B3‐lesions of the breast: Risk of malignancy after vacuum‐assisted breast biopsy versus core needle biopsy diagnosis

Pozzi, Giada; Castellano, Isabella; D'Anna, Maria Rosa; Matteis, A. De; Milanesio, L; Durando, Manuela; Ferrando, Pietro Maria; Bergamasco, Laura; Ala, Ada

doi:10.1111/tbj.13476

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…We found an upgrade rate to malignancy for lesions sampled with a 10G needle of 25.6% which is slightly lower, but not statistically different, than the upgrade rate for lesions sampled with a 14G needle (27.1%) ( Table 6 ). Previous studies have similarly found the upgrade rate for smaller needles to be higher than the larger needles which is logical as larger needles have a smaller chance of sampling errors [ [ 5 , 13 , 18 , 19 ]]. Aside from ADH and lobular neoplasia, the second International Consensus Conference on B3 lesions tended to recommend surveillance following core biopsy with larger needles 7-11G compared to needles 14G or smaller [ 15 ].…”

Section: Discussionmentioning

confidence: 99%

Outcomes of atypical (B3) core biopsy lesions diagnosed across BreastScreen NSW, Australia

Chou,

Tran,

Descallar

et al. 2024

The Breast

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Section: Discussionmentioning

confidence: 99%

Outcomes of atypical (B3) core biopsy lesions diagnosed across BreastScreen NSW, Australia

Chou,

Tran,

Descallar

et al. 2024

The Breast

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“…Identifying the pathological subclasses of benign and malignant breast lesions is of equal significance to assist in assessing the potential risk of deterioration of benign lesions and guiding the selection of surgical procedures (56), as well as predicting the postoperative recurrence rate of malignant lesions (57). For breast pathology, the changes in tissue structure range from non-proliferative changes to proliferative changes, such as usual ductal hyperplasia (UDH), atypical ductal hyperplasia (ADH), DCIS, and invasive ductal carcinoma (IDC) (58).…”

Section: Subclass Identificationmentioning

confidence: 99%

Progress on deep learning in digital pathology of breast cancer: a narrative review

Zhu¹,

Liu²,

Li³

2022

Gland Surg

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Background and Objective: Pathology is the gold standard criteria for breast cancer diagnosis and has important guiding value in formulating the clinical treatment plan and predicting the prognosis.However, traditional microscopic examinations of tissue sections are time consuming and labor intensive, with unavoidable subjective variations. Deep learning (DL) can evaluate and extract the most important information from images with less need for human instruction, providing a promising approach to assist in the pathological diagnosis of breast cancer. To provide an informative and up-to-date summary on the topic of DL-based diagnostic systems for breast cancer pathology image analysis and discuss the advantages and challenges to the routine clinical application of digital pathology.Methods: A PubMed search with keywords ("breast neoplasm" or "breast cancer") and ("pathology" or "histopathology") and ("artificial intelligence" or "deep learning") was conducted. Relevant publications in English published from January 2000 to October 2021 were screened manually for their title, abstract, and even full text to determine their true relevance. References from the searched articles and other supplementary articles were also studied. Key Content and Findings: DL-based computerized image analysis has obtained impressive achievementsin breast cancer pathology diagnosis, classification, grading, staging, and prognostic prediction, providing powerful methods for faster, more reproducible, and more precise diagnoses. However, all artificial intelligence (AI)-assisted pathology diagnostic models are still in the experimental stage. Improving their economic efficiency and clinical adaptability are still required to be developed as the focus of further researches.Conclusions: Having searched PubMed and other databases and summarized the application of DL-based AI models in breast cancer pathology, we conclude that DL is undoubtedly a promising tool for assisting pathologists in routines, but further studies are needed to realize the digitization and automation of clinical pathology.

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“…The key issue is that the management of these lesions is highly controversial and is therefore the center of a lively debate, that could be fittingly approached by building evidence from large series, which are still sporadically available [ 14 , 15 , 16 ]. If performed with large-caliber needles, VAB seems to consistently improve lesion characterization in B3 lesions [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ], even assuming a therapeutic role with vacuum-assisted excision, which is increasingly being proposed with interesting results [ 25 , 26 , 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

B3 Lesions at Vacuum-Assisted Breast Biopsy under Ultrasound or Mammography Guidance: A Single-Center Experience on 3634 Consecutive Biopsies

Girardi

Guaragni

Ruzzenenti

et al. 2021

Cancers

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The rate of upgrade to cancer for breast lesions with uncertain malignant potential (B3 lesions) diagnosed at needle biopsy is highly influenced by several factors, but large series are seldom available. We retrospectively assessed the upgrade rates of a consecutive series of B3 lesions diagnosed at ultrasound- or mammography-guided vacuum-assisted biopsy (VAB) at an EUSOMA-certified Breast Unit over a 7-year timeframe. The upgrade rate was defined as the number of ductal carcinoma in situ (DCIS) or invasive cancer at pathology after excision or during follow-up divided by the total number of B3 lesions. All lesions were reviewed by one of four pathologists with a second opinion for discordant assessments of borderline cases. Excision or surveillance were defined by the multidisciplinary tumor board, with 6- and 12-month follow-up. Out of 3634 VABs (63% ultrasound-guided), 604 (17%) yielded a B3 lesion. After excision, 17/604 B3 lesions were finally upgraded to malignancy (2.8%, 95% confidence interval [CI] 1.8–4.5%), 10/17 (59%) being upgraded to DCIS and 7/17 (41%) to invasive carcinoma. No cases were upgraded during follow-up. B3a lesions showed a significantly lower upgrade rate (0.4%, 95% CI 0.1–2.1%) than B3b lesions (4.7%, 95% CI 2.9–7.5%, p = 0.001), that had a 22.0 adjusted odds ratio for upgrade (95% CI 2.1–232.3). No significant difference was found in upgrade rates according to imaging guidance or needle caliper. Surveillance-oriented management can be considered for B3a lesions, while surgical excision should be pursued for B3b lesions.

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B3‐lesions of the breast: Risk of malignancy after vacuum‐assisted breast biopsy versus core needle biopsy diagnosis

Cited by 5 publications

References 7 publications

Outcomes of atypical (B3) core biopsy lesions diagnosed across BreastScreen NSW, Australia

Outcomes of atypical (B3) core biopsy lesions diagnosed across BreastScreen NSW, Australia

Progress on deep learning in digital pathology of breast cancer: a narrative review

B3 Lesions at Vacuum-Assisted Breast Biopsy under Ultrasound or Mammography Guidance: A Single-Center Experience on 3634 Consecutive Biopsies

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