Screening mammography performance according to breast density: a comparison between radiologists versus standalone intelligence detection
Authors (first, second and last of 17)
Collection
Artificial Intelligence in Breast Imaging
Artificial intelligence (AI) is becoming integrated into many aspects of our day -to -day life, whether its suggestions on movies we should consider, books we may be interested in reading or apparel that may suit our personal taste. In preclinical research, AI provides tools for rapid and robust evaluation of cancer cell and organoid phenotypes or data from small animal imaging. AI is particularly well suited to radiology where it affords opportunities to enhance the speed, accuracy and quality of image interpretation. Rather than eliminating the need for radiologists anytime soon, AI can serve as a valuable adjunct to them allowing resulting in more dependable interpretations of ever more complex technology used in radiology. However, integration of AI to clinical imaging workflows requires careful evaluation of associated ethical, legal, and regulatory challenges.
In this cross-journal collection, we welcome a wide range of articles on AI in breast imaging, including primary research articles, method-based articles, reviews, and perspectives. To express your interest to contribute, please contact the Editor-in-Chief of the respective journal:
Journal of Mammary Gland Biology and Neoplasia: Zuzana Koledova (koledova@med.muni.cz)
Breast Cancer Research & Treatment: William J. Gradishar (w-gradishar@northwestern.edu)
Breast Cancer Research: Lewis A. Chodosh (chodosh@pennmedicine.upenn.edu)
Editors
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Lewis A. Chodosh
University of Pennsylvania, USA
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William J. Gradishar
Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
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Zuzana Koledova
Masaryk University, Czech Republic
Articles (26 in this collection)
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Augmented interpretation of HER2, ER, and PR in breast cancer by artificial intelligence analyzer: enhancing interobserver agreement through a reader study of 201 cases
Authors (first, second and last of 20)
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Are better AI algorithms for breast cancer detection also better at predicting risk? A paired case–control study
Authors (first, second and last of 5)
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Development and prognostic validation of a three-level NHG-like deep learning-based model for histological grading of breast cancer
Authors (first, second and last of 7)
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Development of a machine learning-based radiomics signature for estimating breast cancer TME phenotypes and predicting anti-PD-1/PD-L1 immunotherapy response
Authors (first, second and last of 8)
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Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer
Authors (first, second and last of 10)
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Machine learning prediction of pathological complete response and overall survival of breast cancer patients in an underserved inner-city population
Authors (first, second and last of 11)
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PROACTING: predicting pathological complete response to neoadjuvant chemotherapy in breast cancer from routine diagnostic histopathology biopsies with deep learning
Authors (first, second and last of 16)
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Machine learning radiomics of magnetic resonance imaging predicts recurrence-free survival after surgery and correlation of LncRNAs in patients with breast cancer: a multicenter cohort study
Authors (first, second and last of 18)
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Examination of fully automated mammographic density measures using LIBRA and breast cancer risk in a cohort of 21,000 non-Hispanic white women
Authors (first, second and last of 23)
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Numerical and physical modeling of breast cancer based on image fusion and artificial intelligence
Authors (first, second and last of 12)
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Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review
Authors (first, second and last of 5)
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Classification of breast tumors by using a novel approach based on deep learning methods and feature selection
Authors (first, second and last of 5)
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Breast cancer risk prediction combining a convolutional neural network-based mammographic evaluation with clinical factors
Authors (first, second and last of 9)
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Novel computational biology modeling system can accurately forecast response to neoadjuvant therapy in early breast cancer
Authors (first, second and last of 11)
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Development and validation of an AI-enabled digital breast cancer assay to predict early-stage breast cancer recurrence within 6 years
Authors (first, second and last of 17)
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An integrated deep learning model for the prediction of pathological complete response to neoadjuvant chemotherapy with serial ultrasonography in breast cancer patients: a multicentre, retrospective study
Authors (first, second and last of 15)
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Towards defining morphologic parameters of normal parous and nulliparous breast tissues by artificial intelligence
Authors (first, second and last of 23)
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Serum hormone levels and normal breast histology among premenopausal women
Authors (first, second and last of 18)
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Standardization of the tumor-stroma ratio scoring method for breast cancer research
Authors (first, second and last of 9)
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Multi-center evaluation of artificial intelligent imaging and clinical models for predicting neoadjuvant chemotherapy response in breast cancer
Authors (first, second and last of 25)
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Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review
Authors (first, second and last of 5)
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Artificial image objects for classification of breast cancer biomarkers with transcriptome sequencing data and convolutional neural network algorithms
Authors (first, second and last of 5)
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Machine learning-based image analysis for accelerating the diagnosis of complicated preneoplastic and neoplastic ductal lesions in breast biopsy tissues
Authors (first, second and last of 14)
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Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients
Authors (first, second and last of 7)
