Validation of the Skåne University Hospital nomogram for the preoperative prediction of a disease-free axilla in patients with breast cancer

Majid, Shabaz; Bendahl, Pär Ola; Huss, Linnea; Manjer, Jonas; Rydén, Lisa; Dihge, Looket

doi:10.1093/bjsopen/zrab027

Cited by 5 publications

(3 citation statements)

References 42 publications

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“…Furthermore, the Skåne University Hospital (SUS) nomogram [6], a logistic regression model based on 800 patients in Lund, Sweden, aiming to predict negative sentinel lymph nodes, had an internal validation AUC of 0.74 (95% CI: 0.70–0.79). The nomogram was temporally (n=1318) and geographically (n=1621) externally validated with an AUC of 0.75 (95% CI: 0.70–0.81) and an AUC of 0.73 (95% CI: 0.70–0.76), respectively [24].…”

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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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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“…Furthermore, the Skåne University Hospital nomogram [ 6 ], a logistic regression model based on 800 patients in Lund, Sweden, aiming to predict negative sentinel lymph nodes, had an internal validation AUC of 0.74 (95% CI 0.70-0.79). The nomogram was temporally (n=1318) and geographically (n=1621) externally validated with an AUC of 0.75 (95% CI 0.70–0.81) and an AUC of 0.73 (95% CI 0.70–0.76), respectively [ 24 ].…”

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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“…However, when limited positive SLN were observed, the best treatment for the armpit depends. Based on ACSOG Z0011, AMAROS and OTOASOR study, axillary recurrence (RR) and overall survival (OS) did not differ in patients with low tumor burden and pathologically confirmed 1-2 positive SLN with or without ALND when breast-conserving surgery and radiation were conducted (1,2,4,12,(18)(19)(20)(21)(22)(23). The OTOASOR trial compares completion of axillary lymph node dissection (ALND) to regional nodal irradiation (RNI) in patients with sentinel lymph node metastasis (pN1 SLN) in stage I-II breast cancer.…”

Section: Introductionmentioning

confidence: 99%

A multi-dimensional nomogram to predict non-sentinel lymph node metastases in T1–2HR+ breast cancer

et al. 2023

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BackgroundAxillary lymph node dissection (ALND) could be omitted for T1-2 breast cancer patients with 1-2 positive sentinel lymph node (SLN) after breast-conserving surgery when radiation is planned. However, whether ALND could be replaced by radiation in patients with 1-3 positive SLNs when no more non-SLN metastasis were observed after mastectomy are still controversial. The aim of our study was to develop and validate a nomogram for predicting the possibility of non-SLN metastasis in T1–2 and hormone receptor (HR) positive breast cancer patients with 1-3 positive SLNs after mastectomy.MethodsWe retrospectively reviewed and analyzed the data including the basic information, preoperative sonographic characteristics, and pathological features in breast cancer patients with 1-3 positive SLNs in our medical center between Jan 2016 and Dec 2021. The Chi-square, Fisher’s exact test, and t test were used for comparison of categorical and qualitative variables among patients with or without non-SLN metastasis. Univariate and multivariate logistic regression were used to determine the risk factors for non-SLN metastasis. These predictors were used to build the nomogram. The C-index and area under the receiver operating characteristic curve (AUC) was calculated to assess the accuracy of the model.ResultsA total of 49 in 107 (45.8%) patients were identified with non-SLN metastasis. In multivariate analysis, four variables including younger age, lower estrogen receptor (ER) expression, higher histological score, and cortex thickening of the lymph nodes were determined to be significantly associated with non-SLN metastasis. An individualized nomogram was consequently established with a favorable C-index of 0.822 and verified via two internal validation cohorts.ConclusionsThe current study developed a nomogram predicting non-SLN metastasis for T1–2 and HR+ breast cancer with 1–3 positive SLNs after mastectomy and found that patients in the high-risk group exhibited worse relapse-free survival. The novel nomogram may further help surgeons to determine whether ALND could be omitted when 1-3 positive SLNs were observed in T1–2 and HR+ breast cancer patients.

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Validation of the Skåne University Hospital nomogram for the preoperative prediction of a disease-free axilla in patients with breast cancer

Cited by 5 publications

References 42 publications

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 (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

A multi-dimensional nomogram to predict non-sentinel lymph node metastases in T1–2HR+ breast cancer

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