Construction and validation of prognostic nomogram for metaplastic breast cancer

Li, Yongfeng; Chen, Daobao; Xuan, Haojun; Dragomir, Mihnea P.; Meng, Xuli; Chen, Meng; Jin, Hongchuan

doi:10.17305/bjbms.2021.5911

Cited by 8 publications

(5 citation statements)

References 45 publications

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“…After the screening and statistical analysis, we identified ER status, MFI, first-line therapy options, locoregional therapy, and the number of metastatic sites as independent prognostic factors for OS of HR+/HER2-ABC patients. Interestingly, these prognostic factors extracted from our analysis showed partial consistency with current consensus and guidelines, whereas, in some aspects, differing from other predictive models for ABC patients' survival (10,15,18,22). R. Largillier et al ( 16) used a Cox proportional hazards model to identify age, the number of positive lymph nodes, adjuvant chemotherapy, and site of metastasis as independent predictors of stage IV BC patients' OS.…”

Section: Discussionmentioning

confidence: 67%

A Novel Prognostic Nomogram for Predicting Survival of Hormone Receptor-Positive and HER2 Negative Advanced Breast Cancer Among the Han-Population

Zhu

Wang

2022

Front. Oncol.

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PurposeTo develop a nomogram model to predict overall survival in HR+/HER2- subtype advanced breast cancer.MethodsA total of 3,577 ABC (advanced breast cancer) patients from 21 hospitals in China were involved in this study from January 2012 to December 2014. From all ABC patients, 1,671 HR+/HER2- ABC patients were extracted and enrolled in our study. A nomogram was built based on univariable and multivariable Cox regression analyses, identifying independent predictors. The discriminatory and predictive capacities of the nomogram were assessed using the ROC (receiver operating characteristic) curve and calibration plots.ResultsUnivariable and multivariable analysis found that ER (estrogen receptor) status, MFIs (metastatic-free intervals), first-line therapy options, the number of metastatic sites, and whether local therapy for metastatic sites was chosen, were significantly related to overall survival (all P < 0.05). These variables were incorporated into a nomogram to predict the 2- year, 3-year, and 5-year OS (overall survival) of ABC patients. The AUC (the area under the curve) of the nomogram was 0.748 (95% CI (confidence interval):0.693-0.804) for 5-year OS in the training cohort and 0.732 (95% CI: 0.676-0.789) for the validation cohort. The calibration curves revealed good consistency between actual survival and nomogram prediction in the training and validation cohorts. Additionally, the nomogram showed an excellent ability to stratify patients into different risk cohorts.ConclusionWe established a nomogram that provided a more straightforward predictive model for the outcome of HR+/HER2- ABC subtype patients and, to some extent, assisted physicians in making the personalized therapeutic option.

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Section: Discussionmentioning

confidence: 67%

A Novel Prognostic Nomogram for Predicting Survival of Hormone Receptor-Positive and HER2 Negative Advanced Breast Cancer Among the Han-Population

Zhu

Wang

2022

Front. Oncol.

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“…This discrepancy may be attributed to the limited sample size of patients with MBC in our single-center study. Li et al previously demonstrated that patients aged above 58 years exhibit unfavorable survival outcomes, with poor OS and cancer-specific survival ( 21 ). Furthermore, older patients with MBC are typically diagnosed with higher-risk histological subtypes, which consequently leads to lower survival rates ( 22 ).…”

Section: Discussionmentioning

confidence: 99%

Sonographic and clinicopathologic features of metaplastic breast carcinoma and infiltrating ductal carcinoma: a comparative single-center cohort study

