Artificial Intelligence–Aided Diagnosis of Breast Cancer Lymph Node Metastasis on Histologic Slides in a Digital Workflow

Challa, Bindu; Tahir, Maryam; Hu, Yan; Kellough, David A.; Lujan, Giovani; Sun, Shaoli; Parwani, Anil V.

doi:10.1016/j.modpat.2023.100216

Cited by 14 publications

(6 citation statements)

References 27 publications

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“…Seven deep-learning algorithms outperformed 11 pathologists in time-limited diagnostics [17]. A recent study used 32,000 nodes from 8000 patients to train an AI program, which then demonstrated that AI assistance improved sensitivity and reduced turnaround time [18], showing that AI can identify various phenotypes. AI identified 51 features in breast cancer biopsies, aiding real-time quality control and detecting missed cancers [19].…”

Section: Ai and Pathology/tumor Profilingmentioning

confidence: 99%

The Canadian Breast Cancer Symposium 2023 Meeting Report

Cil,

Boileau,

Chia

et al. 2024

Current Oncology

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On 15–16 June 2023, healthcare professionals and breast cancer patients and advocates from across Canada met in Toronto, Ontario, for the 2023 Canadian Breast Cancer Symposium (CBSC). The CBSC is a national, multidisciplinary event that occurs every 2 years with the goal of developing a personalized approach to the management of breast cancer in Canada. Experts provided state-of-the-art information to help optimally manage breast cancer patients, including etiology, prevention, diagnosis, experimental biology, and therapy of breast cancer and premalignant breast disease. The symposium also had the objectives of increasing communication and collaboration among breast cancer healthcare providers nationwide and providing a comprehensive and real-life review of the many facets of breast cancer. The sessions covered the patient voice, the top breast cancer papers from different disciplines in 2022, artificial intelligence in breast cancer, systemic therapy updates, the management of central nervous system metastases, multidisciplinary management of ductal carcinoma in situ, special populations, optimization-based individual prognostic factors, toxicity management of novel therapeutics, survivorship, and updates in surgical oncology. The key takeaways of these sessions have been summarized in this conference report.

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Section: Ai and Pathology/tumor Profilingmentioning

confidence: 99%

The Canadian Breast Cancer Symposium 2023 Meeting Report

Cil,

Boileau,

Chia

et al. 2024

Current Oncology

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“…Moreover, the study demonstrated the effectiveness of the high magnification network, which also contributed to the high FROC score. A recent application, by Challa et al [ 18 ] proposes the use of a digital imaging analysis “metastasis AI” detection app (Visiopharm Integrator System metastasis AI algorithm) to screen lymph nodes for metastases in BC patients, aiming to improve diagnostic accuracy and pathologists’ efficiency. The AI algorithm demonstrated an overall sensitivity and negative predictive value (NPV) of 100%, making it a promising screening tool before pathologists’ review of H&E-stained slides.…”

Section: Introductionmentioning

confidence: 99%

“…It is also important to assess how assistive reads impact the use of additional diagnostic tests, the categorization of prognosis, and the organization of cases based on algorithm predictions. These evaluations will provide valuable insights into the benefits of intelligent tools in digital pathology [ 15 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

“…The Metastasis Detection APP simplifies lymph node assessment by accurately detecting and measuring metastases in H&E-stained lymph nodes associated with breast and colorectal adenocarcinoma. It improves sensitivity and specificity compared to manual methods and streamlines slide sorting, making it a valuable addition to the diagnostic workflow [ 18 ].

Fig.…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence’s impact on breast cancer pathology: a literature review

Soliman,

Li,

Parwani

2024

Diagn Pathol

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This review discusses the profound impact of artificial intelligence (AI) on breast cancer (BC) diagnosis and management within the field of pathology. It examines the various applications of AI across diverse aspects of BC pathology, highlighting key findings from multiple studies. Integrating AI into routine pathology practice stands to improve diagnostic accuracy, thereby contributing to reducing avoidable errors. Additionally, AI has excelled in identifying invasive breast tumors and lymph node metastasis through its capacity to process large whole-slide images adeptly. Adaptive sampling techniques and powerful convolutional neural networks mark these achievements. The evaluation of hormonal status, which is imperative for BC treatment choices, has also been enhanced by AI quantitative analysis, aiding interobserver concordance and reliability. Breast cancer grading and mitotic count evaluation also benefit from AI intervention. AI-based frameworks effectively classify breast carcinomas, even for moderately graded cases that traditional methods struggle with. Moreover, AI-assisted mitotic figures quantification surpasses manual counting in precision and sensitivity, fostering improved prognosis. The assessment of tumor-infiltrating lymphocytes in triple-negative breast cancer using AI yields insights into patient survival prognosis. Furthermore, AI-powered predictions of neoadjuvant chemotherapy response demonstrate potential for streamlining treatment strategies. Addressing limitations, such as preanalytical variables, annotation demands, and differentiation challenges, is pivotal for realizing AI’s full potential in BC pathology. Despite the existing hurdles, AI’s multifaceted contributions to BC pathology hold great promise, providing enhanced accuracy, efficiency, and standardization. Continued research and innovation are crucial for overcoming obstacles and fully harnessing AI’s transformative capabilities in breast cancer diagnosis and assessment. Graphical Abstract

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“…The literature also shows that pathologists assessing BLN aided by AI also improve their diagnostic efficiency. 7 , 8 …”

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confidence: 99%

Artificial Intelligence Helps Pathologists Increase Diagnostic Accuracy and Efficiency in the Detection of Breast Cancer Lymph Node Metastases

Retamero,

Gulturk,

Bozkurt

et al. 2024

American Journal of Surgical Pathology

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The detection of lymph node metastases is essential for breast cancer staging, although it is a tedious and time-consuming task where the sensitivity of pathologists is suboptimal. Artificial intelligence (AI) can help pathologists detect lymph node metastases, which could help alleviate workload issues. We studied how pathologists’ performance varied when aided by AI. An AI algorithm was trained using more than 32 000 breast sentinel lymph node whole slide images (WSIs) matched with their corresponding pathology reports from more than 8000 patients. The algorithm highlighted areas suspicious of harboring metastasis. Three pathologists were asked to review a dataset comprising 167 breast sentinel lymph node WSIs, of which 69 harbored cancer metastases of different sizes, enriched for challenging cases. Ninety-eight slides were benign. The pathologists read the dataset twice, both digitally, with and without AI assistance, randomized for slide and reading orders to reduce bias, separated by a 3-week washout period. Their slide-level diagnosis was recorded, and they were timed during their reads. The average reading time per slide was 129 seconds during the unassisted phase versus 58 seconds during the AI-assisted phase, resulting in an overall efficiency gain of 55% (P<0.001). These efficiency gains are applied to both benign and malignant WSIs. Two of the 3 reading pathologists experienced significant sensitivity improvements, from 74.5% to 93.5% (P≤0.006). This study highlights that AI can help pathologists shorten their reading times by more than half and also improve their metastasis detection rate.

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Artificial Intelligence–Aided Diagnosis of Breast Cancer Lymph Node Metastasis on Histologic Slides in a Digital Workflow

Cited by 14 publications

References 27 publications

The Canadian Breast Cancer Symposium 2023 Meeting Report

The Canadian Breast Cancer Symposium 2023 Meeting Report

Artificial intelligence’s impact on breast cancer pathology: a literature review

Artificial Intelligence Helps Pathologists Increase Diagnostic Accuracy and Efficiency in the Detection of Breast Cancer Lymph Node Metastases

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