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Radiomics Analysis on Digital Breast Tomosynthesis: Preoperative Evaluation of Lymphovascular Invasion Status in Invasive Breast Cancer

Published:April 08, 2022DOI:https://doi.org/10.1016/j.acra.2022.03.011

      Rationale and Objectives

      To develop a digital breast tomosynthesis (DBT)-based radiomics nomogram for preoperative evaluation of lymphovascular invasion (LVI) status in patients with invasive breast cancer (IBC).

      Materials and Methods

      A total of 135 patients with pathologically confirmed IBC who underwent preoperative DBT from July 2018 to May 2020 were retrospectively enrolled and randomized into the training and validation sets. Radiomics feature extraction was performed on the volume of interest (VOI) manually outlined. A four-step algorithmic was applied to screen the features with the highest predictive power in the training set for constructing the radiomics signature and calculating the correspondent radiomics score (Rad-score). Logistic regression analyses were utilized to develop a combined radiomics model that incorporated the DBT-reported clinicoradiological semantic features and Rad-score, which was visualized as a radiomics nomogram.

      Results

      The percentage of LVI-positive patients was 60.2% and 59.5% in the training and validation sets, respectively. The radiomics signature was constructed based on nine features selected from the 1218 radiomics features extracted. Higher Rad-score, maximum tumor diameter, and spiculate margin were independent risk factors for LVI. The area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, and specificity of the radiomics nomogram were 0.905, 72.7%, and 94.6% in the training set, and 0.835, 80.0%, and 76.5% in the validation set, respectively; this data was higher than models incorporating clinicoradiological semantic features alone or the radiomics signature in both sets.

      Conclusion

      Preoperative DBT-based combined radiomic nomogram could be a potential biomarker for LVI in patients with IBC.

      Key Words

      Abbreviations:

      LVI (lymphovascular invasion), ALN (axillary lymph node), IBC (invasive breast cancer), MRI (magnetic resonance imaging), DCE-MRI (dynamic contrast enhanced MRI), DBT (digital breast tomosynthesis), ER (estrogen receptor), PR (progesterone receptor), AR (androgen receptor), HER-2 (human epidermal growth factor receptor 2), VOI (Volume of Interest), ICC1 (intraclass correlation coefficient), ICC2 (interclass correlation coefficient), LASSO (least absolute shrinkage and selection operator), LR (logistic regression), Rad-score (radiomics score), ROC (receiver operating characteristic curve), 95%CI (95% confidence interval), DCA (decision curve analysis), MTD (maximum tumor diameter), OR (odds ratio)
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      References

