Simple MR Guided Breast Biopsy Strategy: technique and radiological-pathological association

Simple MR Guided Breast Biopsy Strategy: technique and radiological-pathological association | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Simple MR Guided Breast Biopsy Strategy: technique and radiological-pathological association Fattaneh Khalaj, Zahra Moradi, Hamed Ghorani, Amir Kasaeian, Mohammad Hosein Golazar, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4719861/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background MRI is pivotal in breast imaging, encompassing staging, treatment monitoring, and lesion differentiation. While MRI boasts high sensitivity, specificity, and utility in detecting otherwise unseen lesions, challenges persist in accurately distinguishing benign from malignant findings. The study delves into MRI-guided breast biopsy outcomes and highlights the importance of radiologic-pathologic results. Methods This retrospective study analyzed 109 MRI-guided breast biopsies conducted on lesions identified between 2017 and 2023. the patients underwent biopsies for screening and diagnostic purposes. Biopsy procedures involved meticulous MRI guidance using a 1.5 Tesla system. Lesions were categorized based on location and BIRADS lexicon, with biopsy results spanning benign, suspicious, and malignant pathologies. Data collection encompassed a wide array of patient factors and pathology reports, meticulously reviewed by experienced radiologists, shedding light on the efficacy and outcomes of MRI-guided breast biopsies. Results The participants had a mean age of 45 ± 11 years. A significant association was found between the history of pregnancy and breast lesion enhancement. Patients with mass enhancement had a higher BIRADS B4b, B4c, and B5 classification rate, while those with non-mass enhancement were more commonly classified as BIRADS B3 and B4a. Histopathology diagnoses were significant in determining the presence of mass or non-mass lesions. The sensitivity and specificity of MRI for detecting malignancy were high for BIRADS categories 4c and 5 but may result in a higher number of false positives. Conclusions our research highlighted the significance of MRI in the diagnosis of breast cancer, particularly when used in conjunction with high-risk lesions as well as showed the need of sub-classifying BI-RADS-4 lesions to minimize the number of unnecessary biopsies. The results affirm the ongoing use of MRI-guided biopsy for the detection of breast cancer. MRI-guided breast biopsies BIRADS Breast pathologies Background Magnetic resonance imaging (MRI) has an important role in breast imaging for various clinical purposes. These include preoperative staging, monitoring the response to neoadjuvant chemotherapy, differentiating scar tissue from recurrence, assessing breast implants, evaluating patients with cancer of unknown primary origin, and screening high-risk individuals ( 1 , 2 ). When suspicious lesions are identified using mammography, digital breast tomosynthesis, or sonography, MRI may provide additional non-invasive data for decision-making ( 3 , 4 ). MRI can detect lesions that are not visible in ultrasonography (US) or mammography. Moreover, MRI aids in the concurrent staging of breast cancer upon detection or confirmation, assisting in treatment planning ( 5 ). MRI has an overall sensitivity 94.6% (range 85.7–100%) and specificity 74.2% (range 25–100%) ( 6 ). However, distinguishing between benign and malignant lesions may require a breast biopsy ( 7 , 8 ). A limited number of detected lesions need MRI-guided biopsies due to their invisibility in other modalities ( 6 ). The incidence of malignancy in MRI-guided breast biopsies ranges from 18–60%, with most studies indicating rates between 20% and 35% ( 9 – 17 ) . Performing MRI-guided breast biopsies presents challenges for radiologists ( 18 ). These include the availability of an MRI suit, difficulty related to contrast washout causing vanishing lesions, susceptibility artifacts obscuring the biopsy site, and limited access to posteromedial lesions. Achieving radiologic-pathologic concordance is crucial for an accurate diagnosis and appropriate patient management due to the overlapping imaging characteristics between benign and malignant lesions. Different institutions may have varying methods for conducting MRI-guided breast biopsies, leading to differences in clinicopathological outcomes ( 19 , 20 ). The objective of our study was to examine and document the results of MRI-guided breast biopsy, focusing on the indication and accuracy of MRI in comparison to histological assessment of breast masses in our general practice community. Methods Patients: A retrospective analysis was performed on radiological records of 137 lesions from 137 women who had undergone MRI-guided biopsy due to MRI-positive and second-look US-negative findings between 2017 and 2023. The Pardis Noor Medical Imaging Center's ethics review committee (PNMIC-MR-1403-02) approved this study and waived any additional consent because all participants had already given their consent at the time of the biopsy. The study included patients with available previous MRI and subsequent histopathology reports. Twenty-eight results were excluded for various reasons, such as unavailable histopathology, unsuccessful biopsy attempts, and incomplete MRI reports. The final study group consisted of 109 lesions from 109 patients, with an average age of 45 ± 11 years (range: 22–76 years). Patients in this cohort underwent breast MRI due to the following reasons: screening and diagnostic. Screening was performed for asymptomatic women, regardless of previous history of breast carcinoma, who have identifiable risk characteristics for breast cancer in the majority of cases. Diagnostic purposes were employed in women who have recently been diagnosed with malignancy and are undergoing assessment of the breast consistent with MRI. This involves identifying any differences in location, size, or architecture between MRI and sonographic findings in order to determine the extent or appearance of lesions in either the same side or opposite breast. If US guidance was deemed unsuitable for establishing a pathological diagnosis, an MRI-guided core needle biopsy (CNB) was performed. If unintended MRI findings were detected while evaluating mammographic lesions and were not detected through additional mammography or sonography, an MRI-guided CNB was carried out. Additionally, MRI-guided CNB was conducted when mammography guidance was challenging due to limited visibility of the lesion in a single view or its location. MRI-guided biopsy: The patients underwent MRI scans using a 1.5 Tesla MRI system (Achieva, Philips Medical Systems, Best, Netherlands) in prone position. A bilateral dedicated seven-channel phased array breast coil (InVivo, Orlando, FL) was used for the scans. To prepare for a unilateral MRI-guided breast biopsy, compression was applied to the breast being examined using a dedicated sterile biopsy grid. A fiducial marker in the form of a Vitamin E capsule was placed on the biopsy grid in the region where the sample was to be taken. Localizing sequences were obtained, followed by unenhanced axial and contrast-enhanced axial and sagittal images to confirm the presence of the enhancing lesion that was previously observed on the MR image. The MRI procedure consisted of a scan with a high level of detail for 2.5 minutes before and 10 seconds after the contrast was given. This was followed by another detailed scan to evaluate the delayed phase of enhancement. Gadoteric acid (Dotarem, Guerbet, Villepinte, France) is administered intravenously at a rate of 0.2 ml/sec with a dosage of 0.1 mmol/kg. The manual targeting approach utilized a biopsy sheet, necessitating precise translation of the estimated target from the "image" perspective to the "patient" perspective. We determined the coordinates of the augmenting lesion by measuring the distance between the closest reference marker and the lesion, eliminating the need for specialized software for an MRI-guided operation. The MRI table was relocated from the magnet's bore, and the needle guide was modified to match the specified grid address. We administered a local anesthetic by injecting a combination of lidocaine and bicarbonate solution in a 10:1 ratio after positioning the needle guide. Aseptic techniques were used to insert an 18-gauge standard Coaxial needle (manufactured by TSK Laboratory, Tochigi-Ken, Japan) to the predetermined depth, as indicated by the centimeter markings on the needle shaft. The needle was inserted using a lateral approach. Once the coaxial needle was positioned, the inner stylet was extracted. The coaxial cannula was inserted. Then, we moved the MRI table back to its original location inside the magnet's bore. An axial sequence with restricted scope was performed to verify the precise positioning of the cannula in the location of the lesion. If adjustments were required, the cannula was relocated and another restricted axial sequence was captured to verify the location. After confirming the accurate positioning of the cannula, the MRI table was moved away from the magnet. Then, using an 18-gauge TSK semi-automated disposable core biopsy needle, several core samples of the lesion were collected through the coaxial cannula. On average, 5 core biopsies were taken for each lesion. The biopsies were conducted by a radiologist who had an average experience of 20 years. Following the conclusion of the biopsy, the coaxial cannula was extracted. On many occasions, a gold marker (specific manufacturer will be specified later) was inserted into the cannula following the sample process. Hemostasis was achieved by manually applying compression, followed by the application of a sterile dressing. Data Collection: The study involved retrospectively gathering records of various factors such as age, body mass index (BMI), patient symptoms, personal history of breast cancer, family history of breast cancer, pregnancy, menstrual status, risk factors, previous breast surgery, pathology reports, and findings of all MRI-guided breast biopsy cases from 2017 to 2023. Trained personnel collected incomplete data after contacting the patients. A highly skilled radiologist with 20 years of experience in breast imaging reevaluated the imaging findings of the breast biopsy cases. Lesions were categorized based on their location: left outer (LO), left inner (LI), right outer (RO), or right inner (RI). Additionally, the lesions detected through MRI were categorized using the breast imaging reporting and data system (BIRADS) lexicon. Breast core biopsies were further divided into benign, suspicious, or malignant pathological diagnostic categories. Within the benign category, subcategories included benign breast tissue not otherwise specified (n = 30), fibroadenoma (n = 2), proliferative fibrocystic changes not otherwise specified ( 12 ), and non-proliferative fibrocystic mastopathy not otherwise specified (n = 52). Malignant biopsies were classified as invasive ductal carcinoma (n = 5) and metastatic (n = 1), while the high-risk category included atypical ductal hyperplasia (n = 7). Statistical Analysis: Categorical data are presented as frequencies and percentages. The Chi2 and Fisher exact tests were used to compare the distribution of variables between two levels of enhancement. The sensitivity, specificity, positive predictive value, negative predictive value, and ROC curve were used to analyze the efficacy of the model diagnosis based on MRI. The statistical analysis was performed using Stata 16 software (StataCorp. 2019. Stata 16 Base Reference Manual. College Station, TX: Stata Press). Statistical significance was set to P < 0.05. Results This study included 137 potentially eligible MRI-guided breast biopsies. Out of these, 28 lesions were excluded, leaving a total of 109 lesions in 109 patients. Among these, 55 patients had mass enhancement and 54 had non-mass enhancement lesions. The participants had a mean age of 45 ± 11 years (range: 22–76 years), and there was no significant association found between age and the presence of mass or non-mass lesions (p = 0.446). Both groups, with and without mass enhancement, had almost similar rates of normal, overweight, and obese BMI categories, indicating no significant difference between them (p = 0.964). The accompanying symptoms observed in the patients included blood discharge (10/109, 9.17%), milk discharge (4/109, 3.67%), watery discharge (3/109, 2.75%), mass (55/109, 50.46%), check-up (11/109, 10.09%), and pain (26/109, 23.85%). A statistically significant association was found between the history of pregnancy and the type of breast enhancement. Furthermore, Patients with mass enhancement had a higher BIRADS B4b, B4c, and B5 classification rate, while those with non-mass enhancement were more commonly classified as BIRADS B3 and B4a, indicating a lower likelihood of malignancy (p = 0.006). The details of the characteristics of our patients are seen in Table 1 . Lesions that underwent MRI-guided core biopsies showed a significant association between histopathology diagnoses and the presence of mass or non-mass lesions (p = 0.013). Only 1 out of 109 lesions (0.92%) with non-mass enhancement had malignant pathology, while the other 5 malignant lesions (4.59%) had mass enhancement. Furthermore, significance was seen in BIRADS categories, with histopathology having a P-value of 0.000. The majority of lesions with benign histopathology results were classified as BIRADS 4a (79 out of 109, 85.1%). On the other hand, lesions with high-risk or malignant histopathology results were classified as BIRADS 4b, 4c, and 5. Specifically, 12 out of 109 lesions (12.9%) fell into the BIRADS 4b category, 4 out of 109 lesions (4.3%) were categorized as BIRADS 4c, and 4 out of 109 lesions (4.3%) were classified as BIRADS 5 (Table 2 ). We assessed the performance of the MRI BIRADS category in detecting malignancy based on the pathology by evaluating the sensitivity (SE), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and area under ROC curve. Overall, we found that the sensitivity and specificity of MRI for detecting malignancy were high for BIRADS categories 4c and 5, with values of 100% and 86%, respectively. When considering categories 4b, 4c, and 5, the sensitivity and specificity remained relatively high, at 92.3% and 91.7%. This suggests that while MRI with the BIRADS category system can lead to high sensitivity in detecting malignancy, it may also result in a higher number of false positives. Additionally, we discovered that the positive predictive value was relatively low for both categories, indicating a higher likelihood of false positives. However, the negative predictive value was high, demonstrating that MRI effectively identifies benign cases. (Table 3 , 4 ) Table 1 Characteristic of patients based on mass and non-mass enhancement lesions in MRI. Variables Group Total Population (N = 109) Mass Enhancement Non Mass Enhancement P-value Age (years) n (%) = 40 65 (59.63%) 26 (57.78%) 39 (60.94%) BMI n (%) Normal 50 (45.87%) 20 (44.44%) 30 (46.88%) 0.964 Overweight 45 (41.28%) 19 (42.22%) 26 (40.62%) Obese 14 (12.84%) 6 (13.33%) 8 (12.5%) Patient’s Symptoms n (%) Blood Discharge 10 (9.17%) 4 (8.89%) 6 (9.38%) 0.290 Milk Discharge 4 (3.67%) 2 (4.44%) 2 (3.12%) Watery Discharge 3 (2.75%) 3 (6.67%) 0 (0.00%) Mass 55 (50.46%) 19 (42.22%) 36 (56.25%) Check up 11 (10.09%) 4 (8.89%) 7 (10.94%) Pain 26 (23.85%) 13 (28.89%) 13 (20.13%) History of Self-Breast Cancer n (%) No 97 (88.99%) 41 (91.11%) 56 (87.50%) 0.804 Left Breast 7 (6.42%) 3 (6.67%) 4 (6.25%) Right Breast 5 (4.59%) 1 (2.22%) 4 (6.25%) Family History of Breast Cancer n (%) No 54 (49.54%) 26 (57.78%) 28 (43.75%) 0.271 1st Degree 28 (25.69%) 11 (24.44%) 17 (26.56%) 2nd Degree 27 (24.77%) 8 (17.78%) 19 (29.69%) History of Pregnancy n (%) No 21 (19.27%) 10 (22.22%) 11 (17.19%) 0.029 1 30 (27.52%) 7 (15.56%) 23 (35.94%) 2 35 (32.11%) 18 (40%) 17 (26.56%) 3 12 (11.01%) 8 (17.78%) 4 (6.25%) 4 8 (7.34%) 1 (2.22%) 7 (10.94%) 5 2 (1.83%) 1 (2.22%) 1 (1.56%) 6 1 (0.92%) 0 1 (1.56%) Menstrual Cycle n (%) Pre Menopause 46 (42.20%) 13 (28.89%) 33 (51.56%) 0.062 Peri Menopause 16 (14.68%) 6 (13.33%) 10 (15.62%) Post Menopause 39 (35.78%) 22 (48.89%) 17 (26.56%) Unknown 8 (7.34%) 4 (8.89%) 4 (6.25%) History of Previous Breast Surgery n (%) No 83 (76.15%) 32 (71.11%) 51 (79.69%) 0.202 Benign 14 (12.84%) 9 (20%) 5 (7.81%) Malignant 12 (11.01%) 4 (8.89%) 8 (12.50%) Breast Lesion n (%) LO 37 (33.94%) 12 (26.67%) 25 (39.06%) 0.253 LI 12 (11.01%) 6 (13.33%) 6 (9.38%) RO 51 (46.79%) 25 (55.56%) 26 (40.62%) RI 9 (8.26%) 2 (4.44%) 7 (10.94%) BIRADS n (%) B3 10 (9.17%) 1 (2.22%) 9 (14.06%) 0.006 B4a 79 (72.48%) 30 (66.67%) 49 (76.56%) B4b 12 (11.01%) 8 (17.78%) 4 (6.25%) B4c 4 (3.67%) 2 (4.44%) 2 (3.12%) B5 4 (3.67%) 4 (8.89%) 0 (.00%) Table 2 MRI findings based on histopathology. MRI findings Histopathology diagnosis Benign High Risk Malignant P-value Mass n (%) 35 (32.11%) 5 (4.59%) 5 (4.59%) 0.013 Non mass n (%) 61 (55.96%) 2 (1.83%) 1 (0.92%) BIRADS 3 n (%) 10 (9.17%) 0 0 < 0.001 BIRADS 4a n (%) 78 (71.56%) 1 (0.92%) 0 BIRADS 4b n (%) 6 (5.50%) 5 (4.59%) 1 (0.92%) BIRADS 4c n (%) 1 (0.92%) 1 (0.92%) 2 (1.83%) BIRADS 5 n (%) 1 (0.92%) 0 3 (2.75%) Table 3 Accuracy of BIRADS (4b + 4c + 5) in detecting lesions based on pathology Malignant Pathology Benign Pathology Measures Abnormal n (%) 12 ( 11 ) 1 (0.92) Sensitivity: 92.3% (64.0–99.8) Specificity: 91.7% (84.2–96.3) Positive Predictive Value: 60.0% (36.1–80.9) Positive Predictive Value: 98.9% (93.9–100.0) Exact diagnose (ROC area): 92% (0.84–1.00) Normal n (%) 8 (7.34) 88 (80.74) Total n (%) 20 (18.35) 89 (81.65) Table 4 Accuracy of BIRADS (4c + 5) in detecting lesions based on pathology Malignant Pathology Benign Pathology Measures Abnormal (n) 6 (5.51) 0 Sensitivity:100% (54.1–100.0) Specificity: 86% (78.2–92.4) Positive Predictive Value: 30.0% (11.9–54.3) Positive Predictive Value:100.0% (95.9–100.0) Positive Predictive Value: 93% (0.90–0.97) Normal (n) 14 (12.84) 89 (81.65) Total (n) 20 (18.35) 89 (81.65) Discussion MRI is the most sensitive imaging modality for detecting invasive carcinoma and is comparable to mammography for detecting DCIS, among other imaging modalities used for breast cancer diagnosis ( 21 ).The detection rate for contralateral lesions in patients treated with unilateral breast cancer has been reported to range from 3–10% in various studies ( 22 – 25 ). The present study aimed to assess the comparative accuracy of MRI and histological assessment of breast lesions within a general practice population. In women who underwent an MRI-guided biopsy, the incidence of malignancy was 5.50 percent (n = 6), which is almost similar to the findings of Myers et al ( 6 ) .Five individuals were diagnosed with invasive ductal carcinoma, but only one patient presented with metastatic cancer. A strong association between the diagnosis of BIRADS using MRI and the presence of enhancement features in pathology reports was another result of our observation. The MRI demonstrated a 100% sensitivity and 86% specificity in detecting malignant