Elaboration and Validation of a Nomogram Based on Axillary Ultrasound and Tumor Clinicopathological Features to Predict Axillary Lymph Node Metastasis in Patients With Breast Cancer

Liu, Yubo; Ye, Feng; Wang, Yun; Zheng, Xueyi; Huang, Yili; Zhou, Jianhua

doi:10.3389/fonc.2022.845334

Cited by 10 publications

(16 citation statements)

References 31 publications

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“…Finally, we constructed a nomogram to effectively predict survival probability for BC via integrating independent prognostic factors. Although age, TNM stage, and pathologic stage, ER status and PR status were considered as potential prognostic factors of BC [33,34]; however, we found that only four characteristics were the independent prognostic factors. Herein, the OCRL expression-based nomogram as a pretty survival predictive model was constructed to visually and directly show the risk scores of prognostic variables to 1-year, 5-year, 10-year survival, and further guided the clinical treatment and prevention of BC.…”

Section: Discussioncontrasting

confidence: 59%

OCRL is a novel prognostic biomarker and its association with immunoregulation in breast cancer

Chen

Zhu

et al. 2022

Preprint

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Background: OCRL (Oculocerebrorenal syndrome of Lowe Protein) was originally well-known as the Lowe’s protein. However, the expression, significance and regulation mechanisms of OCRL in any cancers were not clear. Methods: The bioinformation of breast cancer (BC) patients was required from TCGA (The Cancer Genome Atlas), and the expression was validated via IHC (immuno-histochemical analysis), qPCR and western blot. The UALCAN database was used to explore the promoter methylation level of OCRL and its role in gene silencing and survival. The prognostic roles of various clinicopathologic characteristics, including the OCRL, were evaluated by univariate and multivariate regression analysis. OCRL-related differentially expressed genes (DEGs) and their functions were explored by LinkedOmics database. The protein-protein interaction (PPI) and immunomodulatory roles were further analyzed with STRING and TISIDB database. Results: Compared with normal and paracancerous samples, the up-regulated expression of OCRL was identified in BC samples. The OCRL was co-expressed with a variety of unfavorable survival-related genes, which also was identified as an independent prognostic factor. Thus, the prognosis of BC patients with overexpressed OCRL was notably more unfavorable from TCGA database. Also, the hypomethylation of OCRL at certain sites was associated with gene silencing and poor survival. Moreover, the overexpression level of OCRL was negatively associated with the infiltration of the most immune cells and the expression of various immune biomarkers in BC. Finally, a OCRL expression-based nomogram integrating independent prognostic factors was constructed to predict at one-, five-, and ten-year the overall survival (OS). Conclusion: OCRL was a promising prognostic predictor and potential immune inhibition modulator.

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Section: Discussioncontrasting

confidence: 59%

OCRL is a novel prognostic biomarker and its association with immunoregulation in breast cancer

Chen

Zhu

et al. 2022

Preprint

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“… 25 , 26 For patients with BC, ultrasound is a conventional imaging method to evaluate lesions and ALN status, as it has low cost and no radiation. 27 In comparison, our study also focused on the combination of preoperative clinicopathological features and DLR methods to supplement more clinical feature information besides ultrasound images. This method can improve the performance and achieve the constraining effect of DLR features to make the model more robust.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Axillary Lymph Node Metastatic Load of Breast Cancer Based on Ultrasound Deep Learning Radiomics Nomogram

Zhang

Zhao

Zhang

et al. 2023

Technol Cancer Res Treat

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Background: Axillary lymph node (ALN) metastatic load is very important in the diagnosis and treatment of breast cancer (BC). We aimed to construct a model for predicting ALN metastatic load using deep learning radiomics (DLR) techniques based on the preoperative ultrasound and clinicopathologic information of patients with stage T1-2 BC. Methods: Retrospective analysis was performed on 176 patients with pathologically confirmed BC in our hospital from February 2018 to April 2020. ALN metastases were divided into a low-load group (< 3 lymph node metastases) and a high-load group (≥ 3 lymph node metastases) according to pathological results. Pyradiomics and pre-trained ResNet50 were used to extract radiomics and deep learning features, respectively. Independent sample T-test, random forest recursive elimination, and Lasso were used to screen the features to construct the deep learning radiomics signature (DLRS). Based on single/multivariate logistic regression analysis results, a DLR nomogram (DLRN) model was constructed by combining valuable clinical features and DLRS. Results: The DLRS was composed of 3 radiomics features and 14 deep learning features and combined with the maximum diameter of lesions to construct the DLRN. The AUCs of the training and test sets were 0.900 (95% CI: 0.853-0.931) and 0.821 (95% CI: 0.769-0.868), respectively. The calibration curve and Hosmer–Lemeshow test confirmed that the DLRN model has a good consistency. The decision curve also confirmed its good clinical practicality. Conclusion: Ultrasound-based DLRN has an excellent performance in predicting ALN load in patients with BC.

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“…Multiple studies have investigated the discriminatory performance of nomograms for predicting N status using retrospective clinicopathological data alone or in combination with imaging features [6,[8][9][10][11][12][13][14][15][16][17]. We aimed to externally validate and further develop a diagnostic tool for the noninvasive staging of N status using only routinely available clinicopathological data to improve the clinical utility of the model.…”

Section: Comparison With Prior Studiesmentioning

confidence: 99%

“…Multiple recent studies have presented prediction models for noninvasive staging of axillary nodal (N) status with the long-term aim of replacing SLNB for subgroups of patients with breast cancer [5][6][7][8][9][10][11][12][13][14][15][16][17]. Only routinely and preoperatively available data should be used for a feasible noninvasive diagnosis of axillary N status aimed at clinical implementation.…”

Section: Introductionmentioning

confidence: 99%

“…Only routinely and preoperatively available data should be used for a feasible noninvasive diagnosis of axillary N status aimed at clinical implementation. 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].…”

Section: Introductionmentioning

confidence: 99%

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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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Elaboration and Validation of a Nomogram Based on Axillary Ultrasound and Tumor Clinicopathological Features to Predict Axillary Lymph Node Metastasis in Patients With Breast Cancer

Cited by 10 publications

References 31 publications

OCRL is a novel prognostic biomarker and its association with immunoregulation in breast cancer

OCRL is a novel prognostic biomarker and its association with immunoregulation in breast cancer

Prediction of Axillary Lymph Node Metastatic Load of Breast Cancer Based on Ultrasound Deep Learning Radiomics Nomogram

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

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