        • Sung H.
        • Ferlay J.
        • Siegel R.L.
        • Laversanne M.
        • Soerjomataram L.
        • Jemal A.
        • et al.
        Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.
        CA Cancer J Clin. 2021; 71: 209-249
        • Liu Y.L.
        • Saraf A.
        • Lee S.M.
        • Zhong X.
        • Hibshoosh H.
        • Kalinsky K.
        • et al.
        Lymphovascular invasion is an independent predictor of survival in breast cancer after neoadjuvant chemotherapy.
        Breast Cancer Res Treat. 2016; 157: 555-564
        • Bae M.S.
        • Moon H.G.
        • Han W.
        • Noh D.Y.
        • Han S.R.
        • Chang J.M.
        • et al.
        Early stage triple-negative breast cancer: imaging and clinical-pathologic factors associated with recurrence.
        Radiology. 2016; 278: 356-364
        • Gujam Fadia J.A.
        • Going J.J.
        • Edwards J.
        • Mohammed Z.M.A.
        • McMillan D.C.
        The role of lymphatic and blood vessel invasion in predicting survival and methods of detection in patients with primary operable breast cancer.
        Crit Rev Oncol/Hematol. 2014; 89: 231-241
        • Rakha E.A.
        • Martin S.
        • Lee A.H.S.
        • Morgan D.
        • Pharoah P.D.P.
        • Hodi Z.
        • et al.
        The prognostic significance of lymphovascular invasion in invasive breast carcinoma.
        Cancer. 2012; 118: 3670-3680
        • Hamy A.-S.
        • Lam G-T.
        • Laas E.
        • Darrigues L.
        • Balezeau T.
        • Guerin J.
        • et al.
        Lymphovascular invasion after neoadjuvant chemotherapy is strongly associated with poor prognosis in breast carcinoma.
        Breast Cancer Res Treat. 2018; 169: 295-304
        • Mori N.
        • Mugikura S.
        • Takasawa C.
        • Miyashita M.
        • Shimauchi A.
        • Ota H.
        • et al.
        Peritumoral apparent diffusion coefficients for prediction of lymphovascular invasion in clinically node-negative invasive breast cancer.
        Eur Radiol. 2016; 26: 331-339
        • Curigliano G.
        • Burstein H.G.
        • Winer E.P.
        • Gnant M.
        • Dubsky P.
        • Loibl S.
        • et al.
        De-escalating and escalating treatments for early-stage breast cancer: the St. gallen international expert consensus conference on the primary therapy of early breast cancer 2017.
        Ann Oncol. 2017; 28: 1700-1712
        • Shen S.
        • Wu G.
        • Xiao G.
        • Du R.
        • Hu N.
        • Xia X.
        • et al.
        Prediction model of lymphovascular invasion based on clinicopathological factors in Chinese patients with invasive breast cancer.
        Medicine (Baltimore). 2018; 97: e12973
        • Igarashi T.
        • Furube H.
        • Ashida H.
        • Ojiri H.
        Breast MRI for prediction of lymphovascular invasion in breast cancer patients with clinically negative axillary lymph nodes.
        Eur J Radiol. 2018; 107: 111-118
        • Ni-Jia-Ti M.-Y.-L.
        • Ai-Hai-Ti D.-L.-M.
        • Huo-Jia A.-S.-J.
        • Wu-Mai-Er P.-L.-M.
        • A-Bu-Li-Zi A.-B.-J.
        • Shi Y
        • et al.
        Development of a risk-stratification scoring system for predicting lymphovascular invasion in breast cancer.
        BMC Cancer. 2020; 20: 94
        • Gillies R.J.
        • Kinahan P.E.
        • Hricak H.
        Radiomics: images are more than pictures, they are data.
        Radiology. 2016; 278: 563-577
        • Lambin P.
        • Leijenaar R.T.H.
        • Deist T.M.
        • Peerlings J.
        • Timmeren J.V.
        • Sanduleanu S.
        • et al.
        Radiomics: the bridge between medical imaging and personalized medicine.
        Nat Rev Clin Oncol. 2017; 14: 749-762
        • Aerts H.J.W.L.
        • Velazquez E.R.
        • Leijenaar R.T.H.
        • Parmar C.
        • Grossmann P.
        • Carvalho S.
        • et al.
        Decoding tumor phenotype by noninvasive imaging using a quantitative radiomics approach.
        Nat Commun. 2014; 5: 4006
        • Zhang Q.
        • Peng Y.
        • Liu W.
        • Bai J.
        • Zheng J.
        • Yang X.
        • et al.
        Radiomics based on multimodal MRI for the differential diagnosis of benign and malignant breast lesions.
        J Magn Reson Imag. 2020; 52: 596-607
        • Li H.
        • Zhu Y.
        • Burnside E.S.
        • Huang E.
        • Drukker K.
