How Reliable Are Gene Expression-Based and Immunohistochemical Biomarkers Assessed on a Core-Needle Biopsy? A Study of Paired Core-Needle Biopsies and Surgical Specimens in Early Breast Cancer

Saghir, Hani; Veerla, Srinivas; Malmberg, Martin; Rydén, Lisa; Ehinger, Anna; Saal, Lao H.; Vallon‐Christersson, Johan; Borg, Åke; Hegardt, Cecilia; Larsson, Christer; Haidar, Alaa; Hedenfalk, Ingrid; Loman, Niklas; Kimbung, Siker

doi:10.3390/cancers14164000

Cited by 7 publications

(4 citation statements)

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“…The Ki67 labelling index, a measure of tumor proliferation, reveals moderate concordance between CNB and surgical specimens. A more reliable and preoperative proliferation measure may be histological grade [14,15].…”

Section: Introductionmentioning

confidence: 99%

A preoperative prediction model for sentinel lymph node status using artificial intelligence on mammographic images and clinicopathological variables in patients with clinically node-negative breast cancer

Rejmer

Dihge

Bendahl

et al. 2023

Preprint

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Background: Cancer detection systems using artificial intelligence (AI) are a rapidly growing research area, in breast cancer. Sentinel lymph node biopsy (SLNB) is the recommended surgical axillary staging method in patients with clinically node-negative breast cancer, although approximately 75–80% have a negative sentinel lymph node (N0) status in the final pathology report. Previous prediction models for N0 status included variables only available postoperatively, thus defeating their purpose. Therefore, we aimed to investigate whether two AI systems, Transpara (Screenpoint Medical) and Laboratory for Individualized Breast Radiodensity Assessment (LIBRA), on mammographic images can be used to improve a previous prediction model for N0 status using only preoperatively available variables. To our knowledge, this is the first preoperative prediction model for N0 status combining AI detection on mammographic images with clinicopathological variables. Methods: This retrospective cohort study included 755 women with primary breast cancer treated at Lund University Hospital between 2009 and 2012. Mammographic images were analyzed using Transpara and LIBRA. Preoperative clinicopathological and radiological variables were used in a multivariable logistic regression analysis to predict N0 status with multiple imputation. The area under the receiver operating curve (AUC) was used to assess model performance and a nomogram was developed. The agreement between preoperative radiological and postoperative pathological tumor size was assessed using correlation. Results: We proposed a preoperative prediction model for N0 status using AI detection on mammographic images and clinicopathological variables, with an AUC of 0.695 (confidence interval: 0.653–0.736). Applying the model, SLNB could be putatively omitted in 23.8% of patients if a false-negative rate of 10% was accepted. The mean difference between radiologic and pathologic tumor size was 0.4 mm and the corresponding Pearson correlation coefficient 0.62. Conclusion: To our knowledge, the prediction model proposed in this manuscript is the first preoperative prediction model for N0 status using AI on mammographic images and routine preoperative patients and tumor characteristics. The correlation between tumor size measurements suggests that radiologic tumor size can replace pathologic size as a predictor of N0 status. Applying this model may enable safe omission of SLNB in 23.8% of patients.

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Section: Introductionmentioning

confidence: 99%

A preoperative prediction model for sentinel lymph node status using artificial intelligence on mammographic images and clinicopathological variables in patients with clinically node-negative breast cancer

Rejmer

Dihge

Bendahl

et al. 2023

Preprint

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“…Most published models include postoperative variables from surgical specimens, including pathological tumor size [10,14], estrogen receptor (ER) status [5,7,13,16], progesterone receptor (PR) status [5,7], human epidermal growth factor receptor 2 (HER2) status [5,7,10,16], proliferation index Ki67 value [5,7,13], Nottingham histological grade (NHG) [5,7,8,12], histological type [5,7,8,12], and lymphovascular invasion (LVI) [6,7,11]. ER, PR, HER2, and Ki67 showed moderate to very good concordance between core needle biopsy (CNB) and surgical specimens [18]. Therefore, these variables have the potential as preoperative predictors of lymph node status.…”

Section: Introductionmentioning

confidence: 99%

“…ER, PR, HER2, and Ki67 showed moderate to very good concordance between core needle biopsy (CNB) and surgical specimens [18]. Therefore, these variables have the potential as preoperative predictors of lymph node status.…”

Section: Introductionmentioning

confidence: 99%

Noninvasive Staging of Lymph Node Status (NILS) in Breast Cancer using Machine Learning: External Validation and Further Model Development

