Clinical and Pathological Factors Affecting the Sentinel Lymph Node Metastasis in Patients with Breast Cancer

Karahallı, Önder; Acar, Turan; Atahan, Kemal; Acar, Nihan; Hacıyanlı, Mehmet; Kamer, Erdinç

doi:10.1007/s12262-016-1500-3

Cited by 11 publications

(7 citation statements)

References 23 publications

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“…A previous study has found a correlation between ALN metastasis and LVI in BC. 33 In this study, MRI-ALN status, as a noninvasive MRI characteristic, showed important discriminative performance for LVI assessment. In addition, previous studies indicated that BES was associated with the prognosis after neoadjuvant chemotherapy and ALN metastasis burden in IBC.…”

Section: Discussionmentioning

confidence: 70%

Using Machine Learning Methods to Assess Lymphovascular Invasion and Survival in Breast Cancer: Performance of Combining Preoperative Clinical and MRI Characteristics

Xie

et al. 2023

Magnetic Resonance Imaging

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BackgroundPreoperative assessment of lymphovascular invasion (LVI) in invasive breast cancer (IBC) is of high clinical relevance for treatment decision‐making and prognosis.PurposeTo investigate the associations of preoperative clinical and magnetic resonance imaging (MRI) characteristics with LVI and disease‐free survival (DFS) by using machine learning methods in patients with IBC.Study TypeRetrospective.PopulationFive hundred and seventy‐five women (range: 24–79 years) with IBC who underwent preoperative MRI examinations at two hospitals, divided into the training (N = 386) and validation datasets (N = 189).Field Strength/SequenceAxial fat‐suppressed T2‐weighted turbo spin‐echo sequence and dynamic contrast‐enhanced with fat‐suppressed T1‐weighted three‐dimensional gradient echo imaging.AssessmentMRI characteristics (clinical T stage, breast edema score, MRI axillary lymph node status, multicentricity or multifocality, enhancement pattern, adjacent vessel sign, and increased ipsilateral vascularity) were reviewed independently by three radiologists. Logistic regression (LR), eXtreme Gradient Boosting (XGBoost), k‐Nearest Neighbor (KNN), and Support Vector Machine (SVM) algorithms were used to establish the models by combing preoperative clinical and MRI characteristics for assessing LVI status in the training dataset, and the methods were further applied in the validation dataset. The LVI score was calculated using the best‐performing of the four models to analyze the association with DFS.Statistical TestsChi‐squared tests, variance inflation factors, receiver operating characteristics (ROC), Kaplan–Meier curve, log‐rank, Cox regression, and intraclass correlation coefficient were performed. The area under the ROC curve (AUC) and hazard ratios (HR) were calculated. A P‐value <0.05 was considered statistically significant.ResultsThe model established by the XGBoost algorithm had better performance than LR, SVM, and KNN models, achieving an AUC of 0.832 (95% confidence interval [CI]: 0.789, 0.876) in the training dataset and 0.838 (95% CI: 0.775, 0.901) in the validation dataset. The LVI score established by the XGBoost model was an independent indicator of DFS (adjusted HR: 2.66, 95% CI: 1.22–5.80).Data ConclusionThe XGBoost model based on preoperative clinical and MRI characteristics may help to investigate the LVI status and survival in patients with IBC.Level of Evidence3Technical EfficacyStage 2

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Section: Discussionmentioning

confidence: 70%

Using Machine Learning Methods to Assess Lymphovascular Invasion and Survival in Breast Cancer: Performance of Combining Preoperative Clinical and MRI Characteristics

Xie

et al. 2023

Magnetic Resonance Imaging

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“…Digital mammography is one of the important imaging tools for breast cancer screening and diagnosis. There have been many reports on prediction of axillary lymph node metastasis [3][4][5] and on digital mammography screening [6][7][8][9]. However, there has been no report on predicting LVI of breast cancer based on imaging patterns of digital mammography, except a little literature in which MRI findings were used for prediction [2,[10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Value of digital mammography in predicting lymphovascular invasion of breast cancer

Liu

Liang

et al. 2020

BMC Cancer

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Background: Lymphovascular invasion (LVI) has never been revealed by preoperative scans. It is necessary to use digital mammography in predicting LVI in patients with breast cancer preoperatively. Methods: Overall 122 cases of invasive ductal carcinoma diagnosed between May 2017 and September 2018 were enrolled and assigned into the LVI positive group (n = 42) and the LVI negative group (n = 80). Independent t-test and χ2 test were performed. Results: Difference in Ki-67 between the two groups was statistically significant (P = 0.012). Differences in interstitial edema (P = 0.013) and skin thickening (P = 0.000) were statistically significant between the two groups. Multiple factor analysis showed that there were three independent risk factors for LVI: interstitial edema (odds ratio [OR] = 12.610; 95% confidence interval [CI]: 1.061-149.922; P = 0.045), blurring of subcutaneous fat (OR = 0.081; 95% CI: 0.012-0.645; P = 0.017) and skin thickening (OR = 9.041; 95% CI: 2.553-32.022; P = 0.001). Conclusions: Interstitial edema, blurring of subcutaneous fat, and skin thickening are independent risk factors for LVI. The specificity of LVI prediction is as high as 98.8% when the three are used together.

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Section: Introductionmentioning

confidence: 99%

Value of digital mammography in predicting lymphovascular invasion of breast cancer

Liu

Liang

et al. 2020

Preprint

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Background: Lymphovascular invasion (LVI) has never been revealed by preoperative scans. It is necessary to use digital mammography in predicting LVI in patients with breast cancer preoperatively. Methods: Overall 122 cases of invasive ductal carcinoma diagnosed between May 2017 and September 2018 were enrolled and assigned into the LVI positive group (n = 42) and the LVI negative group (n = 80). Independent t-test and χ2 test were performed. Results: Difference in Ki-67 between the two groups was statistically significant (P = 0.012). Differences in interstitial edema (P=0.013) and skin thickening (P=0.000) were statistically significant between the two groups. Multiple factor analysis showed that there were three independent risk factors for LVI: interstitial edema (odds ratio [OR] = 12.610; 95% confidence interval [CI]: 1.061-149.922; P=0.045), blurring of subcutaneous fat (OR = 0.081; 95% CI: 0.012-0.645; P = 0.017) and skin thickening (OR = 9.041; 95% CI: 2.553-32.022; P = 0.001). Conclusions: Interstitial edema, blurring of subcutaneous fat, and skin thickening are independent risk factors for LVI. The specificity of LVI prediction is as high as 98.8% when the three are used together.

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Clinical and Pathological Factors Affecting the Sentinel Lymph Node Metastasis in Patients with Breast Cancer

Cited by 11 publications

References 23 publications

Using Machine Learning Methods to Assess Lymphovascular Invasion and Survival in Breast Cancer: Performance of Combining Preoperative Clinical and MRI Characteristics

Using Machine Learning Methods to Assess Lymphovascular Invasion and Survival in Breast Cancer: Performance of Combining Preoperative Clinical and MRI Characteristics

Value of digital mammography in predicting lymphovascular invasion of breast cancer

Value of digital mammography in predicting lymphovascular invasion of breast cancer

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