Title: Diagnostic performance of combining apparent diffusion coefficient and microcalcifications to Kaiser Score in evaluation of BI-RADS 4 breast lesions.

Title: Diagnostic performance of combining apparent diffusion coefficient and microcalcifications to Kaiser Score in evaluation of BI-RADS 4 breast lesions. | 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 Title: Diagnostic performance of combining apparent diffusion coefficient and microcalcifications to Kaiser Score in evaluation of BI-RADS 4 breast lesions. Maryam Hamdy Foaad, Nermin Soliman, Omar Hamdy, Zainab A. Ramadan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7612130/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Jan, 2026 Read the published version in Egyptian Journal of Radiology and Nuclear Medicine → Version 1 posted You are reading this latest preprint version Abstract Background Breast cancer is considered the most commonly diagnosed cancer in the world and is responsible for a high rate of deaths among women. The malignancy risk dramatically increases in Breast Imaging Reporting and Data System (BI-RADS) 4 and 5 lesions. Therefore, this study aimed to evaluate the reliability of adding the apparent diffusion coefficient (ADC) and suspicious microcalcifications (when present) in combination with the Kaiser Score (KS) in improving the accuracy of the evaluation of magnetic resonance imaging (MRI) BI-RADS 4 lesions. Methods A total of 115 patients with 122 breast lesions categorized as BI-RADS 4 on MRI were included in the study. All patients had an MRI and a mammogram. Two observers analyzed images and calculated ADC, KS, KS1, KS2, and KS3. The diagnostic performance was calculated using receiver operating characteristic (ROC) analysis as well as interobserver agreement. Results This study involved 122 breast lesions (mean age: 48.1 years ± 10.3). The sensitivity for KS, KS1, KS2, and KS3 ranged from 85% to 91.84%, with area under the curve (AUC) values of 0.907, 0.916, 0.915, and 0.913, and accuracy rates of 90.98%, 86.89%, 91.8%, and 90.16%, respectively, for the first observer, denoting high and significant sensitivity (P < 0.001). Interobserver agreement was substantial (0.614 and 0.785) for ADC and KS, and perfect (0.822, 0.820, 0.817, and 0.852) for microcalcifications, KS1, KS2, and KS3, respectively, with the highest value for KS3 (0.85). The intraclass correlation coefficient (ICC) was moderate (0.644) for ADC and good (0.830, 0.822, 0.807, and 0.812) for KS, KS1, KS2, and KS3, respectively. Conclusion This study highlighted the value of the KS as a structured diagnostic tool in evaluating BI-RADS 4 breast lesions, particularly when combined with ADC and microcalcifications. KS3, which integrates all three parameters, provided the highest sensitivity and interobserver agreement. The findings uniquely demonstrate that microcalcifications contributed more to sensitivity than ADC when added to the KS framework, while adding ADC improved specificity and accuracy. These results support the use of multiparametric composite scoring to enhance MRI interpretation, reduce unnecessary biopsies, and improve diagnostic confidence in daily practice. Kaiser Score microcalcifications breast lesions BI-RADS 4 MRI mammogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Breast cancer is considered the most commonly diagnosed cancer in the world and is responsible for a high rate of deaths among women ( 1 ). Follow-up and screening of breast cancer can be done by various imaging modalities, especially mammography, ultrasound, and magnetic resonance imaging (MRI) ( 2 ). The Breast Imaging Reporting and Data System (BI-RADS), provided by the American College of Radiology, is considered the standard classification of different breast lesions ( 3 ). It classifies breast imaging findings into seven BI-RADS categories: 0, 1, 2, 3, 4, 5, and 6, according to the probability of malignancy. Breast lesions classified as BI-RADS 4 require biopsy and pathological examination ( 2 ). However, the probability of BI-RADS 4 lesions being malignant ranges from 3% to 94% ( 3 ). Therefore, differentiating benign lesions among the BI-RADS 4 category is mandatory to avoid unnecessary invasive workup. Baltzer et al. ( 4 ) provided a classification tree known as the Kaiser Score (KS) to help differentiate MRI-enhancing breast lesions according to 17 different variables. The KS ranges from 1 to 11, with biopsy recommended if the lesion has a score of more than 4, denoting a higher likelihood of malignancy with an increasing score ( 5 , 6 ). Digital mammography (DM) is considered the most sensitive imaging modality for the detection of microcalcifications ( 7 ). Although the majority of calcifications seen on mammograms are benign and do not require further investigation, some microcalcifications may indicate malignancy, and in certain cases, they may represent the only visible sign of cancer on mammographic imaging, especially ductal carcinoma in situ (DCIS) ( 8 ). A prior study recommended upgrading the Kaiser Score when suspicious microcalcifications are observed on mammography, to avoid DCIS misdiagnosis ( 9 ). Diffusion-weighted imaging (DWI) can provide the apparent diffusion coefficient (ADC), which quantitatively represents tissue fine structure (10). The cellularity of benign lesions is usually lower than that of malignant lesions, so benign lesions have higher ADC values ( 11 ). Therefore, ADC can also help to differentiate benign lesions among BI-RADS 4 lesions, alongside KS ( 12 ). However, its standalone diagnostic accuracy is limited. The structured multiparametric Kaiser Score (KS) has demonstrated higher overall performance, and recent studies suggest that adding ADC to KS can further improve specificity and reduce false-positive findings ( 13 ). Therefore, this study aimed to evaluate the reliability of adding ADC and suspicious microcalcifications (when present) in combination with the KS in improving the accuracy of the evaluation of BI-RADS 4 lesions. Methods Patients This prospective study was conducted in the Diagnostic and Interventional Radiology Department of our institution. This study was approved by the institutional review board (IRB) (Code number: MD.24.04.846). Informed written consent was obtained from all patients included in this study. Personal privacy was respected at all levels of the study. During the period from April 2024 to April 2025, we reviewed 423 female patients who underwent MRI examinations. Three hundred and eight of them were excluded owing to the following reasons: ( 1 ) receiving chemotherapy or having prior surgery (n = 102), ( 2 ) lesions with other BI-RADS categories 2, 3, or 5 on dynamic MRI (n = 138), ( 3 ) lacking histopathological results (n = 10), and ( 4 ) deficient mammogram (n = 58). Finally, a total of 115 patients (seven of them had bilateral BI-RADS 4 breast lesions) were included. Hence, a total of 122 lesions classified as BI-RADS 4 were included in the study. Imaging MRI was performed using a 1.5 T MRI system (Philips Ingenia and Siemens Magnetom Aera). Patients were positioned prone using a breast coil. T2-weighted, short tau inversion recovery (STIR), dynamic post-contrast, subtraction, and DWI sequences were mainly used. A contrast agent (gadopentetate dimeglumine) at a dose of 0.1 ml/kg was injected at a rate of 2 ml/s. Parameters of the different sequences are presented in Table 1 . Mammograms were obtained with a full-field digital mammography unit (Fuji Amulet; Philips), including two views: craniocaudal (CC) and mediolateral oblique (MLO), with parameters of 25 kV/100 mAs. Image processing Post-processing steps involved subtraction imaging by subtracting each pre-contrast image from each post-contrast image series, generation of maximum intensity projection (MIP) images, and generation of a time–signal intensity curve by placing a region of interest (ROI) over the most enhancing part of the lesion. The images were uploaded to the picture archiving and communication system (PACS) of our department. Image interpretation Two radiologists (who were blinded to the histopathological data of the patients) with 11 years and 3 years of experience in breast imaging, respectively, independently interpreted all MR images, calculated the Kaiser score (KS) and ADC for all lesions, and reviewed mammograms for the presence of suspicious microcalcifications following the parameters described in Youk et al. ( 14 ). Primarily, the lesions were evaluated for side and location and classified into mass and non-mass enhancement (NME). The main diagnostic variables of the Kaiser score were assessed (root sign, margin of lesion, presence of edema, internal enhancement of the lesion, and time–intensity curve). Three types of curves were generated: persistent, plateau, and washout. The Kaiser score was calculated from 1 to 11. The workflow of the Kaiser Score system is illustrated in Fig. 1 ( 9 ). ADC was automatically derived from DWI. The value of ADC was calculated by placing