Differentiating Breast Tumors from Background Parenchymal Enhancement at Contrast-Enhanced Mammography: The Role of Radiomics-A Pilot Reader Study

被引:15
作者
Boca , Ioana [1 ]
Ciurea, Anca Ileana [1 ]
Ciortea, Cristiana Augusta [2 ]
Stefan, Paul Andrei [2 ,3 ]
Lisencu, Lorena Alexandra [4 ]
Dudea, Sorin Marian [1 ]
机构
[1] Iuliu Hatieganu Univ Med & Pharm, Dept Radiol, Cluj Napoca 400012, Romania
[2] Emergency Cty Hosp, Dept Radiol, Cluj Napoca 400006, Romania
[3] Iuliu Hatieganu Univ Med & Pharm, Anat & Embryol Morphol Sci Dept, Cluj Napoca 400012, Romania
[4] Iuliu Hatieganu Univ Med & Pharm, Dept Oncol Surg & Gynecol Oncol, Cluj Napoca 400012, Romania
关键词
radiomic analysis; contrast-enhanced spectral mammography; breast cancer; background parenchymal enhancement; SPECTRAL MAMMOGRAPHY; CANCER;
D O I
10.3390/diagnostics11071248
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background: The purpose of this study was to assess the effectiveness of the radiomic analysis of contrast-enhanced spectral mammography (CESM) in discriminating between breast cancers and background parenchymal enhancement (BPE). Methods: This retrospective study included 38 patients that underwent CESM examinations for clinical purposes between January 2019-December 2020. A total of 57 malignant breast lesions and 23 CESM examinations with 31 regions of BPE were assessed through radiomic analysis using MaZda software. The parameters that demonstrated to be independent predictors for breast malignancy were exported into the B11 program and a k-nearest neighbor classifier (k-NN) was trained on the initial groups of patients and was tested using a validation group. Histopathology results obtained after surgery were considered the gold standard. Results: Radiomic analysis found WavEnLL_s_2 parameter as an independent predictor for breast malignancies with a sensitivity of 68.42% and a specificity of 83.87%. The prediction model that included CH1D6SumAverg, CN4D6Correlat, Kurtosis, Perc01, Perc10, Skewness, and WavEnLL_s_2 parameters had a sensitivity of 73.68% and a specificity of 80.65%. Higher values were obtained of WavEnLL_s_2 and the prediction model for tumors than for BPEs. The comparison between the ROC curves provided by the WaveEnLL_s_2 and the entire prediction model did not show statistically significant results (p = 0.0943). The k-NN classifier based on the parameter WavEnLL_s_2 had a sensitivity and specificity on training and validating groups of 71.93% and 45.16% vs. 60% and 44.44%, respectively. Conclusion: Radiomic analysis has the potential to differentiate CESM between malignant lesions and BPE. Further quantitative insight into parenchymal enhancement patterns should be performed to facilitate the role of BPE in personalized clinical decision-making and risk assessment.
引用
收藏
页数:12
相关论文
共 30 条
[1]   Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI [J].
Braman, Nathaniel M. ;
Etesami, Maryam ;
Prasanna, Prateek ;
Dubchuk, Christina ;
Gilmore, Hannah ;
Tiwari, Pallavi ;
Pletcha, Donna ;
Madabhushi, Anant .
BREAST CANCER RESEARCH, 2017, 19
[2]   Influence of MRI acquisition protocols and image intensity normalization methods on texture classification [J].
Collewet, G ;
Strzelecki, M ;
Mariette, F .
MAGNETIC RESONANCE IMAGING, 2004, 22 (01) :81-91
[3]   Breast MRI background parenchymal enhancement as an imaging bridge to molecular cancer sub-type [J].
Dilorenzo, Giuseppe ;
Telegrafo, Michele ;
La Forgia, Daniele ;
Ianora, Amato Antonio Stabile ;
Moschetta, Marco .
EUROPEAN JOURNAL OF RADIOLOGY, 2019, 113 :148-152
[4]  
ElSaid NA, 2017, EGYPT J RADIOL NUC M, V48, P519, DOI 10.1016/j.ejrnm.2017.03.022
[5]   Contrast-enhanced spectral mammography versus MRI: Initial results in the detection of breast cancer and assessment of tumour size [J].
Fallenberg, E. M. ;
Dromain, C. ;
Diekmann, F. ;
Engelken, F. ;
Krohn, M. ;
Singh, J. M. ;
Ingold-Heppner, B. ;
Winzer, K. J. ;
Bick, U. ;
Renz, D. M. .
EUROPEAN RADIOLOGY, 2014, 24 (01) :256-264
[6]   Fully Automated Support System for Diagnosis of Breast Cancer in Contrast-Enhanced Spectral Mammography Images [J].
Fanizzi, Annarita ;
Losurdo, Liliana ;
Basile, Teresa Maria A. ;
Bellotti, Roberto ;
Bottigli, Ubaldo ;
Delogu, Pasquale ;
Diacono, Domenico ;
Didonna, Vittorio ;
Fausto, Alfonso ;
Lombardi, Angela ;
Lorusso, Vito ;
Massafra, Raffaella ;
Tangaro, Sabina ;
La Forgia, Daniele .
JOURNAL OF CLINICAL MEDICINE, 2019, 8 (06)
[7]   Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment [J].
Gastounioti, Aimilia ;
Conant, Emily F. ;
Kontos, Despina .
BREAST CANCER RESEARCH, 2016, 18
[8]   Contrast-enhanced spectral mammography (CESM) and contrast enhanced MRI (CEMRI): Patient preferences and tolerance [J].
Hobbs, Max M. ;
Taylor, Donna B. ;
Buzynski, Sebastian ;
Peake, Rachel E. .
JOURNAL OF MEDICAL IMAGING AND RADIATION ONCOLOGY, 2015, 59 (03) :300-305
[9]   Bilateral Contrast-enhanced Dual-Energy Digital Mammography: Feasibility and Comparison with Conventional Digital Mammography and MR Imaging in Women with Known Breast Carcinoma [J].
Jochelson, Maxine S. ;
Dershaw, D. David ;
Sung, Janice S. ;
Heerdt, Alexandra S. ;
Thornton, Cynthia ;
Moskowitz, Chaya S. ;
Ferrara, Jessica ;
Morris, Elizabeth A. .
RADIOLOGY, 2013, 266 (03) :743-751
[10]   Radiomic Analysis in Contrast-Enhanced Spectral Mammography for Predicting Breast Cancer Histological Outcome [J].
La Forgia, Daniele ;
Fanizzi, Annarita ;
Campobasso, Francesco ;
Bellotti, Roberto ;
Didonna, Vittorio ;
Lorusso, Vito ;
Moschetta, Marco ;
Massafra, Raffaella ;
Tamborra, Pasquale ;
Tangaro, Sabina ;
Telegrafo, Michele ;
Pastena, Maria Irene ;
Zito, Alfredo .
DIAGNOSTICS, 2020, 10 (09)