histopathological abnormalities. In addition, the inclusion of high-risk lesions resulted in a shift in these values to 92.3% and 91.7%, respectively. Furthermore, we found that 6.42% of patients had high-risk pathology lesions which is almost lower than the results of a systematic review and meta-analysis conducted by Ozcan et al. ( 18 ). They aimed to assess the major performance metrics of MRI-guided breast biopsies on a sample of 11,087 patients. This review showed the pooled rates of histopathological outcomes for benign, high-risk, and malignant lesions, which were found to be 65.06% (95% CI: 59.15–70.54%), 16.69% (95% CI: 9.96–26.64%), and 29.64% (95% CI: 23.58–36.52%), respectively ( 18 ). It seems that the variation is likely attributed to disparities in patient demographics, research methodologies, and radiologists' criteria for suggesting biopsy ( 6 ). While we typically collect an average of 5 core biopsy samples at our institution, many institutions have reported receiving up to 12 samples ( 9 ). Given that our high-risk lesions are almost lower than the reported range, it may be suggested that more than the average of five consecutive samples were needed. In our research, most of the high-risk and malignant lesions exhibited mass enhancement. We discovered a notable association between the enhancement characteristics seen during MRI-guided breast biopsies and the histology results. It is important to mention that prior research has shown that the risk of malignancy is greater in ME lesions compared to NME, with rates of 34–60% and 27–41%, respectively ( 14 , 16 , 26 ). However, Schnall et al. found a 94% negative predictive value for NME for invasive carcinoma but noted that the lack of enhancement on MRI did not rule out malignancy. This was further addressed by a retrospective assessment of tiny invasive carcinomas (< 5 mm) that did not enhance. This research indicated that NME is not always benign and that a large percentage of benign lesions may appear as EM and enhancement focus (EF).( 8 ). The application of the BIRADS classification to breast MRI presents challenges due to the absence of established criteria for categorizing BIRADS 4 in the official BIRADS lexicon for MRI. Consequently, there is a considerable variation in the probability of malignancy (ranging from > 2–95%) within this category, leading to the recommendation for biopsy in nearly all cases of BI-RADS 4 findings ( 27 , 28 ). Nevertheless, the latest investigations have endeavored to further categorize BI-RADS 4 into subcategories A (indicating low suspicion), B (indicating moderate suspicion), and C (indicating strong suspicion), similar to other modes of breast imaging. We observed a significant association between MRI BIRADS and histopathology. Similarly, the study conducted by Cha et al. examined 65 lesions using biopsied guided MRI and demonstrated a noteworthy association with lesions that they categorized as BIRADS subcategories ( 29 ). In a separate investigation conducted by Almeida et al., a total of 103 MRI-guided biopsies were obtained from 83 individuals. The researchers found that the BIRADS subcategory 4C exhibited the highest predictive value for malignancy (odds ratio [OR] 13.48; 95% CI 2.27–79.98) ( 30 ). Both the Cha et al. and Maltez de Almeida et al. research were limited by their failure to provide specific information regarding the division of 4A/B/C groups. Currently, our center endeavors to categorize BIRADS category 4 lesions during the interpretation of the MRI. Nevertheless, using explicit BIRADS 4A, 4B, and 4C classifications and conducting meticulous comparisons between radiology and pathology should potentially reduce the occurrence of superfluous MRI-guided biopsies. Recent research has indicated that MRI exhibits a sensitivity of 94.6% (with a range of 85.7–100%) and a specificity of 74.2% (with a range of 25–100%)( 31 ). The moderate specificity of breast MRI, coupled with its high sensitivity, contributes to a higher incidence of false positives and consequently leads to an elevated number of MRI-guided biopsies for benign tumors ( 21 ). Despite the existence of strict criteria for interpreting breast MRI, they lack the necessary specificity to identify whether a diagnosis is benign or malignant unambiguously. The breast MRI BI-RADS (4c + 5) and MRI BIRADS (4b + 4c + 5) had a sensitivity of 100.0% (54.1–100.0) and 92.3% (64.0–99.8), respectively, according to our data. The specificity rates for breast MRI BI-RADS (4c + 5) and breast MRI BIRADS (4b + 4c + 5) were found to be 86.4% (78.2–92.4) and 91.7% (84.2–96.3), respectively. The importance of radiologic and pathologic association in breast MRI interpretation should be considered, as it plays a critical role in reducing the occurrence of benign biopsies, relieving patient distress, and reducing healthcare expenses. Prakash et al. conducted a study that found a association between peer review in radiology-pathology and a decrease in surgical excisions. The study revealed that weekly association conferences had an impact on up to 5.3% of cases, leading to surgery avoidance in 2.1% of cases ( 32 ). There are various limitations in our investigation. The retrospective nature of this investigation resulted in a lack of specificity in the descriptions of lesions. Furthermore, in a few instances where patients underwent MRI prior to biopsy and outside of our medical facility, the image quality was occasionally insufficient, or just the reports were accessible. One further constraint pertains to the inadequate monitoring of multiple instances that exhibited inconsistent results, a pathologic diagnosis that was either high-risk or borderline, and the postponement of biopsy procedures. An effort for thorough follow-up was requested and could have influenced some of the results of this study. Conclusion In summary, our study shows that MRI-guided biopsies are highly precise and responsive in identifying malignant breast lesions even with manual calculation method. The importance of MRI in diagnosing breast cancer is emphasized by its high sensitivity and specificity for malignant histopathological lesions, especially when used with high-risk lesions. The study also highlights the need to clearly sub-classify BI-RADS 4 lesions to reduce unnecessary biopsies using MRI. Additionally, this study emphasizes the importance in interpreting breast MRI to minimize false positives and unnecessary biopsies. Furthermore, these findings support the continued use of MRI guided biopsy as a crucial tool in diagnosing breast cancer and emphasize the need for ongoing progress and consistency in interpreting breast MRI. Abbreviations MRI: Magnetic resonance imaging US: ultrasonography CNB: core needle biopsy LO: left outer LI: left inner RO: right outer RI: right inner BMI: body mass index BIRADS :breast imaging reporting and data system SE: sensitivity SP: specificity PPV: positive predictive value NPV: negative predictive value Declarations Funding : No fund Data Availability : The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Pardis Noor Medical Imaging Center and written informed consent was waived any additional consent because all participants had already given their consent at the time of the biopsy (PNMIC-MR-1403-02). Authors’ contributions: Fattaneh Khalaj: Writing – original draft, Conceptualization Zahra Moradi: Writing – original draft Amir Kasaeian: Data curation; Formal analysis Hamed Ghorani, Mohammad Hosein Golazar: Investigation Shahram Akhlaghpoor: Conceptualization; Resources; Writing– review & editing. Acknowledgements : Not applicable Consent for publication: Not applicable. Competing interests: The authors declare that they have no competing interests. References Sardanelli F, Boetes C, Borisch B, Decker T, Federico M, Gilbert FJ, et al. 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Nunes LW, Schnall MD, Orel SG. Update of breast MR imaging architectural interpretation model. Radiology. 2001;219(2):484-94. Morris E, Comstock C, Lee C, Lehman C, Ikeda D, Newstead G. ACR BI-RADS® magnetic resonance imaging. ACR BI-RADS® atlas, breast imaging reporting and data system. 2013;5. Cha SY, Ko EY, Han B-K, Ko ES, Choi JS, Park KW, Lee JE. Magnetic resonance imaging-guided breast biopsy in Korea: a 10-year follow-up experience. Journal of Breast Cancer. 2021;24(4):377. Maltez de Almeida JR, Gomes AB, Barros TP, Fahel PE, de Seixas Rocha M. Subcategorization of suspicious breast lesions (BI-RADS category 4) according to MRI criteria: role of dynamic contrast-enhanced and diffusion-weighted imaging. American Journal of Roentgenology. 2015;205(1):222-31. Aristokli N, Polycarpou I, Themistocleous S, Sophocleous D, Mamais I. Comparison of the diagnostic performance of Magnetic Resonance Imaging (MRI), ultrasound and mammography for detection of breast cancer based on tumor type, breast density and patient's history: A review. Radiography. 2022;28(3):848-56. Prakash S, Venkataraman S, Slanetz PJ, Dialani V, Fein-Zachary V, Littlehale N, Mehta TS. Improving patient care by incorporation of multidisciplinary breast radiology-pathology correlation conference. Canadian Association of Radiologists Journal. 2016;67(2):122-9. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4719861","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":339686463,"identity":"e2561a2a-83cb-4af9-8d58-cd5f8ec1eaea","order_by":0,"name":"Fattaneh Khalaj","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Fattaneh","middleName":"","lastName":"Khalaj","suffix":""},{"id":339686464,"identity":"dd8426d3-f912-4772-94f8-2a2f69f484d0","order_by":1,"name":"Zahra Moradi","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zahra","middleName":"","lastName":"Moradi","suffix":""},{"id":339686465,"identity":"5d653121-fb64-4a64-b88c-f6d48bd0abf4","order_by":2,"name":"Hamed Ghorani","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hamed","middleName":"","lastName":"Ghorani","suffix":""},{"id":339686466,"identity":"a6336514-6b61-4b60-82e6-44f0c2b8f0ce","order_by":3,"name":"Amir Kasaeian","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Amir","middleName":"","lastName":"Kasaeian","suffix":""},{"id":339686467,"identity":"729c0653-4b30-4b2b-aef1-9a8908f56d34","order_by":4,"name":"Mohammad Hosein Golazar","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Hosein","lastName":"Golazar","suffix":""},{"id":339686468,"identity":"d6011c74-2632-459e-ba46-e86919d03cb5","order_by":5,"name":"Shahram Akhlaghpoor","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIiWNgGAWjYBACNh4exgMMDAdAbMYHIKKNkBZ+Hh4GmBZmA6K0SPYgtLBJgLQ0ENJicObsgQM/ft3J42fvfVZdUHNPto//7MEHH/cwyPOLHcCu5WxfwsHevmfFkj3HzW7POFZs3CaRl2w44xmD4czZCdi1nOcxOMDbczhxw400tts8bAmJbRI8ZtI8BxgSDG5j12IP1HLwL1RLMc8/oBb+M/i1GJztMTjM8wOihZm3DaiFIYeAljNnDA7LNhwG+uUYs/TMvgSgX3KMDWcckMDtlzM5hg/f/DkMDLE2xs8F3xJk5/efMXzw4YCNPL80di1gAIw+sCwzkpgEbuVg8AdTyygYBaNgFIwCOAAA1tJoTLk65dAAAAAASUVORK5CYII=","orcid":"","institution":"Pardis Noor Medical Imaging and Cancer Center","correspondingAuthor":true,"prefix":"","firstName":"Shahram","middleName":"","lastName":"Akhlaghpoor","suffix":""}],"badges":[],"createdAt":"2024-07-10 18:21:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4719861/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4719861/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86858748,"identity":"2ea87ecd-fe0c-4bc8-946c-24f57ca74c3e","added_by":"auto","created_at":"2025-07-16 11:38:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1132081,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4719861/v1/8eca32b2-4f8d-48ea-a864-1c0fcc13fcc8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Simple MR Guided Breast Biopsy Strategy: technique and radiological-pathological association","fulltext":[{"header":"Background","content":"\u003cp\u003eMagnetic resonance imaging (MRI) has an important role in breast imaging for various clinical purposes. These include preoperative staging, monitoring the response to neoadjuvant chemotherapy, differentiating scar tissue from recurrence, assessing breast implants, evaluating patients with cancer of unknown primary origin, and screening high-risk individuals (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). When suspicious lesions are identified using mammography, digital breast tomosynthesis, or sonography, MRI may provide additional non-invasive data for decision-making (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). MRI can detect lesions that are not visible in ultrasonography (US) or mammography. Moreover, MRI aids in the concurrent staging of breast cancer upon detection or confirmation, assisting in treatment planning (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). MRI has an overall sensitivity 94.6% (range 85.7\u0026ndash;100%) and specificity 74.2% (range 25\u0026ndash;100%) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). However, distinguishing between benign and malignant lesions may require a breast biopsy (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). A limited number of detected lesions need MRI-guided biopsies due to their invisibility in other modalities (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The incidence of malignancy in MRI-guided breast biopsies ranges from 18\u0026ndash;60%, with most studies indicating rates between 20% and 35% (\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14 CR15 CR16\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) .\u003c/p\u003e \u003cp\u003ePerforming MRI-guided breast biopsies presents challenges for radiologists (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). These include the availability of an MRI suit, difficulty related to contrast washout causing vanishing lesions, susceptibility artifacts obscuring the biopsy site, and limited access to posteromedial lesions. Achieving radiologic-pathologic concordance is crucial for an accurate diagnosis and appropriate patient management due to the overlapping imaging characteristics between benign and malignant lesions. Different institutions may have varying methods for conducting MRI-guided breast biopsies, leading to differences in clinicopathological outcomes (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e The objective of our study was to examine and document the results of MRI-guided breast biopsy, focusing on the indication and accuracy of MRI in comparison to histological assessment of breast masses in our general practice community.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients:\u003c/h2\u003e \u003cp\u003eA retrospective analysis was performed on radiological records of 137 lesions from 137 women who had undergone MRI-guided biopsy due to MRI-positive and second-look US-negative findings between 2017 and 2023. The Pardis Noor Medical Imaging Center's ethics review committee (PNMIC-MR-1403-02) approved this study and waived any additional consent because all participants had already given their consent at the time of the biopsy. The study included patients with available previous MRI and subsequent histopathology reports. Twenty-eight results were excluded for various reasons, such as unavailable histopathology, unsuccessful biopsy attempts, and incomplete MRI reports. The final study group consisted of 109 lesions from 109 patients, with an average age of 45\u0026thinsp;\u0026plusmn;\u0026thinsp;11 years (range: 22\u0026ndash;76 years). Patients in this cohort underwent breast MRI due to the following reasons: screening and diagnostic. Screening was performed for asymptomatic women, regardless of previous history of breast carcinoma, who have identifiable risk characteristics for breast cancer in the majority of cases. Diagnostic purposes were employed in women who have recently been diagnosed with malignancy and are undergoing assessment of the breast consistent with MRI. This involves identifying any differences in location, size, or architecture between MRI and sonographic findings in order to determine the extent or appearance of lesions in either the same side or opposite breast. If US guidance was deemed unsuitable for establishing a pathological diagnosis, an MRI-guided core needle biopsy (CNB) was performed. If unintended MRI findings were detected while evaluating mammographic lesions and were not detected through additional mammography or sonography, an MRI-guided CNB was carried out. Additionally, MRI-guided CNB was conducted when mammography guidance was challenging due to limited visibility of the lesion in a single view or its location.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMRI-guided biopsy:\u003c/h2\u003e \u003cp\u003eThe patients underwent MRI scans using a 1.5 Tesla MRI system (Achieva, Philips Medical Systems, Best, Netherlands) in prone position. A bilateral dedicated seven-channel phased array breast coil (InVivo, Orlando, FL) was used for the scans. To prepare for a unilateral MRI-guided breast biopsy, compression was applied to the breast being examined using a dedicated sterile biopsy grid. A fiducial marker in the form of a Vitamin E capsule was placed on the biopsy grid in the region where the sample was to be taken. Localizing sequences were obtained, followed by unenhanced axial and contrast-enhanced axial and sagittal images to confirm the presence of the enhancing lesion that was previously observed on the MR image.\u003c/p\u003e \u003cp\u003eThe MRI procedure consisted of a scan with a high level of detail for 2.5 minutes before and 10 seconds after the contrast was given. This was followed by another detailed scan to evaluate the delayed phase of enhancement. Gadoteric acid (Dotarem, Guerbet, Villepinte, France) is administered intravenously at a rate of 0.2 ml/sec with a dosage of 0.1 mmol/kg. The manual targeting approach utilized a biopsy sheet, necessitating precise translation of the estimated target from the \"image\" perspective to the \"patient\" perspective. We determined the coordinates of the augmenting lesion by measuring the distance between the closest reference marker and the lesion, eliminating the need for specialized software for an MRI-guided operation. The MRI table was relocated from the magnet's bore, and the needle guide was modified to match the specified grid address. We administered a local anesthetic by injecting a combination of lidocaine and bicarbonate solution in a 10:1 ratio after positioning the needle guide. Aseptic techniques were used to insert an 18-gauge standard Coaxial needle (manufactured by TSK Laboratory, Tochigi-Ken, Japan) to the predetermined depth, as indicated by the centimeter markings on the needle shaft. The needle was inserted using a lateral approach. Once the coaxial needle was positioned, the inner stylet was extracted. The coaxial cannula was inserted. Then, we moved the MRI table back to its original location inside the magnet's bore.