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01-12-2021 | Breast Cancer | Original Article

Radiomic features of breast parenchyma: assessing differences between FOR PROCESSING and FOR PRESENTATION digital mammography

Authors: Mario Sansone, Roberta Grassi, Maria Paola Belfiore, Gianluca Gatta, Francesca Grassi, Fabio Pinto, Giorgia Viola La Casella, Roberta Fusco, Salvatore Cappabianca, Vincenza Granata, Roberto Grassi

Published in: Insights into Imaging | Issue 1/2021

Abstract

Objective

To assess the similarity and differences of radiomics features on full field digital mammography (FFDM) in FOR PROCESSING and FOR PRESENTATION data.

Methods

165 consecutive women who underwent FFDM were included. Breasts have been segmented into “dense” and “non-dense” area using the software LIBRA. Segmentation of both FOR PROCESSING and FOR PRESENTATION images have been evaluated by Bland–Altman, Dice index and Cohen’s kappa analysis. 74 textural features were computed: 18 features of First Order (FO), 24 features of Gray Level Co-occurrence Matrix (GLCM), 16 features of Gray Level Run Length Matrix (GLRLM) and 16 features of Gray Level Size Zone Matrix (GLSZM). Paired Wilcoxon test, Spearman’s rank correlation, intraclass correlation and canonical correlation have been used. Bilateral symmetry and percent density (PD) were also evaluated.

Results

Segmentation from FOR PROCESSING and FOR PRESENTATION gave very different results. Bilateral symmetry was higher when evaluated on features computed using FOR PROCESSING images. All features showed a positive Spearman’s correlation coefficient and many FOR-PROCESSING features were moderately or strongly correlated to their corresponding FOR-PRESENTATION counterpart. As regards the correlation analysis between PD and textural features from FOR-PRESENTATION a moderate correlation was obtained only for Gray Level Non Uniformity from GLRLM both on “dense” and “non dense” area; as regards correlation between PD and features from FOR-PROCESSING a moderate correlation was observed only for Maximal Correlation Coefficient from GLCM both on “dense” and “non dense” area.

Conclusions

Texture features from FOR PROCESSING mammograms seem to be most suitable for assessing breast density.

Sung H, Ferlay J, Siegel RL et al (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71(3):209–249. https://doi.org/10.3322/caac.21660CrossRefPubMed

Deandrea S, Cavazzana L, Principi N et al (2021) Screening of women with aesthetic prostheses in dedicated sessions of a population-based breast cancer screening programme. Radiol Med 126(7):946–955. https://doi.org/10.1007/s11547-021-01357-5CrossRefPubMedPubMedCentral

Pediconi F, Galati F, Bernardi D et al (2020) Breast imaging and cancer diagnosis during the COVID-19 pandemic: recommendations from the Italian College of Breast Radiologists by SIRM. Radiol Med 125(10):926–930. https://doi.org/10.1007/s11547-020-01254-3CrossRefPubMedPubMedCentral

Pinker K (2019) Beyond breast density: radiomic phenotypes enhance assessment of breast cancer risk. Radiology 290(1):50–51. https://doi.org/10.1148/radiol.2018182296CrossRefPubMed

Kontos D, Winham SJ, Oustimov A et al (2019) Radiomic phenotypes of mammographic parenchymal complexity: toward augmenting breast density in breast cancer risk assessment. Radiology 290(1):41–49. https://doi.org/10.1148/radiol.2018180179CrossRefPubMed

Gastounioti A, Conant EF, Kontos D (2016) Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment. Breast Cancer Res 18(1):91. https://doi.org/10.1186/s13058-016-0755-8CrossRefPubMedPubMedCentral

Gastounioti A, Oustimov A, Keller BM et al (2016) Breast parenchymal patterns in processed versus raw digital mammograms: a large population study toward assessing differences in quantitative measures across image representations. Med Phys 43(11):5862. https://doi.org/10.1118/1.4963810CrossRefPubMedPubMedCentral

Keller BM, Chen J, Daye D, Conant EF, Kontos D (2015) Preliminary evaluation of the publicly available Laboratory for Breast Radiodensity Assessment (LIBRA) software tool: comparison of fully automated area and volumetric density measures in a case-control study with digital mammography. Breast Cancer Res 25(17):117. https://doi.org/10.1186/s13058-015-0626-8CrossRef

Keller BM, Nathan DL, Wang Y et al (2012) Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation. Med Phys 39(8):4903–4917. https://doi.org/10.1118/1.4736530CrossRefPubMedPubMedCentral

10.

Granata V, Fusco R, Barretta ML et al (2021) Radiomics in hepatic metastasis by colorectal cancer. Infect Agent Cancer 16(1):39. https://doi.org/10.1186/s13027-021-00379-yCrossRefPubMedPubMedCentral

11.

Fusco R, Piccirillo A, Sansone M et al (2021) Radiomics and artificial intelligence analysis with textural metrics extracted by contrast-enhanced mammography in the breast lesions classification. Diagnostics (Basel) 11(5):815. https://doi.org/10.3390/diagnostics11050815CrossRef

12.

