Siegel RL, Miller KD, Fuchs HE, Jemal A (2022) Cancer statistics, 2022. CA Cancer J Clin 72:7–33
Akram M, Iqbal M, Daniyal M, Khan AU (2017) Awareness and current knowledge of breast cancer. Biol Res 50:33
Article PubMed PubMed Central Google Scholar
Monsuez JJ, Charniot JC, Vignat N, Artigou JY (2010) Cardiac side-effects of cancer chemotherapy. Int J Cardiol 144:3–15
Bikiewicz A, Banach M, von Haehling S, Maciejewski M, Bielecka-Dabrowa A (2021) Adjuvant breast cancer treatments cardiotoxicity and modern methods of detection and prevention of cardiac complications. ESC Heart Fail 8:2397–2418
Article PubMed PubMed Central Google Scholar
Abdel-Qadir H, Austin PC, Lee DS et al (2017) A population-based study of cardiovascular mortality following early-stage breast cancer. JAMA Cardiol 2:88–93
Tagliafico AS, Piana M, Schenone D, Lai R, Massone AM, Houssami N (2020) Overview of radiomics in breast cancer diagnosis and prognostication. Breast 49:74–80
van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B (2020) Radiomics in medical imaging-"how-to" guide and critical reflection. Insights Imaging 11:91
Article PubMed PubMed Central Google Scholar
Crivelli P, Ledda RE, Parascandolo N, Fara A, Soro D, Conti M (2018) A new challenge for radiologists: radiomics in breast cancer. Biomed Res Int 2018:6120703
Article PubMed PubMed Central Google Scholar
Yala A, Lehman C, Schuster T, Portnoi T, Barzilay R (2019) A deep learning mammography-based model for improved breast cancer risk prediction. Radiology 292:60–66
Jiang B, Guo N, Ge Y, Zhang L, Oudkerk M, Xie X (2020) Development and application of artificial intelligence in cardiac imaging. Br J Radiol 93:20190812
Article PubMed PubMed Central Google Scholar
Knackstedt C, Bekkers SC, Schummers G et al (2015) Fully automated versus standard tracking of left ventricular ejection fraction and longitudinal strain: the FAST-EFs multicenter study. J Am Coll Cardiol 66:1456–1466
Demissei BG, Fan Y, Qian Y et al (2021) Left ventricular segmental strain and the prediction of cancer therapy-related cardiac dysfunction. Eur Heart J Cardiovasc Imaging 22:418–426
Kagiyama N, Shrestha S, Cho JS et al (2020) A low-cost texture-based pipeline for predicting myocardial tissue remodeling and fibrosis using cardiac ultrasound. EBioMedicine 54:102726
Article PubMed PubMed Central Google Scholar
Hathaway QA, Yanamala N, Siva NK, Adjeroh DA, Hollander JM, Sengupta pp. (2022) Ultrasonic texture features for assessing cardiac remodeling and dysfunction. J Am Coll Cardiol. 80:2187–2201
Bozkurt B, Coats AJ, Tsutsui H et al (2021) Universal definition and classification of heart failure: a report of the heart failure society of America, heart failure association of the European society of cardiology, Japanese heart failure society and writing committee of the universal definition of heart failure. J Card Fail. https://doi.org/10.1002/ejhf.2115
Kuhn M (2008) Building predictive models in R using the caret package. J Stat Softw 28:1–26
Nioche C, Orlhac F, Boughdad S et al (2018) LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res 78:4786–4789
Article CAS PubMed Google Scholar
Nioche C. LIFEx. Online: The LIFEx team, 2024.
Team RC. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing, 2021.
Therneau TM. A Package for Survival Analysis in R. 2022:R package version 3.4–0.
