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The impact of body mass index (BMI) on MRI diagnostic performance and surgical management for axillary lymph node in breast cancer

Abstract

Background

We hypothesized that different BMI might have different impact on pre-operative MRI axillary lymph node (ALN) prediction accuracy and thereby subsequent surgical lymph node management. The aim of this study is to evaluate the effect of BMI on presentation, surgical treatment, and MRI performance characteristics of breast cancer with the main focus on ALN metastasis evaluation.

Methods

The medical records of patients with primary invasive breast cancer who had pre-operative breast MRI and underwent surgical resection were retrospectively reviewed. They were categorized into 3 groups in this study: underweight (BMI < 18.5), normal (BMI of 18.5 to 24), and overweight (BMI > 24). Patients’ characteristics, surgical management, and MRI performance for axillary evaluation between the 3 groups were compared.

Results

A total of 2084 invasive breast cancer patients with a mean age of 53.4 ± 11.2 years were included. Overweight women had a higher rate of breast conserving surgery (56.7% vs. 54.5% and 52.1%) and initial axillary lymph node dissection (15.9% vs. 12.2% and 8.5%) if compared to normal and underweight women. Although the post-operative ALN positive rates were similar between the 3 groups, overweight women were significantly found to have more axillary metastasis on MRI compared with normal and underweight women (50.2% vs 37.7% and 18.3%). There was lower accuracy in terms of MRI prediction in overweight women (65.1%) than in normal and underweight women (67.8% and 76.1%).

Conclusion

Our findings suggest that BMI may influence the diagnostic performance on MRI on ALN involvement and the surgical management of the axilla in overweight to obese women with breast cancer.

Background

Breast cancer is the most common cancer in women worldwide [1]. Body mass index (BMI) has become the most well-adopted index of body weight [2, 3] and obesity had been shown in many studies to be related to breast cancer incidence and outcome [4,5,6]. Furthermore, increase in the incidence of breast cancer-specific death was reported in obese ladies in a recent meta-analysis [7]. A previous large, population-based case–cohort study [8] also found that obesity appeared to influence breast cancer survival in part by greater tumor size, positive nodal status, and distant metastasis, with a 1.7-fold increased risk of stage III/IV disease in obese women compared to normal weight women.

Axillary lymph node (ALN) staging is an important part in the surgical management of breast cancer. Axillary nodal involvement is a well-known indicator of poor prognosis. Previously, axillary lymph node dissection (ALND) was the gold standard in determining the status of ALNs in patients with breast cancer. Sentinel lymph node biopsy (SLNB), which was associated with less morbidity, had gradually replaced ALND for surgical ALN evaluation in patients with early breast cancer [9,10,11]. ALND was performed if nodal metastasis was confirmed on SLNB or preoperative percutaneous biopsy. However, two landmark trials (NSABP-04 and ASCOG-Z0011) had caused a paradigm shift as these trials had shown that for breast cancer patients with T1–T2 stage and clinically negative lymph node (cN0), ALND could be safely omitted in selected patients having one or two sentinel lymph node (SLN) metastasis after breast preservation surgery and whole breast radiotherapy [12, 13]. Since ALND may not be necessary in women with metastatic axillary disease who meet the trial criteria, these studies had changed the role of pre-operative axillary imaging from identifying ALN metastasis to detecting patients with advanced (more than 2 metastatic LNs) or high-level axillary lymph nodes (metastasis in level II or III LNs). In other words, pre-operative axillary imaging plays an important role on identifying patients who are suitable for SLNB [14] or even omitting biopsy in some conditions [15, 16].

For pre-operative staging and evaluation of ALN status, both ultrasound and MRI are commonly used non-invasive modalities [17]. Previous study had shown the sensitivity of pre-operative ultrasound for detecting ALN metastasis was similar in obese and non-obese patients [18]. However, there was paucity of data regarding the effect of BMI on the performance of MRI in ALN evaluation. Hence, our analysis might provide a better understanding of the effect of BMI on axillary lymph node evaluation especially with pre-operative MRI.

Therefore, the aim of this study is to evaluate the effect of BMI on presentation, surgical treatment and MRI performance characteristics of breast cancer with the main focus on axillary lymph nodes metastasis evaluation. We hypothesized that different BMI might have different impact on pre-operative MRI axillary lymph node prediction accuracy and thereby subsequent surgical lymph node management.

