Artificial intelligence-based, semi-automated segmentation for the extraction of ultrasound-derived radiomics features in breast cancer: a prospective multicenter study.
Artificial intelligence
Breast cancer
Machine learning
Ultrasound
Journal
La Radiologia medica
ISSN: 1826-6983
Titre abrégé: Radiol Med
Pays: Italy
ID NLM: 0177625
Informations de publication
Date de publication:
09 May 2024
09 May 2024
Historique:
received:
27
07
2023
accepted:
29
04
2024
medline:
10
5
2024
pubmed:
10
5
2024
entrez:
9
5
2024
Statut:
aheadofprint
Résumé
To investigate the feasibility of an artificial intelligence (AI)-based semi-automated segmentation for the extraction of ultrasound (US)-derived radiomics features in the characterization of focal breast lesions (FBLs). Two expert radiologists classified according to US BI-RADS criteria 352 FBLs detected in 352 patients (237 at Center A and 115 at Center B). An AI-based semi-automated segmentation was used to build a machine learning (ML) model on the basis of B-mode US of 237 images (center A) and then validated on an external cohort of B-mode US images of 115 patients (Center B). A total of 202 of 352 (57.4%) FBLs were benign, and 150 of 352 (42.6%) were malignant. The AI-based semi-automated segmentation achieved a success rate of 95.7% for one reviewer and 96% for the other, without significant difference (p = 0.839). A total of 15 (4.3%) and 14 (4%) of 352 semi-automated segmentations were not accepted due to posterior acoustic shadowing at B-Mode US and 13 and 10 of them corresponded to malignant lesions, respectively. In the validation cohort, the characterization made by the expert radiologist yielded values of sensitivity, specificity, PPV and NPV of 0.933, 0.9, 0.857, 0.955, respectively. The ML model obtained values of sensitivity, specificity, PPV and NPV of 0.544, 0.6, 0.416, 0.628, respectively. The combined assessment of radiologists and ML model yielded values of sensitivity, specificity, PPV and NPV of 0.756, 0.928, 0.872, 0.855, respectively. AI-based semi-automated segmentation is feasible, allowing an instantaneous and reproducible extraction of US-derived radiomics features of FBLs. The combination of radiomics and US BI-RADS classification led to a potential decrease of unnecessary biopsy but at the expense of a not negligible increase of potentially missed cancers.
Identifiants
pubmed: 38724697
doi: 10.1007/s11547-024-01826-7
pii: 10.1007/s11547-024-01826-7
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s).
Références
International agency for research on cancer (2020) The global cancer observatory. World Health Organization
Mokhtari-Hessari P, Montazeri A (2020) Health-related quality of life in breast cancer patients: review of reviews from 2008 to 2018. Health Qual Life Outcomes 18:338
pubmed: 33046106
pmcid: 7552560
doi: 10.1186/s12955-020-01591-x
Brown C, Nazeer R, Gibbs A et al (2023) Breaking bias: the role of artificial intelligence in improving clinical decision-making. Cureus 15:e36415
pubmed: 37090406
pmcid: 10115193
Smith H, Fotheringham K (2020) Artificial intelligence in clinical decision-making: rethinking liability. Med Law Int 20:096853322094576
doi: 10.1177/0968533220945766
Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577
pubmed: 26579733
doi: 10.1148/radiol.2015151169
Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762
pubmed: 28975929
doi: 10.1038/nrclinonc.2017.141
Trister AD, Buist DSM, Lee CI (2017) Will machine learning tip the balance in breast cancer screening? JAMA Oncol 3:1463–1464
pubmed: 28472204
pmcid: 8855965
doi: 10.1001/jamaoncol.2017.0473
Kabiraj S, Raihan M, Alvi N, et al (2020) Breast cancer risk prediction using XGBoost and random forest algorithm. In: 2020 11th International conference on computing, communication and networking technologies (ICCCNT). IEEE
Ghiasi MM, Zendehboudi S (2021) Application of decision tree-based ensemble learning in the classification of breast cancer. Comput Biol Med 128:104089
pubmed: 33338982
doi: 10.1016/j.compbiomed.2020.104089
Lin A, Kolossváry M, Yuvaraj J et al (2020) myocardial infarction associates with a distinct pericoronary adipose tissue radiomic phenotype: a prospective case-control study. JACC Cardiovasc Imaging 13:2371–2383
pubmed: 32861654
pmcid: 7996075
doi: 10.1016/j.jcmg.2020.06.033
Chen W, Liu B, Peng S et al (2018) Computer-aided grading of gliomas combining automatic segmentation and radiomics. Int J Biomed Imaging 2018:2512037
pubmed: 29853828
pmcid: 5964423
doi: 10.1155/2018/2512037
Tagliafico AS, Piana M, Schenone D et al (2020) Overview of radiomics in breast cancer diagnosis and prognostication. Breast 49:74–80
