Al-Thelaya K, Gilal NU, Alzubaidi M, Majeed F, Agus M, Schneider J, Househ M (2023) Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: a survey. J Pathol Inf 14:100335

Article  Google Scholar 

Albusayli R, Graham JD, Pathmanathan N, Shaban M, Raza SEA, Minhas F, Armes JE (2023) Rajpoot, N. Artificial intelligence-based digital scores of stromal tumour-infiltrating lymphocytes and tumour-associated stroma predict disease-specific survival in triple-negative breast cancer. J Pathol 260:32–42

Article  PubMed  CAS  Google Scholar 

Ali R, Balamurali M, Varamini PD (2022) Learning-based Artificial Intelligence to investigate targeted nanoparticles’ Uptake in TNBC cells. Int J Mol Sci 23:16070

Article  PubMed  PubMed Central  CAS  Google Scholar 

Bai X, Ni J, Beretov J, Graham P, Li Y (2021) Triple-negative breast cancer therapeutic resistance: where is the Achilles’ heel? Cancer Lett 497:100–111

Article  PubMed  CAS  Google Scholar 

Ben Azzouz F, Michel B, Lasla H, Gouraud W, François AF, Girka F et al (2021) Development of an absolute assignment predictor for triple-negative breast cancer subtyping using machine learning approaches. Comput Biol Med 129:104171

Article  PubMed  Google Scholar 

Bhinder B, Gilvary C, Madhukar NS, Elemento O (2021) Artificial intelligence in cancer research and precision medicine. Cancer Discov 11:900–915

Article  PubMed  PubMed Central  CAS  Google Scholar 

Bissanum R, Chaichulee S, Kamolphiwong R, Navakanitworakul R, Kanokwiroon K (2021) Molecular classification models for triple negative breast cancer subtype using machine learning. J Pers Med 11:881

Article  PubMed  PubMed Central  Google Scholar 

Bluemke DA, Moy L, Bredella MA (2020) Assessing radiology research on artifcial intelligence: a brief guide for authors, reviewers, and readers-from the Radiology editorial board. Radiology 294(3):487–489

Article  PubMed  Google Scholar 

Boulenger A, Luo Y, Zhang C, Zhao C, Gao Y, Xiao M, Zhu Q, Tang J (2023) Deep learning-based system for automatic prediction of triple-negative breast cancer from ultrasound images. Med Biol Eng Comput 61(2):567–578

Article  PubMed  Google Scholar 

Chen Z, Wang M, Feng R, Su M, Torres-de la Roche LA et al (2021) A machine learning model to predict the triple negative breast cancer immune subtype. Front Immunol 12:749459

Article  PubMed  PubMed Central  CAS  Google Scholar 

Dodington DW, Lagree A, Tabbarah S, Mohebpour M, Sadeghi-Naini A, Tran WT et al (2021) Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients. Breast Cancer Res Treat 186:379–389

Article  PubMed  CAS  Google Scholar 

Ensenyat-Mendez M, Llinàs-Arias P, Orozco JIJ, Íñiguez-Muñoz S, Salomon MP, Sesé B et al (2021) Current triple-negative breast cancer subtypes: dissecting the most aggressive form of breast cancer. Front Oncol 11:681476

Article  PubMed  PubMed Central  CAS  Google Scholar 

Gadag S, Sinha S, Nayak Y, Garg S, Nayak UY (2020) Combination therapy and nanoparticulate systems: smart approaches for the effective treatment of breast cancer. Pharmaceutics 12:524

Article  PubMed  PubMed Central  CAS  Google Scholar 

Gautam P, Jaiswal A, Aittokallio T, Al-Ali H, Wennerberg K (2019) Phenotypic screening combined with machine learning for efficient identification of breast cancerselective therapeutic targets. Cell Chem Biol 26:970e4–9e4

Article  Google Scholar 

Guan H, Su Y, Guo W, Chen C, Xie X, Lv XA (2023) Prognostic model of genetic markers for triple-negative breast Cancer based on Machine Learning and Bioinformatics Analysis. Stud Health Technol Inf 308:303–312

Google Scholar 

Hou X, Li X, Han Y, Xu H, Xie Y, Zhou T, Xue T, Qian X, Li J, Wang HC, Yan J, Guo X, Liu Y, Liu J (2024) Triple-negative breast cancer survival prediction using artificial intelligence through integrated analysis of tertiary lymphoid structures and tumor budding. Cancer 15:1499–1512