Methods

Patients

The study was approved by the Institutional Review Board of the Changhua Christian Hospital (CCH) (CCH IRB No. 141224 and No. 210519) and granted a waiver of informed consent. Women with invasive breast cancer and underwent surgical resection during the period of January 2010 to December 2020 were retrospectively recruited from the breast cancer database of CCH in Taiwan. Patients with non-primary breast cancer, carcinoma in situ only lesions, those who did not receive pre-operative MRI study, or patients who underwent neoadjuvant chemotherapy were excluded from the study. A flow chart of the patient selection process was shown in Fig. 1.

Fig. 1
figure 1

Flow chart of patient exclusion criteria

The clinicopathologic factors collected from the database include BMI, age, tumor location, biopsy method, types of breast operation, axillary LN staging method, pathologic tumor size, histology, tumor grade, status of estrogen receptor (ER), progesterone receptor (PR), human epithelial growth factor receptor 2 (HER-2) expression, and Ki-67 percentage, and axillary LN status.

BMI was calculated as weight in kilograms divided by height in meters squared (BMI = kg/m2). Using Taiwanese definition, BMI was categorized into four groups: underweight (BMI < 18.5), normal (BMI of 18.5 to 24), overweight (BMI of 24.1 to 26.9), and obese (BMI ≥ 27). Patients were categorized into 3 groups for the purpose of this study: underweight (BMI < 18.5), normal (BMI of 18.5 to 24), overweight (BMI > 24). We merged overweight and obese patients into one group because of the relatively small proportion of people with a BMI > 27 in our study cohort.

Magnetic resonance imaging (MRI) protocol and evaluation

MR imaging was performed with a Siemens MAGNETOM Verio 3.0 Tesla MRI machine. All patients were imaged in the prone position with both breasts placed into a dedicated 16-channel breast coil. MR imaging protocols included the following: bilateral axial turbo-spin-echo fat-suppressed T2-weighted imaging (TR/TE 4630/70 ms; field of view 320 mm; slice thickness 3 mm; number of excitations 1), axial turbo-spin-echo T1-weighted imaging (TR/TE 736/9.1 ms; field of view 320 mm; slice thickness 3 mm; number of excitations 1), and diffusion weighted imaging (DWI) (TR/TE 5800/82 ms; field of view 360 mm; slice thickness 3 mm, with b values of 0, 400, and 800 s/mm2). Dynamic contrast enhanced MR images (DCE-MRI) were obtained with a three-dimensional fat-suppressed volumetric interpolated breath-hold examination (VIBE) sequence with parallel acquisition once before and five times after a bolus injection of gadobenate dimeglumine (0.1 mmol/kg). Both breasts were examined in the transverse plane at 60 s intervals in each phase of the dynamic studies. The dynamic MRI parameters were as follows: TR/TE 4.36/1.58 ms; field of view 320 mm; slice thickness 1 mm. The following criteria were used to identify suspicious metastatic lymph nodes: marked enhancement, loss of fatty hilum, cortical thickening (> 3 mm), round, or irregular shape [19, 20]. The whole-breast MRI readings were carried out by two experienced, board-certified breast radiologists (HKW, CYL, and WPW).

Statistical analyses

Data were expressed as mean ± standard deviation for continuous variables. The Kolmogorov-Smirnov test demonstrated that the samples followed a normal distribution. The independent t test was used to compare continuous variables. Categorical variables were compared using the chi-square test or Fisher’s exact test, as appropriate.

The chi-square test was used to assess the associations between BMI and the evaluation of ALN metastasis on MRI. Final surgical histopathologic findings at either SLNB or ALND were used as reference standards for ALN evaluation. Diagnostic performance parameters (sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy) in each BMI subgroup were calculated for breast MRI regarding the surgical axillary LN status. True negative was defined as MRI showing negative ALN lymph nodes and surgical lymph node report also revealing no ALN metastasis. True positive was defined as MRI ALN evaluation as metastasis followed by final surgical outcome also confirming ALN metastasis. Sensitivity and specificity were defined as probabilities that in the case of positive ALN in the pathologic reports, MRI reported suspicious ALNs. Statistical analyses were performed by using Statistical Product and Service Solutions (SPSS) for Windows (Version 19.0, SPSS Inc., Chicago, IL).

