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2000
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Objective

This study aimed to determine the utility of a radiomic nomogram combined with clinical imaging and radiomic features based on MRI for the diagnosis of triple-negative breast cancer.

Methods

Multi-parametric MRI images of 136 breast cancer patients were retrospectively analyzed, 95 cases were stratified into the training cohort, and 41 cases were selected for the test group. According to the pathological molecular typing, the patients were divided into 23 cases of triple-negative breast cancer and 113 cases of non-triple-negative breast cancer. ITK software was used to manually delineate the lesion volume region of interest (VOI), and the Pyradiomics package was used to extract radiomic features for screening and model building. The platform was then used to analyze the clinical and imaging risk factors of breast cancer to build a characteristic model separately. Finally, a radiomic nomogram was constructed by integrating the radiomic and independent clinical image features. The diagnostic performance of the model was assessed using ROC curves.

Results

Univariate and multivariate analyses showed that the menstrual cycle, glandular density, and skin thickening were risk factors for clinical imaging characteristics of triple-negative breast cancer. The Area Under the Curve (AUC) was 0.839 and 0.826 for univariate and multivariate analysis, respectively. After screening, 11 radiomic features participated in the calculation of the radiomic score, and its AUC in the test set was 0.803. Combining it further with clinical models, the AUC improved to 0.899.

Conclusion

The radiomic nomogram developed in this study has great value in the diagnosis of triple-negative breast cancer.

© 2024 The Author(s). Published by Bentham Science Publisher.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2025-09-12

