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ORIGINAL RESEARCH

ABVS-Based Radiomics for Early Predicting the Efficacy of Neoadjuvant Chemotherapy in Patients with Breast Cancers

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Pages 625-636 | Received 24 Apr 2023, Accepted 11 Aug 2023, Published online: 15 Aug 2023

References

  • Montemurro F, Nuzzolese I, Ponzone R. Neoadjuvant or adjuvant chemotherapy in early breast cancer? Expert Opin Pharmacother. 2020;21(9):1071–1082. doi:10.1080/14656566.2020.1746273
  • Wang H, Mao X. Evaluation of the efficacy of neoadjuvant chemotherapy for breast cancer. Drug Des Devel Ther. 2020;14:2423–2433. doi:10.2147/dddt.s253961
  • Wang J, Chu Y, Wang B, Jiang T. A narrative review of ultrasound technologies for the prediction of neoadjuvant chemotherapy response in breast cancer. Cancer Management Res. 2021;13:7885–7895. doi:10.2147/cmar.s331665
  • Yin XX, Hadjiloucas S, Zhang Y, Tian Z. MRI radiogenomics for intelligent diagnosis of breast tumors and accurate prediction of neoadjuvant chemotherapy responses-a review. Comput Methods Programs Biomed. 2022;214:106510. doi:10.1016/j.cmpb.2021.106510
  • Mao N, Shi Y, Lian C, et al. Intratumoral and peritumoral radiomics for preoperative prediction of neoadjuvant chemotherapy effect in breast cancer based on contrast-enhanced spectral mammography. Eur Radiolo. 2022;32(5):3207–3219. doi:10.1007/s00330-021-08414-7
  • Fowler AM, Mankoff DA, Joe BN. Imaging neoadjuvant therapy response in breast cancer. Radiology. 2017;285(2):358–375. doi:10.1148/radiol.2017170180
  • Schaefgen B, Mati M, Sinn HP, et al. Can routine imaging after neoadjuvant chemotherapy in breast cancer predict pathologic complete response? Ann Surg Oncol. 2016;23(3):789–795. doi:10.1245/s10434-015-4918-0
  • Mayerhoefer ME, Materka A, Langs G, et al. Introduction to radiomics. J Nucl Med. 2020;61(4):488–495. doi:10.2967/jnumed.118.222893
  • Tagliafico AS, Piana M, Schenone D, Lai R, Massone AM, Houssami N. Overview of radiomics in breast cancer diagnosis and prognostication. Breast. 2020;49:74–80. doi:10.1016/j.breast.2019.10.018
  • Conti A, Duggento A, Indovina I, Guerrisi M, Toschi N. Radiomics in breast cancer classification and prediction. Semin Cancer Biol. 2021;72:238–250. doi:10.1016/j.semcancer.2020.04.002
  • Pesapane F, Rotili A, Agazzi GM, et al. Recent radiomics advancements in breast cancer: lessons and pitfalls for the next future. Curr Oncol. 2021;28(4):2351–2372. doi:10.3390/curroncol28040217
  • Dighe SP, Shinde RK, Shinde SJ, Anand A. Review on assessment of response of neo-adjuvant chemotherapy in patients of carcinoma breast by high frequency ultrasound. J Evolution Med Dental Sci. 2020;9:3873+.
  • Gu J, Tong T, He C, et al. Deep learning radiomics of ultrasonography can predict response to neoadjuvant chemotherapy in breast cancer at an early stage of treatment: a prospective study. Int J Med. 2022;32(3):2099–2109. doi:10.1007/s00330-021-08293-y
  • Vourtsis A. Three-dimensional automated breast ultrasound: technical aspects and first results. Diagn Interv Imaging. 2019;100(10):579–592. doi:10.1016/j.diii.2019.03.012
  • Meng Z, Chen C, Zhu Y, et al. Diagnostic performance of the automated breast volume scanner: a systematic review of inter-rater reliability/agreement and meta-analysis of diagnostic accuracy for differentiating benign and malignant breast lesions. Eur Radiol. 2015;25(12):3638–3647. doi:10.1007/s00330-015-3759-3
  • Hellgren R, Dickman P, Leifland K, Saracco A, Hall P, Celebioglu F. Comparison of handheld ultrasound and automated breast ultrasound in women recalled after mammography screening. Acta Radiol. 2017;58(5):515–520. doi:10.1177/0284185116665421
  • Eisenhauer EA, Therasse P, Bogaerts J, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2009;45(2):228–247. doi:10.1016/j.ejca.2008.10.026
  • St. Croix B, Man S, Kerbel RS. Reversal of intrinsic and acquired forms of drug resistance by hyaluronidase treatment of solid tumors. Cancer Lett. 1998;131(1):35–44. doi:10.1016/S0304-3835(98)00199-2
  • Junttila MR, de Sauvage FJ. Influence of tumour micro-environment heterogeneity on therapeutic response. Nature. 2013;501(7467):346–354. doi:10.1038/nature12626
  • Pietras K, Östman A. Hallmarks of cancer: interactions with the tumor stroma. Exp Cell Res. 2010;316(8):1324–1331. doi:10.1016/j.yexcr.2010.02.045
  • Iwamoto T, Kajiwara Y, Zhu Y, Iha S. Biomarkers of neoadjuvant/adjuvant chemotherapy for breast cancer. Chin Clin Oncol. 2020;9(3):27. doi:10.21037/cco.2020.01.06
