Molybdenum target X-ray examination and multimodality MRI in the diagnosis of breast cancer

Breast cancer is one of the most common malignant diseases, with a high mortality rate, affecting mostly females. This study aims to assess the diagnostic value of Molybdenum target X-ray examination and multimodality Magnetic Resonance Imaging (MRI) in breast cancer diagnosis. A total of 60 patients with suspected breast cancer were screened and included in the study. All patients underwent Molybdenum target X-ray and multimodality MRI, and the results were compared to pathological examination, which served as the reference standard for evaluating the diagnostic efficacy of the different screening methods. Molybdenum target X-ray examination identified 19 positive cases and 41 negative cases. Comparatively, multimodality MRI detected 43 positive cases and 17 negative cases. Compared to Molybdenum target X-ray, multimodality MRI demonstrated higher diagnostic accuracy, specificity, and sensitivity. Further analysis revealed that among the 45 positive patients, 13 were classified as stage 1, 20 as stage 2, 9 as stage 3, and 3 as stage 4. The pathological types were categorized as invasive ductal carcinoma, intraductal carcinoma, and ductal carcinoma in situ, with 25, 6 and 14 cases, respectively. Intraductal carcinoma exhibited higher levels of enhancement rate and signal enhancement ratio, as well as shorter peak time, compared to the other two types. No significant difference was observed between invasive ductal carcinoma and ductal carcinoma in situ. In the clinical diagnosis of breast cancer, multimodality MRI examination proves to be more comprehensive and accurate in determining the tumor’s nature and the type of disease, with significant clinical value in the field.

Molybdenum target X-ray; Multimodality MRI; Breast cancer; Diagnostic efficacy

[1] Lee H, Lee JE, Jeong WG, Ki SY, Park MH, Lee JS, et al. HER2-positive breast cancer: association of MRI and clinicopathologic features with tumor-infiltrating lymphocytes. American Journal of Roentgenology. 2022; 218: 258–269.

[2] Calabrese A, Santucci D, Landi R, Beomonte Zobel B, Faiella E, de Felice C. Radiomics MRI for lymph node status prediction in breast cancer patients: the state of art. Journal of Cancer Research and Clinical Oncology. 2021; 147: 1587–1597.

[3] Backhaus P, Burg MC, Roll W, Büther F, Breyholz H, Weigel S, et al. Simultaneous FAPI PET/MRI targeting the fibroblast-activation protein for breast cancer. Radiology. 2022; 302: 39–47.

[4] Lee JY, Lee K, Seo BK, Cho KR, Woo OH, Song SE, et al. Radiomic machine learning for predicting prognostic biomarkers and molecular subtypes of breast cancer using tumor heterogeneity and angiogenesis properties on MRI. European Radiology. 2022; 32: 650–660.

[5] Morawitz J, Bruckmann N, Dietzel F, Ullrich T, Bittner A, Hoffmann O, et al. Comparison of nodal staging between CT, MRI, and [18F]-FDG PET/MRI in patients with newly diagnosed breast cancer. European Journal of Nuclear Medicine and Molecular Imaging. 2022; 49: 992–1001.

[6] Romeo V, Clauser P, Rasul S, Kapetas P, Gibbs P, Baltzer PAT, et al. AI-enhanced simultaneous multiparametric 18F-FDG PET/MRI for accurate breast cancer diagnosis. European Journal of Nuclear Medicine and Molecular Imaging. 2022; 49: 596–608.

[7] Onega T, Zhu W, Kerlikowske K, Miglioretti DL, Lee CI, Henderson LM, et al. Preoperative MRI in breast cancer: effect of breast density on biopsy rate and yield. Breast Cancer Research and Treatment. 2022; 191: 177–190.

[8] Kwon M, Choi JS, Won H, Ko EY, Ko ES, Park KW, et al. Breast cancer screening with abbreviated breast MRI: 3-year outcome analysis. Radiology. 2021; 299: 73–83.

[9] Yamaguchi K, Hara Y, Kitano I, Hamamoto T, Kiyomatsu K, Yamasaki F, et al. Relationship between MRI findings and invasive breast cancer with podoplanin-positive cancer-associated fibroblasts. Breast Cancer. 2021; 28: 572–580.

[10] Yeh E, Rives A, Nakhlis F, Bay C, Harrison BT, Bellon JR, et al. MRI changes in breast skin following preoperative therapy for patients with inflammatory breast cancer. Academic Radiology. 2022; 29: 637–647.

[11] Bian T, Wu Z, Lin Q, Mao Y, Wang H, Chen J, et al. Evaluating tumor-infiltrating lymphocytes in breast cancer using preoperative MRI-based radiomics. Journal of Magnetic Resonance Imaging. 2022; 55: 772–784.

[12] Thawani R, Gao L, Mohinani A, Tudorica A, Li X, Mitri Z, et al. Quantitative DCE-MRI prediction of breast cancer recurrence following neoadjuvant chemotherapy: a preliminary study. BMC Med Imaging. 2022; 22: 182.