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References

  1. KimY. YooK.Y. GoodmanM.T. Differences in incidence, mortality and survival of breast cancer by regions and countries in Asia and contributing factors.Asian Pac. J. Cancer Prev.20151672857287010.7314/APJCP.2015.16.7.285725854374
     [Google Scholar]
  2. NguyenP.L. TaghianA.G. KatzM.S. NiemierkoA. Abi RaadR.F. BoonW.L. BellonJ.R. WongJ.S. SmithB.L. HarrisJ.R. Breast cancer subtype approximated by estrogen receptor, progesterone receptor, and HER-2 is associated with local and distant recurrence after breast-conserving therapy.J. Clin. Oncol.200826142373237810.1200/JCO.2007.14.428718413639
     [Google Scholar]
  3. JohanssonA.L.V. TrewinC.B. HjerkindK.V. Ellingjord-DaleM. JohannesenT.B. UrsinG. Breast cancer-specific survival by clinical subtype after 7 years follow-up of young and elderly women in a nationwide cohort.Int. J. Cancer201914461251126110.1002/ijc.3195030367449
     [Google Scholar]
  4. VagiaE. MahalingamD. CristofanilliM. The landscape of targeted therapies in TNBC.Cancers202012491610.3390/cancers1204091632276534
     [Google Scholar]
  5. NiM. ZhouX. LiuJ. YuH. GaoY. ZhangX. LiZ. Prediction of the clinicopathological subtypes of breast cancer using a fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI.BMC Cancer2020201107310.1186/s12885‑020‑07557‑y33167903
     [Google Scholar]
  6. LiQ. DormerJ.D. DaryaniP. ChenD. ZhangZ. FeiB. Radiomics analysis of MRI for predicting molecular subtypes of breast cancer in young women.Proc. SPIE20191095014810.1117/12.251205632528211
     [Google Scholar]
  7. FanM. LiH. WangS. ZhengB. ZhangJ. LiL. Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer.PLoS One2017122e017168310.1371/journal.pone.017168328166261
     [Google Scholar]
  8. LeithnerD. HorvatJ.V. MarinoM.A. Bernard-DavilaB. JochelsonM.S. Ochoa-AlbizteguiR.E. MartinezD.F. MorrisE.A. ThakurS. PinkerK. Radiomic signatures with contrast-enhanced magnetic resonance imaging for the assessment of breast cancer receptor status and molecular subtypes: Initial results.Breast Cancer Res.201921110610.1186/s13058‑019‑1187‑z31514736
     [Google Scholar]
  9. LambinP. Rios-VelazquezE. LeijenaarR. CarvalhoS. van StiphoutR.G.P.M. GrantonP. ZegersC.M.L. GilliesR. BoellardR. DekkerA. AertsH.J.W.L. Radiomics: Extracting more information from medical images using advanced feature analysis.Eur. J. Cancer201248444144610.1016/j.ejca.2011.11.03622257792
     [Google Scholar]
  10. IyengarN.M. MorrisP.G. ZhouX.K. GucalpA. GiriD. HarbusM.D. FalconeD.J. KrasneM.D. VahdatL.T. SubbaramaiahK. MorrowM. HudisC.A. DannenbergA.J. Menopause is a determinant of breast adipose inflammation.Cancer Prev. Res.20158534935810.1158/1940‑6207.CAPR‑14‑024325720743
     [Google Scholar]
  11. MannR.M. AthanasiouA. BaltzerP.A.T. Camps-HerreroJ. ClauserP. FallenbergE.M. ForraiG. FuchsjägerM.H. HelbichT.H. Killburn-ToppinF. LesaruM. PanizzaP. PediconiF. PijnappelR.M. PinkerK. SardanelliF. SellaT. Thomassin-NaggaraI. ZackrissonS. GilbertF.J. KuhlC.K. Breast cancer screening in women with extremely dense breasts recommendations of the European Society of Breast Imaging (EUSOBI).Eur. Radiol.20223264036404510.1007/s00330‑022‑08617‑635258677
     [Google Scholar]
  12. McNeishD.M. Using lasso for predictor selection and to assuage overfitting: A method long overlooked in behavioral sciences.Multivariate Behav. Res.201550547148410.1080/00273171.2015.103696526610247
     [Google Scholar]
  13. GoldhirschA. WinerE.P. CoatesA.S. GelberR.D. Piccart-GebhartM. ThürlimannB. SennH.J. AlbainK.S. AndréF. BerghJ. BonnefoiH. Bretel-MoralesD. BursteinH. CardosoF. Castiglione-GertschM. CoatesA.S. ColleoniM. CostaA. CuriglianoG. DavidsonN.E. Di LeoA. EjlertsenB. ForbesJ.F. GelberR.D. GnantM. GoldhirschA. GoodwinP. GossP.E. HarrisJ.R. HayesD.F. HudisC.A. IngleJ.N. JassemJ. JiangZ. KarlssonP. LoiblS. MorrowM. NamerM. Kent OsborneC. PartridgeA.H. Penault-LlorcaF. PerouC.M. Piccart-GebhartM.J. PritchardK.I. RutgersE.J.T. SedlmayerF. SemiglazovV. ShaoZ-M. SmithI. ThürlimannB. ToiM. TuttA. UntchM. VialeG. WatanabeT. WilckenN. WinerE.P. WoodW.C. Personalizing the treatment of women with early breast cancer: Highlights of the ST gallen international expert consensus on the primary therapy of early breast cancer 2013.Ann. Oncol.20132492206222310.1093/annonc/mdt30323917950
