10
Views
0
CrossRef citations to date
0
Altmetric
ORIGINAL RESEARCH

Multimodal Machine Learning-Based Ductal Carcinoma in situ Prediction from Breast Fibromatosis

, , , & ORCID Icon
Pages 811-823 | Received 23 Apr 2024, Accepted 26 Jun 2024, Published online: 12 Jul 2024

References

  • Liu Y, Zhao S, Zhang Y, Onwuka JU, Zhang Q, Liu X. Bisphosphonates and breast cancer survival: a meta-analysis and trial sequential analysis of 81508 participants from 23 prospective epidemiological studies. Aging. 2021;13(15):19835–19866. doi:10.18632/aging.203395
  • Manley H, Mutasa S, Chang P, Desperito E, Crew K, Ha R. Dynamic changes of convolutional neural network-based mammographic breast cancer risk score among women undergoing chemoprevention treatment. Clin Breast Cancer. 2021;21(4):e312–e318. doi:10.1016/j.clbc.2020.11.007
  • Li J, Song Y, Xu S, et al. Predicting underestimation of ductal carcinoma in situ: a comparison between radiomics and conventional approaches. Int J Comput Assist Radiol Surg. 2019;14(4):709–721. doi:10.1007/s11548-018-1900-x
  • Venkatesan A, Chu P, Kerlikowske K, Sickles EA, Smith-Bindman R. Positive predictive value of specific mammographic findings according to reader and patient variables. Radiology. 2009;250(3):648–657. doi:10.1148/radiol.2503080541
  • Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin. 2022;72(1):7–33. doi:10.3322/caac.21708
  • Hophan SL, Odnokoz O, Liu H, et al. Ductal carcinoma in situ of breast: from molecular etiology to therapeutic management. Endocrinology. 2022;163(4). doi:10.1210/endocr/bqac027
  • Liu H, Zeng H, Zhang H, et al. Breast fibromatosis: imaging and clinical findings. Breast J. 2020;26(11):2217–2222. doi:10.1111/tbj.14008
  • Friedrich-Rust M, Meyer G, Dauth N, et al. Interobserver agreement of thyroid imaging reporting and data system (TIRADS) and strain elastography for the assessment of thyroid nodules. PLoS One. 2013;8(10):e77927. doi:10.1371/journal.pone.0077927
  • 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
  • Wu J, Fang Q, Yao J, et al. Integration of ultrasound radiomics features and clinical factors: a nomogram model for identifying the Ki-67 status in patients with breast carcinoma. Front Oncol. 2022;12:979358. doi:10.3389/fonc.2022.979358
  • Wang X, Agyekum EA, Ren Y, et al. A radiomic nomogram for the ultrasound-based evaluation of extrathyroidal extension in papillary thyroid carcinoma. Front Oncol. 2021;11:625646. doi:10.3389/fonc.2021.625646
  • Zhou SC, Liu TT, Zhou J, et al. An ultrasound radiomics nomogram for preoperative prediction of central neck lymph node metastasis in papillary thyroid carcinoma. Front Oncol. 2020;10:1591. doi:10.3389/fonc.2020.01591
  • Wang R, Dai W, Gong J, et al. Development of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients. J Hematol Oncol. 2022;15(1):11. doi:10.1186/s13045-022-01225-3
  • Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278(2):563–577. doi:10.1148/radiol.2015151169
  • Sala E, Mema E, Himoto Y, et al. Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. Clin Radiol. 2017;72(1):3–10. doi:10.1016/j.crad.2016.09.013
  • Zou Y, Shi Y, Liu J, et al. A comparative analysis of six machine learning models based on ultrasound to distinguish the possibility of central cervical lymph node metastasis in patients with papillary thyroid carcinoma. Front Oncol. 2021;11:656127. doi:10.3389/fonc.2021.656127
  • Masuda T, Nakaura T, Funama Y, et al. Machine learning to identify lymph node metastasis from thyroid cancer in patients undergoing contrast-enhanced CT studies. Radiography. 2021;27(3):920–926. doi:10.1016/j.radi.2021.03.001
