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Preoperative MRI Features Associated With Axillary Nodal Burden and Disease-Free Survival in Patients With Early-Stage Breast Cancer

  • Junjie Zhang (Cancer Hospital Affiliated to Shanxi Medical University/Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences) ;
  • Zhi Yin (College of Medical Imaging, Shanxi Medical University) ;
  • Jianxin Zhang (Cancer Hospital Affiliated to Shanxi Medical University/Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences) ;
  • Ruirui Song (Cancer Hospital Affiliated to Shanxi Medical University/Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences) ;
  • Yanfen Cui (Cancer Hospital Affiliated to Shanxi Medical University/Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences) ;
  • Xiaotang Yang (Cancer Hospital Affiliated to Shanxi Medical University/Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences)
  • 투고 : 2023.11.29
  • 심사 : 2024.06.27
  • 발행 : 2024.09.01

초록

Objective: To investigate the potential association among preoperative breast MRI features, axillary nodal burden (ANB), and disease-free survival (DFS) in patients with early-stage breast cancer. Materials and Methods: We retrospectively reviewed 297 patients with early-stage breast cancer (cT1-2N0M0) who underwent preoperative MRI between December 2016 and December 2018. Based on the number of positive axillary lymph nodes (LNs) determined by postoperative pathology, the patients were divided into high nodal burden (HNB; ≥3 positive LNs) and non-HNB (<3 positive LNs) groups. Univariable and multivariable logistic regression analyses were performed to identify independent risk factors associated with ANB. Predictive efficacy was evaluated using the receiver operating characteristic (ROC) curve and area under the curve (AUC). Univariable and multivariable Cox proportional hazards regression analyses were performed to determine preoperative features associated with DFS. Results: We included 47 and 250 patients in the HNB and non-HNB groups, respectively. Multivariable logistic regression analysis revealed that multifocality/multicentricity (adjusted odds ratio [OR] = 3.905, 95% confidence interval [CI]: 1.685-9.051, P = 0.001) and peritumoral edema (adjusted OR = 3.734, 95% CI: 1.644-8.479, P = 0.002) were independent risk factors for HNB. Combined peritumoral edema and ultifocality/multicentricity achieved an AUC of 0.760 (95% CI: 0.707-0.807) for predicting HNB, with a sensitivity and specificity of 83.0% and 63.2%, respectively. During the median follow-up period of 45 months (range, 5-61 months), 26 cases (8.75%) of breast cancer recurrence were observed. Multivariable Cox proportional hazards regression analysis indicated that younger age (adjusted hazard ratio [HR] = 3.166, 95% CI: 1.200-8.352, P = 0.021), larger tumor size (adjusted HR = 4.370, 95% CI: 1.671-11.428, P = 0.002), and multifocality/multicentricity (adjusted HR = 5.059, 95% CI: 2.166-11.818, P < 0.001) were independently associated with DFS. Conclusion: Preoperative breast MRI features may be associated with ANB and DFS in patients with early-stage breast cancer.

키워드

과제정보

This study has received funding by the Fundamental Research Program of Shanxi Province (No. 202203021212062 and 202103021222014), the National Natural Science Foundation of China (No. 82171923 and 82371952), the Central Government Guided Local Science and Technology Development Fund Project (No. YDZJSX20231B012), the Project of Shanxi Provincial Health Commission (No. 2021XM51 and 2023XM014), and the National Cancer Regional Medical Center Science and Education Cultivation Fund (No. BD2023003 and SD2023001).