  • Marinovich ML, Sardanelli F, Ciatto S, et al. Early prediction of pathologic response to neoadjuvant therapy in breast cancer: systematic review of the accuracy of MRI. Breast. 2012;21(5):669–677. doi:10.1016/j.breast.2012.07.006
  • Group BC, Branch of Oncologist CM, Society IM, Chinese Anti-Cancer Association. Chinese expert consensus of albumin-bound paclitaxel in the treatment of breast cancer. Zhonghua Zhong Liu Za Zhi. 2023;45(3):203–211. doi:10.3760/cma.j.cn112152-20230103-00006
  • Wang Y, Zhang C, Liu J, Huang G. Is 18F-FDG PET accurate to predict neoadjuvant therapy response in breast cancer? A meta-analysis. Breast Cancer Res Treat. 2012;131(2):357–369. doi:10.1007/s10549-011-1780-z
  • Derks MGM, van de Velde CJH. Neoadjuvant chemotherapy in breast cancer: more than just downsizing. Lancet Oncol. 2018;19(1):2–3. doi:10.1016/s1470-2045(17)30914-2
  • Aerts HJ. The potential of radiomic-based phenotyping in precision medicine: a review. JAMA Oncol. 2016;2(12):1636–1642. doi:10.1001/jamaoncol.2016.2631
  • Liu Z, Wang S, Dong D, et al. The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges. Theranostics. 2019;9(5):1303–1322. doi:10.7150/thno.30309
  • Ng F, Kozarski R, Ganeshan B, Goh V. Assessment of tumor heterogeneity by CT texture analysis: can the largest cross-sectional area be used as an alternative to whole tumor analysis? Eur J Radiol. 2013;82(2):342–348. doi:10.1016/j.ejrad.2012.10.023
  • D’Angelo A, Orlandi A, Bufi E, Mercogliano S, Belli P, Manfredi R. Automated breast volume scanner (ABVS) compared to handheld ultrasound (HHUS) and contrast-enhanced magnetic resonance imaging (CE-MRI) in the early assessment of breast cancer during neoadjuvant chemotherapy: an emerging role to monitoring tumor response? Radiol Med. 2021;126(4):517–526. doi:10.1007/s11547-020-01319-3
  • Guo Y, Hu Y, Qiao M, et al. Radiomics analysis on ultrasound for prediction of biologic behavior in breast invasive ductal carcinoma. Clin Breast Cancer. 2018;18(3):e335–e344. doi:10.1016/j.clbc.2017.08.002
  • Hu S, Xu C, Guan W, Tang Y, Liu Y. Texture feature extraction based on wavelet transform and gray-level co-occurrence matrices applied to osteosarcoma diagnosis. Biomed Mater Eng. 2014;24(1):129–143. doi:10.3233/bme-130793
  • McAnena P, Moloney BM, Browne R, et al. A radiomic model to classify response to neoadjuvant chemotherapy in breast cancer. BMC Med Imaging. 2022;22(1):225. doi:10.1186/s12880-022-00956-6
  • Chen S, Shu Z, Li Y, et al. Machine learning-based radiomics nomogram using magnetic resonance images for prediction of neoadjuvant chemotherapy efficacy in breast cancer patients. Front Oncol. 2020;10:1410. doi:10.3389/fonc.2020.01410
  • Li Y, Fan Y, Xu D, et al. Deep learning radiomic analysis of DCE-MRI combined with clinical characteristics predicts pathological complete response to neoadjuvant chemotherapy in breast cancer. Front Oncol. 2022;12:1041142. doi:10.3389/fonc.2022.1041142
  • Saberioon M, Císař P, Labbé L, Souček P, Pelissier P, Kerneis T. Comparative performance analysis of support vector machine, random forest, logistic regression and k-nearest neighbours in rainbow trout (Oncorhynchus mykiss) classification using image-based features. Sensors. 2018;18(4):1027.
  • Maroco J, Silva D, Rodrigues A, Guerreiro M, Santana I, de Mendonça A. Data mining methods in the prediction of Dementia: a real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests. BMC Res Notes. 2011;4(1):299. doi:10.1186/1756-0500-4-299
  • Sarica A, Cerasa A, Quattrone A. Random forest algorithm for the classification of neuroimaging data in alzheimer’s disease: a systematic review. Front Aging Neurosci. 2017;9:329. doi:10.3389/fnagi.2017.00329
  • Huang S, Cai N, Pacheco PP, Narrandes S, Wang Y, Xu W. Applications of Support Vector Machine (SVM) Learning in Cancer Genomics. Cancer Genomics Proteomics. 2018;15(1):41–51. doi:10.21873/cgp.20063
  • Shipe ME, Deppen SA, Farjah F, Grogan EL. Developing prediction models for clinical use using logistic regression: an overview. J Thorac Dis. 2019;11(Suppl 4):S574–s584. doi:10.21037/jtd.2019.01.25
  • Oberije C, Nalbantov G, Dekker A, et al. A prospective study comparing the predictions of doctors versus models for treatment outcome of lung cancer patients: a step toward individualized care and shared decision making. Radiother Oncol. 2014;112(1):37–43. doi:10.1016/j.radonc.2014.04.012
  • Guo L, Du S, Gao S, et al. Delta-radiomics based on dynamic contrast-enhanced MRI predicts pathologic complete response in breast cancer patients treated with neoadjuvant chemotherapy. Cancers. 2022;14(14):3515.
  • Braman NM, Etesami M, Prasanna P, et al. Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI. Breast Cancer Res. 2017;19(1):57. doi:10.1186/s13058-017-0846-1