an ROI (area 5–10 mm²) on the part of the lesion having the lowest signal, avoiding areas of hemorrhage and necrosis. The two observers evaluated microcalcifications seen on mammography, focusing on suspicious microcalcifications according to a scoring system outlined by Youk et al. ( 14 ), to classify microcalcifications. The combination between KS, suspicious microcalcifications, and ADC was then applied. The parameter KS1 was derived by incorporating microcalcification findings into the original KS. For lesions with suspicious microcalcifications, 2 points were added to the original KS score to calculate KS1, based on the method proposed by Dietzel et al. ( 9 ). KS2 was obtained by combining the ADC values with the KS, as described in prior studies ( 9 , 13 ). A value of 1.4 × 10⁻³ mm²/s was identified as the optimal threshold ( 13 ). Based on this, if a lesion's ADC exceeded 1.4 × 10⁻³ mm²/s, the Kaiser score (with a value of > 4) was reduced by 1 point, as this yielded the best diagnostic accuracy in our testing. If the ADC was below this threshold, the Kaiser score remained unchanged. KS3 involved microcalcifications, ADC values, and the original KS score. Reference standard Histopathological examination was the reference standard for all lesions. Specimens were obtained either through biopsy or surgical excision and were independently reviewed by two board-certified pathologists with 4 and 11 years of experience, respectively. Statistical analysis The collected data were coded, processed, and analyzed using the Statistical Package for the Social Sciences (SPSS) version 15 for Windows (SPSS Inc., Chicago, IL, USA). Qualitative data were presented as number and percentage. Normally distributed data were presented as mean ± SD. Non-parametric data were presented as minimum–maximum and median. P < 0.05 was considered statistically significant. To assess the diagnostic performance of the Kaiser score, a receiver operating characteristic (ROC) analysis was performed, with calculation of the area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). To assess interobserver agreement, Cohen’s kappa coefficient (κ) was used for categorical variables, and the intraclass correlation coefficient (ICC) was used for continuous or ordinal variables. Kappa values were interpreted as follows: 0.80 = almost perfect agreement ( 15 ). For ICC, a two-way random-effects model (ICC 2,1) was applied, and interpretation followed conventional thresholds: 0.90 = excellent agreement ( 16 ). Results A total of 115 patients (mean age: 48.1 years ± 10.3, age range: 23–77 years) were included in the study. Among these patients, 7 had bilateral breast lesions (hence, 122 BI-RADS 4 breast lesions were evaluated): 5 had bilateral benign lesions, and 2 had 1 benign lesion and 1 malignant lesion in both breasts. Of the 122 lesions, 49 (40.16%) were malignant (mean age: 51.2 years ± 11.3) and 73 (59.84%) were benign (mean age: 46.05 years ± 9.35). There were 60 (49.18%) mass lesions and 62 (50.82%) NME lesions ( Table 2 ). The median KS value for malignant lesions was 6 (range: 5–9), while for benign lesions it was 3 (range: 3–5). Malignant lesions had a lower mean ADC value (1.1 ± 0.32) compared to benign lesions (1.3 ± 0.37). The pathological diagnoses of lesions are provided in Table 3 . The validity for ADC, KS, KS1, KS2, and KS3 was tested in this study with AUC values of (0.638, 0.907, 0.916, 0.915, 0.913) and (0.699, 0.818, 0.852, 0.847, 0.854) for the 1st and 2nd observers, respectively. The accuracy of detecting suspicious microcalcifications, ADC, KS, KS1, KS2, and KS3 was (66.39%, 60.66%, 90.98%, 86.89%, 91.8%, 90.16%) and (65.57%, 61.48%, 81.97%, 79.51%, 82.79%, 82.79%) for the 1st and 2nd observers, respectively ( Table 4, Fig. 2 ). Inter-observer agreement was substantial (0.614 and 0.785) for ADC and KS, respectively. It was perfect (0.822, 0.820, 0.817, and 0.852) for microcalcifications, KS1, KS2, and KS3, respectively, with the highest value for KS3 (0.852). The intraclass correlation coefficient was moderate (0.644) for ADC and good (0.830, 0.822, 0.807, 0.812) for KS, KS1, KS2, and KS3, respectively ( Table 5 ). Examples of the lesions involved in the study are represented in (Figs. 3–6). Discussion BI-RADS 4 category can involve both benign and malignant lesions ( 3 ). The Kaiser score enhances the ability to differentiate between them ( 13 ). Suspicious mammographic microcalcifications are more prevalent in malignant lesions, particularly in DCIS ( 17 ). ADC values are significantly lower in malignant lesions ( 18 ). Hence, this study aimed to evaluate the reliability of combining ADC and suspicious microcalcifications (when present) with the KS in differentiating benign from malignant BI-RADS 4 lesions. This study demonstrated that the median KS was significantly higher in malignant lesions compared to benign ones (range: 5–9 for malignant, 3–5 for benign). Furthermore, the mean ADC value for malignant lesions was significantly lower than for benign lesions (1.1 ± 0.32 for malignant, 1.3 ± 0.37 for benign), consistent with findings from both Meng et al. ( 19 ) and Pan et al. ( 20 ). In the study by Aslan and Oktay ( 21 ), the Kaiser score achieved perfect sensitivity (100%) for both observers, but only moderate specificity (52.38% and 47.62%) and overall accuracy (75.61% and 73.17%) for the 1st and 2nd observers, respectively. Similarly, in our study, the Kaiser score alone demonstrated excellent sensitivity (91.84% and 85.71%) for the 1st and 2nd observers, respectively; however, for the 1st observer, no significant increase in sensitivity was achieved when microcalcifications or ADC were added, in contrast to Aslan and Oktay’s 1st observer, who already reached 100% sensitivity with KS alone. Instead, the additional value in our study was the improvement in specificity and accuracy when using KS2 (91.78% and 91.80% for the 1st observer), values that clearly exceeded those reported by Aslan and Oktay ( 21 ). For the 2nd observer, KS1 and KS3 improved sensitivity (89.80%), slightly higher than KS2 (85.71%) and the original KS (85.71%). This gain in sensitivity came at the expense of specificity, with KS1 showing the lowest specificity (72.60%), whereas KS2 maintained the highest specificity (80.82%). These findings suggest that, unlike Aslan and Oktay’s results ( 21 )—where sensitivity was maximized at the expense of specificity—our approach using composite scores yielded a more balanced diagnostic profile by improving specificity and accuracy while maintaining high sensitivity, especially for less experienced readers. In contrast, ADC alone demonstrated the weakest performance, with accuracies of only 60.66% and 61.48% for the 1st and 2nd observers, respectively, and specificity falling below 50% in both cases. This denotes its limited value as a standalone diagnostic tool and supports its use only in combination with other parameters, aligning with findings from Pan et al. ( 20 ), who also reported moderate discriminative ability of ADC. Compared to the higher AUC of 0.901 reported by An et al. ( 22 ), our results indicate a lower diagnostic performance (AUC 0.638 and 0.699 for the 1st and 2nd observers, respectively). This is also consistent with findings from Dietzel et al. ( 13 ) and Meng et al. ( 19 ), who reported a superior AUC for KS over ADC alone and that combining KS with ADC increased specificity. When comparing our findings to previous studies, our results are largely consistent with those reported by Wengert et al. ( 5 ), who demonstrated that KS significantly reduced unnecessary stereotactic biopsies and had broad applicability across mass and non-mass lesions. Similar to their study, we found that lower Kaiser scores (≤ 4) were associated with benign lesions and that the KS helped to safely exclude malignancy in many cases. The findings in this study are also consistent with those of Pan et al. ( 20 ), showing that the median KS was significantly higher in malignant lesions than in benign ones. When applying a KS cut-off value of > 4, this study yielded four false-negative cases: two DCIS, one invasive ductal carcinoma, and one case of Paget’s disease, all with borderline KS values between 3 and 4, comparable to Pan et al.’s report of 12 false-negative lesions with most KS values ranging from 3 to 4. Despite these limitations, the KS in our study correctly identified 66 (90%) of 73 benign lesions initially labeled as BI-RADS 4, allowing us to potentially avoid unnecessary biopsies in the majority of these patients, higher than Pan et al.’s reported biopsy avoidance rate of 60.9%. This reinforces the utility of KS in refining biopsy decisions and improving diagnostic efficacy, particularly in BI-RADS 4 cases. Similar results were found regarding the performance of KS and its modified versions (KS1, KS2, and KS3) in evaluating BI-RADS 4 breast lesions by Pan et al. ( 20 ). In both studies, KS1 showed the highest sensitivity, denoting that it was best at detecting malignant