참고문헌

  1. Higgins T, Mittendorf EA. Peritumoral lidocaine injection: a low-cost, easily implemented intervention to improve outcomes in early-stage breast cancer. J Clin Oncol 2023;41:3287-3290
  2. Boughey JC, Ballman KV, Hunt KK, McCall LM, Mittendorf EA, Ahrendt GM, et al. Axillary ultrasound after neoadjuvant chemotherapy and its impact on sentinel lymph node surgery: results from the American College of Surgeons Oncology Group Z1071 trial (Alliance). J Clin Oncol 2015;33:3386-3393
  3. Giuliano AE, Hunt KK, Ballman KV, Beitsch PD, Whitworth PW, Blumencranz PW, et al. Axillary dissection vs no axillary dissection in women with invasive breast cancer and sentinel node metastasis: a randomized clinical trial. JAMA 2011;305:569-575
  4. Lim GH, Upadhyaya VS, Acosta HA, Lim JMA, Allen JC Jr, Leong LCH. Preoperative predictors of high and low axillary nodal burden in Z0011 eligible breast cancer patients with a positive lymph node needle biopsy result. Eur J Surg Oncol 2018;44:945-950
  5. Kim WH, Kim HJ, Lee SM, Cho SH, Shin KM, Lee SY, et al. Prediction of high nodal burden with ultrasound and magnetic resonance imaging in clinically node-negative breast cancer patients. Cancer Imaging 2019;19:4
  6. Lim GH, Teo SY, Allen JC Jr, Chinthala JP, Leong LCH. Determining whether high nodal burden in early breast cancer patients can be predicted preoperatively to avoid sentinel lymph node biopsy. J Breast Cancer 2019;22:67-76
  7. Pilewskie M, Morrow M. Axillary nodal management following neoadjuvant chemotherapy: a review. JAMA Oncol 2017;3:549-555
  8. Chang JM, Leung JWT, Moy L, Ha SM, Moon WK. Axillary nodal evaluation in breast cancer: state of the art. Radiology 2020;295:500-515
  9. Kuijs VJ, Moossdorff M, Schipper RJ, Beets-Tan RG, Heuts EM, Keymeulen KB, et al. The role of MRI in axillary lymph node imaging in breast cancer patients: a systematic review. Insights Imaging 2015;6:203-215
  10. Yi CB, Ding ZY, Deng J, Ye XH, Chen L, Zong M, et al. Combining the ultrasound features of primary tumor and axillary lymph nodes can reduce false-negative rate during the prediction of high axillary node burden in BI-RADS category 4 or 5 breast cancer lesions. Ultrasound Med Biol 2020;46:1941-1948
  11. Li JW, Tong YY, Jiang YZ, Shui XJ, Shi ZT, Chang C. Clinicopathologic and ultrasound variables associated with a heavy axillary nodal tumor burden in invasive breast carcinoma. J Ultrasound Med 2019;38:1747-1755
  12. Dihge L, Bendahl PO, Ryden L. Nomograms for preoperative prediction of axillary nodal status in breast cancer. Br J Surg 2017;104:1494-1505
  13. Zhao F, Cai C, Liu M, Xiao J. Identification of the lymph node metastasis-related automated breast volume scanning features for predicting axillary lymph node tumor burden of invasive breast cancer via a clinical prediction model. Front Endocrinol (Lausanne) 2022;13:881761
  14. Mann RM, Cho N, Moy L. Breast MRI: state of the art. Radiology 2019;292:520-536
  15. Xu Z, Ding Y, Zhao K, Han C, Shi Z, Cui Y, et al. MRI characteristics of breast edema for assessing axillary lymph node burden in early-stage breast cancer: a retrospective bicentric study. Eur Radiol 2022;32:8213-8225
  16. Li J, Ma W, Jiang X, Cui C, Wang H, Chen J, et al. Development and validation of nomograms predictive of axillary nodal status to guide surgical decision-making in early-stage breast cancer. J Cancer 2019;10:1263-1274
  17. Cheon H, Kim HJ, Lee SM, Cho SH, Shin KM, Kim GC, et al. Preoperative MRI features associated with lymphovascular invasion in node-negative invasive breast cancer: a propensity-matched analysis. J Magn Reson Imaging 2017;46:1037-1044
  18. Zhang S, Zhang D, Yi S, Gong M, Lu C, Cai Y, et al. The relationship of lymphatic vessel density, lymphovascular invasion, and lymph node metastasis in breast cancer: a systematic review and meta-analysis. Oncotarget 2017;8:2863-2873