pubmed: 31739125
doi: 10.1016/j.breast.2019.10.018
Militello C, Rundo L, Dimarco M et al (2022) 3D DCE-MRI radiomic analysis for malignant lesion prediction in breast cancer patients. Acad Radiol 29:830–840
pubmed: 34600805
doi: 10.1016/j.acra.2021.08.024
Gu J, Jiang TA (2022) Ultrasound radiomics in personalized breast management: current status and future prospects. Front Oncol 17(12):963612
doi: 10.3389/fonc.2022.963612
Guo Y, Hu Y, Qiao M et al (2018) Radiomics analysis on ultrasound for prediction of biologic behavior in breast invasive ductal carcinoma. Clin Breast Cancer 18:e335–e344
pubmed: 28890183
doi: 10.1016/j.clbc.2017.08.002
Bove S, Comes MC, Lorusso V et al (2022) A ultrasound-based radiomic approach to predict the nodal status in clinically negative breast cancer patients. Sci Rep 12:7914
pubmed: 35552476
pmcid: 9098914
doi: 10.1038/s41598-022-11876-4
Jiang M, Zhang D, Tang S-C 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
pubmed: 33226454
doi: 10.1007/s00330-020-07544-8
Ciritsis A, Rossi C, Eberhard M et al (2019) Automatic classification of ultrasound breast lesions using a deep convolutional neural network mimicking human decision-making. Eur Radiol 29:5458–5468
pubmed: 30927100
doi: 10.1007/s00330-019-06118-7
Scapicchio C, Gabelloni M, Barucci A et al (2021) A deep look into radiomics. Radiol Med 126:1296–1311
pubmed: 34213702
pmcid: 8520512
doi: 10.1007/s11547-021-01389-x
O’Connell AM, Bartolotta TV, Orlando A et al (2022) Diagnostic performance of an artificial intelligence system in breast ultrasound. J Ultrasound Med 41:97–105
pubmed: 33665833
doi: 10.1002/jum.15684
Bartolotta TV, Orlando A, Cantisani V et al (2018) Focal breast lesion characterization according to the BI-RADS US lexicon: role of a computer-aided decision-making support. Radiol Med 123:498–506
pubmed: 29569216
doi: 10.1007/s11547-018-0874-7
World Medical Association (2013) World medical association declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA 310:2191–2194
doi: 10.1001/jama.2013.281053
D’Orsi C, Bassett L, Feig S, Others (2018) Breast imaging reporting and data system (BI-RADS). Breast imaging atlas, 4th edn American College of Radiology, Reston
Bartolotta TV, Orlando AAM, Di Vittorio ML et al (2021) S-Detect characterization of focal solid breast lesions: a prospective analysis of inter-reader agreement for US BI-RADS descriptors. J Ultrasound 24:143–150
pubmed: 32447631
doi: 10.1007/s40477-020-00476-5
Zwanenburg A, Vallières M, Abdalah MA et al (2020) The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295:328–338
pubmed: 32154773
doi: 10.1148/radiol.2020191145
Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybernet 6:610–621
doi: 10.1109/TSMC.1973.4309314
Thibault G, Angulo J, Meyer F (2014) Advanced statistical matrices for texture characterization: application to cell classification. IEEE Trans Biomed Eng 61:630–637
pubmed: 24108747
doi: 10.1109/TBME.2013.2284600
Galloway MM (1975) Texture analysis using gray level run lengths. Comput Graph Image Process 4:172–179
doi: 10.1016/S0146-664X(75)80008-6
Sun C, Wee WG (1983) Neighboring gray level dependence matrix for texture classification. Comput Vis Graph Image Process 23:341–352
doi: 10.1016/0734-189X(83)90032-4
Amadasun M, King R (1989) Textural features corresponding to textural properties. IEEE Trans Syst Man Cybernet 19(5):1264–1274
doi: 10.1109/21.44046
Papanikolaou N, Matos C, Koh DM (2020) How to develop a meaningful radiomic signature for clinical use in oncologic patients. Cancer Imaging 20:33
pubmed: 32357923
pmcid: 7195800
doi: 10.1186/s40644-020-00311-4
Prinzi F, Militello C, Conti V, Vitabile S (2023) Impact of wavelet kernels on predictive capability of radiomic features: a case study on COVID-19 chest X-ray images. J Imaging Sci Technol. https://doi.org/10.3390/jimaging9020032
doi: 10.3390/jimaging9020032
Liang W, Luo S, Zhao G, Wu H (2020) Predicting hard rock pillar stability using GBDT, XGBoost, and LightGBM algorithms. Sci China Ser A Math 8:765
Shwartz-Ziv R, Armon A (2022) Tabular data: deep learning is not all you need. Inf Fusion 81:84–90
doi: 10.1016/j.inffus.2021.11.011
Prinzi F, Orlando A, Gaglio S, Vitabile S (2024) Interpretable radiomic signature for breast microcalcification detection and classification. J Imaging Inform Med. https://doi.org/10.1007/s10278-024-01012-1
doi: 10.1007/s10278-024-01012-1
pubmed: 38351223
Menze BH, Kelm BM, Masuch R et al (2009) A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinformatics 10:213
pubmed: 19591666