lesions. Pan et al. ( 20 ) reported a sensitivity of 100%, while in our study KS1 had sensitivity of (89.80%–91.84%). However, this came with a lower specificity (56%) in Pan’s study ( 20 ), while in our case it was (72.60%–83.56%). In Pan et al.’s study ( 20 ), KS2 sensitivity ranged from 77.1% to 91.3% and specificity from 64.0% to 69.4%, depending on lesion type. In contrast, the current results demonstrated higher diagnostic performance of KS2, with sensitivity of 91.84% and 85.71% and specificities of 91.78% and 80.82% for the 1st and 2nd observers, respectively. Furthermore, accuracy reached 91.8% for the 1st observer, compared to Pan’s lower overall accuracy estimates. This difference may reflect variations in lesion characteristics or reader experience, suggesting that ADC may provide greater added value in certain clinical settings, especially when interpreted consistently. With KS3, Pan et al. ( 20 ) found a high sensitivity of 94.3% and a specificity of 60%. Our results were similar in sensitivity (91.84% and 89.80%) but showed better specificity (89.04% and 78.08%). This indicates that KS3, in both studies, maintains high cancer detection while offering a modest improvement in avoiding unnecessary biopsies in our dataset. Lastly, for the original KS, Pan ( 20 ) reported sensitivity of 82.9% and specificity of 60%, while our study showed higher sensitivity (91.84% and 85.71%) and specificity (90.41% and 79.45%). In this study, interobserver agreement showed a high level of consistency between observers. The kappa agreement was substantial (0.614 and 0.785) for both ADC values and the overall KS, indicating reliable reproducibility in these assessments. Moreover, perfect agreement (0.822, 0.820, 0.817, and 0.852) was observed for microcalcifications, as well as KS1, KS2, and KS3, respectively. Among these, KS3 showed the highest kappa value (κ = 0.85), reflecting excellent interobserver reliability. This suggests that structured decision tools enriched by objective metrics like ADC values and the presence of microcalcifications can help reduce subjectivity in MRI interpretation, an area traditionally challenged by variability in reader experience and lesion complexity. This agreement is consistent with Istomin A et al. ( 23 ), who had excellent interobserver agreement for KS (0.882), and higher than Milos RI et al.’s ( 12 ) agreement, which was fair to moderate (0.393–0.560). The ICC was moderate (0.644) for ADC and good (0.830, 0.822, 0.807, 0.812) for KS, KS1, KS2, and KS3, respectively. That is consistent with Meng et al. ( 19 ), who also reported excellent ICC for both the Kaiser score and ADC measurements (0.912 for KS and 0.997 for ADC) and also with Aslan and Oktay ( 21 ), whose coefficient was excellent (0.964) for Kaiser score. This study had some limitations that must be acknowledged. First, while KS1 and KS3 improved overall accuracy and sensitivity, false positives and false negatives were still encountered. These limitations underscore the importance of integrating imaging with clinical and histological information. Additionally, when measuring ADC values, ROI was manually drawn on two-dimensional images, carefully excluding areas of visible necrosis, cystic change, or hemorrhage. However, this approach may have overlooked the impact of intralesional heterogeneity on diffusion measurements. Future multi-center studies with larger sample volumes, more readers, and using deep learning and feature tracking are recommended. Conclusion This study highlighted the value of the KS as a structured diagnostic tool in evaluating BI-RADS 4 breast lesions, particularly when combined with ADC and microcalcifications. KS3, which integrates all three parameters, provided the highest sensitivity and interobserver agreement. The findings uniquely demonstrate that microcalcifications contributed more to sensitivity than ADC when added to the KS framework, while adding ADC improved specificity and accuracy. These results support the use of multiparametric composite scoring to enhance MRI interpretation, reduce unnecessary biopsies, and improve diagnostic confidence in daily practice. Abbreviations ADC: Apparent Diffusion Coefficient AUC: Area Under the Curve BI-RADS: Breast Imaging Reporting and Data System DCIS: Ductal Carcinoma in Situ DM: Digital Mammography DCE-MRI: Dynamic Contrast-Enhanced Magnetic Resonance Imaging DWI: Diffusion-Weighted Imaging SPSS: Statistical Package for the Social Sciences ICC: intraclass correlation coefficient KS: Kaiser Score MIP: Maximum Intensity Projection MRI: Magnetic Resonance Imaging NME : Non-Mass Enhancement NPV: Negative Predictive Value PACS: Picture Archiving and Communication System PPV: Positive Predictive Value ROC: Receiver Operating Characteristic ROI: Region of Interest STIR: Short Tau Inversion Recovery TIC: Time Intensity Curve Declarations Ethics approval and consent to participate This study was approved by institutional review board on April 21, 2024. (Code number: MD.24.04.846). All patients included in this study gave written informed consent to participate in the research. Consent for publication: All patients included in this study gave written informed consent to publish the data contained in this study. Availability of data and materials: Available on request with the corresponding author. Competing interests: The authors declare that they have no competing interests. Funding: Not applicable (no funding was received for this study). Declarations : The authors have no relevant financial or non-financial interests to disclose. Acknowledgement : Not applicable Author Contribution MH and NA designed the research. MH performed the research and wrote the manuscript. MH and ZA analyzed the collected data. ZA and OH revised the data and manuscript. All authors read and approved the final manuscript. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Eur J Radiol 138:109659 Tables Table (1): Parameters of MRI sequences used Parameters/sequences T2 T1 STIR Dynamic T1 DWI TR/TE (ms) 2000/80 450/14 7000/70 4-8/2 5800/139 Slice thickness 3 3 3-4 3 4 Inter slice gap (mm) 1 1 1 0 1 FOV 300/360 300/360 300/360 300/360 300/360 Scan plane axial Axial axial axial axial Type of Sequence FSE FSE FSE with inversion (TI 150 ms) FLASH 3D GRE-T1W1 Single shot spin EPI B value 0,500,1000 TR: repetition time, TE: echo time, TI: inversion time, FOV: Field of view, FSE: fast spin echo, FLASH: fast low angle shot, GRE: gradient echo, EPI: echo planar imaging Table (2): characteristics of patients and lesions: TIC: Time intensity curve Table (3): Final histopathological diagnosis of the lesions in the study Subtypes Number 122 Malignant (number 49, 40.16%) Invasive ductal carcinoma 27 (22.12%) Invasive lobular carcinoma 6 (4.92%) Papillary carcinoma 1 (0.82%) Paget’s disease of the nipple 2 (1.64%) DCIS 12 (9.84%) micro cystic adnexal carcinoma of sweet gland 1 (0.82%) Benign (number 73, 59.84%) Fibroadenoma 4 (3.28%) Phyllodes 1 (0.82%) Intraductal papilloma 13 (10.65%) Intraductal papilloma with fibroadenoma 1 (0.82%) Intraductal papillomatosis 6 (4.92%) Fibrocystic changes/ fibroadenosis 21 (17.21%) Sclerosing adenosis 3 (2.46%) Breast tissue 4 (3.28%) Fat necrosis 2 (1.64%) Apocrine metaplasia 1 (0.82%) Mammary ductectasia 2 (1.64%) Stromal fibrosis 5 (4.1%) Inflammatory / Granulomatous mastitis 6 (4.92%) Benign proliferative breast lesion 4 (3.28%) DCIS: Ductal carcinoma in situ. Table (4): Validity of KS, KS1, KS2, and KS3 for characterization of benign versus malignant lesions Sensitivity% Specificity % NPV % PPV % Accuracy % AUC P value Micro-calcifications 1 st observer 2 nd observer 30.61 34.69 90.41 86.30 66.00 66.32 68.18 62.96 66.39 65.57 ADC 1 st observer 2 nd observer 79.59 89.80 47.95 42.47 77.78 86.11 50.65 51.16 60.66 61.48 0.638 0.699 0.010 < 0.001 KS 1 st observer 2 nd observer 91.84 85.71 90.41 79.45 94.29 89.23 86.54 73.68 90.98 81.97 0.907 0.818 < 0.001 < 0.001 KS1 1 st observer 2 nd observer 91.84 89.80 83.56 72.60 93.85 91.38 78.95 68.75 86.89 79.51 0.916 0.852 < 0.001 < 0.001 KS2 1 st observer 2 nd observer 91.84 85.71 91.78 80.82 94.37 89.39 88.24 75.00 91.80 82.79 0.915 0.847 < 0.001 < 0.001 KS3 1 st observer 2 nd observer 91.84 89.80 89.04 78.08 94.20 91.94 84.91 73.33 90.16 82.79 0.913 0.854 < 0.001 < 0.001 NPV: negative predictive value, PPV: positive predictive value, ADC: apparent diffusion coefficient, KS: Kaiser score. Table (5): Interobserver kappa agreement and intra class correlation coefficient according to type of variables 2 nd Observer 1st Observer k P value ICC 95% CI KS score - - 0.830 (0.766-0.880) KS (benign versus malignant) 0.785 < 0.001 - - ADC value - - 0.644 (0.528-0.736) ADC (benign versus malignant) 0.614 < 0.001 - - KS1 score - - 0.822 (0.755- 0.872) KS1 score (benign versus malignant) 0.820 < 0.001 - - KS2 score - - 0.807 (0.735-0.861) KS2 score (benign versus malignant) 0.817 < 0.001 - - KS3 score - - 0.812 (0.742-0.865) KS3 score (benign versus malignant) 0.852 < 0.001 - - Microcalcifications 0.822 < 0.001 K: kappa, ICC: intraclass correlation coefficient, CI: confidence interval, KS: Kaiser score, ADC: Apparent diffusion coefficient Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 31 Jan, 2026 Read the published version in Egyptian Journal of Radiology and Nuclear Medicine → 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7612130","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":538642630,"identity":"da706928-a069-41cd-bd2a-6967a572bf51","order_by":0,"name":"Maryam Hamdy Foaad","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYPCCA0CcwPgAyjMgWgszTCnxWtgkiNKi2372mcTPnDvyuu05ZlU3au7YM7A3b5NgqKjFqcXsTLqZZO+2Z4bbzrwxu51z7FliA8+xMgmGM8dxazmQxibBu+0w47YbOUAtbIcTGCRyzCQY247h1nL+GZvk322H7UFainP+HbZnkH8D1PIPj5YbaWzSQFsSQVqYc9sOMzZI8AC1NNTg0fKM2Vp22+HkbWeeFUvn9h1ObONJK7ZIOHYAj8PSGG++3XbYdtvx5I2fc74dtudnP7zxxoeaOpxagIAFGh0ckOhgAxEJDIfxaWH+AKHZHyCL4rVlFIyCUTAKRhYAAPqFXcalDimVAAAAAElFTkSuQmCC","orcid":"","institution":"Mansoura University","correspondingAuthor":true,"prefix":"","firstName":"Maryam","middleName":"Hamdy","lastName":"Foaad","suffix":""},{"id":538642631,"identity":"9049e486-2c39-41d5-b096-677f78221ff4","order_by":1,"name":"Nermin Soliman","email":"","orcid":"","institution":"Mansoura University","correspondingAuthor":false,"prefix":"","firstName":"Nermin","middleName":"","lastName":"Soliman","suffix":""},{"id":538642632,"identity":"12c5761f-06f2-4850-ae64-f2aa04e82ebb","order_by":2,"name":"Omar Hamdy","email":"","orcid":"","institution":"Mansoura University","correspondingAuthor":false,"prefix":"","firstName":"Omar","middleName":"","lastName":"Hamdy","suffix":""},{"id":538642633,"identity":"62d31fc1-2592-4334-98ae-dc17a05fc7f0","order_by":3,"name":"Zainab A. 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06:29:38","extension":"html","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":111681,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7612130/v1/e25baec18148b0e5c1726007.html"},{"id":95226749,"identity":"60389080-e68c-4a8f-907a-b5ac2a69ca52","added_by":"auto","created_at":"2025-11-05 16:31:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":228151,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic flowchart of the Kaiser score adapted from Dietzel et al (9).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7612130/v1/f48dd03167ac7a351bf9b0f0.png"},{"id":95170836,"identity":"180c05a8-3421-4943-8066-f2e7d5ffea4f","added_by":"auto","created_at":"2025-11-05 06:29:37","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":95435,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves show the diagnostic performance of KS, KS1, KS2, KS3 and ADC alone, AUC values (0.907, 0.916, 0.915, 0.913, and 0.638) and the accuracy was (90.98%, 86.89%, 91.8%, 90.16 %, and 60.66 %) respectively for the 1\u003csup\u003est\u003c/sup\u003e observer.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7612130/v1/4d907471ff0cda015c68ad79.jpg"},{"id":95170840,"identity":"cf8f6c2f-9244-4147-9624-58a4617f45d4","added_by":"auto","created_at":"2025-11-05 06:29:37","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":100685,"visible":true,"origin":"","legend":"\u003cp\u003e44 years old female patient with nipple discharge. Mammogram and MR images of the right breast: (a) MLO mammogram shows no microcalcifications, (b) STIR and (c) dynamic subtraction images show dilated ducts with few homogenously enhancing intra ductal masses seen at 5 o'clock zone a, (d) TIC shows type 1 persistent curve, (e) DWI and (f) ADC show diffusion restriction with a mean ADC of 0.9. KS was 3, KS1 was 3, while KS2 was 3, and KS3 was 3. The pathology revealed major duct ectasia with foci of low grade ductal carcinoma in situ.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7612130/v1/53aaea5fda760801c2c75f6e.jpg"},{"id":95227476,"identity":"61860dda-11c5-4b40-8e2c-efcd8a396333","added_by":"auto","created_at":"2025-11-05 16:32:32","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":118467,"visible":true,"origin":"","legend":"\u003cp\u003e54 years old female patient with nipple discharge. Mammogram and MR images of the left breast: (a) MLO mammogram shows grouped amorphous microcalcifications, (b) T2 and (c) dynamic subtraction images show ductal dilatation with linear homogenous non mass enhancement in the ductal distribution, (d) TIC shows type 1 persistent curve, (e) DWI and (f) ADC show restricted diffusion with corresponding small area of low ADC, mean ADC of darkest area is 1.2. KS was 3, KS1 was 5, while KS2 was 3, and KS3 was 5. The pathology revealed sclerosing intra ductal papillomatosis.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7612130/v1/243c299408d9e83859d9bda0.jpg"},{"id":95226308,"identity":"24b2c83b-9498-4a1a-83b6-c32b3351f00d","added_by":"auto","created_at":"2025-11-05 16:30:55","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":90353,"visible":true,"origin":"","legend":"\u003cp\u003e56 years old female patient with mastalgia. Mammogram and MR images of the right breast: (a) MLO mammogram shows no microcalcifications, (b) T2 and (c) dynamic subtraction images show A non-circumscribed heterogeneously enhancing mass lesion at 3-4 o'clock with +ve root sign “red arrow”, (d) TIC shows type 1 persistent curve, (e) DWI and (f) ADC show diffusion restriction with a mean ADC of 0.9. KS was 6, KS1 was 6, while KS2 was 6, and KS3 was 6. The pathology revealed lobular mastitis.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7612130/v1/0197fa5c4d7a51afd8009c43.jpg"},{"id":95226453,"identity":"29a77e58-a42d-406a-b05c-7062e0378e7f","added_by":"auto","created_at":"2025-11-05 16:31:10","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":123254,"visible":true,"origin":"","legend":"\u003cp\u003e65 years old female patient with left breast lump. Mammogram and MR images of the left breast: (a) CC mammogram shows no micro calcifications, (b) STIR and (c) dynamic subtraction images show a small heterogeneously enhanced speculated mass at 11 o'clock zone a with +ve root sign “blue arrow”, (d) TIC shows type 3 washout curve, (e) DWI and (f) ADC show restricted diffusion with a mean ADC of the darkest area of 0.9. KS was 7, KS1 was 7, while KS2 was 7, and KS3 was 7. The pathology revealedgrade II Invasive ductal carcinoma with low grade DCIS component.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7612130/v1/d452ba46b1bf3479bba945e9.jpg"},{"id":101690636,"identity":"ad33d6a8-801a-45ec-92fd-605a676b723f","added_by":"auto","created_at":"2026-02-02 16:06:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1863775,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7612130/v1/6be5b446-9b42-4991-a6f4-4770d3fad130.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Title: Diagnostic performance of combining apparent diffusion coefficient and microcalcifications to Kaiser Score in evaluation of BI-RADS 4 breast lesions.","fulltext":[{"header":"Background","content":"\u003cp\u003eBreast cancer is considered the most commonly diagnosed cancer in the world and is responsible for a high rate of deaths among women (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFollow-up and screening of breast cancer can be done by various imaging modalities, especially mammography, ultrasound, and magnetic resonance imaging (MRI) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe Breast Imaging Reporting and Data System (BI-RADS), provided by the American College of Radiology, is considered the standard classification of different breast lesions (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). It classifies breast imaging findings into seven BI-RADS categories: 0, 1, 2, 3, 4, 5, and 6, according to the probability of malignancy. Breast lesions classified as BI-RADS 4 require biopsy and pathological examination (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). However, the probability of BI-RADS 4 lesions being malignant ranges from 3% to 94% (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Therefore, differentiating benign lesions among the BI-RADS 4 category is mandatory to avoid unnecessary invasive workup. Baltzer et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) provided a classification tree known as the Kaiser Score (KS) to help differentiate MRI-enhancing breast lesions according to 17 different variables. The KS ranges from 1 to 11, with biopsy recommended if the lesion has a score of more than 4, denoting a higher likelihood of malignancy with an increasing score (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDigital mammography (DM) is considered the most sensitive imaging modality for the detection of microcalcifications (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Although the majority of calcifications seen on mammograms are benign and do not require further investigation, some microcalcifications may indicate malignancy, and in certain cases, they may represent the only visible sign of cancer on mammographic imaging, especially ductal carcinoma in situ (DCIS) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). A