Gerds TA, Kattan MW (2021) Medical risk prediction models: with ties to machine learning. Chapman and Hall/CRC, Boca Raton
DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845
Article CAS PubMed Google Scholar
Mogensen UB, Ishwaran H, Gerds TA (2012) Evaluating random forests for survival analysis using prediction error curves. J Stat Softw 50:1–23
Article PubMed PubMed Central Google Scholar
Kundu S, Aulchenko YS, van Duijn CM, Janssens AC (2011) PredictABEL: an R package for the assessment of risk prediction models. Eur J Epidemiol 26:261–264
Article PubMed PubMed Central Google Scholar
Kerr KF, Wang Z, Janes H, McClelland RL, Psaty BM, Pepe MS (2014) Net reclassification indices for evaluating risk prediction instruments: a critical review. Epidemiology 25:114–121
Article PubMed PubMed Central Google Scholar
Frantz S, Hundertmark MJ, Schulz-Menger J, Bengel FM, Bauersachs J (2022) Left ventricular remodelling post-myocardial infarction: pathophysiology, imaging, and novel therapies. Eur Heart J 43:2549–2561
Article CAS PubMed PubMed Central Google Scholar
Mehta LS, Watson KE, Barac A et al (2018) Cardiovascular disease and breast cancer: where these entities intersect: a scientific statement from the American Heart Association. Circulation 137:e30–e66
Article PubMed PubMed Central Google Scholar
Subramaniam S, Kong YC, Zaharah H et al (2021) Baseline cardiovascular comorbidities, and the influence on cancer treatment decision-making in women with breast cancer. Ecancermedicalscience 15:1293
Article PubMed PubMed Central Google Scholar
Kabore EG, Macdonald C, Kabore A et al (2023) Risk prediction models for cardiotoxicity of chemotherapy among patients with breast cancer: a systematic review. JAMA Netw Open 6:e230569
Article PubMed PubMed Central Google Scholar
Ezaz G, Long JB, Gross CP, Chen J (2014) Risk prediction model for heart failure and cardiomyopathy after adjuvant trastuzumab therapy for breast cancer. J Am Heart Assoc 3:e000472
Article PubMed PubMed Central Google Scholar
Fogarassy G, Vathy-Fogarassy A, Kenessey I, Kasler M, Forster T (2019) Risk prediction model for long-term heart failure incidence after epirubicin chemotherapy for breast cancer - A real-world data-based, nationwide classification analysis. Int J Cardiol 285:47–52
Kim DY, Park MS, Youn JC et al (2021) Development and validation of a risk score model for predicting the cardiovascular outcomes after breast cancer therapy: the CHEMO-RADIAT score. J Am Heart Assoc 10:e021931
Article CAS PubMed PubMed Central Google Scholar
Goel S, Liu J, Guo H et al (2019) Decline in left ventricular ejection fraction following anthracyclines predicts trastuzumab cardiotoxicity. JACC Heart Fail 7:795–804
Romond EH, Jeong JH, Rastogi P et al (2012) Seven-year follow-up assessment of cardiac function in NSABP B-31, a randomized trial comparing doxorubicin and cyclophosphamide followed by paclitaxel (ACP) with ACP plus trastuzumab as adjuvant therapy for patients with node-positive, human epidermal growth factor receptor 2-positive breast cancer. J Clin Oncol 30:3792–3799
Article CAS PubMed PubMed Central Google Scholar
Upshaw JN, Ruthazer R, Miller KD et al (2019) Personalized decision making in early stage breast cancer: applying clinical prediction models for anthracycline cardiotoxicity and breast cancer mortality demonstrates substantial heterogeneity of benefit-harm trade-off. Clin Breast Cancer 19(259–267):e1
Chang WT, Liu CF, Feng YH et al (2022) An artificial intelligence approach for predicting cardiotoxicity in breast cancer patients receiving anthracycline. Arch Toxicol 96:2731–2737
Article CAS PubMed Google Scholar
Mango VL, Sun M, Wynn RT, Ha R (2020) Should we ignore, follow, or biopsy? Impact of artificial intelligence decision support on breast ultrasound lesion assessment. AJR Am J Roentgenol 214:1445–1452
Article PubMed PubMed Central Google Scholar
Qian X, Pei J, Zheng H et al (2021) Prospective assessment of breast cancer risk from multimodal multiview ultrasound images via clinically applicable deep learning. Nat Biomed Eng 5:522–532
Shen Y, Shamout FE, Oliver JR et al (2021) Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams. Nat Commun 12:5645
Article CAS PubMed PubMed Central Google Scholar
Jiang M, Li CL, Luo XM et al (2022) Radiomics model based on shear-wave elastography in the assessment of axillary lymph node status in early-stage breast cancer. Eur Radiol 32:2313–2325
Jiang M, Zhang D, Tang SC et al (2021) Deep learning with convolutional neural network in the assessment of breast cancer molecular subtypes based on US images: a multicenter retrospective study. Eur Radiol 31:3673–3682
留言 (0)