye¹,

Suo²,

Liang³

et al. 2020

Transl Cancer Res TCR

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“…However, external validation studies should be performed to determine whether our results are applicable in other patient populations [26]. Another strength of this study is the use of X-tile software, which is a robust graphical tool [27], to determine the optimal cutoff values for serum bilirubin and albumin levels.…”

Section: Discussionmentioning

confidence: 99%

A nomogram based on serum bilirubin and albumin levels predicts survival in gastric cancer patients

Sun

Nie

et al. 2017

Oncotarget

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Decreases in serum bilirubin and albumin levels are associated with poorer prognoses in some types of cancer. Here, we examined the predictive value of serum bilirubin and albumin levels in 778 gastric cancer patients from a single hospital in China who were divided among prospective training and retrospective validation cohorts. X-tile software was used to identify optimal cutoff values for separating training cohort patients into higher and lower overall survival (OS) groups, based on total bilirubin (TBIL) and albumin levels. In univariate analysis, tumor grade and TNM stage were associated with OS. After adjusting for tumor grade and TNM stage, TBIL and albumin levels were still clearly associated with OS. These results were confirmed in the 299 patients in the validation cohort. A nomogram based on TBIL and albumin levels was more accurate than the TNM staging system for predicting prognosis in both cohorts. These results suggest that serum TBIL and albumin levels are independent predictors of OS in gastric cancer patients, and that an index that combines TBIL and albumin levels with the TNM staging system might have more predictive value than any of these measures alone.

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“…Compared with the conventional TNM staging system, nomograms could integrate all parameters related to survival to provide a more accurate estimation 21 . Recently, several nomograms have been constructed in metastatic BC patients [22][23][24][25] . Nevertheless, there is no nomogram built for BC patients with lung metastasis.…”

Section: Introductionmentioning

confidence: 99%

A Risk Stratification Model for Predicting Survival in Breast Cancer Patients With Lung Metastasis: A Study of the SEER Database and A Chinese Cohort

Pei

Wang

et al. 2021

Preprint

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Background Distant metastases are the leading cause of death among breast cancer (BC) patients, and lung is the second preferential colonized sits. However, there has been no prognostic evaluation of BC patients with lung metastases so far. This research aimed to predict the overall survival (OS) and breast cancer-specific survival (BCSS) for BC patients with lung metastases and stratify them into different risk groups.Result A total of 3888 patients from the SEER database were eligible for the subsequent analyses, with the training cohort of 1944 patients and the validation cohort I of 1944 patients. In addition, 374 patients from the Chinese XXX database were assigned as the validation II. Race, age, grade, subtype, number of metastatic sites, surgery, and chemotherapy were identified as determinant prognostic variables and integrated to construct the nomogram. Ultimately, a risk stratification model was established, and all patients were classified as three risk groups. Conclusion We constructed prognostic prediction and risk stratification models for BC patients with lung metastases and validated it in both the SEER cohort and the Chinese cohort; these could assist clinicians in risk prediction, prognosis evaluation, and decision making on an individualized basis.

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Intrinsic and extrinsic flaws of the nomogram predicting bone-only metastasis in women with early breast cancer: An external validation study

Cited by 7 publications

References 35 publications

Nomogram for predicting the risk of bone metastasis in breast cancer: a SEER population-based study

Nomogram for predicting the risk of bone metastasis in breast cancer: a SEER population-based study

A nomogram based on serum bilirubin and albumin levels predicts survival in gastric cancer patients

A Risk Stratification Model for Predicting Survival in Breast Cancer Patients With Lung Metastasis: A Study of the SEER Database and A Chinese Cohort

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