Collection
Artificial Intelligence in Breast Imaging
- Submission status
- Open
- Open for submission from
- 19 October 2021
- Submission deadline
- Ongoing
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
-
Lewis A. Chodosh
University of Pennsylvania, USA
-
William J. Gradishar
Northwestern University, Feinberg School of Medicine, Chicago, IL, USA
-
Zuzana Koledova
Masaryk University, Czech Republic
Articles (33 in this collection)
-
-
Revolutionizing breast cancer Ki-67 diagnosis: ultrasound radiomics and fully connected neural networks (FCNN) combination method
Authors (first, second and last of 5)
- Yanfeng Li
- Wengxing Long
- Hui Xie
- Content type: Research
- Published: 09 June 2024
- Breast Cancer Research and Treatment
-
Frequency and characteristics of errors by artificial intelligence (AI) in reading screening mammography: a systematic review
Authors (first, second and last of 5)
- Aileen Zeng
- Nehmat Houssami
- M. Luke Marinovich
- Content type: Review
- Open Access
- Published: 09 June 2024
- Breast Cancer Research and Treatment
- Pages: 1 - 13
-
Deep learning-based risk stratification of preoperative breast biopsies using digital whole slide images
Authors (first, second and last of 8)
- Constance Boissin
- Yinxi Wang
- Mattias Rantalainen
- Content type: Research
- Open Access
- Published: 03 June 2024
- Breast Cancer Research
- Article: 90
-
Deep learning of mammogram images to reduce unnecessary breast biopsies: a preliminary study
Authors (first, second and last of 6)
- Chang Liu
- Min Sun
- Shandong Wu
- Content type: Research
- Open Access
- Published: 24 May 2024
- Breast Cancer Research
- Article: 82
-
Pathomic model based on histopathological features and machine learning to predict IDO1 status and its association with breast cancer prognosis
Authors (first, second and last of 4)
- Xiaohua Zhuo
- Hailong Deng
- Xiaoming Qiu
- Content type: Research
- Open Access
- Published: 23 May 2024
- Breast Cancer Research and Treatment
- Pages: 151 - 165
-
Enhancing pathological complete response prediction in breast cancer: the role of dynamic characterization of DCE-MRI and its association with tumor heterogeneity
Authors (first, second and last of 5)
- Xinyu Zhang
- Xinzhi Teng
- Jing Cai
- Content type: Research
- Open Access
- Published: 14 May 2024
- Breast Cancer Research
- Article: 77
-
Screening mammography performance according to breast density: a comparison between radiologists versus standalone intelligence detection
Authors (first, second and last of 17)
- Mi-ri Kwon
- Yoosoo Chang
- Seungho Ryu
- Content type: Research
- Open Access
- Published: 22 April 2024
- Breast Cancer Research
- Article: 68
-
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)
- Minsun Jung
- Seung Geun Song
- So-Woon Kim
- Content type: Research
- Open Access
- Published: 23 February 2024
- Breast Cancer Research
- Article: 31
-
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)
- Ruggiero Santeramo
- Celeste Damiani
- Adam R. Brentnall
- Content type: Research
- Open Access
- Published: 07 February 2024
- Breast Cancer Research
- Article: 25
-
Improving lesion detection in mammograms by leveraging a Cycle-GAN-based lesion remover
Authors
- Juhun Lee
- Robert M. Nishikawa
- Content type: Research
- Open Access
- Published: 01 February 2024
- Breast Cancer Research
- Article: 21
-
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)
- Abhinav Sharma
- Philippe Weitz
- Mattias Rantalainen
- Content type: Research
- Open Access
- Published: 29 January 2024
- Breast Cancer Research
- Article: 17
-
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)
- Xiaorui Han
- Yuan Guo
- Changhong Liang
- Content type: Research
- Open Access
- Published: 29 January 2024
- Breast Cancer Research
- Article: 18
-
Digital image analysis and machine learning-assisted prediction of neoadjuvant chemotherapy response in triple-negative breast cancer
Authors (first, second and last of 10)
- Timothy B. Fisher
- Geetanjali Saini
- Ritu Aneja
- Content type: Research
- Open Access
- Published: 18 January 2024
- Breast Cancer Research
- Article: 12
-
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)
- Kevin Dell’Aquila
- Abhinav Vadlamani
- Tim Q. Duong
- Content type: Research
- Open Access
- Published: 10 January 2024
- Breast Cancer Research
- Article: 7
-
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)
- Witali Aswolinskiy
- Enrico Munari
- Francesco Ciompi
- Content type: Research
- Open Access