Results

Patient and clinicopathological characteristics

According to the inclusion and exclusion criteria, a total of 2084 women with primary operable invasive breast cancer patients who received pre-operative breast MRI and surgical treatment were selected from CCH Breast Cancer Database. Among the 2084 women included in the study, 71 (3.4%) were considered as underweight (BMI < 18.5), 1045 (50.1%) as normal (18.5 ≤ BMI < 24), and 968 (46.5%) as overweight (BMI ≥ 24). The mean age of this cohort was 53.4 ± 11.2 years, and mean tumor size 2.3 ± 1.6 cm (Table 1). Overweight women were significantly older (56.1 ± 11.1 years) compared with normal (51.4 ± 10.7 years) and underweight women (47.6 ± 11.6 years, p < 0.01). The pathologic tumor size was significant larger in overweight women (p < 0.01). Underweight women (52.1%) tend to present with early stage (stage I) breast cancer at diagnosis if compared with overweight (34.5%) or normal (46.4%) women (p < 0.01). No significant differences between BMI groups were found for histological subtype, histologic grade, ER/PR/HER-2, and Ki-67 status (Table 1).

Table 1 Demographic and clinical characteristics by body mass index (BMI) subgroups

The mean total mastectomy specimen weight in the study cohort was 346.5 ± 200.1 g, and overweight women were significantly associated with larger specimen weight (466.9 ± 221.7) than normal (280.8 ± 140.8), and underweight (153.7 ± 97.6) patients (p < 0.01). Compared with underweight (52.1%) and normal (54.5%) women, overweight (56.7%) women had a higher chance of receiving breast conserving surgery (BCS). On the contrary, overweight women had higher rate of ALND as initial approach than normal and underweight women (15.9% vs. 12.2% and 8.5%, p < 0.01, Table 1).

Diagnostic performance of prediction ALN status on MRI & impact of BMI

A total of 2084 invasive breast cancer patients with pre-operative ALN MRI evaluation and post-operative ALN pathologic results were available for concordance analysis (Figs. 2, 3, and 4). Overweight women were significantly reported to have higher incidence of axillary metastasis on MRI if compared with underweight and normal weight women (50.2% vs 18.3% and 37.7%, p < 0.01, Table 2). The post-operative ALN positive rates were similar between all the 3 BMI subgroups (37.8%, 31%, 33.5%, p = 0.09).

Fig. 2
figure 2

Breast MRI in a 40-year-old women in the overweight group (BMI 28.8) with invasive carcinoma in the right breast. a STIR axial image. b T1-weighted axial image shows suspicious lymph nodes in right axilla (arrowhead). But the pathology shows no metastatic lymph nodes are noted

Fig. 3
figure 3

A female patient aged 38 years in the underweight group (BMI 17.7) with invasive ductal carcinoma in the left breast. MRI demonstrates enlarged lymph nodes (LN) in ipsilateral axilla (arrowhead). a STIR pulse sequence. b Pre-contrast T1-weighted pulse sequence. c The contrast-enhanced T1 fat-saturated pulse sequence. The pathology report confirmed axillary LN metastasis

Fig. 4
figure 4

Breast MRI in a 40-year-old women in the normal group (BMI 21.2) with invasive carcinoma in the left breast. a STIR axial image. b T1-weighted axial image. No suspicious lymph nodes are seen. But the pathology shows several metastatic lymph nodes

Table 2 Diagnostic performance of MRI on axillary lymph node evaluation between BMI groups

For the concordance of ALN status between MRI and final pathology in the BMI subgroups analysis, overweight women significantly have the highest sensitivity (70.2%) but lowest specificity (62.0%). On the contrary, underweight women have the highest specificity (91.8%), and PPV (69.2%). NPV was similar between three groups: 77.6%, 77.6%, and 77.4% in underweight, normal, and overweight women respectively. Overall, the accuracy of breast MRI for detecting metastatic ALN was lower in overweight women (65.1%) than in normal and underweight women (67.8% and 76.1%, Table 2)

Discussion

As the prevalence of obesity continues to increase in our population, understanding the effect of BMI on pre-operative clinical imaging of breast cancer is of paramount importance. Our current study showed that BMI influenced the diagnostic performance on MRI and the surgical management of ALN in overweight (BMI > 24) women with breast cancer and this was an important but rarely reported finding prior. We found that the accuracy of ALN prediction in breast MRI was lower in overweight women (65.1%) owing to the higher rate of false-positive prediction of pre-operative MRI imaging assessment.