     [Google Scholar]
  14. HanY. MaY. WuZ. ZhangF. ZhengD. LiuX. TaoL. LiangZ. YangZ. LiX. HuangJ. GuoX. Histologic subtype classification of non-small cell lung cancer using PET/CT images.Eur. J. Nucl. Med. Mol. Imaging202148235036010.1007/s00259‑020‑04771‑532776232
     [Google Scholar]
  15. DalalV. CarmichealJ. DhaliwalA. JainM. KaurS. BatraS.K. Radiomics in stratification of pancreatic cystic lesions: Machine learning in action.Cancer Lett.202046922823710.1016/j.canlet.2019.10.02331629933
     [Google Scholar]
  16. FontaineP. RietF.G. CastelliJ. GnepK. DepeursingeA. CrevoisierR.D. AcostaO. Comparison of feature selection in radiomics for the prediction of overall survival after radiotherapy for hepatocellular carcinoma.Annu. Int. Conf. IEEE Eng. Med. Biol. Soc.202020201667167010.1109/EMBC44109.2020.917672433018316
     [Google Scholar]
  17. YinL. DuanJ.J. BianX.W. YuS. Triple-negative breast cancer molecular subtyping and treatment progress.Breast Cancer Res.20202216110.1186/s13058‑020‑01296‑532517735
     [Google Scholar]
  18. DaveyM.G. DaveyM.S. BolandM.R. RyanÉ.J. LoweryA.J. KerinM.J. Radiomic differentiation of breast cancer molecular subtypes using pre-operative breast imaging - A systematic review and meta-analysis.Eur. J. Radiol.202114410999610.1016/j.ejrad.2021.10999634624649
     [Google Scholar]
  19. JimenezJ.E. AbdelhafezA. MittendorfE.A. ElshafeeyN. YungJ.P. LittonJ.K. AdradaB.E. CandelariaR.P. WhiteJ. ThompsonA.M. HuoL. WeiP. TripathyD. ValeroV. YamC. HazleJ.D. MoulderS.L. YangW.T. RauchG.M. A model combining pretreatment MRI radiomic features and tumor-infiltrating lymphocytes to predict response to neoadjuvant systemic therapy in triple-negative breast cancer.Eur. J. Radiol.202214911022010.1016/j.ejrad.2022.11022035193025
     [Google Scholar]
  20. KamiyaS. SatakeH. HayashiY. IshigakiS. ItoR. KawamuraM. TaokaT. IwanoS. NaganawaS. Features from MRI texture analysis associated with survival outcomes in triple-negative breast cancer patients.Breast Cancer202229116417310.1007/s12282‑021‑01294‑134529241
     [Google Scholar]
  21. AngeliniG. MariniC. IacconiC. MazzottaD. MorettiM. PicanoE. MorgantiR. Magnetic resonance (MR) features in triple negative breast cancer (TNBC) vs receptor positive cancer (nTNBC).Clin. Imaging201849121610.1016/j.clinimag.2017.10.01629120811
     [Google Scholar]
  22. UematsuT. KasamiM. YuenS. Triple-negative breast cancer: Correlation between MR imaging and pathologic findings.Radiology2009250363864710.1148/radiol.250308105419244039
     [Google Scholar]
  23. PapanikolaouN. MatosC. KohD.M. How to develop a meaningful radiomic signature for clinical use in oncologic patients.Cancer Imaging20202013310.1186/s40644‑020‑00311‑432357923
     [Google Scholar]
  24. ChoyG. KhalilzadehO. MichalskiM. DoS. SamirA.E. PianykhO.S. GeisJ.R. PandharipandeP.V. BrinkJ.A. DreyerK.J. Current applications and future impact of machine learning in radiology.Radiology2018288231832810.1148/radiol.201817182029944078
     [Google Scholar]
  25. ParkE.K. LeeK. SeoB.K. ChoK.R. WooO.H. SonG.S. LeeH.Y. ChangY.W. Machine learning approaches to radiogenomics of breast cancer using low-dose perfusion computed tomography: Predicting prognostic biomarkers and molecular subtypes.Sci. Rep.2019911784710.1038/s41598‑019‑54371‑z31780739
     [Google Scholar]
  26. EunN.L. KangD. SonE.J. ParkJ.S. YoukJ.H. KimJ.A. GweonH.M. Texture analysis with 3.0-T MRI for association of response to neoadjuvant chemotherapy in breast cancer.Radiology20202941314110.1148/radiol.201918271831769740
     [Google Scholar]
  27. ParmarC. GrossmannP. BussinkJ. LambinP. AertsH.J.W.L. Machine learning methods for quantitative radiomic biomarkers.Sci. Rep.2015511308710.1038/srep1308726278466
     [Google Scholar]
  28. ZhangB. HeX. OuyangF. GuD. DongY. ZhangL. MoX. HuangW. TianJ. ZhangS. Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma.Cancer Lett.2017403212710.1016/j.canlet.2017.06.00428610955
     [Google Scholar]
  29. ChenT. GuestrinC. XGBoost: A scalable tree boosting system.The 22nd ACM SIGKDD International Conference.New York201678594
     [Google Scholar]
  30. MontiS. AielloM. IncoronatoM. GrimaldiA.M. MoscarinoM. MirabelliP. FerboU. CavaliereC. SalvatoreM. DCE-MRI pharmacokinetic-based phenotyping of invasive ductal carcinoma:A radiomic study for prediction of histological outcomes.Contrast Media Mol. Imaging2018201811110.1155/2018/507626929581709