prior study recommended upgrading the Kaiser Score when suspicious microcalcifications are observed on mammography, to avoid DCIS misdiagnosis (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDiffusion-weighted imaging (DWI) can provide the apparent diffusion coefficient (ADC), which quantitatively represents tissue fine structure (10). The cellularity of benign lesions is usually lower than that of malignant lesions, so benign lesions have higher ADC values (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Therefore, ADC can also help to differentiate benign lesions among BI-RADS 4 lesions, alongside KS (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). However, its standalone diagnostic accuracy is limited. The structured multiparametric Kaiser Score (KS) has demonstrated higher overall performance, and recent studies suggest that adding ADC to KS can further improve specificity and reduce false-positive findings (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTherefore, this study aimed to evaluate the reliability of adding ADC and suspicious microcalcifications (when present) in combination with the KS in improving the accuracy of the evaluation of BI-RADS 4 lesions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePatients\u003c/h2\u003e\u003cp\u003eThis prospective study was conducted in the Diagnostic and Interventional Radiology Department of our institution. This study was approved by the institutional review board (IRB) (Code number: MD.24.04.846). Informed written consent was obtained from all patients included in this study. Personal privacy was respected at all levels of the study.\u003c/p\u003e\u003cp\u003e During the period from April 2024 to April 2025, we reviewed 423 female patients who underwent MRI examinations. Three hundred and eight of them were excluded owing to the following reasons: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) receiving chemotherapy or having prior surgery (n\u0026thinsp;=\u0026thinsp;102), (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) lesions with other BI-RADS categories 2, 3, or 5 on dynamic MRI (n\u0026thinsp;=\u0026thinsp;138), (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) lacking histopathological results (n\u0026thinsp;=\u0026thinsp;10), and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) deficient mammogram (n\u0026thinsp;=\u0026thinsp;58). Finally, a total of 115 patients (seven of them had bilateral BI-RADS 4 breast lesions) were included. Hence, a total of 122 lesions classified as BI-RADS 4 were included in the study.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eImaging\u003c/h3\u003e\n\u003cp\u003eMRI was performed using a 1.5 T MRI system (Philips Ingenia and Siemens Magnetom Aera). Patients were positioned prone using a breast coil. T2-weighted, short tau inversion recovery (STIR), dynamic post-contrast, subtraction, and DWI sequences were mainly used. A contrast agent (gadopentetate dimeglumine) at a dose of 0.1 ml/kg was injected at a rate of 2 ml/s. Parameters of the different sequences are presented in \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e. Mammograms were obtained with a full-field digital mammography unit (Fuji Amulet; Philips), including two views: craniocaudal (CC) and mediolateral oblique (MLO), with parameters of 25 kV/100 mAs.\u003c/p\u003e\n\u003ch3\u003eImage processing\u003c/h3\u003e\n\u003cp\u003ePost-processing steps involved subtraction imaging by subtracting each pre-contrast image from each post-contrast image series, generation of maximum intensity projection (MIP) images, and generation of a time\u0026ndash;signal intensity curve by placing a region of interest (ROI) over the most enhancing part of the lesion. The images were uploaded to the picture archiving and communication system (PACS) of our department.\u003c/p\u003e\n\u003ch3\u003eImage interpretation\u003c/h3\u003e\n\u003cp\u003eTwo radiologists (who were blinded to the histopathological data of the patients) with 11 years and 3 years of experience in breast imaging, respectively, independently interpreted all MR images, calculated the Kaiser score (KS) and ADC for all lesions, and reviewed mammograms for the presence of suspicious microcalcifications following the parameters described in Youk et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePrimarily, the lesions were evaluated for side and location and classified into mass and non-mass enhancement (NME). The main diagnostic variables of the Kaiser score were assessed (root sign, margin of lesion, presence of edema, internal enhancement of the lesion, and time\u0026ndash;intensity curve). Three types of curves were generated: persistent, plateau, and washout. The Kaiser score was calculated from 1 to 11. The workflow of the Kaiser Score system is illustrated in \u003cb\u003eFig.\u0026nbsp;1\u003c/b\u003e (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eADC was automatically derived from DWI. The value of ADC was calculated by placing an ROI (area 5\u0026ndash;10 mm\u0026sup2;) on the part of the lesion having the lowest signal, avoiding areas of hemorrhage and necrosis.\u003c/p\u003e\u003cp\u003eThe two observers evaluated microcalcifications seen on mammography, focusing on suspicious microcalcifications according to a scoring system outlined by Youk et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), to classify microcalcifications.\u003c/p\u003e\u003cp\u003eThe combination between KS, suspicious microcalcifications, and ADC was then applied. The parameter KS1 was derived by incorporating microcalcification findings into the original KS. For lesions with suspicious microcalcifications, 2 points were added to the original KS score to calculate KS1, based on the method proposed by Dietzel et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). KS2 was obtained by combining the ADC values with the KS, as described in prior studies (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). A value of 1.4 \u0026times; 10⁻\u0026sup3; mm\u0026sup2;/s was identified as the optimal threshold (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Based on this, if a lesion's ADC exceeded 1.4 \u0026times; 10⁻\u0026sup3; mm\u0026sup2;/s, the Kaiser score (with a value of \u0026gt;\u0026thinsp;4) was reduced by 1 point, as this yielded the best diagnostic accuracy in our testing. If the ADC was below this threshold, the Kaiser score remained unchanged. KS3 involved microcalcifications, ADC values, and the original KS score.\u003c/p\u003e\u003cp\u003e\u003cb\u003eReference standard\u003c/b\u003e\u003c/p\u003e\u003cp\u003eHistopathological examination was the reference standard for all lesions. Specimens were obtained either through biopsy or surgical excision and were independently reviewed by two board-certified pathologists with 4 and 11 years of experience, respectively.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe collected data were coded, processed, and analyzed using the Statistical Package for the Social Sciences (SPSS) version 15 for Windows (SPSS Inc., Chicago, IL, USA). Qualitative data were presented as number and percentage. Normally distributed data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Non-parametric data were presented as minimum\u0026ndash;maximum and median. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003cp\u003eTo assess the diagnostic performance of the Kaiser score, a receiver operating characteristic (ROC) analysis was performed, with calculation of the area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). To assess interobserver agreement, Cohen\u0026rsquo;s kappa coefficient (κ) was used for categorical variables, and the intraclass correlation coefficient (ICC) was used for continuous or ordinal variables. Kappa values were interpreted as follows: \u0026lt;0.20\u0026thinsp;=\u0026thinsp;poor, 0.21\u0026ndash;0.40\u0026thinsp;=\u0026thinsp;fair, 0.41\u0026ndash;0.60\u0026thinsp;=\u0026thinsp;moderate, 0.61\u0026ndash;0.80\u0026thinsp;=\u0026thinsp;substantial, and \u0026gt;\u0026thinsp;0.80\u0026thinsp;=\u0026thinsp;almost perfect agreement (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). For ICC, a two-way random-effects model (ICC 2,1) was applied, and interpretation followed conventional thresholds: \u0026lt;0.50\u0026thinsp;=\u0026thinsp;poor, 0.50\u0026ndash;0.75\u0026thinsp;=\u0026thinsp;moderate, 0.75\u0026ndash;0.90\u0026thinsp;=\u0026thinsp;good, and \u0026gt;\u0026thinsp;0.90\u0026thinsp;=\u0026thinsp;excellent agreement (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 115 patients (mean age: 48.1 years\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3, age range: 23\u0026ndash;77 years) were included in the study. Among these patients, 7 had bilateral breast lesions (hence, 122 BI-RADS 4 breast lesions were evaluated): 5 had bilateral benign lesions, and 2 had 1 benign lesion and 1 malignant lesion in both breasts. Of the 122 lesions, 49 (40.16%) were malignant (mean age: 51.2 years\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3) and 73 (59.84%) were benign (mean age: 46.05 years\u0026thinsp;\u0026plusmn;\u0026thinsp;9.35). There were 60 (49.18%) mass lesions and 62 (50.82%) NME lesions (\u003cb\u003eTable\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eThe median KS value for malignant lesions was 6 (range: 5\u0026ndash;9), while for benign lesions it was 3 (range: 3\u0026ndash;5). Malignant lesions had a lower mean ADC value (1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32) compared to benign lesions (1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37). The pathological diagnoses of lesions are provided in \u003cb\u003eTable\u0026nbsp;3\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eThe validity for ADC, KS, KS1, KS2, and KS3 was tested in this study with AUC values of (0.638, 0.907, 0.916, 0.915, 0.913) and (0.699, 0.818, 0.852, 0.847, 0.854) for the 1st and 2nd observers, respectively. The accuracy of detecting suspicious microcalcifications, ADC, KS, KS1, KS2, and KS3 was (66.39%, 60.66%, 90.98%, 86.89%, 91.8%, 90.16%) and (65.57%, 61.48%, 81.97%, 79.51%, 82.79%, 82.79%) for the 1st and 2nd observers, respectively (\u003cb\u003eTable\u0026nbsp;4, Fig.