  • Frizzell JD, Liang L, Schulte PJ, et al. Prediction of 30-day all-cause readmissions in patients hospitalized for heart failure: comparison of machine learning and other statistical approaches. JAMA Cardiol. 2017;2(2):204–209. doi:10.1001/jamacardio.2016.3956
  • Rahbar G, Sie AC, Hansen GC, et al. Benign versus malignant solid breast masses: US differentiation. Radiology. 1999;213(3):889–894. doi:10.1148/radiology.213.3.r99dc20889
  • Woodard GA, Price ER. Qualitative radiogenomics: association between BI-RADS calcification descriptors and recurrence risk as assessed by the oncotype DX ductal carcinoma in situ score. AJR Am J Roentgenol. 2019;212(4):919–924. doi:10.2214/AJR.18.20306
  • Luo WQ, Huang QX, Huang XW, Hu HT, Zeng FQ, Wang W. Predicting breast cancer in breast imaging reporting and data system (BI-RADS) ultrasound category 4 or 5 lesions: a nomogram combining radiomics and BI-RADS. Sci Rep. 2019;9(1):11921. doi:10.1038/s41598-019-48488-4
  • Cha H, Chang YW, Lee EJ, et al. Ultrasonographic features of pure ductal carcinoma in situ of the breast: correlations with pathologic features and biological markers. Ultrasonography. 2018;37(4):307–314. doi:10.14366/usg.17039
  • Radswiki SR, Ashraf A. Ductal carcinoma in situ. The Radswiki; 2010.
  • Limkin EJ, Sun R, Dercle L, et al. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol. 2017;28(6):1191–1206. doi:10.1093/annonc/mdx034
  • 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
  • Morris LG, Riaz N, Desrichard A, et al. Pan-cancer analysis of intratumor heterogeneity as a prognostic determinant of survival. Oncotarget. 2016;7(9):10051–10063. doi:10.18632/oncotarget.7067
  • Yu Q, Liu J, Lin H, Lei P, Fan B. Application of radiomics model of CT images in the identification of ureteral calculus and phlebolith. Int J Clin Pract. 2022;2022:5478908. doi:10.1155/2022/5478908
  • Gu S, Qian J, Yang L, et al. Multiparametric MRI radiomics for the differentiation of brain glial cell hyperplasia from low-grade glioma. BMC Med Imaging. 2023;23(1):116. doi:10.1186/s12880-023-01086-3
  • Tian H, Wu H, Wu G, Xu G. Noninvasive prediction of TERT promoter mutations in high-grade glioma by radiomics analysis based on multiparameter MRI. Biomed Res Int. 2020;2020:3872314. doi:10.1155/2020/3872314
  • Sasaki T, Kinoshita M, Fujita K, et al. Radiomics and MGMT promoter methylation for prognostication of newly diagnosed glioblastoma. Sci Rep. 2019;9(1):14435. doi:10.1038/s41598-019-50849-y
  • Salman R, Alzaatreh A, Sulieman H, Faisal S. A bootstrap framework for aggregating within and between feature selection methods. Entropy. 2021;23(2):200. doi:10.3390/e23020200
  • Wang X, Wang Y, Xu Z, Xiong Y, Wei DQ. ATC-NLSP: prediction of the classes of anatomical therapeutic chemicals using a network-based label space partition method. Front Pharmacol. 2019;10:971. doi:10.3389/fphar.2019.00971
  • Jin P, Chen J, Dong Y, et al. Ultrasound-based radiomics nomogram combined with clinical features for the prediction of central lymph node metastasis in papillary thyroid carcinoma patients with Hashimoto’s thyroiditis. Front Endocrinol. 2022;13:993564. doi:10.3389/fendo.2022.993564
  • Mao B, Ma J, Duan S, Xia Y, Tao Y, Zhang L. Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics. Eur Radiol. 2021;31(7):4576–4586. doi:10.1007/s00330-020-07562-6
  • Hu HT, Wang Z, Huang XW, et al. Ultrasound-based radiomics score: a potential biomarker for the prediction of microvascular invasion in hepatocellular carcinoma. Eur Radiol. 2019;29(6):2890–2901. doi:10.1007/s00330-018-5797-0
  • Li G, Li L, Li Y, et al. An MRI radiomics approach to predict survival and tumour-infiltrating macrophages in gliomas. Brain. 2022;145(3):1151–1161. doi:10.1093/brain/awab340