        • Hoadley K.A.
        • et al.
        Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set.
        NPJ Breast Cancer. 2016; 2: 16012
        • Zheng X.
        • Yao Z.
        • Huang Y.
        • Yu Y.
        • Wang Y.
        • Liu Y.
        • et al.
        Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer.
        Nat Commun. 2020; 11: 1236
        • Conti A.
        • Duggento A.
        • Indovina I.
        • Guerrisi M.
        • Toschi N.
        Radiomics in breast cancer classification and prediction.
        Semin Cancer Biol. 2020; https://doi.org/10.1016/j.semcancer.2020.04.002
        • Zhang J.
        • Wang G.
        • Ren J.
        • Yang Z.
        • Li D.
        • Cui Y.
        • et al.
        Multiparametric MRI-based radiomics nomogram for preoperative prediction of lymphovascular invasion and clinical outcomes in patients with breast invasive ductal carcinoma.
        Eur Radiol. 2022; https://doi.org/10.1007/s00330-021-08504-6
        • Liu Z.
        • Feng B.
        • Li C.
        • Chen Y.
        • Chen Q.
        • Li X.
        • et al.
        Preoperative prediction of lymphovascular invasion in invasive breast cancer with dynamic contrast-enhanced-MRI-based radiomics.
        J Magn Reson Imaging. 2019; 50: 847-857
        • Friedewald S.M.
        • Rafferty E.A.
        • Rose S.L.
        • Durand M.A.
        • Plecha D.M
        • Greenberg J.S.
        • et al.
        Breast cancer screening using tomosynthesis in combination with digital mammography.
        JAMA. 2014; 311: 2499-2507
        • Alabousi M.
        • Zha N.
        • Salameh J.-P.
        • Samoilov L.
        • Sharifabadi A.D.
        • Pozdnyakov A.
        • et al.
        Digital breast tomosynthesis for breast cancer detection: a diagnostic test accuracy systematic review and meta-analysis.
        Eur Radiol. 2020; 30: 2058-2071
        • Huang Y.Q.
        • Liang C.-H.
        • He L.
        • Tian J.
        • Liang C.S.
        • Chen X.
        • et al.
        Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer.
        J Clin Oncol. 2016; 34: 2157-2164
        • Yang L.
        • Gu D.
        • Wei J.
        • Yang C.
        • Rao S.
        • Wang W.
        • et al.
        A radiomics nomogram for preoperative prediction of microvascular invasion in hepatocellular carcinoma.
        Liver Cancer. 2019; 8: 373-386
        • Li H.
        • Mendel K.R.
        • Lan L.
        • Sheth D.
        • Giger M.L.
        Digital mammography in breast cancer: additive value of radiomics of breast parenchyma.
        Radiology. 2019; 291: 15-20
        • Zwanenburg A.
        • Leger S.
        • Vallières M.
        • et al.
        Image biomarker standardisation initiative.
        Radiology. 2020; 295: 328-338
        • Rawashdeh M.A.
        • Bourne R.M.
        • Ryan E.A.
        • Lee W.B.
        • Pietrzyk M.W.
        • Reed W.M.
        • et al.
        Quantitative measures confirm the inverse relationship between lesion spiculation and detection of breast masses.
        Acad Radiol. 2013; 20: 576-580
        • Gardezi S.J.S.
        • Elazab A.
        • Lei B.
        • Wang T.
        Breast cancer detection and diagnosis using mammographic data: systematic review.
        Journal of medical Internet research. 2019; 21: e14464
        • Ferranti C.
        • Coopmans de Yoldi G.
        • Biganzoli E.
        • Bergonzi S.
        • Mariani L.
        • Scaperrotta G.
        • et al.
        Relationships between age, mammographic features and pathological tumor characteristics in non-palpable breast cancer.
        Br J Radiol. 2000; 73: 698-705
        • Wu T.
        • Dai Y.
        Tumor microenvironment and therapeutic response.
        Cancer Lett. 2017; 387: 61-68
        • Roayaie S.
        • Blume I.N.
        • Thung S.N.
        • Guido M.
        • Fiel M.-L.
        • Hiotis S.
        • et al.
        A system of classifying microvascular invasion to predict outcome after resection in patients with hepatocellular carcinoma.
        Gastroenterology. 2009; 137: 850-855
        • Tamaki K.
        • Ishida T.
        • Miyashita M.
        • Amari M.
        • Ohuchi N.
        • Tamaki N.
        • et al.
        Correlation between mammographic findings and corresponding histopathology: potential predictors for biological characteristics of breast diseases.
        Cancer Sci. 2011; 102: 2179-2185