\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eInter-observer agreement was substantial (0.614 and 0.785) for ADC and KS, respectively. It was perfect (0.822, 0.820, 0.817, and 0.852) for microcalcifications, KS1, KS2, and KS3, respectively, with the highest value for KS3 (0.852). The intraclass correlation coefficient was moderate (0.644) for ADC and good (0.830, 0.822, 0.807, 0.812) for KS, KS1, KS2, and KS3, respectively (\u003cb\u003eTable\u0026nbsp;5\u003c/b\u003e). Examples of the lesions involved in the study are represented in (Figs.\u0026nbsp;3\u0026ndash;6).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBI-RADS 4 category can involve both benign and malignant lesions (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The Kaiser score enhances the ability to differentiate between them (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Suspicious mammographic microcalcifications are more prevalent in malignant lesions, particularly in DCIS (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). ADC values are significantly lower in malignant lesions (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Hence, this study aimed to evaluate the reliability of combining ADC and suspicious microcalcifications (when present) with the KS in differentiating benign from malignant BI-RADS 4 lesions.\u003c/p\u003e\u003cp\u003eThis study demonstrated that the median KS was significantly higher in malignant lesions compared to benign ones (range: 5\u0026ndash;9 for malignant, 3\u0026ndash;5 for benign). Furthermore, the mean ADC value for malignant lesions was significantly lower than for benign lesions (1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32 for malignant, 1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37 for benign), consistent with findings from both Meng et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) and Pan et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the study by Aslan and Oktay (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), the Kaiser score achieved perfect sensitivity (100%) for both observers, but only moderate specificity (52.38% and 47.62%) and overall accuracy (75.61% and 73.17%) for the 1st and 2nd observers, respectively. Similarly, in our study, the Kaiser score alone demonstrated excellent sensitivity (91.84% and 85.71%) for the 1st and 2nd observers, respectively; however, for the 1st observer, no significant increase in sensitivity was achieved when microcalcifications or ADC were added, in contrast to Aslan and Oktay\u0026rsquo;s 1st observer, who already reached 100% sensitivity with KS alone. Instead, the additional value in our study was the improvement in specificity and accuracy when using KS2 (91.78% and 91.80% for the 1st observer), values that clearly exceeded those reported by Aslan and Oktay (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor the 2nd observer, KS1 and KS3 improved sensitivity (89.80%), slightly higher than KS2 (85.71%) and the original KS (85.71%). This gain in sensitivity came at the expense of specificity, with KS1 showing the lowest specificity (72.60%), whereas KS2 maintained the highest specificity (80.82%). These findings suggest that, unlike Aslan and Oktay\u0026rsquo;s results (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u0026mdash;where sensitivity was maximized at the expense of specificity\u0026mdash;our approach using composite scores yielded a more balanced diagnostic profile by improving specificity and accuracy while maintaining high sensitivity, especially for less experienced readers.\u003c/p\u003e\u003cp\u003eIn contrast, ADC alone demonstrated the weakest performance, with accuracies of only 60.66% and 61.48% for the 1st and 2nd observers, respectively, and specificity falling below 50% in both cases. This denotes its limited value as a standalone diagnostic tool and supports its use only in combination with other parameters, aligning with findings from Pan et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), who also reported moderate discriminative ability of ADC. Compared to the higher AUC of 0.901 reported by An et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), our results indicate a lower diagnostic performance (AUC 0.638 and 0.699 for the 1st and 2nd observers, respectively).\u003c/p\u003e\u003cp\u003eThis is also consistent with findings from Dietzel et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) and Meng et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), who reported a superior AUC for KS over ADC alone and that combining KS with ADC increased specificity.\u003c/p\u003e\u003cp\u003eWhen comparing our findings to previous studies, our results are largely consistent with those reported by Wengert et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), who demonstrated that KS significantly reduced unnecessary stereotactic biopsies and had broad applicability across mass and non-mass lesions. Similar to their study, we found that lower Kaiser scores (\u0026le;\u0026thinsp;4) were associated with benign lesions and that the KS helped to safely exclude malignancy in many cases.\u003c/p\u003e\u003cp\u003eThe findings in this study are also consistent with those of Pan et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), showing that the median KS was significantly higher in malignant lesions than in benign ones. When applying a KS cut-off value of \u0026gt;\u0026thinsp;4, this study yielded four false-negative cases: two DCIS, one invasive ductal carcinoma, and one case of Paget\u0026rsquo;s disease, all with borderline KS values between 3 and 4, comparable to Pan et al.\u0026rsquo;s report of 12 false-negative lesions with most KS values ranging from 3 to 4. Despite these limitations, the KS in our study correctly identified 66 (90%) of 73 benign lesions initially labeled as BI-RADS 4, allowing us to potentially avoid unnecessary biopsies in the majority of these patients, higher than Pan et al.\u0026rsquo;s reported biopsy avoidance rate of 60.9%. This reinforces the utility of KS in refining biopsy decisions and improving diagnostic efficacy, particularly in BI-RADS 4 cases.\u003c/p\u003e\u003cp\u003eSimilar results were found regarding the performance of KS and its modified versions (KS1, KS2, and KS3) in evaluating BI-RADS 4 breast lesions by Pan et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In both studies, KS1 showed the highest sensitivity, denoting that it was best at detecting malignant lesions. Pan et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) reported a sensitivity of 100%, while in our study KS1 had sensitivity of (89.80%\u0026ndash;91.84%). However, this came with a lower specificity (56%) in Pan\u0026rsquo;s study (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), while in our case it was (72.60%\u0026ndash;83.56%). In Pan et al.\u0026rsquo;s study (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), KS2 sensitivity ranged from 77.1% to 91.3% and specificity from 64.0% to 69.4%, depending on lesion type. In contrast, the current results demonstrated higher diagnostic performance of KS2, with sensitivity of 91.84% and 85.71% and specificities of 91.78% and 80.82% for the 1st and 2nd observers, respectively. Furthermore, accuracy reached 91.8% for the 1st observer, compared to Pan\u0026rsquo;s lower overall accuracy estimates. This difference may reflect variations in lesion characteristics or reader experience, suggesting that ADC may provide greater added value in certain clinical settings, especially when interpreted consistently.