     [Google Scholar]
  31. PerouC.M. SørlieT. EisenM.B. van de RijnM. JeffreyS.S. ReesC.A. PollackJ.R. RossD.T. JohnsenH. AkslenL.A. FlugeØ. PergamenschikovA. WilliamsC. ZhuS.X. LønningP.E. Børresen-DaleA.L. BrownP.O. BotsteinD. Molecular portraits of human breast tumours.Nature2000406679774775210.1038/3502109310963602
     [Google Scholar]
  32. AmornsiripanitchN. BickelhauptS. ShinH.J. DangM. RahbarH. PinkerK. PartridgeS.C. Diffusion-weighted MRI for unenhanced breast cancer screening.Radiology2019293350452010.1148/radiol.201918278931592734
     [Google Scholar]
  33. YangX. DongM. LiS. ChaiR. ZhangZ. LiN. ZhangL. Diffusion-weighted imaging or dynamic contrast-enhanced curve: A retrospective analysis of contrast-enhanced magnetic resonance imaging–based differential diagnoses of benign and malignant breast lesions.Eur. Radiol.20203094795480510.1007/s00330‑020‑06883‑w32350660
     [Google Scholar]
  34. ZhangQ. PengY. LiuW. BaiJ. ZhengJ. YangX. ZhouL. Radiomics based on multimodal MRI for the differential diagnosis of benign and malignant breast lesions.J. Magn. Reson. Imaging202052259660710.1002/jmri.2709832061014
     [Google Scholar]
  35. MaM. GanL. JiangY. QinN. LiC. ZhangY. WangX. Radiomics analysis based on automatic image segmentation of DCE-MRI for predicting triple-negative and nontriple-negative breast cancer.Comput. Math. Methods Med.202120211710.1155/2021/214046534422088
     [Google Scholar]
  36. GrimmL.J. ZhangJ. MazurowskiM.A. Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms.J. Magn. Reson. Imaging201542490290710.1002/jmri.2487925777181
     [Google Scholar]
  37. WaughS.A. PurdieC.A. JordanL.B. VinnicombeS. LerskiR.A. MartinP. ThompsonA.M. Magnetic resonance imaging texture analysis classification of primary breast cancer.Eur. Radiol.201626232233010.1007/s00330‑015‑3845‑626065395
     [Google Scholar]
  38. XieT. WangZ. ZhaoQ. BaiQ. ZhouX. GuY. PengW. WangH. Machine learning-based analysis of MR multiparametric radiomics for the subtype classification of breast cancer.Front. Oncol.2019950510.3389/fonc.2019.0050531259153
     [Google Scholar]
  39. ShaY.S. ChenJ.F. MRI-based radiomics for the diagnosis of triple-negative breast cancer: A meta-analysis.Clin. Radiol.202277965566310.1016/j.crad.2022.04.01535641339
     [Google Scholar]
  40. LeithnerD. MayerhoeferM.E. MartinezD.F. JochelsonM.S. MorrisE.A. ThakurS.B. PinkerK. Non-invasive assessment of breast cancer molecular subtypes with multi-parametric magnetic resonance imaging radiomics.J. Clin. Med.202096185310.3390/jcm906185332545851
     [Google Scholar]
  41. LeithnerD. Bernard-DavilaB. MartinezD.F. HorvatJ.V. JochelsonM.S. MarinoM.A. AvendanoD. Ochoa-AlbizteguiR.E. SuttonE.J. MorrisE.A. ThakurS.B. PinkerK. Radiomic signatures derived from diffusion-weighted imaging for the assessment of breast cancer receptor status and molecular subtypes.Mol. Imaging Biol.202022245346110.1007/s11307‑019‑01383‑w31209778
     [Google Scholar]
  42. WangQ. MaoN. LiuM. ShiY. MaH. DongJ. ZhangX. DuanS. WangB. XieH. Radiomic analysis on magnetic resonance diffusion weighted image in distinguishing triple-negative breast cancer from other subtypes: A feasibility study.Clin. Imaging20217213614110.1016/j.clinimag.2020.11.02433242692
     [Google Scholar]
  43. WuJ. SunX. WangJ. CuiY. KatoF. ShiratoH. IkedaD.M. LiR. Identifying relations between imaging phenotypes and molecular subtypes of breast cancer: Model discovery and external validation.J. Magn. Reson. Imaging20174641017102710.1002/jmri.2566128177554
     [Google Scholar]
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The Diagnostic Value of a Nomogram based on Clinical Imaging and MRI-based Radiomic Features in Triple-Negative Breast Cancer
Curr. Med. Imaging 20, e15734056227812 (2024); https://doi.org/10.2174/0115734056227812231016112438
/content/journals/cmir/10.2174/0115734056227812231016112438
/content/journals/cmir/10.2174/0115734056227812231016112438
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  • Article Type:     Research Article
Keyword(s): AUC; Clinical models; Magnetic resonance imaging; Radiomics; ROC; Triple-negative breast cancer

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