\u003c/p\u003e \u003cp\u003eAn axial sequence with restricted scope was performed to verify the precise positioning of the cannula in the location of the lesion. If adjustments were required, the cannula was relocated and another restricted axial sequence was captured to verify the location. After confirming the accurate positioning of the cannula, the MRI table was moved away from the magnet. Then, using an 18-gauge TSK semi-automated disposable core biopsy needle, several core samples of the lesion were collected through the coaxial cannula. On average, 5 core biopsies were taken for each lesion. The biopsies were conducted by a radiologist who had an average experience of 20 years. Following the conclusion of the biopsy, the coaxial cannula was extracted. On many occasions, a gold marker (specific manufacturer will be specified later) was inserted into the cannula following the sample process. Hemostasis was achieved by manually applying compression, followed by the application of a sterile dressing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData Collection:\u003c/h2\u003e \u003cp\u003eThe study involved retrospectively gathering records of various factors such as age, body mass index (BMI), patient symptoms, personal history of breast cancer, family history of breast cancer, pregnancy, menstrual status, risk factors, previous breast surgery, pathology reports, and findings of all MRI-guided breast biopsy cases from 2017 to 2023. Trained personnel collected incomplete data after contacting the patients. A highly skilled radiologist with 20 years of experience in breast imaging reevaluated the imaging findings of the breast biopsy cases. Lesions were categorized based on their location: left outer (LO), left inner (LI), right outer (RO), or right inner (RI). Additionally, the lesions detected through MRI were categorized using the breast imaging reporting and data system (BIRADS) lexicon. Breast core biopsies were further divided into benign, suspicious, or malignant pathological diagnostic categories. Within the benign category, subcategories included benign breast tissue not otherwise specified (n\u0026thinsp;=\u0026thinsp;30), fibroadenoma (n\u0026thinsp;=\u0026thinsp;2), proliferative fibrocystic changes not otherwise specified (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), and non-proliferative fibrocystic mastopathy not otherwise specified (n\u0026thinsp;=\u0026thinsp;52). Malignant biopsies were classified as invasive ductal carcinoma (n\u0026thinsp;=\u0026thinsp;5) and metastatic (n\u0026thinsp;=\u0026thinsp;1), while the high-risk category included atypical ductal hyperplasia (n\u0026thinsp;=\u0026thinsp;7).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis:\u003c/h2\u003e \u003cp\u003eCategorical data are presented as frequencies and percentages. The Chi2 and Fisher exact tests were used to compare the distribution of variables between two levels of enhancement. The sensitivity, specificity, positive predictive value, negative predictive value, and ROC curve were used to analyze the efficacy of the model diagnosis based on MRI. The statistical analysis was performed using Stata 16 software (StataCorp. 2019. Stata 16 Base Reference Manual. College Station, TX: Stata Press). Statistical significance was set to \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThis study included 137 potentially eligible MRI-guided breast biopsies. Out of these, 28 lesions were excluded, leaving a total of 109 lesions in 109 patients. Among these, 55 patients had mass enhancement and 54 had non-mass enhancement lesions. The participants had a mean age of 45\u0026thinsp;\u0026plusmn;\u0026thinsp;11 years (range: 22\u0026ndash;76 years), and there was no significant association found between age and the presence of mass or non-mass lesions (p\u0026thinsp;=\u0026thinsp;0.446). Both groups, with and without mass enhancement, had almost similar rates of normal, overweight, and obese BMI categories, indicating no significant difference between them (p\u0026thinsp;=\u0026thinsp;0.964). The accompanying symptoms observed in the patients included blood discharge (10/109, 9.17%), milk discharge (4/109, 3.67%), watery discharge (3/109, 2.75%), mass (55/109, 50.46%), check-up (11/109, 10.09%), and pain (26/109, 23.85%). A statistically significant association was found between the history of pregnancy and the type of breast enhancement. Furthermore, Patients with mass enhancement had a higher BIRADS B4b, B4c, and B5 classification rate, while those with non-mass enhancement were more commonly classified as BIRADS B3 and B4a, indicating a lower likelihood of malignancy (p\u0026thinsp;=\u0026thinsp;0.006). The details of the characteristics of our patients are seen in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eLesions that underwent MRI-guided core biopsies showed a significant association between histopathology diagnoses and the presence of mass or non-mass lesions (p\u0026thinsp;=\u0026thinsp;0.013). Only 1 out of 109 lesions (0.92%) with non-mass enhancement had malignant pathology, while the other 5 malignant lesions (4.59%) had mass enhancement. Furthermore, significance was seen in BIRADS categories, with histopathology having a P-value of 0.000. The majority of lesions with benign histopathology results were classified as BIRADS 4a (79 out of 109, 85.1%). On the other hand, lesions with high-risk or malignant histopathology results were classified as BIRADS 4b, 4c, and 5. Specifically, 12 out of 109 lesions (12.9%) fell into the BIRADS 4b category, 4 out of 109 lesions (4.3%) were categorized as BIRADS 4c, and 4 out of 109 lesions (4.3%) were classified as BIRADS 5 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe assessed the performance of the MRI BIRADS category in detecting malignancy based on the pathology by evaluating the sensitivity (SE), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and area under ROC curve. Overall, we found that the sensitivity and specificity of MRI for detecting malignancy were high for BIRADS categories 4c and 5, with values of 100% and 86%, respectively. When considering categories 4b, 4c, and 5, the sensitivity and specificity remained relatively high, at 92.3% and 91.7%. This suggests that while MRI with the BIRADS category system can lead to high sensitivity in detecting malignancy, it may also result in a higher number of false positives. Additionally, we discovered that the positive predictive value was relatively low for both categories, indicating a higher likelihood of false positives. However, the negative predictive value was high, demonstrating that MRI effectively identifies benign cases. (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e,\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristic of patients based on mass and non-mass enhancement lesions in MRI.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal Population\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;109)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMass Enhancement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon Mass Enhancement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAge (years) n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e=\u0026lt; 40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44 (40.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (42.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25 (39.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026gt;\u0026thinsp;40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65 (59.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (57.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39 (60.94%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eBMI n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNormal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50 (45.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (44.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30 (46.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOverweight\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45 (41.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (42.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26 (40.62%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eObese\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (12.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (13.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003ePatient\u0026rsquo;s Symptoms n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBlood Discharge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (9.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (8.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6 (9.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMilk Discharge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (3.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (4.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2 (3.12%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWatery Discharge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (2.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (6.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0 (0.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMass\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55 (50.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (42.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36 (56.25%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCheck up\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (10.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (8.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7 (10.94%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePain\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26 (23.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (28.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13 (20.13%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eHistory of Self-Breast Cancer n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97 (88.