Development and Validation of Web-Based Nomograms to Predict Postoperative Invasive Component in Ductal Carcinoma<i>in Situ</i>at Needle Breast Biopsy

Lee, Seong Cheol; Chang, Moo Hwan

doi:10.4258/hir.2014.20.2.152

Cited by 7 publications

(5 citation statements)

References 3 publications

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“…14,17,22,24,28 Each study with a prediction model used different risk factors and therefore the models are not easily comparable. This has also been demonstrated in external validation of studies that applied published models to their cases; one study demonstrated a tendency towards lower or higher numbers of observed underestimates than expected 29 , and another previous study demonstrated validation AUCs of 0.59-0.66, whereas the studies they validated reported validities of 0.70-0.85 14 . The low AUC in this study could also be due to the absence of certain data that might have been important, such as the type of biopsy device and the size of the lesion on mammography.…”

Section: Discussionmentioning

confidence: 74%

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A prediction model for underestimation of invasive breast cancer after a biopsy diagnosis of ductal carcinoma in situ: based on 2892 biopsies and 589 invasive cancers

Meurs¹,

Rosmalen

Menke-Pluijmers

et al. 2018

Br J Cancer

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BACKGROUND: Patients with a biopsy diagnosis of ductal carcinoma in situ (DCIS) might be diagnosed with invasive breast cancer at excision, a phenomenon known as underestimation. Patients with DCIS are treated based on the risk of underestimation or progression to invasive cancer. The aim of our study was to expand the knowledge on underestimation and to develop a prediction model. METHODS: Population-based data were retrieved from the Dutch Pathology Registry and the Netherlands Cancer Registry for DCIS between January 2011 and June 2012. RESULTS: Of 2892 DCIS biopsies, 21% were underestimated invasive breast cancers. In multivariable analysis, risk factors were highgrade DCIS (odds ratio (OR) 1.43, 95% confidence interval (CI): 1.05-1.95), a palpable tumour (OR 2.22, 95% CI: 1.76-2.81), a BI-RADS (Breast Imaging Reporting and Data System) score 5 (OR 2.36, 95% CI: 1.80-3.09) and a suspected invasive component at biopsy (OR 3.84, 95% CI: 2.69-5.46). The predicted risk for underestimation ranged from 9.5 to 80.2%, with a median of 14.7%. Of the 596 invasive cancers, 39% had unfavourable features. CONCLUSIONS: The risk for an underestimated diagnosis of invasive breast cancer after a biopsy diagnosis of DCIS is considerable. With our prediction model, the individual risk of underestimation can be calculated based on routinely available preoperatively known risk factors (https://www.evidencio.com/models/show/1074). BACKGROUNDPatients with ductal carcinoma in situ (DCIS) are treated based on the risk of underestimation or progression to invasive cancer. The standard treatment for patients with a biopsy diagnosis of DCIS is wide local excision with radiation or mastectomy. Often a sentinel lymph node (SLN) biopsy is advised for axillary staging. 1,2 Both the standard treatment and the use of the SLN biopsy can constitute overtreatment. The standard treatment might be disproportionate for screen-detected DCIS patients who have a high chance that the DCIS would not even have been detected during their lifetime. 3 It has been estimated that between 14 and 53% of DCIS progress into invasive breast cancer. 4,5 To address overtreatment, phase III trials investigate the safety of active surveillance of DCIS patients at low risk for developing or having invasive breast cancer. 6-11 Active surveillance is based on the result of the biopsy. By modelling of active surveillance of DCIS patients, the disease-specific cumulative mortality was related to underestimation. 12,13 Underestimation is the phenomenon that the invasive breast cancer is undetected at preoperative biopsy and only becomes evident after pathological examination of the excision material. The use of the SLN biopsy can also constitute overtreatment. The SLN biopsy is done if a mastectomy is chosen, and also for patients undergoing wide local excision who are at high risk of having an underestimated invasive breast cancer. 1,2 The reported risk of underestimation varies from 14 to 43% 14,15 , and in a meta-analysis it was estimated to be 25.9% (95% confidence inte...