  19. D'Orsi CJ, Sickles EA, Mendelson EB, Morris EA. ACR BI-RADS atlas: breast imaging reporting and data system. 5th ed. Reston: American College of Radiology, 2013
  20. Byon JH, Park YV, Yoon JH, Moon HJ, Kim EK, Kim MJ, et al. Added value of MRI for invasive breast cancer including the entire axilla for evaluation of high-level or advanced axillary lymph node metastasis in the post-ACOSOG Z0011 trial era. Radiology 2021;300:46-54
  21. Harada TL, Uematsu T, Nakashima K, Sugino T, Nishimura S, Takahashi K, et al. Is the presence of edema and necrosis on T2WI pretreatment breast MRI the key to predict pCR of triple negative breast cancer? Eur Radiol 2020;30:3363-3370
  22. Abdelhafez AH, Musall BC, Adrada BE, Hess K, Son JB, Hwang KP, et al. Tumor necrosis by pretreatment breast MRI: association with neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC). Breast Cancer Res Treat 2021;185:1-12
  23. Wolff AC, Hammond MEH, Allison KH, Harvey BE, Mangu PB, Bartlett JMS, et al. Human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline focused update. J Clin Oncol 2018;36:2105-2122
  24. Goldhirsch A, Winer EP, Coates AS, Gelber RD, Piccart-Gebhart M, Thurlimann B, et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen international expert consensus on the primary therapy of early breast cancer 2013. Ann Oncol 2013;24:2206-2223
  25. Park SH, Han K, Park SY. Mistakes to avoid for accurate and transparent reporting of survival analysis in imaging research. Korean J Radiol 2021;22:1587-1593
  26. Camp RL, Dolled-Filhart M, Rimm DL. X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res 2004;10:7252-7259
  27. Cheon H, Kim HJ, Kim TH, Ryeom HK, Lee J, Kim GC, et al. Invasive breast cancer: prognostic value of peritumoral edema identified at preoperative MR imaging. Radiology 2018;287:68-75
  28. Kwon BR, Shin SU, Kim SY, Choi Y, Cho N, Kim SM, et al. Microcalcifications and peritumoral edema predict survival outcome in luminal breast cancer treated with neoadjuvant chemotherapy. Radiology 2022;304:310-319
  29. Baltzer PA, Yang F, Dietzel M, Herzog A, Simon A, Vag T, et al. Sensitivity and specificity of unilateral edema on T2w-TSE sequences in MR-mammography considering 974 histologically verified lesions. Breast J 2010;16:233-239
  30. Koyama H, Kobayashi N, Harada M, Takeoka M, Kawai Y, Sano K, et al. Significance of tumor-associated stroma in promotion of intratumoral lymphangiogenesis: pivotal role of a hyaluronan-rich tumor microenvironment. Am J Pathol 2008;172:179-193
  31. Song SE, Park EK, Cho KR, Seo BK, Woo OH, Jung SP, et al. Additional value of diffusion-weighted imaging to evaluate multifocal and multicentric breast cancer detected using pre-operative breast MRI. Eur Radiol 2017;27:4819-4827
  32. Grimm LJ, Johnson KS, Marcom PK, Baker JA, Soo MS. Can breast cancer molecular subtype help to select patients for preoperative MR imaging? Radiology 2015;274:352-358
  33. Weissenbacher TM, Zschage M, Janni W, Jeschke U, Dimpfl T, Mayr D, et al. Multicentric and multifocal versus unifocal breast cancer: is the tumor-node-metastasis classification justified? Breast Cancer Res Treat 2010;122:27-34
  34. Fushimi A, Yoshida A, Yagata H, Takahashi O, Hayashi N, Suzuki K, et al. Prognostic impact of multifocal and multicentric breast cancer versus unifocal breast cancer. Surg Today 2019;49:224-230
  35. Giuliano AE, Edge SB, Hortobagyi GN. Eighth edition of the AJCC cancer staging manual: breast cancer. Ann Surg Oncol 2018;25:1783-1785
  36. Bitencourt AGV, Eugenio DSG, Souza JA, Souza JO, Makdissi FBA, Marques EF, et al. Prognostic significance of preoperative MRI findings in young patients with breast cancer. Sci Rep 2019;9:3106