\u003c/p\u003e\u003cp\u003eWith KS3, Pan et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) found a high sensitivity of 94.3% and a specificity of 60%. Our results were similar in sensitivity (91.84% and 89.80%) but showed better specificity (89.04% and 78.08%). This indicates that KS3, in both studies, maintains high cancer detection while offering a modest improvement in avoiding unnecessary biopsies in our dataset. Lastly, for the original KS, Pan (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) reported sensitivity of 82.9% and specificity of 60%, while our study showed higher sensitivity (91.84% and 85.71%) and specificity (90.41% and 79.45%).\u003c/p\u003e\u003cp\u003eIn this study, interobserver agreement showed a high level of consistency between observers. The kappa agreement was substantial (0.614 and 0.785) for both ADC values and the overall KS, indicating reliable reproducibility in these assessments. Moreover, perfect agreement (0.822, 0.820, 0.817, and 0.852) was observed for microcalcifications, as well as KS1, KS2, and KS3, respectively. Among these, KS3 showed the highest kappa value (κ\u0026thinsp;=\u0026thinsp;0.85), reflecting excellent interobserver reliability. This suggests that structured decision tools enriched by objective metrics like ADC values and the presence of microcalcifications can help reduce subjectivity in MRI interpretation, an area traditionally challenged by variability in reader experience and lesion complexity. This agreement is consistent with Istomin A et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), who had excellent interobserver agreement for KS (0.882), and higher than Milos RI et al.\u0026rsquo;s (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) agreement, which was fair to moderate (0.393\u0026ndash;0.560).\u003c/p\u003e\u003cp\u003eThe ICC was moderate (0.644) for ADC and good (0.830, 0.822, 0.807, 0.812) for KS, KS1, KS2, and KS3, respectively. That is consistent with Meng et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), who also reported excellent ICC for both the Kaiser score and ADC measurements (0.912 for KS and 0.997 for ADC) and also with Aslan and Oktay (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), whose coefficient was excellent (0.964) for Kaiser score.\u003c/p\u003e\u003cp\u003eThis study had some limitations that must be acknowledged. First, while KS1 and KS3 improved overall accuracy and sensitivity, false positives and false negatives were still encountered. These limitations underscore the importance of integrating imaging with clinical and histological information. Additionally, when measuring ADC values, ROI was manually drawn on two-dimensional images, carefully excluding areas of visible necrosis, cystic change, or hemorrhage. However, this approach may have overlooked the impact of intralesional heterogeneity on diffusion measurements. Future multi-center studies with larger sample volumes, more readers, and using deep learning and feature tracking are recommended.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlighted the value of the KS as a structured diagnostic tool in evaluating BI-RADS 4 breast lesions, particularly when combined with ADC and microcalcifications. KS3, which integrates all three parameters, provided the highest sensitivity and interobserver agreement. The findings uniquely demonstrate that microcalcifications contributed more to sensitivity than ADC when added to the KS framework, while adding ADC improved specificity and accuracy. These results support the use of multiparametric composite scoring to enhance MRI interpretation, reduce unnecessary biopsies, and improve diagnostic confidence in daily practice.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eADC:\u003c/strong\u003e Apparent Diffusion Coefficient \u003cstrong\u003eAUC:\u003c/strong\u003e Area Under the Curve \u0026nbsp;\u003cstrong\u003eBI-RADS:\u003c/strong\u003e\u0026nbsp; Breast Imaging Reporting and Data System \u003cstrong\u003eDCIS:\u003c/strong\u003e\u0026nbsp; Ductal Carcinoma in Situ \u003cstrong\u003eDM:\u003c/strong\u003e\u0026nbsp; Digital Mammography \u003cstrong\u003eDCE-MRI:\u003c/strong\u003e\u0026nbsp; Dynamic Contrast-Enhanced Magnetic Resonance Imaging \u0026nbsp; \u003cstrong\u003eDWI:\u003c/strong\u003e Diffusion-Weighted Imaging \u003cstrong\u003eSPSS:\u003c/strong\u003e\u0026nbsp; Statistical Package for the Social Sciences \u003cstrong\u003eICC:\u003c/strong\u003e intraclass correlation coefficient\u003cstrong\u003e\u0026nbsp;KS:\u003c/strong\u003e\u0026nbsp; Kaiser Score \u003cstrong\u003eMIP:\u003c/strong\u003e\u0026nbsp; Maximum Intensity Projection \u003cstrong\u003eMRI:\u003c/strong\u003e\u0026nbsp; Magnetic Resonance Imaging \u003cstrong\u003eNME\u003c/strong\u003e: \u0026nbsp;Non-Mass Enhancement \u003cstrong\u003eNPV:\u003c/strong\u003e\u0026nbsp; Negative Predictive Value \u003cstrong\u003ePACS:\u003c/strong\u003e\u0026nbsp; Picture Archiving and Communication System \u003cstrong\u003ePPV:\u003c/strong\u003e\u0026nbsp; Positive Predictive Value \u003cstrong\u003eROC:\u003c/strong\u003e\u0026nbsp; Receiver Operating Characteristic \u003cstrong\u003eROI:\u003c/strong\u003e Region of Interest \u003cstrong\u003eSTIR:\u003c/strong\u003e Short Tau Inversion Recovery \u003cstrong\u003eTIC:\u003c/strong\u003e\u0026nbsp; Time Intensity Curve\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by institutional review board on April 21, 2024. (Code number: MD.24.04.846).\u003c/p\u003e\n\u003cp\u003eAll patients included in this study gave written informed consent to participate in the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e All patients included in this study gave written informed consent to publish the data contained in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e Available on request with the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e Not applicable (no funding was received for this study).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e: The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e: Not applicable\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMH and NA designed the research. MH performed the research and wrote the manuscript. MH and ZA analyzed the collected data. ZA and OH revised the data and manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71:209\u0026ndash;249\u003c/li\u003e\n\u003cli\u003eSch\u0026uuml;nemann HJ, Lerda D, Quinn C, Follmann M, Alonso-Coello P, Rossi PG, Lebeau A, Nystr\u0026ouml;m L, Broeders M, Ioannidou-Mouzaka L, Duffy SW (2020) Breast cancer screening and diagnosis: a synopsis of the European Breast Guidelines. Ann Intern Med 172:46\u0026ndash;56\u003c/li\u003e\n\u003cli\u003eJajodia A, Sindhwani G, Pasricha S, Prosch H, Puri S, Dewan A, Batra U, Doval DC, Mehta A, Chaturvedi AK (2021) Application of the Kaiser score to increase diagnostic accuracy in equivocal lesions on diagnostic mammograms referred for MR mammography. Eur J Radiol 134:109413\u003c/li\u003e\n\u003cli\u003eBaltzer PA, Dietzel M, Kaiser WA (2013) A simple and robust classification tree for differentiation between benign and malignant lesions in MR-mammography. Eur Radiol 23:2051\u0026ndash;2060\u003c/li\u003e\n\u003cli\u003eWengert GJ, Pipan F, Almohanna J, Bickel H, Polanec S, Kapetas P, Clauser P, Pinker K, Helbich TH, Baltzer PA (2020) Impact of the Kaiser score on clinical decision-making in BI-RADS 4 mammographic calcifications examined with breast MRI. Eur Radiol 30:1451\u0026ndash;1459\u003c/li\u003e\n\u003cli\u003eZhang B, Feng L, Wang L, Chen X, Li X, Yang Q (2020) Kaiser score for diagnosis of breast lesions presenting as non-mass enhancement on MRI. Nan Fang Yi Ke Da Xue Xue Bao 40:562\u0026ndash;566\u003c/li\u003e\n\u003cli\u003eD\u0026rsquo;Orsi CJ, Sickles EA, Mendelson EB, Morris EA (2013) ACR BI-RADS atlas: breast imaging reporting and data system. American College of Radiology, Reston\u003c/li\u003e\n\u003cli\u003eZhang L, Hao C, Wu Y, Zhu Y, Ren Y, Tong Z (2019) Microcalcification and BMP-2 in breast cancer: correlation with clinicopathological features and outcomes. Onco Targets Ther 12:2023\u0026ndash;2033\u003c/li\u003e\n\u003cli\u003eDietzel M, Baltzer PA (2018) How to use the Kaiser score as a clinical decision rule for diagnosis in multiparametric breast MRI: a pictorial essay. Insights Imaging 9:325\u0026ndash;335\u003c/li\u003e\n\u003cli\u003ePartridge SC, Nissan N, Rahbar H, Kitsch AE, Sigmund EE (2017) Diffusion weighted breast MRI: Clinical applications and emerging techniques. J Magn Reson Imaging 45:337\u0026ndash;355\u003c/li\u003e\n\u003cli\u003eBickel H, Pinker K, Polanec S, Magometschnigg H, Wengert G, Spick C, Bogner W, Bago-Horvath Z, Helbich TH, Baltzer P (2017) Diffusion-weighted imaging of breast lesions: Region-of-interest placement and different ADC parameters influence apparent diffusion coefficient values. Eur Radiol 27:1883\u0026ndash;1892\u003c/li\u003e\n\u003cli\u003eMilos RI, Pipan F, Kalovidouri A, Clauser P, Kapetas P, Bernathova M, Helbich TH, Baltzer PA (2020) The Kaiser score reliably excludes malignancy in benign contrast-enhancing lesions classified as BI-RADS 4 on breast MRI high-risk screening exams. Eur Radiol 30:6052\u0026ndash;6061\u003c/li\u003e\n\u003cli\u003eDietzel M, Krug