Yao,

Luo,

et al. 2024

Quant Imaging Med Surg

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Background The rarity of metaplastic breast carcinoma (MBC) has resulted in limited sonographic data. Given the inferior prognosis of MBC compared to invasive ductal carcinoma (IDC), accurate preoperative differentiation between the two is imperative for effective treatment planning and prognostic prediction. The objective of this study was to assess the diagnostic accuracy of MBC and differentiate it from IDC by analyzing sonographic and clinicopathologic features. Methods In this retrospective cohort study, 197 women comprising 200 IDC lesions were enrolled between January 2012 and December 2021 and 20 women comprising 20 pure MBC lesions were enrolled between January 2019 and December 2019. A comparison was made between the sonographic and clinicopathologic characteristics of MBC and IDC. Results The results indicated that patients with MBC had a higher proportion of tumor grade 3 (95.0% vs. 32.5%; P<0.001), high Ki-67 expression (100.0% vs. 75.0%; P<0.001), and the triple-negative subtype (90.0% vs. 13.0%; P<0.001) as compared to those with IDC. On ultrasound (US) findings, MBC lesions tended to have a larger size (≥5 cm: 45.0% vs. 1.5%; P<0.001), regular shape (45.0% vs. 1.5%, P<0.001), circumscribed margin (40.0% vs. 0.5%, P<0.001), a complex cystic and solid echo pattern (50.0% vs. 3.5%; P<0.001), and posterior acoustic enhancement (95.0% vs. 14.5%; P<0.001). Additionally, MBC was more likely to be misinterpreted as a benign lesion by sonographers than was IDC (30.0% vs. 4.5%; P<0.001). Multilayer perceptron analysis revealed posterior acoustic enhancement, circumscribed margins, and size as distinguishing factors between these two tumor types. The estimated rates of local recurrence, distant metastasis, and 5-year overall survival in 19 cases with MBC were found to be 10.5%, 31.6%, and 65.0%, respectively. Conclusions MBC typically presents as a large breast mass with more benign US features in older women, findings which may facilitate its accurate diagnosis and differentiation from other breast masses.

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“…This study included a total of 8813 EOCRC patients from the SEER database between 2008 and 2017. Using R software, we randomly assigned the 8813 EOCRC patients into a training cohort (n = 6610) and a internal validation cohort (n = 2203) at a 7:3 ratio [28][29][30] . Cumulative incidence functions (CIFs) were calculated for the training group to assess the probabilities of experiencing various events at 1, 3, and 5 years.…”

Section: Nomogram Construction and Validationmentioning

confidence: 99%

Nomogram model for predicting cause-specific mortality in patients with Early-Onset Colorectal Cancer: a competing risk analysis Insight from the SEER Database and a Chinese Cohort.

Fu,

Chen,

Fan

et al. 2023

Preprint

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Objective This study aims to analyze the risk factors for Cancer-Specific Mortality (CSM) and Other-Cause Mortality (OCM) in early-onset colorectal cancer (EOCRC) patients,and to construct a nomogram for predicting CSM based on a competitive risk model and validate it using training, internal, and external cohorts. Methods EOCRC patients from the SEER database(2008–2017). Furthermore, EOCRC patients treated at a Northeast China tertiary hospital were included(2014–2020). The SEER data were randomly divided into training and validation sets at a 7:3 ratio. Univariate COX regression model was used to screen for prognostic correlates. Multivariate Cox regression models were then employed to identify independent risk factors. A nomogram visualized results, assessed by C-index,AUC and calibration curves. DCA evaluated clinical utility. Results A total of 8,813 patients were collected from the SEER database, divided into training (N = 6,610) and validation (N = 2,203) sets. 76 patients were included from the Chinese cohort(N = 76). Multivariable Cox regression models revealed that race, tumor differentiation, carcinoembryonic antigen (CEA), marital status, histological type, AJCC stage, and surgical status were independent risk factors for CSM in EOCRC patients. The nomogram constructed based on those independent risk factors had good performance with C-index of 0.806 ,0.801and 0.810 for the training, internal validation and external validation cohorts, respectively.Calibration curves and AUC also indicated the nomogram's accuracy and discriminative ability. Also DCA reflects the good clinical value of the model. Conclusion This study successfully established a competing risk model for CSM in EOCRC patients, demonstrating good predictive value, which may help clinicians to make better treatment decision making.

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Construction and validation of prognostic nomogram for metaplastic breast cancer

Cited by 8 publications

References 45 publications

A Novel Prognostic Nomogram for Predicting Survival of Hormone Receptor-Positive and HER2 Negative Advanced Breast Cancer Among the Han-Population

A Novel Prognostic Nomogram for Predicting Survival of Hormone Receptor-Positive and HER2 Negative Advanced Breast Cancer Among the Han-Population

Sonographic and clinicopathologic features of metaplastic breast carcinoma and infiltrating ductal carcinoma: a comparative single-center cohort study

Nomogram model for predicting cause-specific mortality in patients with Early-Onset Colorectal Cancer: a competing risk analysis Insight from the SEER Database and a Chinese Cohort.

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