Hjärtström

Dihge

Bendahl

et al. 2023

Preprint

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Background:Most patients diagnosed with breast cancer present with node–negative disease. Sentinel lymph node biopsy (SLNB) is routinely used to stage the axilla, leaving patients with healthy axillary lymph nodes without therapeutic effects but at risk of morbidities from the intervention. Numerous studies have developed nodal (N) status prediction models for noninvasive axillary staging using postoperative data or imaging features that are not part of the diagnostic workup. Lymphovascular invasion (LVI) is a top-ranked predictor of N metastasis; however, it is challenging to assess preoperatively.Objective:To externally validate a multilayer perceptron (MLP) model for noninvasive lymph node staging (NILS) in a large population-based register cohort (n=18 633) while developing a new N MLP in the same cohort. Data were extracted from the Swedish National Quality Register for Breast Cancer (NKBC), 2014–2017, comprising only routinely and preoperatively available documented clinicopathological variables. Furthermore, we aimed to develop and validate an LVI MLP to predict missing values of LVI status to increase the preoperative feasibility of the original NILS model.Methods:Three non-overlapping cohorts were used for model development and validation. Four N MLPs and one LVI MLP were developed using 11–12 routinely available predictors. Three N models were used to account for the different availabilities of LVI status in the cohorts and external validation in NKBC. The fourth N model was developed for 80% of NKBC cases (n=14 906) and validated in the remaining 20% (n=3727). Three alternatives for imputing missing values of the LVI status were compared using the LVI model. The discriminatory capacity was evaluated by validation area under the receiver operating characteristics curve (AUC) in three of the N models. The clinical feasibility of the models was evaluated using calibration and decision curve analyses.Results:External validation of the original NILS model was performed in NKBC (AUC 0.699, 95% CI: 0.690–0.708) with good calibration and the potential of sparing 16% of patients with node-negative disease from SLNB. The LVI model was externally validated (AUC 0.747, 95% CI: 0.694–0.799) with good calibration but did not improve the discriminatory performance of the N models. The new N model was developed in NKBC without information on LVI (AUC 0.709, 95% CI: 0.688–0.729) with excellent calibration in the hold-out internal validation cohort, resulting in the potential omission of 24% of patients from unnecessary SLNB.Conclusions:The NILS model was externally validated in NKBC, where the imputation of LVI status did not improve the model′s discriminatory performance. A new N model demonstrated the feasibility of using register data comprising only the variables available in the preoperative setting for NILS using machine learning. Future steps include ongoing preoperative validation of the NILS model and extending the model with, for example, mammography images.

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“…ER, PR, HER2, and Ki67 showed moderate to very good concordance between core needle biopsy (CNB) and surgical specimens [ 18 ]. Therefore, these variables have potential as preoperative predictors of lymph node status.…”

Section: Introductionmentioning

confidence: 99%

Noninvasive Staging of Lymph Node Status in Breast Cancer Using Machine Learning: External Validation and Further Model Development

Hjärtström,

Dihge,

Bendahl

et al. 2023

JMIR Cancer

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Background Most patients diagnosed with breast cancer present with a node-negative disease. Sentinel lymph node biopsy (SLNB) is routinely used for axillary staging, leaving patients with healthy axillary lymph nodes without therapeutic effects but at risk of morbidities from the intervention. Numerous studies have developed nodal status prediction models for noninvasive axillary staging using postoperative data or imaging features that are not part of the diagnostic workup. Lymphovascular invasion (LVI) is a top-ranked predictor of nodal metastasis; however, its preoperative assessment is challenging. Objective This paper aimed to externally validate a multilayer perceptron (MLP) model for noninvasive lymph node staging (NILS) in a large population-based cohort (n=18,633) and develop a new MLP in the same cohort. Data were extracted from the Swedish National Quality Register for Breast Cancer (NKBC, 2014-2017), comprising only routinely and preoperatively available documented clinicopathological variables. A secondary aim was to develop and validate an LVI MLP for imputation of missing LVI status to increase the preoperative feasibility of the original NILS model. Methods Three nonoverlapping cohorts were used for model development and validation. A total of 4 MLPs for nodal status and 1 LVI MLP were developed using 11 to 12 routinely available predictors. Three nodal status models were used to account for the different availabilities of LVI status in the cohorts and external validation in NKBC. The fourth nodal status model was developed for 80% (14,906/18,663) of NKBC cases and validated in the remaining 20% (3727/18,663). Three alternatives for imputation of LVI status were compared. The discriminatory capacity was evaluated using the validation area under the receiver operating characteristics curve (AUC) in 3 of the nodal status models. The clinical feasibility of the models was evaluated using calibration and decision curve analyses. Results External validation of the original NILS model was performed in NKBC (AUC 0.699, 95% CI 0.690-0.708) with good calibration and the potential of sparing 16% of patients with node-negative disease from SLNB. The LVI model was externally validated (AUC 0.747, 95% CI 0.694-0.799) with good calibration but did not improve the discriminatory performance of the nodal status models. A new nodal status model was developed in NKBC without information on LVI (AUC 0.709, 95% CI: 0.688-0.729), with excellent calibration in the holdout internal validation cohort, resulting in the potential omission of 24% of patients from unnecessary SLNBs. Conclusions The NILS model was externally validated in NKBC, where the imputation of LVI status did not improve the model’s discriminatory performance. A new nodal status model demonstrated the feasibility of using register data comprising only the variables available in the preoperative setting for NILS using machine learning. Future steps include ongoing preoperative validation of the NILS model and extending the model with, for example, mammography images.

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How Reliable Are Gene Expression-Based and Immunohistochemical Biomarkers Assessed on a Core-Needle Biopsy? A Study of Paired Core-Needle Biopsies and Surgical Specimens in Early Breast Cancer

Cited by 7 publications

References 54 publications

A preoperative prediction model for sentinel lymph node status using artificial intelligence on mammographic images and clinicopathological variables in patients with clinically node-negative breast cancer

A preoperative prediction model for sentinel lymph node status using artificial intelligence on mammographic images and clinicopathological variables in patients with clinically node-negative breast cancer

Noninvasive Staging of Lymph Node Status (NILS) in Breast Cancer using Machine Learning: External Validation and Further Model Development

Noninvasive Staging of Lymph Node Status in Breast Cancer Using Machine Learning: External Validation and Further Model Development

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