Establishment of a model for predicting sentinel lymph node metastasis in early breast cancer based on contrast-enhanced ultrasound and clinicopathological features

Wang, Lina; Li, Juntao; Qiao, Jie; Guo, Xiaomao; Bian, Xiaolin; Guo, Lanwei; Liu, Zhenzhen; Lu, Zhenwei

doi:10.21037/gs-21-245

Cited by 7 publications

(5 citation statements)

References 23 publications

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“…Breast cancer is the most common malignant tumor in women worldwide, which seriously threatens the lives and health of patients ( 7 , 8 ). With medical research developments, the early diagnosis and treatment of breast cancer have greatly improved, and the mortality rate has decreased; however, the high incidence of breast cancer remains a public health problem globally ( 9 ). ALNM is a common type of breast cancer metastasis, and epidemiological statistics show that the probability of ALNM occurring in patients with breast cancer is as high as 15%.…”

Section: Discussionmentioning

confidence: 99%

“…It has been suggested that CEUS, real-time elastography and hemodynamics have certain value in the diagnosis of breast cancer, and can provide great improvements in sensitivity and specificity ( 3 ). In addition, CEUS has been described as having a high diagnostic rate and strong sensitivity for breast cancer, which facilitates evaluation of the efficacy of chemotherapy for breast cancer and guides the therapeutic schedule ( 9 ). A previous study showed that CEUS features are associated with biological prognostic factors in patients with breast cancer, and play an important role in the diagnosis of breast cancer and evaluation of its prognosis ( 12 ).…”

Section: Discussionmentioning

confidence: 99%

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Association and predictive value of contrast‑enhanced ultrasound features with axillary lymph node metastasis in primary breast cancer

Kuang,

Lin,

Yuan

et al. 2024

Oncol Lett

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Primary breast cancer is the most common malignant tumor in women worldwide, and axillary lymph node metastasis (ALNM) is an important marker of disease progression in patients with breast cancer. The objective of the present study was to analyze the association between contrast-enhanced ultrasound (CEUS) features and ALNM in primary breast cancer and its predictive value. A total of 120 patients with breast cancer were assigned to the non-metastatic group (n=70) and metastatic group (n=50). The factors influencing ALNM were explored by multivariate logistic regression analysis. The consistency of CEUS, ordinary ultrasonography and pathological examination in the diagnosis of the ALNM of breast cancer was evaluated by consistency testing. The sensitivity, specificity and consistency rate of CEUS features and ordinary ultrasonography were analyzed by receiver operating characteristic curve and four-fold table analyses. High enhancement amplitude, centripetal enhancement sequence, increased maximum cortical thickness, high peak intensity and a larger area under the curve of lymph nodes were more commonly found in the metastatic group than in the non-metastatic group. The lymph node aspect ratio and time to peak were lower in the metastatic group than the non-metastatic group. The time to peak was a protective factor for ALNM in patients with breast cancer. The sensitivity, specificity and coincidence rate with pathological examination of CEUS in the diagnosis of ALNM were 92.00, 90.00 and 90.83%, while these of ordinary ultrasonography were 76.00, 80.00 and 78.33%, respectively. The consistency test indicated that CEUS and pathological examination were consistent in the diagnosis of ALNM in patients with breast cancer, with a κ value of 0.816, indicating a good consistency. The κ value of ordinary ultrasonography and pathological examination was 0.763, also indicating a good consistency. However, these results indicate that CEUS is more valuable than ordinary ultrasonography in the diagnosis of ALNM in cases of breast cancer. In conclusion, the present study indicates that CEUS features were influencing factors associated with ALNM in patients with breast cancer and may serve as an important reference for the preoperative prediction of ALNM in breast cancer.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Association and predictive value of contrast‑enhanced ultrasound features with axillary lymph node metastasis in primary breast cancer

Kuang,

Lin,

Yuan

et al. 2024

Oncol Lett

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“…51 Moreover, pioneering research has suggested the potential of DL for detecting breast cancer from gene expression data and identifying high-risk genes. 52 In terms of predicting lymph node metastasis, DL models exploiting radiomics, such as magnetic resonance imaging, 24 shear wave elastography, 25 contrast-enhanced ultrasonography, 53 and positron emission tomography, 26 have demonstrated excellent performance, with ROC AUCs between 0.82 and 0.94. However, these imaging features are not always available in routine breast cancer work-ups.…”

Section: Discussionmentioning

confidence: 99%

Identification of Sentinel Lymph Node Macrometastasis in Breast Cancer by Deep Learning Based on Clinicopathological Characteristics