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41 (91.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56 (87.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLeft Breast\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (6.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (6.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4 (6.25%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRight Breast\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (4.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (2.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4 (6.25%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eFamily History of Breast Cancer n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54 (49.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (57.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28 (43.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1st Degree\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (25.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (24.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17 (26.56%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2nd Degree\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (24.77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (17.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19 (29.69%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003eHistory of Pregnancy n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21 (19.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (22.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11 (17.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30 (27.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (15.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23 (35.94%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35 (32.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17 (26.56%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (11.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (17.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4 (6.25%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (7.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (2.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7 (10.94%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (1.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (2.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1 (1.56%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (0.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1 (1.56%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eMenstrual Cycle\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePre Menopause\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46 (42.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (28.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33 (51.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePeri Menopause\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16 (14.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (13.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10 (15.62%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePost Menopause\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39 (35.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (48.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17 (26.56%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eUnknown\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (7.34%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (8.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4 (6.25%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eHistory of Previous Breast Surgery\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83 (76.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (71.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51 (79.69%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBenign\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (12.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5 (7.81%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMalignant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (11.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (8.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8 (12.50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eBreast Lesion n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37 (33.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (26.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25 (39.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (11.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (13.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6 (9.38%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51 (46.79%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25 (55.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26 (40.62%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (8.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (4.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7 (10.94%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eBIRADS n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eB3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (9.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (2.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9 (14.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eB4a\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79 (72.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (66.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49 (76.56%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eB4b\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (11.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (17.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4 (6.25%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eB4c\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (3.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (4.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2 (3.12%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eB5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (3.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4 (8.89%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0 (.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMRI findings based on histopathology.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMRI findings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eHistopathology diagnosis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBenign\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh Risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMalignant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMass n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (32.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (4.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (4.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon mass n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (55.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.92%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBIRADS 3 n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (9.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBIRADS 4a n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (71.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBIRADS 4b n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (5.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (4.59%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.92%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBIRADS 4c n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (1.83%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBIRADS 5 n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (2.75%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAccuracy of BIRADS (4b\u0026thinsp;+\u0026thinsp;4c\u0026thinsp;+\u0026thinsp;5) in detecting lesions based on pathology\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMalignant Pathology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBenign Pathology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMeasures\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbnormal n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (0.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eSensitivity: 92.3% (64.0\u0026ndash;99.8)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eSpecificity: 91.7% (84.2\u0026ndash;96.3)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ePositive Predictive Value: 60.0% (36.1\u0026ndash;80.9)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ePositive Predictive Value: 98.9% (93.9\u0026ndash;100.0)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eExact diagnose (ROC area): 92% (0.84\u0026ndash;1.00)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNormal\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (7.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88 (80.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (18.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89 (81.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAccuracy of BIRADS (4c\u0026thinsp;+\u0026thinsp;5) in detecting lesions based on pathology\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMalignant Pathology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBenign Pathology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMeasures\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbnormal\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (5.