- Published: 13 November 2023
- Breast Cancer Research
- Article: 142
-
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)
- Yunfang Yu
- Wei Ren
- Herui Yao
- Content type: Research
- Open Access
- Published: 01 November 2023
- Breast Cancer Research
- Article: 132
-
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)
- Laurel A. Habel
- Stacey E. Alexeeff
- Weiva Sieh
- Content type: Research
- Open Access
- Published: 06 August 2023
- Breast Cancer Research
- Article: 92
-
Numerical and physical modeling of breast cancer based on image fusion and artificial intelligence
Authors (first, second and last of 12)
- Bartosz Dołęga-Kozierowski
- Piotr Kasprzak
- Mariusz Ptak
- Content type: Preclinical study
- Open Access
- Published: 25 July 2023
- Breast Cancer Research and Treatment
- Pages: 33 - 43
-
Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review
Authors (first, second and last of 5)
- Richard Adam
- Kevin Dell’Aquila
- Tim Q. Duong
- Content type: Review
- Open Access
- Published: 24 July 2023
- Breast Cancer Research
- Article: 87
-
Classification of breast tumors by using a novel approach based on deep learning methods and feature selection
Authors (first, second and last of 5)
- Nizamettin Kutluer
- Ozgen Arslan Solmaz
- Huseyin Eristi
- Content type: Preclinical study
- Published: 21 May 2023
- Breast Cancer Research and Treatment
- Pages: 183 - 192
-
Breast cancer risk prediction combining a convolutional neural network-based mammographic evaluation with clinical factors
Authors (first, second and last of 9)
- Alissa Michel
- Vicky Ro
- Katherine D. Crew
- Content type: Epidemiology
- Published: 20 May 2023
- Breast Cancer Research and Treatment
- Pages: 237 - 245
-
Novel computational biology modeling system can accurately forecast response to neoadjuvant therapy in early breast cancer
Authors (first, second and last of 11)
- Joseph R. Peterson
- John A. Cole
- Vinita Takiar
- Content type: Research
- Open Access
- Published: 10 May 2023
- Breast Cancer Research
- Article: 54
-
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)
- Gerardo Fernandez
- Marcel Prastawa
- Michael J. Donovan
- Content type: Research
- Open Access
- Published: 20 December 2022
- Breast Cancer Research
- Article: 93
-
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)
- Lei Wu
- Weitao Ye
- Ying Wang
- Content type: Research
- Open Access
- Published: 21 November 2022
- Breast Cancer Research
- Article: 81
-
Towards defining morphologic parameters of normal parous and nulliparous breast tissues by artificial intelligence
Authors (first, second and last of 23)
- Joshua Ogony
- Thomas de Bel
- Mark E. Sherman
- Content type: Research
- Open Access
- Published: 11 July 2022
- Breast Cancer Research
- Article: 45
-
Serum hormone levels and normal breast histology among premenopausal women
Authors (first, second and last of 18)
- Mark E. Sherman
- Thomas de Bel
- Jeroen van der Laak
- Content type: Epidemiology
- Published: 03 May 2022
- Breast Cancer Research and Treatment
- Pages: 149 - 158
-
Standardization of the tumor-stroma ratio scoring method for breast cancer research
Authors (first, second and last of 9)
- Sophie C. Hagenaars
- Kiki M. H. Vangangelt
- Wilma E. Mesker
- Content type: Review
- Open Access
- Published: 16 April 2022
- Breast Cancer Research and Treatment
- Pages: 545 - 553
-
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)
- Tan Hong Qi
- Ong Hiok Hian
- Ai3
- Content type: Clinical Trial
- Published: 09 March 2022
- Breast Cancer Research and Treatment
- Pages: 121 - 138
-
Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review
Authors (first, second and last of 5)
- Aimilia Gastounioti
- Shyam Desai
- Despina Kontos
- Content type: Review
- Open Access
- Published: 20 February 2022
- Breast Cancer Research
- Article: 14
-
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)
- Xiangning Chen
- Daniel G. Chen
- Jingchun Chen
- Content type: Research article
- Open Access
- Published: 10 October 2021
- Breast Cancer Research
- Article: 96
-
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)
- Shinya Sato
- Satoshi Maki
- Yohei Miyagi
- Content type: Clinical trial
- Published: 01 May 2021
- Breast Cancer Research and Treatment
- Pages: 649 - 659
-
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)
- David W. Dodington
- Andrew Lagree
- Fang-I Lu
- Content type: Preclinical study
- Published: 23 January 2021
- Breast Cancer Research and Treatment
- Pages: 379 - 389