ALN status is no doubt a critical component in surgical decision-making and in determining therapeutic strategies with significant impact on overall prognosis. Surgical axillary staging is still the gold standard for evaluating the status of ALN in breast cancer patients, be it either ALND or SLNB. Currently, SLNB had replaced ALND for surgical ALN evaluation in patients with clinically node negative breast cancer [21]. Meanwhile, pre-operative imaging assessment of the axilla has become increasingly more common in the current day breast cancer management. The value of ultrasound on axillary evaluation had been well documented [22,23,24,25]. However, the exact diagnostic performance of MRI for discriminating axillary lymph node involvement has inconsistent results reported by previous studies [26,27,28,29,30,31,32,33,34,35] (Table 3). As the results, to enhance the use of MRI on pre-operative axillary evaluation, identifying subgroup who would be benefit or less advantage from pre-operative MRI axillary evaluation is important. There were numerous studies evaluating the link between BMI and the risk, prognosis, and management of breast cancer [36, 37]. However, the impacts of BMI on pre-operative imaging assessment modalities were still not well established. Shah et al. showed that the sensitivity of pre-operative ultrasound assessment for detecting nodal metastasis was similar in newly diagnosed invasive breast cancer patients regardless of BMI [18]. On the contrary, we found that the sensitivity, specificity, and overall accuracy of ALN status as evaluated on MRI were significantly affected by BMI.

Table 3 Literature review of MRI diagnostic performance on axillary lymph node

The sensitivity, specificity and accuracy of lymph node evaluation in overweight group (BMI > 24) were 70.2%, 62.0%, and 65.1%, respectively, while in the normal weight group they were 58.3%, 72.7%, and 67.8%, respectively. The lower specificity and accuracy were likely attributed to more false positive nodes on MRI in the overweight group. This result may imply that obesity could have a significant impact on nodal morphology features or size on MRI leading to limited diagnostic value. Alexander et al. found a highly significant association between increasing BMI and axillary LN dimensions, which was driven by expansion of the LN hilum secondary to fat infiltration [38]. Thus, using conventional MRI morphologic criteria to determine ALN metastasis may be limited. Other than morphologic features, functional and physiological assessments of the lymph nodes may be useful in detecting ALN metastasis. Buus et al. [39] found metastatic lymph node fat fraction on Dixon sequence were significantly lower than non-metastatic ipsilateral (p < 0.001) and contralateral lymph nodes (p < 0.001). Xing et al. [40] conducted a meta-analysis and found sensitivity of 0.86 and specificity of 0.86 for ADC values to discriminate between metastatic and non-metastatic axillary lymph nodes with AUC of 0.93. Superparamagnetic iron oxide (SPIO) MRI showed potential for a non-invasive modality of lymph node (LN) metastases evaluation [41]. However, whether BMI has impact on the diagnostic performance of these alternative methods remains unclear. Further studies combining additional sequence or specific contrast agent in the MRI assessment of the axilla in higher BMI breast cancer patients may be warranted.

Overweight to obese women, which was defined as BMI > 24 in current study, were usually correlated with larger breasts, and higher BCS rate compared with normal or underweight patients (Table 1). Similar to a previous study [42], our results showed that elevated BMI was not associated with a higher likelihood of ALN metastasis. However, we found a difference in surgical axillary staging between BMI categories, in that overweight to obese women had a higher proportion of receiving ALND as the initial approach. ALND is reserved as a secondary treatment procedure for patients with positive findings on SLNB or in clinically positive ALN patients. Higher ALND rate in the overweight group (BMI > 24) could be attributed to higher false-positive nodes on pre-operative breast imaging, like MRI.