Granata V, Fusco R, Avallone A et al (2021) Radiomics-derived data by contrast enhanced magnetic resonance in ras mutations detection in colorectal liver metastases. Cancers (Basel) 13(3):453. https://doi.org/10.3390/cancers13030453CrossRef

13.

Danti G, Berti V, Abenavoli E et al (2020) Diagnostic imaging of typical lung carcinoids: relationship between MDCT, (111) In-Octreoscan and (18)F-FDG-PET imaging features with Ki-67 index. Radiol Med 125:715–729. https://doi.org/10.1007/s11547-020-01172-4CrossRefPubMed

14.

Hu HT, Shan QY, Chen SL et al (2020) CT-based radiomics for preoperative prediction of early recurrent hepatocellular carcinoma: technical reproducibility of acquisition and scanners. Radiol Med 125:697–705. https://doi.org/10.1007/s11547-020-01174-2CrossRefPubMed

15.

Farchione A, Larici AR, Masciocchi C et al (2020) Exploring technical issues in personalized medicine: NSCLC survival prediction by quantitative image analysis-usefulness of density correction of volumetric CT data. Radiol Med 125:625–635. https://doi.org/10.1007/s11547-020-01157-3CrossRefPubMed

16.

Li H, Mendel KR, Lan L, Sheth D, Giger ML (2019) Digital mammography in breast cancer: additive value of radiomics of breast parenchyma. Radiology 291(1):15–20. https://doi.org/10.1148/radiol.2019181113CrossRefPubMed

17.

Mazo C, Kearns C, Mooney C, Gallagher WM (2020) Clinical decision support systems in breast cancer: a systematic review. Cancers (Basel) 12(2):369. https://doi.org/10.3390/cancers12020369CrossRef

18.

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(2):328–338. https://doi.org/10.1148/radiol.2020191145CrossRefPubMed

19.

van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77(21):e104–e107. https://doi.org/10.1158/0008-5472.CAN-17-0339CrossRefPubMedPubMedCentral

20.

American College of Radiology (2013) ACR BI-RADS Atlas—mammography, 5th edn. American College of Radiology, Reston. https://doi.org/10.1118/1.4736530CrossRef

21.

Oduko JM, Young KC, Gundogdu O, Alsager A (2008) Effect of using tungsten-anode X-ray tubes on dose and image quality in full-field digital mammography. In: Krupinski EA (ed) Digital mammography. IWDM 2008. Lecture notes in computer science 2008, vol 5116. Springer, Berlin. https://doi.org/10.1007/978-3-540-70538-3_73CrossRef

22.

Borg M (2019) Application of the European protocol in the evaluation of digital mammography units with tungsten target tubes. Radiat Prot Dosimetry 185(4):507–518. https://doi.org/10.1093/rpd/ncz044CrossRefPubMed

23.

Van Rossum G, Drake FL (2009) Python 3 reference manual. CreateSpace, Scotts Valley

24.

Taha AA, Hanbury A (2015) Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging 15:29. https://doi.org/10.1186/s12880-015-0068-xCrossRefPubMedPubMedCentral

25.

Härdle WK, Simar L (2015) Applied multivariate statistical analysis. Springer, Berlin

26.

Tuerlinckx F, Rijmen F, Verbeke G, De Boeck P (2006) Statistical inference in generalized linear mixed models: a review. Br J Math Stat Psychol 59(Pt 2):225–255. https://doi.org/10.1348/000711005X79857CrossRefPubMed

27.

Kallenberg MG, Lokate M, van Gils CH, Karssemeijer N (2011) Automatic breast density segmentation: an integration of different approaches. Phys Med Biol 56(9):2715–2729. https://doi.org/10.1088/0031-9155/56/9/005CrossRefPubMed

28.

Tagliafico A, Tagliafico G, Tosto S et al (2009) Mammographic density estimation: comparison among BI-RADS categories, a semi-automated software and a fully automated one. Breast 18(1):35–40. https://doi.org/10.1016/j.breast.2008.09.005CrossRefPubMed

29.

Glide-Hurst CK, Duric N, Littrup P (2007) A new method for quantitative analysis of mammographic density. Med Phys 34(11):4491–4498. https://doi.org/10.1118/1.2789407CrossRefPubMed

Title: Radiomic features of breast parenchyma: assessing differences between FOR PROCESSING and FOR PRESENTATION digital mammography
Authors: Mario Sansone
Roberta Grassi
Maria Paola Belfiore
Gianluca Gatta
Francesca Grassi
Fabio Pinto
Giorgia Viola La Casella
Roberta Fusco
Salvatore Cappabianca
Vincenza Granata
Roberto Grassi
Publication date: 01-12-2021
Publisher: Springer International Publishing
Keywords: Breast Cancer
Mammography
Breast Cancer
Mammography
Published in: Insights into Imaging / Issue 1/2021
Electronic ISSN: 1869-4101
DOI: https://doi.org/10.1186/s13244-021-01093-4

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Radiomic features of breast parenchyma: assessing differences between FOR PROCESSING and FOR PRESENTATION digital mammography

Abstract

Objective

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Objective

Methods

Results

Conclusions

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