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eSensitivity:100% (54.1\u0026ndash;100.0)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eSpecificity: 86% (78.2\u0026ndash;92.4)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ePositive Predictive Value: 30.0% (11.9\u0026ndash;54.3)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ePositive Predictive Value:100.0% (95.9\u0026ndash;100.0)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ePositive Predictive Value: 93% (0.90\u0026ndash;0.97)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNormal\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (12.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89 (81.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(n)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20 (18.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89 (81.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMRI is the most sensitive imaging modality for detecting invasive carcinoma and is comparable to mammography for detecting DCIS, among other imaging modalities used for breast cancer diagnosis (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).The detection rate for contralateral lesions in patients treated with unilateral breast cancer has been reported to range from 3\u0026ndash;10% in various studies (\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe present study aimed to assess the comparative accuracy of MRI and histological assessment of breast lesions within a general practice population. In women who underwent an MRI-guided biopsy, the incidence of malignancy was 5.50 percent (n\u0026thinsp;=\u0026thinsp;6), which is almost similar to the findings of Myers et al (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) .Five individuals were diagnosed with invasive ductal carcinoma, but only one patient presented with metastatic cancer.\u003c/p\u003e \u003cp\u003eA strong association between the diagnosis of BIRADS using MRI and the presence of enhancement features in pathology reports was another result of our observation. The MRI demonstrated a 100% sensitivity and 86% specificity in detecting malignant histopathological abnormalities. In addition, the inclusion of high-risk lesions resulted in a shift in these values to 92.3% and 91.7%, respectively. Furthermore, we found that 6.42% of patients had high-risk pathology lesions which is almost lower than the results of a systematic review and meta-analysis conducted by Ozcan et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). They aimed to assess the major performance metrics of MRI-guided breast biopsies on a sample of 11,087 patients. This review showed the pooled rates of histopathological outcomes for benign, high-risk, and malignant lesions, which were found to be 65.06% (95% CI: 59.15\u0026ndash;70.54%), 16.69% (95% CI: 9.96\u0026ndash;26.64%), and 29.64% (95% CI: 23.58\u0026ndash;36.52%), respectively (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). It seems that the variation is likely attributed to disparities in patient demographics, research methodologies, and radiologists' criteria for suggesting biopsy (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). While we typically collect an average of 5 core biopsy samples at our institution, many institutions have reported receiving up to 12 samples (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Given that our high-risk lesions are almost lower than the reported range, it may be suggested that more than the average of five consecutive samples were needed.\u003c/p\u003e \u003cp\u003eIn our research, most of the high-risk and malignant lesions exhibited mass enhancement. We discovered a notable association between the enhancement characteristics seen during MRI-guided breast biopsies and the histology results. It is important to mention that prior research has shown that the risk of malignancy is greater in ME lesions compared to NME, with rates of 34\u0026ndash;60% and 27\u0026ndash;41%, respectively (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). However, Schnall et al. found a 94% negative predictive value for NME for invasive carcinoma but noted that the lack of enhancement on MRI did not rule out malignancy. This was further addressed by a retrospective assessment of tiny invasive carcinomas (\u0026lt;\u0026thinsp;5 mm) that did not enhance. This research indicated that NME is not always benign and that a large percentage of benign lesions may appear as EM and enhancement focus (EF).(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe application of the BIRADS classification to breast MRI presents challenges due to the absence of established criteria for categorizing BIRADS 4 in the official BIRADS lexicon for MRI. Consequently, there is a considerable variation in the probability of malignancy (ranging from \u0026gt;\u0026thinsp;2\u0026ndash;95%) within this category, leading to the recommendation for biopsy in nearly all cases of BI-RADS 4 findings (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Nevertheless, the latest investigations have endeavored to further categorize BI-RADS 4 into subcategories A (indicating low suspicion), B (indicating moderate suspicion), and C (indicating strong suspicion), similar to other modes of breast imaging.\u003c/p\u003e \u003cp\u003eWe observed a significant association between MRI BIRADS and histopathology. Similarly, the study conducted by Cha et al. examined 65 lesions using biopsied guided MRI and demonstrated a noteworthy association with lesions that they categorized as BIRADS subcategories (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). In a separate investigation conducted by Almeida et al., a total of 103 MRI-guided biopsies were obtained from 83 individuals. The researchers found that the BIRADS subcategory 4C exhibited the highest predictive value for malignancy (odds ratio [OR] 13.48; 95% CI 2.27\u0026ndash;79.98) (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Both the Cha et al. and Maltez de Almeida et al. research were limited by their failure to provide specific information regarding the division of 4A/B/C groups. Currently, our center endeavors to categorize BIRADS category 4 lesions during the interpretation of the MRI. Nevertheless, using explicit BIRADS 4A, 4B, and 4C classifications and conducting meticulous comparisons between radiology and pathology should potentially reduce the occurrence of superfluous MRI-guided biopsies.\u003c/p\u003e \u003cp\u003eRecent research has indicated that MRI exhibits a sensitivity of 94.6% (with a range of 85.7\u0026ndash;100%) and a specificity of 74.2% (with a range of 25\u0026ndash;100%)(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The moderate specificity of breast MRI, coupled with its high sensitivity, contributes to a higher incidence of false positives and consequently leads to an elevated number of MRI-guided biopsies for benign tumors (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Despite the existence of strict criteria for interpreting breast MRI, they lack the necessary specificity to identify whether a diagnosis is benign or malignant unambiguously. The breast MRI BI-RADS (4c\u0026thinsp;+\u0026thinsp;5) and MRI BIRADS (4b\u0026thinsp;+\u0026thinsp;4c\u0026thinsp;+\u0026thinsp;5) had a sensitivity of 100.0% (54.1\u0026ndash;100.0) and 92.3% (64.0\u0026ndash;99.8), respectively, according to our data. The specificity rates for breast MRI BI-RADS (4c\u0026thinsp;+\u0026thinsp;5) and breast MRI BIRADS (4b\u0026thinsp;+\u0026thinsp;4c\u0026thinsp;+\u0026thinsp;5) were found to be 86.4% (78.2\u0026ndash;92.4) and 91.7% (84.2\u0026ndash;96.3), respectively.\u003c/p\u003e \u003cp\u003eThe importance of radiologic and pathologic association in breast MRI interpretation should be considered, as it plays a critical role in reducing the occurrence of benign biopsies, relieving patient distress, and reducing healthcare expenses. Prakash et al. conducted a study that found a association between peer review in radiology-pathology and a decrease in surgical excisions. The study revealed that weekly association conferences had an impact on up to 5.3% of cases, leading to surgery avoidance in 2.1% of cases (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere are various limitations in our investigation. The retrospective nature of this investigation resulted in a lack of specificity in the descriptions of lesions. Furthermore, in a few instances where patients underwent MRI prior to biopsy and outside of our medical facility, the image quality was occasionally insufficient, or just the reports were accessible. One further constraint pertains to the inadequate monitoring of multiple instances that exhibited inconsistent results, a pathologic diagnosis that was either high-risk or borderline, and the postponement of biopsy procedures. An effort for thorough follow-up was requested and could have influenced some of the results of this study.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, our study shows that MRI-guided biopsies are highly precise and responsive in identifying malignant breast lesions even with manual calculation method. The importance of MRI in diagnosing breast cancer is emphasized by its high sensitivity and specificity for malignant histopathological lesions, especially when used with high-risk lesions. The study also highlights the need to clearly sub-classify BI-RADS 4 lesions to reduce unnecessary biopsies using MRI. Additionally, this study emphasizes the importance in interpreting breast MRI to minimize false positives and unnecessary biopsies. Furthermore, these findings support the continued use of MRI guided biopsy as a crucial tool in diagnosing breast cancer and emphasize the need for ongoing progress and consistency in interpreting breast MRI.