Findings of suspicious ALN metastasis on imaging may subject patients to upfront ALND, especially if the patient did not receive neoadjuvant chemotherapy. Another possible explanation would be surgeons’ concern about the technical difficulty of ultrasound-guided fine needle aspiration/core needle biopsy and higher failure rate of SLNB in overweight/obesity patients. Although the precise reasons are largely unknown, several studies have reported lower SLN identification rates in obese women with dual-modality method for high BMI [43, 44]. Obese women who underwent ALND could potentially developed unfavorable effect after breast cancer surgery. Meijer et al. revealed ALND and high BMI are risk factors of developing breast cancer related lymphedema [45].

This study is limited by its retrospective nature and a single institution cohort which could lead to bias in treatment preferences. We aim to provide a general and complete information regarding the surgical management of breast tumor and BMI, and our patient cohort is large and representative of the contemporary distribution of BMI in Asian population. Our cohort of more than 2000 primary invasive operable breast cancer patients with pre-operative MRI evaluation and detailed post-operative pathologic reports enabled us to analyze the impact of BMI on the evaluation ALN status with MRI.

Conclusions

Our study demonstrated that the diagnostic performance of MRI on pre-operative axillary lymph node assessment and hence surgical management was affected in overweight to obese women with breast cancer. Clinicians should therefore be cautious of using pre-operative MRI alone for the evaluation of ALN status, and specific strategies are needed to optimize the care of overweight to obese women with breast cancer.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

BMI:

Body mass index

ALN:

Axillary lymph node

ALND:

Axillary lymph node dissection

SLN:

Sentinel lymph node

SLNB:

Sentinel lymph node biopsy

ER:

Estrogen receptor

PR:

Progesterone receptor

HER-2:

Human epithelial growth factor receptor 2

PPV:

Positive predict value

NPV:

Negative predictive value

References

  1. Engin A. Obesity-associated breast cancer: analysis of risk factors. Adv Exp Med Biol. 2017;960:571–606.

    CAS  PubMed  Google Scholar 

  2. Flegal KM, Kit BK, Orpana H, Graubard BI. Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. JAMA. 2013;309:71–82.

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Garrow JS, Webster J. Quetelet's index (W/H2) as a measure of fatness. Int J Obes. 1985;9:147–53.

    CAS  PubMed  Google Scholar 

  4. Kang C, LeRoith D, Gallagher EJ. Diabetes, obesity, and breast cancer. Endocrinology. 2018;159:3801–12.

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Neuhouser ML, Aragaki AK, Prentice RL, Manson JE, Chlebowski R, Carty CL, et al. Overweight, obesity, and postmenopausal invasive breast cancer risk: a secondary analysis of the women's health initiative randomized clinical trials. JAMA Oncol. 2015;1:611–21.

    PubMed  PubMed Central  Google Scholar 

  6. Protani M, Coory M, Martin JH. Effect of obesity on survival of women with breast cancer: systematic review and meta-analysis. Breast Cancer Res Treat. 2010;123:627–35.

    PubMed  Google Scholar 

  7. Chan DSM, Vieira AR, Aune D, Bandera EV, Greenwood DC, McTiernan A, et al. Body mass index and survival in women with breast cancer-systematic literature review and meta-analysis of 82 follow-up studies. Ann Oncol. 2014;25:1901–14.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Blair CK, Wiggins CL, Nibbe AM, Storlie CB, Prossnitz ER, Royce M, et al. Obesity and survival among a cohort of breast cancer patients is partially mediated by tumor characteristics. NPJ Breast Cancer. 2019;5:33.

    PubMed  PubMed Central  Google Scholar 

  9. Hennigs A, Riedel F, Feisst M, Kopke M, Rezai M, Nitz U, et al. Evolution of the use of completion axillary lymph node dissection in patients with T1/2N0M0 breast cancer and tumour-involved sentinel lymph nodes undergoing mastectomy: a cohort study. Ann Surg Oncol. 2019;26:2435–43.

    PubMed  Google Scholar 

  10. Rao R, Euhus D, Mayo HG, Balch C. Axillary node interventions in breast cancer: a systematic review. JAMA. 2013;310:1385–94.

    CAS  PubMed  Google Scholar 

  11. Cirocchi R, Amabile MI, De Luca A, Frusone F, Tripodi D, Gentile P, et al. New classifications of axillary lymph nodes and their anatomical-clinical correlations in breast surgery. World J Surg Oncol. 2021;19:93.

