Artificial Intelligence in Breast Cancer Screening and Diagnosis

Dileep, Gayathri; Gyani, Sanjeev G Gianchandani

doi:10.7759/cureus.30318

Cited by 20 publications

(20 citation statements)

References 44 publications

(46 reference statements)

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“…Considering the prevalent discordance in HER2 status between primary and metastatic disease that affects a substantial portion of cases, it's prudent to consider evaluating the HER2-low status using metastatic tissues initially. This viewpoint suggests that optimizing AI models by training them on extensive HER2-stained datasets enriched with metastatic samples could enhance both precision and efficiency, thereby addressing subjectivity and variability concerns [ 18 ]. To ensure a comprehensive approach to interpreting HER2 IHC expression in the low range, ongoing education and updates remain vital components.…”

Section: Conclusion and Future Implicationsmentioning

confidence: 99%

Standardized pathology report for HER2 testing in compliance with 2023 ASCO/CAP updates and 2023 ESMO consensus statements on HER2-low breast cancer

Ivanova,

Porta,

D’Ercole

et al. 2023

Virchows Arch

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Since the release of the DESTINY-Breast04 (DB-04) trial findings in June 2022, the field of pathology has seen a renaissance of HER2 as a predictive biomarker in breast cancer. The trial focused on patients with metastatic breast cancer who were classified as “HER2-low,” i.e., those with immunohistochemistry (IHC) HER2 1 + or 2 + and negative in situ hybridization (ISH) results. The study revealed that treating these patients with trastuzumab deruxtecan (T-DXd) instead of the oncologist’s chosen chemotherapy led to outstanding improvements in survival. This has challenged the existing binary HER2 pathological classification system, which categorized tumors as either positive (overexpression/amplification) or negative, as per the ASCO/CAP 2018 guideline reaffirmed by ASCO/CAP 2023 guideline update. Given that DB-04 excluded patients with HER2 IHC score 0 status, the results of the ongoing DB-06 trial may shed further light on the potential benefits of T-DXd therapy for these patients. Roughly half of all breast cancers are estimated to belong to the HER2-low category, which does not represent a distinct or specific subtype of cancer. Instead, it encompasses a diverse group of tumors that exhibit clinical, morphological, immunohistochemical, and molecular variations. However, HER2-low offers a distinctive biomarker status that identifies a specific therapeutic regimen (i.e., T-DXd) linked to a favorable prognosis in breast cancer. This unique association emphasizes the importance of accurately identifying these tumors. Differentiating between a HER2 IHC score 0 and score 1 + has not been clinically significant until now. To ensure accurate classification and avoid misdiagnosis, it is necessary to adopt standardized procedures, guidelines, and specialized training for pathologists in interpreting HER2 expression in the lower spectrum. Additionally, the utilization of artificial intelligence holds promise in supporting this endeavor. Here, we address the current state of the art and unresolved issues in assessing HER2-low status, with a particular emphasis on the score 0. We explore the dilemma surrounding the exclusion of HER2-zero patients from potentially beneficial therapy based on traditional HER2 testing. Additionally, we examine the clinical context, considering that DB-04 primarily involved heavily pretreated late-stage metastatic breast cancers. We also delve into emerging evidence suggesting that extrapolating HER2-low status from the original diagnosis may lead to misleading results. Finally, we provide recommendations for conducting high-quality testing and propose a standardized pathology report in compliance with 2023 ASCO/CAP updates and 2023 ESMO consensus statements on HER2-low breast cancer.

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Section: Conclusion and Future Implicationsmentioning

confidence: 99%

Standardized pathology report for HER2 testing in compliance with 2023 ASCO/CAP updates and 2023 ESMO consensus statements on HER2-low breast cancer

Ivanova,

Porta,

D’Ercole

et al. 2023

Virchows Arch

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“…The potential of these AI techniques is immense in unveiling critical insights into the role of the microbiome in human health and developing personalized treatments. Specifically, in the context of breast cancer, AI can revolutionize diagnostics and therapeutics by identifying microbial biomarkers associated with subtypes or treatment outcomes 128,129 . This information can facilitate personalized therapies targeting specific microbial communities, and AI algorithms can predict patients' response to breast cancer treatments, thus minimizing side effects.…”

Section: Technological Advances In Breast/gut Microbiome Research: a ...mentioning

confidence: 99%

Exploring the human microbiome – A step forward for precision medicine in breast cancer

Jotshi,

Sukla,

Haque

et al. 2023

Cancer Reports

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BackgroundThe second most frequent cancer in the world and the most common malignancy in women is breast cancer. Breast cancer is a significant health concern in India with a high mortality‐to‐incidence ratio and presentation at a younger age.Recent FindingsRecent studies have identified gut microbiota as a significant factor that can have an influence on the development, treatment, and prognosis of breast cancer. This review article aims to describe the influence of microbial dysbiosis on breast cancer occurrence and the possible interactions between oncobiome and specific breast cancer molecular subtypes. The review further also discusses the role of epigenetics and diet/nutrition in the regulation of the gut and breast microbiome and its association with breast cancer prevention, therapy, and recurrence. Additionally, the recent technological advances in microbiome research, including next‐generation sequencing (NGS) technologies, genome sequencing, single‐cell sequencing, and microbial metabolomics along with recent advances in artificial intelligence (AI) have also been reviewed. This is an attempt to present a comprehensive status of the microbiome as a key cancer biomarker.ConclusionWe believe that correlating microbiome and carcinogenesis is important as it can provide insights into the mechanisms by which microbial dysbiosis can influence cancer development and progression, leading to the potential use of the microbiome as a tool for prognostication and personalized therapy.

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“…There is a need for a proper feature selection technique which can consider maximum genetic anomaly simultaneously and efficiently [6].Since breast cancer is a complex disease and its complexity differs from person to person so one cannot depend on a generalized medication. Hence, there should be a criteria which should have scalable drug sensitivity.…”

Section: Model Scalabilitymentioning

confidence: 99%

“…There are many techniques that have been introduced in the field of breast cancer prediction. Techniques such as deep learning [4], machine learning [5], and artificial intelligence [6]. Several ML techniques, including Support Vector Machine [7], Genetic [8], and classification techniques, are used to predict breast cancer.…”

Section: Introductionmentioning

confidence: 99%

A Review on Prediction Model for Breast Cancer Using Machine Learning

2023

IRJMETS

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One of the commonly encountered in which blood cells begin to proliferate erratically is breast cancer. There are several forms of breast cancer that can be fatal to the patient who's been diagnosed with it. Numerous things, such as genes, changes in the economy, social circumstances, and lifestyle choices, might contribute to it. Several research and articles have been written in this area to address the problem of such a terminal disease and offer answers by figuring out how to assist patients.In order to better comprehend the project's approach and to compare and contrast various machine learning algorithms, this paper is aimed at informing the reader about breast cancer. Machine learning can be employed to identify undetected trends for the identification of breast cancer by emphasizing the creation of a model with a high level of accuracy. The suggested strategy can be integrated with other machine learning approaches depending on the appropriateness and requirements of the project.

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Artificial Intelligence in Breast Cancer Screening and Diagnosis

Cited by 20 publications

References 44 publications

Standardized pathology report for HER2 testing in compliance with 2023 ASCO/CAP updates and 2023 ESMO consensus statements on HER2-low breast cancer

Standardized pathology report for HER2 testing in compliance with 2023 ASCO/CAP updates and 2023 ESMO consensus statements on HER2-low breast cancer

Exploring the human microbiome – A step forward for precision medicine in breast cancer

A Review on Prediction Model for Breast Cancer Using Machine Learning

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