Staging of the Axilla in Breast Cancer and the Evolving Role of Axillary Ultrasound

Chen, Michael Y; Gillanders, William E.

doi:10.2147/bctt.s273039

Cited by 8 publications

(5 citation statements)

References 85 publications

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“…Evaluating the risk of upstaging of ADH/DCIS is a popular research topic of great clinical importance. Currently, patients with invasive diseases require axillary nodal staging, 29 and preoperative assessment of upstaging risk could allow patient counselling regarding axillary staging at the time of definitive surgery. In the future, patients with low upstaging risk and low histological grade may even opt for active surveillance without the need for wide excision 12 .…”

Section: Discussionmentioning

confidence: 99%

Deep‐learning model to improve histological grading and predict upstaging of atypical ductal hyperplasia / ductal carcinoma in situ on breast biopsy

Huang,

Chang,

Lin

et al. 2024

Histopathology

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AimsRisk stratification of atypical ductal hyperplasia (ADH) and ductal carcinoma in situ (DCIS), diagnosed using breast biopsy, has great clinical significance. Clinical trials are currently exploring the possibility of active surveillance for low‐risk lesions, whereas axillary lymph node staging may be considered during surgical planning for high‐risk lesions. We aimed to develop a machine‐learning algorithm based on whole‐slide images of breast biopsy specimens and clinical information to predict the risk of upstaging to invasive breast cancer after wide excision.Methods and ResultsPatients diagnosed with ADH/DCIS on breast biopsy were included in this study, comprising 592 (740 slides) and 141 (198 slides) patients in the development and independent testing cohorts, respectively. Histological grading of the lesions was independently evaluated by two pathologists. Clinical information, including biopsy method, lesion size, and Breast Imaging Reporting and Data System (BI‐RADS) classification of ultrasound and mammograms, were collected. Deep DCIS consisted of three deep neural networks to evaluate nuclear grade, necrosis, and stromal reactivity. Deep DCIS output comprised five parameters: total patches, lesion extent, Deep Grade, Deep Necrosis, and Deep Stroma. Deep DCIS highly correlated with the pathologists' evaluations of both slide‐ and patient‐level labels. All five parameters of Deep DCIS were significantly associated with upstaging to invasive carcinoma in subsequent wide excisional specimens. Using multivariate logistic regression, Deep DCIS predicted upstaging to invasive carcinoma with an area under the curve (AUC) of 0.81, outperforming pathologists' evaluation (AUC, 0.71 and 0.69). After including clinical and hormone receptor status information, performance further improved (AUC, 0.87). This combined model retained its predictive power in two subgroup analyses: the first subgroup included unequivocal DCIS (excluding cases of ADH and DCIS suspicious for microinvasion) (AUC, 0.83), while the second excluded cases of high‐grade DCIS (AUC, 0.81). The model was validated in an independent testing cohort (AUC, 0.81).ConclusionThis study demonstrated that deep‐learning models can refine histological evaluation of ADH and DCIS on breast biopsies, which may help guide future treatment planning.

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Section: Discussionmentioning

confidence: 99%

Deep‐learning model to improve histological grading and predict upstaging of atypical ductal hyperplasia / ductal carcinoma in situ on breast biopsy

Huang,

Chang,

Lin

et al. 2024

Histopathology

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“…Before deciding about scalonating treatment (local or systemic) because of a suspicious lymph node image, it is essential to confirm the finding with FNAC or core needle biopsy (CNB) [ 29 ]. The PPV of the US alone is not high enough to directly proceed to ALND without tissue sampling, ranging from 56 to 90% [ 29 , 30 ]. Our data are in accordance; we found a PPV of US of 71%.…”

Section: Discussionmentioning

confidence: 99%

Axillary ultrasound and fine-needle aspiration cytology to predict clinically relevant nodal burden in breast cancer patients

et al. 2021

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Background Axillary lymph node involvement is one important prognostic factor in breast cancer, but the way to access this information has been modified over the years. This study evaluated if axillary ultrasound (US) coupled with fine-needle aspiration cytology (FNAC) can accurately predict clinically relevant node metastasis in patients with breast cancer, and thus assist clinical decisions Methods This is a cross-sectional study with retrospective data collection of 241 individuals (239 women and 2 men) with unilateral operable breast cancer who were submitted to preoperative axillary assessment by physical exam, US and FNAC if suspicious nodes by imaging. We calculated sensitivity, specificity, and accuracy of the methods. We compared the patient's characteristics using chi-square test, parametrics and non-parametrics statistics according to the variable. Results The most sensible method was US (0.59; 95% CI, 0.50–0.69), and the most specific was US coupled with FNAC (0.97; 95% CI, 0.92–0.99). Only 2.7% of the patients with normal axillary US had more than 2 metastatic nodes in the axillary lymph node dissection, against 50% of the patients with suspicious lymph nodes in the US and positive FNAC. Conclusions Axillary US coupled with FNAC can sort patients who have a few metastatic nodes at most from those with heavy axillary burden and could be one more tool to initially evaluate patients and define treatment strategies.

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“…For traditional radical mastectomy, as the standard treatment, axillary lymph node dissection (ALND) is an indispensable part of the operation in patients with axillary negative or positive (1)(2)(3)(4)(5). The impact of ALND is to assist clinicians in planning their treatment and to provide more information on prognosis.…”

Section: Introductionmentioning

confidence: 99%

“…Nowadays, as the first station lymphatic node receiving lymphatic drainage from primary breast tumors, the sentinel lymph node biopsy (SLNB) has become a standard tool for evaluating axillary lymph node status in early breast cancer. Breast cancer patients who have negative sentinel lymph node (SLN) could avoid ALND (2)(3)(4)(5)(11)(12)(13)(14)(15)(16)(17). However, when limited positive SLN were observed, the best treatment for the armpit depends.…”

Section: Introductionmentioning

confidence: 99%

“…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%

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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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Staging of the Axilla in Breast Cancer and the Evolving Role of Axillary Ultrasound

Cited by 8 publications

References 85 publications

Deep‐learning model to improve histological grading and predict upstaging of atypical ductal hyperplasia / ductal carcinoma in situ on breast biopsy

Deep‐learning model to improve histological grading and predict upstaging of atypical ductal hyperplasia / ductal carcinoma in situ on breast biopsy

Axillary ultrasound and fine-needle aspiration cytology to predict clinically relevant nodal burden in breast cancer patients

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

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