    PubMed  PubMed Central  Google Scholar 

  12. Giuliano AE, Ballman KV, McCall L, Beitsch PD, Brennan MB, Kelemen PR, et al. Effect of axillary dissection vs no axillary dissection on 10-year overall survival among women with invasive breast cancer and sentinel node metastasis: the ACOSOG Z0011 (Alliance) randomized clinical trial. JAMA. 2017;318:918–26.

    PubMed  PubMed Central  Google Scholar 

  13. Krag DN, Anderson SJ, Julian TB, Brown AM, Harlow SP, Ashikaga T, et al. Technical outcomes of sentinel-lymph-node resection and conventional axillary-lymph-node dissection in patients with clinically node-negative breast cancer: results from the NSABP B-32 randomised phase III trial. Lancet Oncol. 2007;8:881–8.

    CAS  PubMed  Google Scholar 

  14. Sun SX, Moseley TW, Kuerer HM, Yang WT. Imaging-based approach to axillary lymph node staging and sentinel lymph node biopsy in patients with breast cancer. AJR Am J Roentgenol. 2020;214:249–58.

    PubMed  Google Scholar 

  15. Lyman GH, Temin S, Edge SB, Newman LA, Turner RR, Weaver DL, et al. Sentinel lymph node biopsy for patients with early-stage breast cancer: American Society of Clinical Oncology clinical practice guideline update. J Clin Oncol. 2014;32:1365–83.

    PubMed  Google Scholar 

  16. Shapiro-Wright HM, Julian TB. Sentinel lymph node biopsy and management of the axilla in ductal carcinoma in situ. J Natl Cancer Inst Monogr. 2010;2010:145–9.

    PubMed  PubMed Central  Google Scholar 

  17. Leenders M, Kramer G, Belghazi K, Duvivier K, van den Tol P, Schreurs H. Can we identify or exclude extensive axillary nodal involvement in breast cancer patients preoperatively? J Oncol. 2019;2019:8404035.

    PubMed  PubMed Central  Google Scholar 

  18. Shah AR, Glazebrook KN, Boughey JC, Hoskin TL, Shah SS, Bergquist JR, et al. Does BMI affect the accuracy of preoperative axillary ultrasound in breast cancer patients? Ann Surg Oncol. 2014;21:3278–83.

    PubMed  Google Scholar 

  19. Murray AD, Staff RT, Redpath TW, Gilbert FJ, Ah-See AK, Brookes JA, et al. Dynamic contrast enhanced MRI of the axilla in women with breast cancer: comparison with pathology of excised nodes. Br J Radiol. 2002;75:220–8.

    CAS  PubMed  Google Scholar 

  20. Ecanow JS, Abe H, Newstead GM, Ecanow DB, Jeske JM. Axillary staging of breast cancer: what the radiologist should know. Radiographics. 2013;33:1589–612.

    PubMed  Google Scholar 

  21. Galimberti V, Cole BF, Zurrida S, Viale G, Luini A, Veronesi P, et al. Axillary dissection versus no axillary dissection in patients with sentinel-node micrometastases (IBCSG 23-01): a phase 3 randomised controlled trial. Lancet Oncol. 2013;14:297–305.

    PubMed  PubMed Central  Google Scholar 

  22. Lowes S, Leaver A, Cox K, Satchithananda K, Cosgrove D, Lim A. Evolving imaging techniques for staging axillary lymph nodes in breast cancer. Clin Radiol. 2018;73:396–409.

    CAS  PubMed  Google Scholar 

  23. Choi HY, Park M, Seo M, Song E, Shin SY, Sohn YM. Preoperative axillary lymph node evaluation in breast cancer: current issues and literature review. Ultrasound Q. 2017;33:6–14.

    PubMed  Google Scholar 

  24. Alvarez S, Anorbe E, Alcorta P, Lopez F, Alonso I, Cortes J. Role of sonography in the diagnosis of axillary lymph node metastases in breast cancer: a systematic review. AJR Am J Roentgenol. 2006;186:1342–8.