[13] Pan I, Oeffinger KC, Shih YT. Cost-sharing and out-of-pocket cost for women who received MRI for breast cancer screening. Journal of the National Cancer Institute. 2022; 114: 254–262.

[14] Freitas V, Li X, Amitai Y, Au F, Kulkarni S, Ghai S, et al. Contralateral breast screening with preoperative MRI: long-term outcomes for newly diagnosed breast cancer. Radiology. 2022; 304: 297–307.

[15] Rivlin M, Anaby D, Nissan N, Zaiss M, Deshmane A, Navon G, et al. Breast cancer imaging with glucosamine CEST (chemical exchange saturation transfer) MRI: first human experience. European Radiology. 2022; 32: 7365–7373.

[16] Wang LC. Skin changes in inflammatory breast cancer: role of MRI in evaluation of treatment response. Academic Radiology. 2022; 29: 648–649.

[17] Taourel P. MRI to detect contralateral breast cancer in patients with newly diagnosed breast cancer: an increase in overall survival to be confirmed. Radiology. 2022; 304: 308–309.

[18] Militello C, Rundo L, Dimarco M, Orlando A, Woitek R, D’Angelo I, et al. 3D DCE-MRI radiomic analysis for malignant lesion prediction in breast cancer patients. Academic Radiology. 2022; 29: 830–840.

[19] Cozzi A, Buragina G, Spinelli D, Schiaffino S, Zanardo M, Di Leo G, et al. Accuracy and inter-reader agreement of breast MRI for cancer staging using 0.08 mmol/kg of gadobutrol. Clinical Imaging. 2021; 72: 154–161.

[20] Hollingsworth AB, Pearce MR, Stough RG. Breast cancer survival following MRI detection in a high-risk screening program. The Breast Journal. 2020; 26: 991–994.

[21] DelPriore MR, Biswas D, Hippe DS, Zecevic M, Parsian S, Scheel JR, et al. Breast cancer conspicuity on computed versus acquired high b-value diffusion-weighted MRI. Academic Radiology. 2021; 28: 1108–1117.

[22] Wang H, Velden BHM, Chan HSM, Loo CE, Viergever MA, Gilhuijs KGA. Synchronous breast cancer: phenotypic similarities on MRI. Journal of Magnetic Resonance Imaging. 2020; 51: 1858–1867.

[23] You C, Xiao Q, Zhu X, Sun Y, Di G, Liu G, et al. The clinicopathological and MRI features of patients with BRCA1/2 mutations in familial breast cancer. Gland Surgery. 2021; 10: 262–272.

[24] Bruckmann NM, Sawicki LM, Kirchner J, Martin O, Umutlu L, Herrmann K, et al. Prospective evaluation of whole-body MRI and 18F-FDG PET/MRI in N and M staging of primary breast cancer patients. European Journal of Nuclear Medicine and Molecular Imaging. 2020; 47: 2816–2825.

[25] Zhang H, Guo L, Tao W, Zhang J, Zhu Y, Abdelrahim MEA, et al. Possible breast cancer risk related to background parenchymal enhancement at breast MRI: a meta-analysis study. Nutrition and Cancer. 2021; 73: 1371–1377.

[26] Gonçalves MA, Pereira BTL, Tavares CA, Santos TMR, da Cunha EFF, Ramalho TC. Value of contrast-enhanced magnetic resonance imaging (MRI) in the diagnosis of breast cancer. Mini-Reviews in Medicinal Chemistry. 2022; 22: 865–872.

[27] Sonni I, Minamimoto R, Baratto L, Gambhir SS, Loening AM, Vasanawala SS, et al. Simultaneous PET/MRI in the evaluation of breast and prostate cancer using combined Na[18F] F and [18F]FDG: a focus on skeletal lesions. Molecular Imaging and Biology. 2020; 22: 397–406.

[28] Brooks JD, Christensen RAG, Sung JS, Pike MC, Orlow I, Bernstein JL, et al. MRI background parenchymal enhancement, breast density and breast cancer risk factors: a cross-sectional study in pre- and post-menopausal women. NPJ Breast Cancer. 2022; 8: 97.

[29] Wang X, Chang MD, Lee MC, Niell BL. The Breast Cancer Screening and Timing of Breast MRI-experience in a genetic high-risk screening clinic in a comprehensive cancer center. Current Oncology. 2022; 29: 2119–2131.

[30] Moran CJ. Editorial for “evaluating tumor-infiltrating lymphocytes in breast cancer using preoperative MRI-based radiomics”. Journal of Magnetic Resonance Imaging. 2022; 55: 785–786.

[31] Andreassen MMS, Rodríguez-Soto AE, Conlin CC, Vidić I, Seibert TM, Wallace AM, et al. Discrimination of breast cancer from healthy breast tissue using a three-component diffusion-weighted MRI model. Clinical Cancer Research. 2021; 27: 1094–1104.