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Section: Discussionmentioning

confidence: 74%

“…An overview of the found risk factors for underestimation is given in Table 1. Based on risk factors, several studies developed prediction models with the purpose to select patients for SLN biopsy 14,17,24,[28][29][30] .…”

mentioning

confidence: 99%

A prediction model for underestimation of invasive breast cancer after a biopsy diagnosis of ductal carcinoma in situ: based on 2892 biopsies and 589 invasive cancers

Meurs¹,

Rosmalen

Menke-Pluijmers

et al. 2018

Br J Cancer

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“…A nomogram can be validated by both internal and external validation. (32) In this study, internal validation used the data of the same cohort for the generation of the nomogram, and external validation used the data from another institution. Both internal and external validation indicated good agreement between the prediction and the actual diagnosis in the probability.…”

Section: Discussionmentioning

confidence: 99%

Performances of Clinical Characteristics and Radiological Findings in Identifying COVID- 19 From Suspected Cases

Zhao

Yiping

et al. 2021

Preprint

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Background: To identify effective factors and establish a model to distinguish COVID-19 patients from suspected cases.Methods: The clinical characteristics, laboratory results and initial chest CT findings of suspected COVID-19 patients in 3 institutions were retrospectively reviewed. Univariate and multivariate logistic regression were performed to identify significant features. A nomogram was constructed, with calibration validated internally and externally.Results: 239 patients from 2 institutions were enrolled in the primary cohort including 157 COVID-19 and 82 non-COVID-19 patients. 11 features were included for multivariate logistic regression analysis after LASSO selection. We found that the COVID-19 group are more likely to have fever (OR, 4.22), contact history (OR, 284.73), lower WBC count (OR, 0.63), left lower lobe involvement (OR, 9.42), multifocal lesions (OR, 8.98), pleual thickening (OR, 5.59), peripheral distribution (OR, 0.09), and less mediastinal lymphadenopathy (OR, 0.037). The nomogram developed accordingly for clinical practice showed satisfactory internal and external validation. Conclusions: In conclusion, fever, contact history, decreased WBC count, left lower lobe involvement, pleural thickening, multifocal lesions, peripheral distribution and absence of mediastinal lymphadenopathy are able to distinguish COVID-19 patients from other suspected patients. The corresponding nomogram is a useful tool in clinical practice.

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“…A few validation studies of models were published, which showed that the prediction models performed statistically slightly worse in the validation cohorts than in the patient population in which the model was developed. 7 , 8 Comparison of the available models is complicated by the large variety of risk factors that are used in prediction models. Furthermore, comparison of the models is not always fully possible because some studies did not report the intercept of their model.…”

mentioning

confidence: 99%

“…Within the populations on which the models were developed, the rates varied from 14% to 37%. 7 , 8 This variation might not only be due to differences in the selection of patients but also to differences in diagnostic workup between hospitals or countries or over time. When using a prediction model, it therefore is important that the cohort has some similarity with the model development cohort.…”

mentioning

confidence: 99%

Validation and Clinical Utility of a Prediction Model for the Risk of Upstaging to Invasive Breast Cancer After a Biopsy Diagnosis Ductal Carcinoma In Situ

et al. 2023

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Background This study aimed to validate the DCIS-upstage model, a previously developed model to predict the risk of upstaging to invasive breast cancer in patients with biopsy-proven ductal carcinoma in situ (DCIS) in a more recent cohort and to assess the model’s clinical utility. Methods The model was validated in a registry cohort (n = 2269) and in an institution cohort (n = 302). A calibration plot was made, followed by a decision curve analysis (DCA). The model’s area under the curve (AUC) was compared with the AUC of another published model and with the AUCs of new models using the risk factors of the DCIS-upstage model and additional risk factors. Results The DCIS-upstage model had an AUC of 0.67 at development; in the validation, the AUC was 0.65 in the registry cohort and 0.73 in the institution cohort. The DCA showed that the model has clinical utility. The other published model had an AUC of 0.66 in the institution cohort. Adding risk factors to the DCIS-upstage model slightly increased the AUC. Conclusions The DCIS-upstage prediction model is valid in other cohorts. The model has clinical utility and may be used to select patients with biopsy-proven DCIS for sentinel lymph node biopsy.

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Development and Validation of Web-Based Nomograms to Predict Postoperative Invasive Component in Ductal Carcinomain Situat Needle Breast Biopsy

Cited by 7 publications

References 3 publications

A prediction model for underestimation of invasive breast cancer after a biopsy diagnosis of ductal carcinoma in situ: based on 2892 biopsies and 589 invasive cancers

A prediction model for underestimation of invasive breast cancer after a biopsy diagnosis of ductal carcinoma in situ: based on 2892 biopsies and 589 invasive cancers

Performances of Clinical Characteristics and Radiological Findings in Identifying COVID- 19 From Suspected Cases

Validation and Clinical Utility of a Prediction Model for the Risk of Upstaging to Invasive Breast Cancer After a Biopsy Diagnosis Ductal Carcinoma In Situ

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