    PubMed  Google Scholar 

  25. Balasubramanian I, Fleming CA, Corrigan MA, Redmond HP, Kerin MJ, Lowery AJ. Meta-analysis of the diagnostic accuracy of ultrasound-guided fine-needle aspiration and core needle biopsy in diagnosing axillary lymph node metastasis. Br J Surg. 2018;105:1244–53.

    CAS  PubMed  Google Scholar 

  26. Yoshimura G, Sakurai T, Oura S, Suzuma T, Tamaki T, Umemura T, et al. Evaluation of axillary lymph node status in breast cancer with MRI. Breast Cancer. 1999;6:249–58.

    CAS  PubMed  Google Scholar 

  27. Kvistad KA, Rydland J, Smethurst HB, Lundgren S, Fjosne HE, Haraldseth O. Axillary lymph node metastases in breast cancer: preoperative detection with dynamic contrast-enhanced MRI. Eur Radiol. 2000;10:1464–71.

    CAS  PubMed  Google Scholar 

  28. He N, Xie C, Wei W, Pan C, Wang W, Lv N, et al. A new, preoperative, MRI-based scoring system for diagnosing malignant axillary lymph nodes in women evaluated for breast cancer. Eur J Radiol. 2012;81:2602–12.

    PubMed  Google Scholar 

  29. Scaranelo AM, Eiada R, Jacks LM, Kulkarni SR, Crystal P. Accuracy of unenhanced MR imaging in the detection of axillary lymph node metastasis: study of reproducibility and reliability. Radiology. 2012;262:425–34.

    PubMed  Google Scholar 

  30. Hwang SO, Lee SW, Kim HJ, Kim WW, Park HY, Jung JH. The comparative study of ultrasonography, contrast-enhanced MRI, and (18)F-FDG PET/CT for detecting axillary lymph node metastasis in T1 breast cancer. J Breast Cancer. 2013;16:315–21.

    PubMed  PubMed Central  Google Scholar 

  31. Hieken TJ, Trull BC, Boughey JC, Jones KN, Reynolds CA, Shah SS, et al. Preoperative axillary imaging with percutaneous lymph node biopsy is valuable in the contemporary management of patients with breast cancer. Surgery. 2013;154(831-838):discussion 838-840.

    Google Scholar 

  32. Abe H, Schacht D, Kulkarni K, Shimauchi A, Yamaguchi K, Sennett CA, et al. Accuracy of axillary lymph node staging in breast cancer patients: an observer-performance study comparison of MRI and ultrasound. Acad Radiol. 2013;20:1399–404.

    PubMed  Google Scholar 

  33. An YS, Lee DH, Yoon JK, Lee SJ, Kim TH, Kang DK, et al. Diagnostic performance of 18F-FDG PET/CT, ultrasonography and MRI. Detection of axillary lymph node metastasis in breast cancer patients. Nuklearmedizin. 2014;53:89–94.

    CAS  PubMed  Google Scholar 

  34. Hyun SJ, Kim EK, Moon HJ, Yoon JH, Kim MJ. Preoperative axillary lymph node evaluation in breast cancer patients by breast magnetic resonance imaging (MRI): Can breast MRI exclude advanced nodal disease? Eur Radiol. 2016;26:3865–73.

    PubMed  Google Scholar 

  35. Barco I, Chabrera C, Garcia-Fernandez A, Fraile M, Gonzalez S, Canales L, et al. Role of axillary ultrasound, magnetic resonance imaging, and ultrasound-guided fine-needle aspiration biopsy in the preoperative triage of breast cancer patients. Clin Transl Oncol. 2017;19:704–10.

    CAS  PubMed  Google Scholar 

  36. Ozmen V, Ilgun S, Celet Ozden B, Ozturk A, Aktepe F, Agacayak F, et al. Comparison of breast cancer patients who underwent partial mastectomy (PM) with mini latissimus dorsi flap (MLDF) and subcutaneous mastectomy with implant (M + I) regarding quality of life (QOL), cosmetic outcome and survival rates. World J Surg Oncol. 2020;18:87.

    PubMed  PubMed Central  Google Scholar 

  37. Wang XL, Jia CX, Liu LY, Zhang Q, Li YY, Li L. Obesity, diabetes mellitus, and the risk of female breast cancer in Eastern China. World J Surg Oncol. 2013;11:71.