Dihge,

Edén,

Svensson

et al. 2024

Preprint

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The axillary lymph node status remains an important prognostic factor in breast cancer, and nodal staging using sentinel lymph node biopsy (SLNB) is routine. Randomized clinical trials provide evidence supporting de-escalation of axillary surgery and omission of SLNB in patients at low risk. However, identifying sentinel lymph node macrometastases (macro-SLNMs) is crucial for planning treatment tailored to the individual patient. This study is the first to explore the capacity of deep learning (DL) models to identify macro-SLNMs based on preoperative clinicopathological characteristics. We trained and validated five multivariable models using a population-based cohort of 18,185 patients. DL models outperform logistic regression under the constraint that the sensitivity is no less than 90%, reflecting the sensitivity of SLNB. This highlights the feasibility of noninvasive macro-SLNM prediction using DL. Feature importance analysis revealed that patients with similar characteristics exhibited different nodal status predictions, indicating the need for additional predictors for further improvement.

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“…The American College of Surgeons Oncology Group (ACOSOG) Z0011 study found that for T1–2 breast cancer patients with no clinically palpable axillary adenopathy, the complete dissection of ALNs is not warranted in women with fewer than three involved ALNs who undergo breast-conserving surgery and whole-breast radiotherapy [ 3 ]. Among all clinically node-negative patients, approximately 25–35% harbor nodal metastases, while approximately 10% have ≥ 3 metastatic ALNs [ 4 – 7 ]. Preoperative identification of ≥ 3 metastatic ALNs can be an indication of neoadjuvant systemic treatment or direct ALN dissection (ALND) [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting three or more metastatic nodes using contrast-enhanced lymphatic US findings in early breast cancer

Niu,

Hao,

Gao

et al. 2024

Insights Imaging

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Objectives To develop and validate a nomogram for predicting ≥ 3 metastatic axillary lymph nodes (ALNs) in early breast cancer with no palpable axillary adenopathy by clinicopathologic data, contrast-enhanced (CE) lymphatic ultrasound (US), and grayscale findings of sentinel lymph nodes (SLNs). Materials and methods Women with T1-2N0 invasive breast cancer were consecutively recruited for the CE lymphatic US. Patients from Center 1 were grouped into development and internal validation cohorts at a ratio of 2:1. The external validation cohort was constructed from Center 2. The clinicopathologic data and US findings of SLNs were analyzed. A nomogram was developed to predict women with ≥ 3 metastatic ALNs. Nomogram performance was assessed with the area under the receiver operating characteristic curve (AUC) and calibration curve analysis. Results One hundred seventy-nine from Center 1 were considered the development cohorts. The remaining 90 participants from Center 1 were internal cohorts and 197 participants from Center 2 were external validation cohorts. The US findings of no enhancement (odds ratio (OR), 15.3; p = 0.01), diffuse (OR, 19.1; p = 0.01) or focal eccentric (OR, 27.7; p = 0.003) cortical thickening, and absent hilum (OR, 169.7; p < 0.001) were independently associated with ≥ 3 metastatic ALNs. Compared to grayscale US or CE lymphatic US alone, the nomogram showed the highest AUC of 0.88 (0.85, 0.91). The nomogram showed a calibration slope of 1.0 (p = 0.80–0.81; Brier = 0.066–0.067) in validation cohorts in predicting ≥ 3 metastatic ALNs. Conclusion Patients likely to have ≥ 3 metastatic ALNs were identified by combining the lymphatic and grayscale US findings of SLNs. Our nomogram could aid in multidisciplinary treatment decision-making. Trial registration This trial is registered on www.chictr.org.cn: ChiCTR2000031231. Registered March 25, 2020. Critical relevance statement A nomogram combining lymphatic CEUS and grayscale US findings of SLNs could identify early breast cancer patients with low or high axillary tumor burden preoperatively, which is more applicable to the Z0011 era. Our nomogram could be useful in aiding multidisciplinary treatment decision-making for patients with early breast cancer. Key points • CEUS can help identify and diagnose SLN in early breast cancer preoperatively. • Combining lymphatic and grayscale US findings can predict axillary tumor burden. • The nomogram showed a high diagnostic value in validation cohorts. Graphical Abstract

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Establishment of a model for predicting sentinel lymph node metastasis in early breast cancer based on contrast-enhanced ultrasound and clinicopathological features

Cited by 7 publications

References 23 publications

Association and predictive value of contrast‑enhanced ultrasound features with axillary lymph node metastasis in primary breast cancer

Association and predictive value of contrast‑enhanced ultrasound features with axillary lymph node metastasis in primary breast cancer

Identification of Sentinel Lymph Node Macrometastasis in Breast Cancer by Deep Learning Based on Clinicopathological Characteristics

Predicting three or more metastatic nodes using contrast-enhanced lymphatic US findings in early breast cancer

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