pmcid: 2724423
doi: 10.1186/1471-2105-10-213
Altman DG, Bland JM (1994) Diagnostic tests 2: predictive values. BMJ 309:102
pubmed: 8038641
pmcid: 2540558
doi: 10.1136/bmj.309.6947.102
van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107
pubmed: 29092951
pmcid: 5672828
doi: 10.1158/0008-5472.CAN-17-0339
Wei Q, Yan Y-J, Wu G-G et al (2022) The diagnostic performance of ultrasound computer-aided diagnosis system for distinguishing breast masses: a prospective multicenter study. Eur Radiol 32:4046–4055
pubmed: 35066633
doi: 10.1007/s00330-021-08452-1
Dong F, She R, Cui C et al (2021) One step further into the blackbox: a pilot study of how to build more confidence around an AI-based decision system of breast nodule assessment in 2D ultrasound. Eur Radiol 31:4991–5000
pubmed: 33404698
doi: 10.1007/s00330-020-07561-7
Li J-W, Cao Y-C, Zhao Z-J et al (2022) Prediction for pathological and immunohistochemical characteristics of triple-negative invasive breast carcinomas: the performance comparison between quantitative and qualitative sonographic feature analysis. Eur Radiol 32:1590–1600
pubmed: 34519862
doi: 10.1007/s00330-021-08224-x
Jiang M, Li C-L, Chen R-X et al (2021) Management of breast lesions seen on US images: dual-model radiomics including shear-wave elastography may match performance of expert radiologists. Eur J Radiol 141:109781
pubmed: 34029933
doi: 10.1016/j.ejrad.2021.109781
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
pubmed: 33875840
doi: 10.1038/s41551-021-00711-2
Kapetas P, Clauser P, Woitek R et al (2019) Quantitative multiparametric breast ultrasound: application of contrast-enhanced ultrasound and elastography leads to an improved differentiation of benign and malignant lesions. Invest Radiol 54:257–264
pubmed: 30632985
pmcid: 8284878
doi: 10.1097/RLI.0000000000000543
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
pubmed: 34561440
pmcid: 8463596
doi: 10.1038/s41467-021-26023-2
Romeo V, Cuocolo R, Apolito R et al (2021) Clinical value of radiomics and machine learning in breast ultrasound: a multicenter study for differential diagnosis of benign and malignant lesions. Eur Radiol 31:9511–9519
pubmed: 34018057
pmcid: 8589755
doi: 10.1007/s00330-021-08009-2
Gu Y, Xu W, Liu T et al (2023) Ultrasound-based deep learning in the establishment of a breast lesion risk stratification system: a multicenter study. Eur Radiol 33:2954–2964
pubmed: 36418619
doi: 10.1007/s00330-022-09263-8
Dietzel M, Clauser P, Kapetas P et al (2021) Images are data: a breast imaging perspective on a contemporary paradigm. Rofo 193:898–908
pubmed: 33535260
doi: 10.1055/a-1346-0095
Rodriguez-Ruiz A, Lång K, Gubern-Merida A et al (2019) Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists. J Natl Cancer Inst 111:916–922
pubmed: 30834436
pmcid: 6748773
doi: 10.1093/jnci/djy222
Stelzer PD, Steding O, Raudner MW et al (2020) Combined texture analysis and machine learning in suspicious calcifications detected by mammography: potential to avoid unnecessary stereotactical biopsies. Eur J Radiol 132:109309
pubmed: 33010682
doi: 10.1016/j.ejrad.2020.109309
Dietzel M, Schulz-Wendtland R, Ellmann S et al (2020) Automated volumetric radiomic analysis of breast cancer vascularization improves survival prediction in primary breast cancer. Sci Rep 10:3664
pubmed: 32111898
pmcid: 7048934
doi: 10.1038/s41598-020-60393-9
Pötsch N, Dietzel M, Kapetas P et al (2021) An A.I. Classifier derived from 4D radiomics of dynamic contrast-enhanced breast MRI data: potential to avoid unnecessary breast biopsies. Eur Radiol 31:5866–5876
pubmed: 33744990
pmcid: 8270804
doi: 10.1007/s00330-021-07787-z
Tahmassebi A, Wengert GJ, Helbich TH et al (2019) Impact of machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy and survival outcomes in breast cancer patients. Invest Radiol 54:110–117
pubmed: 30358693
pmcid: 6310100
doi: 10.1097/RLI.0000000000000518
Mao L, Chen H, Liang M et al (2019) Quantitative radiomic model for predicting malignancy of small solid pulmonary nodules detected by low-dose CT screening. Quant Imaging Med Surg 9:263–272
pubmed: 30976550
pmcid: 6414768
doi: 10.21037/qims.2019.02.02
Kamiya A, Murayama S, Kamiya H et al (2014) Kurtosis and skewness assessments of solid lung nodule density histograms: differentiating malignant from benign nodules on CT. Jpn J Radiol 32:14–21
pubmed: 24248771
doi: 10.1007/s11604-013-0264-y
Zarcaro C, Clauser P (2023) Artificial intelligence clinical applications in breast diagnostic imaging. J Radiol Rev 10:127–137. https://doi.org/10.23736/S2723-9284.23.00246-9
doi: 10.23736/S2723-9284.23.00246-9