    PubMed  PubMed Central  Google Scholar 

  38. diFlorio Alexander RM, Haider SJ, MacKenzie T, Goodrich ME, Weiss J, Onega T. Correlation between obesity and fat-infiltrated axillary lymph nodes visualized on mammography. Br J Radiol. 2018;91:20170110.

    PubMed  Google Scholar 

  39. Buus TW, Sivesgaard K, Fris TL, Christiansen PM, Jensen AB, Pedersen EM. Fat fractions from high-resolution 3D radial Dixon MRI for predicting metastatic axillary lymph nodes in breast cancer patients. Eur J Radiol Open. 2020;7:100284.

    PubMed  PubMed Central  Google Scholar 

  40. Xing H, Song CL, Li WJ. Meta analysis of lymph node metastasis of breast cancer patients: clinical value of DWI and ADC value. Eur J Radiol. 2016;85:1132–7.

    PubMed  Google Scholar 

  41. Pouw JJ, Grootendorst MR, Bezooijen R, Klazen CA, De Bruin WI, Klaase JM, et al. Pre-operative sentinel lymph node localization in breast cancer with superparamagnetic iron oxide MRI: the SentiMAG Multicentre Trial imaging subprotocol. Br J Radiol. 2015;88:20150634.

    PubMed  PubMed Central  Google Scholar 

  42. McCartan D, Stempel M, Eaton A, Morrow M, Pilewskie M. Impact of body mass index on clinical axillary nodal assessment in breast cancer patients. Ann Surg Oncol. 2016;23:3324–9.

    PubMed  PubMed Central  Google Scholar 

  43. Derossis AM, Fey JV, Cody HS 3rd, Borgen PI. Obesity influences outcome of sentinel lymph node biopsy in early-stage breast cancer. J Am Coll Surg. 2003;197:896–901.

    PubMed  Google Scholar 

  44. Cox CE, Dupont E, Whitehead GF, Ebert MD, Nguyen K, Peltz ES, et al. Age and body mass index may increase the chance of failure in sentinel lymph node biopsy for women with breast cancer. Breast J. 2002;8:88–91.

    PubMed  Google Scholar 

  45. Meijer EF, Bouta EM, Mendonca C, Skolny MN, Salama LW, Taghian AG, Padera TP. A retrospective analysis of commonly prescribed medications and the risk of developing breast cancer related lymphedema. Clin Res Trials. 2020;6(1). https://doi.org/10.15761/crt.1000293.

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Acknowledgements

The authors would like thank Shu-Hsin Pai, Yi-Yuan Lee, Chin-Mei Tai, Yi-Ru Ke, Ya Ting Zhung, and Yun-Ting Chang for the assistance in this study.

Funding

This study was funded by the Ministry of Science and Technology of Taiwan (MOST 110-2314-B-371-009). This study was also sponsored by research funding provided by the Changhua Christian Hospital (109-CCH-IRP-093, 110-CCH-IRP-042, and 110-CCH-ICO-155). The funding body did not play any role in design, in the collection, analysis, and interpretation of data; in the writing of the manuscript; and in the decision to submit the manuscript for publication.

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HWL contributed to study conception and design, general supervision of the research group, and also critical revised the manuscript. STC involved in data interpretation and was a major contributor in writing the manuscript. STC performed the statistical analysis. CWM contributed to English language reviewing of the manuscript. WPW and HKW contributed to imaging interpretation and annotation. DRC and DRC engaged in data acquisition and analysis, as well as imaging and figures processing. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Hung-Wen Lai.

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Ethics approval and consent to participate

This study was approved by the institutional review board (IRB) of Changhua Christian Hospital (CCH IRB No. 141224 and No. 210519), and all patients gave written informed consent.

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Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Chen, ST., Lai, HW., Wu, WP. et al. The impact of body mass index (BMI) on MRI diagnostic performance and surgical management for axillary lymph node in breast cancer. World J Surg Onc 20, 45 (2022). https://doi.org/10.1186/s12957-022-02520-6

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  • DOI: https://doi.org/10.1186/s12957-022-02520-6

Keywords

  • Body mass index
  • Breast cancer
  • MRI
  • Axillary lymph node