Article  Google Scholar 

Hu Z et al (2020) Multi-cancer analysis of clonality and the timing of systemic spread in paired primary tumors and metastases. Nat Genet 52:701–708

Article  PubMed  PubMed Central  CAS  Google Scholar 

Hu H, Tong K, Tsang JY, Ko CW, Tam F, Loong TC, Tse GM (2024) Subtyping of triple-negative breast cancers: its prognostication and implications in diagnosis of breast origin. ESMO Open 9:102993

Article  PubMed  PubMed Central  CAS  Google Scholar 

Huang Y, Wei L, Hu Y, Shao N, Lin Y, He S et al (2021) Multi-parametric MRI-based radiomics models for predicting molecular subtype and androgen receptor expression in breast cancer. Front Oncol 11:706733

Article  PubMed  PubMed Central  CAS  Google Scholar 

Huang Z, Shao W, Han Z, Alkashash AM, Dela SC, Parwani AV, Nitta H, Hou Y, Wang T, Salama P, Rizkalla M, Zhang J, Huang K, Li Z (2023) Artificial intelligence reveals features associated with breast cancer neoadjuvant chemotherapy responses from multi-stain histopathologic images. NPJ Precis Oncol 27:14

Article  Google Scholar 

Irajizad E, Wu R, Vykoukal J, Murage E, Spencer R, Dennison JB et al (2022) Application of artificial intelligence to plasma metabolomics profiles to predict response to neoadjuvant chemotherapy in triple-negative breast cancer. Front Artif Intell 5:876100

Article  PubMed  PubMed Central  Google Scholar 

Jé zé quel P, Kerdraon O, Hondermarck H, Gué rin-Charbonnel C, Lasla H, Gouraud W et al (2019) Identification of three subtypes of triple-negative breast cancer with potential therapeutic implications. Breast Cancer Res 21:65–14

Article  Google Scholar 

Jiang Y, Yang M, Wang S, Li X, Sun Y (2020) Emerging role of deep learning-based artifcial intelligence in tumor pathology. Cancer Commun (lond) 40(4):154–166

Article  PubMed  Google Scholar 

Kim J, Yu D, Kwon Y, Lee KS, Sim SH, Kong SY et al (2020) Genomic characteristics of triple-negative breast cancer nominate molecular subtypes that predict chemotherapy response. Mol Cancer Res 18:253–263

Article  PubMed  CAS  Google Scholar 

Kothari C, Osseni MA, Agbo L, Ouellette G, Déraspe M, Laviolette F et al (2020) Machine learning analysis identifies genes differentiating triple negative breast cancers. Sci Rep 10:10464

Article  PubMed  PubMed Central  CAS  Google Scholar 

Kumar Y, Koul A, Singla R, Ijaz MF (2022) Artifcial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. J Ambient Intell Humaniz Comput 14:1–28

Google Scholar 

Leithner D, Mayerhoefer ME, Martinez DF, Jochelson MS, Morris EA, Thakur SB et al (2020) Non-invasive assessment of breast cancer molecular subtypes with multiparametric magnetic resonance imaging radiomics. J Clin Med 9:1853

Article  PubMed  PubMed Central  CAS  Google Scholar 

Li H, Ye J, Liu H, Wang Y, Shi B, Chen J et al (2021) Application of deep learning in the detection of breast lesions with four different breast densities. Cancer Med 10:4994–5000

Article  PubMed  PubMed Central  Google Scholar 

Li S, Zhang N, Zhang H et al (2023) Artificial intelligence learning landscape of triple-negative breast cancer uncovers new opportunities for enhancing outcomes and immunotherapy responses. J Big Data 10:132

Article  CAS  Google Scholar 

Liu J, Su R, Zhang J, Wei L (2021) Classification and gene selection of triple-negative breast cancer subtype embedding gene connectivity matrix in deep neural network. Brief Bioinform 22(5):bbaa395

Article  PubMed  Google Scholar 

Ma M, Liu R, Wen C, Xu W, Xu Z, Wang S et al (2022) Predicting the molecular subtype of breast cancer and identifying interpretable imaging features using machine learning algorithms. Eur Radiol 32:1652–1662

Article  PubMed  CAS  Google Scholar 

Mendelson EB (2019) Artifcial intelligence in breast imaging: potentials and limitations. Am J Roentgenol 212(2):293–299