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eMRI: Magnetic resonance imaging\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUS: ultrasonography\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCNB: core needle biopsy\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLO: left outer \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLI: left inner\u003c/p\u003e\n\u003cp\u003eRO: right outer\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRI: right inner\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBMI: body mass index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBIRADS :breast imaging reporting and data system\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSE: sensitivity\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSP: specificity\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePPV: positive predictive value\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNPV: negative predictive value\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eNo fund\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki, and\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ethe protocol was approved by the Ethics Committee of Pardis Noor Medical Imaging Center and written informed consent was\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ewaived any additional consent because all participants had already given their consent at the time of the biopsy (PNMIC-MR-1403-02).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFattaneh Khalaj: Writing \u0026ndash; original draft, Conceptualization\u003c/p\u003e\n\u003cp\u003eZahra Moradi: Writing \u0026ndash; original draft\u003c/p\u003e\n\u003cp\u003eAmir Kasaeian: Data curation; Formal analysis\u003c/p\u003e\n\u003cp\u003eHamed Ghorani, Mohammad Hosein Golazar: Investigation\u003c/p\u003e\n\u003cp\u003eShahram Akhlaghpoor: Conceptualization; Resources; Writing\u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSardanelli F, Boetes C, Borisch B, Decker T, Federico M, Gilbert FJ, et al. 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Outcome of MRI-guided breast biopsy. American Journal of Roentgenology. 2008;191(6):1798-804.\u003c/li\u003e\n\u003cli\u003eLiberman L, Bracero N, Morris E, Thornton C, Dershaw DD. MRI-guided 9-gauge vacuum-assisted breast biopsy: initial clinical experience. American Journal of Roentgenology. 2005;185(1):183-93.\u003c/li\u003e\n\u003cli\u003eMalhaire C, El Khoury C, Thibault F, Athanasiou A, Petrow P, Ollivier L, Tardivon A. Vacuum-assisted biopsies under MR guidance: results of 72 procedures. European radiology. 2010;20:1554-62.\u003c/li\u003e\n\u003cli\u003eRauch GM, Dogan BE, Smith TB, Liu P, Yang WT. Outcome analysis of 9-gauge MRI-guided vacuum-assisted core needle breast biopsies. American Journal of Roentgenology. 2012;198(2):292-9.\u003c/li\u003e\n\u003cli\u003e\u0026Ouml;zcan BB, Yan J, Xi Y, Baydoun S, Scoggins ME, Doğan BE. Performance benchmark metrics and clinicopathologic outcomes of MRI-guided breast biopsies: a systematic review and meta-analysis. European Journal of Breast Health. 2023;19(1):1.\u003c/li\u003e\n\u003cli\u003eMcGrath AL, Price ER, Eby PR, Rahbar H. MRI-guided breast interventions. Journal of Magnetic Resonance Imaging. 2017;46(3):631-45.\u003c/li\u003e\n\u003cli\u003ePapalouka V, Kilburn-Toppin F, Gaskarth M, Gilbert F. MRI-guided breast biopsy: a review of technique, indications, and radiological\u0026ndash;pathological correlations. Clinical Radiology. 2018;73(10):908.e17-.e25.\u003c/li\u003e\n\u003cli\u003eMoreno G, Molina M, Wu R, Sullivan JR, Jorns JM. Unveiling the histopathologic spectrum of MRI-guided breast biopsies: an institutional pathological-radiological correlation. Breast Cancer Research and Treatment. 2021;187(3):673-80.\u003c/li\u003e\n\u003cli\u003eTeller P, Jefford VJ, Gabram SG, Newell M, Carlson GW. The utility of breast MRI in the management of breast cancer. The Breast Journal. 2010;16(4):394-403.\u003c/li\u003e\n\u003cli\u003eHeywang-K\u0026ouml;brunner SH, Hacker A, Sedlacek S. Magnetic resonance imaging: the evolution of breast imaging. The Breast. 2013;22:S77-S82.\u003c/li\u003e\n\u003cli\u003eBerg WA, Gutierrez L, NessAiver MS, Carter WB, Bhargavan M, Lewis RS, Ioffe OB. Diagnostic accuracy of mammography, clinical examination, US, and MR imaging in preoperative assessment of breast cancer. radiology. 2004;233(3):830-49.\u003c/li\u003e\n\u003cli\u003eLehman CD, Gatsonis C, Kuhl CK, Hendrick RE, Pisano ED, Hanna L, et al. MRI evaluation of the contralateral breast in women with recently diagnosed breast cancer. New England Journal of Medicine. 2007;356(13):1295-303.\u003c/li\u003e\n\u003cli\u003eLiberman L, Morris EA, Dershaw DD, Thornton CM, Van Zee KJ, Tan LK. Fast MRI-guided vacuum-assisted breast biopsy: initial experience. American Journal of Roentgenology. 2003;181(5):1283-93.\u003c/li\u003e\n\u003cli\u003eNunes LW, Schnall MD, Orel SG. Update of breast MR imaging architectural interpretation model. Radiology. 2001;219(2):484-94.\u003c/li\u003e\n\u003cli\u003eMorris E, Comstock C, Lee C, Lehman C, Ikeda D, Newstead G. ACR BI-RADS\u0026reg; magnetic resonance imaging. ACR BI-RADS\u0026reg; atlas, breast imaging reporting and data system. 2013;5.\u003c/li\u003e\n\u003cli\u003eCha SY, Ko EY, Han B-K, Ko ES, Choi JS, Park KW, Lee JE. Magnetic resonance imaging-guided breast biopsy in Korea: a 10-year follow-up experience. Journal of Breast Cancer. 2021;24(4):377.\u003c/li\u003e\n\u003cli\u003eMaltez de Almeida JR, Gomes AB, Barros TP, Fahel PE, de Seixas Rocha M. Subcategorization of suspicious breast lesions (BI-RADS category 4) according to MRI criteria: role of dynamic contrast-enhanced and diffusion-weighted imaging. American Journal of Roentgenology. 2015;205(1):222-31.\u003c/li\u003e\n\u003cli\u003eAristokli N, Polycarpou I, Themistocleous S, Sophocleous D, Mamais I. Comparison of the diagnostic performance of Magnetic Resonance Imaging (MRI), ultrasound and mammography for detection of breast cancer based on tumor type, breast density and patient\u0026apos;s history: A review. Radiography. 2022;28(3):848-56.\u003c/li\u003e\n\u003cli\u003ePrakash S, Venkataraman S, Slanetz PJ, Dialani V, Fein-Zachary V, Littlehale N, Mehta TS. Improving patient care by incorporation of multidisciplinary breast radiology-pathology correlation conference. Canadian Association of Radiologists Journal. 2016;67(2):122-9.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"MRI-guided breast biopsies, BIRADS, Breast pathologies","lastPublishedDoi":"10.21203/rs.3.rs-4719861/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4719861/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMRI is pivotal in breast imaging, encompassing staging, treatment monitoring, and lesion differentiation. While MRI boasts high sensitivity, specificity, and utility in detecting otherwise unseen lesions, challenges persist in accurately distinguishing benign from malignant findings. The study delves into MRI-guided breast biopsy outcomes and highlights the importance of radiologic-pathologic results.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective study analyzed 109 MRI-guided breast biopsies conducted on lesions identified between 2017 and 2023. the patients underwent biopsies for screening and diagnostic purposes. Biopsy procedures involved meticulous MRI guidance using a 1.5 Tesla system. Lesions were categorized based on location and BIRADS lexicon, with biopsy results spanning benign, suspicious, and malignant pathologies. Data collection encompassed a wide array of patient factors and pathology reports, meticulously reviewed by experienced radiologists, shedding light on the efficacy and outcomes of MRI-guided breast biopsies.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe participants had a mean age of 45\u0026thinsp;\u0026plusmn;\u0026thinsp;11 years. A significant association was found between the history of pregnancy and breast lesion enhancement. Patients with mass enhancement had a higher BIRADS B4b, B4c, and B5 classification rate, while those with non-mass enhancement were more commonly classified as BIRADS B3 and B4a. Histopathology diagnoses were significant in determining the presence of mass or non-mass lesions. The sensitivity and specificity of MRI for detecting malignancy were high for BIRADS categories 4c and 5 but may result in a higher number of false positives.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eour research highlighted the significance of MRI in the diagnosis of breast cancer, particularly when used in conjunction with high-risk lesions as well as showed the need of sub-classifying BI-RADS-4 lesions to minimize the number of unnecessary biopsies. The results affirm the ongoing use of MRI-guided biopsy for the detection of breast cancer.\u003c/p\u003e","manuscriptTitle":"Simple MR Guided Breast Biopsy Strategy: technique and radiological-pathological association","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-16 06:35:18","doi":"10.21203/rs.3.rs-4719861/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c167e738-25ca-41e5-af11-3ac4cdee2cce","owner":[],"postedDate":"August 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-16T11:38:39+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-16 06:35:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4719861","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4719861","identity":"rs-4719861","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}