B, Clauser P, Burke C, Hellmich M, Maintz D, Uder M, Bickel H, Helbich T, Baltzer PA (2021) A multicentric comparison of apparent diffusion coefficient mapping and the Kaiser score in the assessment of breast lesions. Investig Radiol 56:274\u0026ndash;282\u003c/li\u003e\n\u003cli\u003eYouk JH, Gweon HM, Son EJ, Eun NL, Choi EJ, Kim JA (2019) Scoring system to stratify malignancy risks for mammographic microcalcifications based on breast imaging reporting and data system 5th edition descriptors. Korean J Radiol 20:1646\u0026ndash;1652\u003c/li\u003e\n\u003cli\u003eRau G, Shih YS (2021) Evaluation of Cohen\u0026apos;s kappa and other measures of inter-rater agreement for genre analysis and other nominal data. J Engl Acad Purp 53:101026\u003c/li\u003e\n\u003cli\u003eKoo TK, Li MY (2016) A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 15:155\u0026ndash;163\u003c/li\u003e\n\u003cli\u003eBennani-Baiti B, Dietzel M, Baltzer PA (2017) MRI for the assessment of malignancy in BI-RADS 4 mammographic microcalcifications. PLoS One 12\\:e0188679\u003c/li\u003e\n\u003cli\u003eIima M, Honda M, Sigmund EE, Ohno Kishimoto A, Kataoka M, Togashi K (2020) Diffusion MRI of the breast: Current status and future directions. J Magn Reson Imaging 52:70\u0026ndash;90\u003c/li\u003e\n\u003cli\u003eMeng L, Zhao X, Lu L, Xing Q, Wang K, Guo Y, Shang H, Chen Y, Huang M, Sun Y, Zhang X (2021) A comparative assessment of MR BI-RADS 4 breast lesions with Kaiser score and apparent diffusion coefficient value. Front Oncol 11:779642\u003c/li\u003e\n\u003cli\u003ePan J, Huang X, Yang S, Ouyang F, Ouyang L, Wang L, Chen M, Zhou L, Du Y, Chen X, Deng L (2023) The added value of apparent diffusion coefficient and microcalcifications to the Kaiser score in the evaluation of BI-RADS 4 lesions. Eur J Radiol 165:110920\u003c/li\u003e\n\u003cli\u003eAslan O, Oktay A (2024) Diagnostic accuracy of the breast MRI Kaiser score in suspected architectural distortions and its comparison with mammography. Sci Rep 14:447\u003c/li\u003e\n\u003cli\u003eAn Y, Mao G, Ao W, Mao F, Zhang H, Cheng Y, Yang G (2022) Can DWI provide additional value to Kaiser score in evaluation of breast lesions. Eur Radiol 32:5964\u0026ndash;5973\u003c/li\u003e\n\u003cli\u003eIstomin A, Masarwah A, Vanninen R, Okuma H, Sudah M (2021) Diagnostic performance of the Kaiser score for characterizing lesions on breast MRI with comparison to a multiparametric classification system. Eur J Radiol 138:109659\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable (1): Parameters of MRI sequences used\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"608\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameters/sequences\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSTIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDynamic T1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDWI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTR/TE (ms)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e2000/80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e450/14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e7000/70\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e4-8/2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e5800/139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSlice thickness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e3-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInter slice gap (mm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFOV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e300/360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e300/360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e300/360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e300/360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e300/360\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScan plane\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eaxial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eAxial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eaxial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eaxial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eaxial\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of Sequence\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eFSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eFSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eFSE with inversion (TI 150 ms)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003eFLASH 3D GRE-T1W1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eSingle shot spin EPI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 103px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e0,500,1000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTR: repetition time, TE: echo time, TI: inversion time, FOV: Field of view, FSE: fast spin echo, FLASH: fast low angle shot, GRE: gradient echo, EPI: echo planar imaging\u003c/p\u003e\n\u003cp\u003eTable (2): characteristics of patients and lesions:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1762272689.png\" width=\"993\" height=\"641\"\u003e\u003c/p\u003e\n\u003cp\u003eTIC: Time intensity curve\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable (3): Final histopathological diagnosis of the lesions in the study\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"657\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubtypes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber 122\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalignant (number 49, 40.16%)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003eInvasive ductal carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e27 (22.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003eInvasive lobular carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e6 (4.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003ePapillary carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1 (0.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003ePaget\u0026rsquo;s disease of the nipple\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2 (1.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003eDCIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e12 (9.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003emicro cystic adnexal carcinoma of sweet gland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1 (0.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"14\" valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBenign (number 73, 59.84%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003eFibroadenoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e4 (3.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003ePhyllodes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1 (0.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003eIntraductal papilloma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e13 (10.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003eIntraductal papilloma with fibroadenoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1 (0.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003eIntraductal papillomatosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e6 (4.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003eFibrocystic changes/ fibroadenosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e21 (17.21%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003eSclerosing adenosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e3 (2.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003eBreast tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e4 (3.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003eFat necrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2 (1.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003eApocrine metaplasia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1 (0.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003eMammary ductectasia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e2 (1.64%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003eStromal fibrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e5 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003eInflammatory / Granulomatous mastitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e6 (4.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 334px;\"\u003e\n \u003cp\u003eBenign proliferative breast lesion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e4 (3.28%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eDCIS: Ductal carcinoma in situ. Table (4): Validity of KS, KS1, KS2, and KS3 for characterization of benign versus malignant lesions\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"690\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePPV %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAccuracy %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMicro-calcifications\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003csup\u003est\u003c/sup\u003e observer\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003csup\u003end\u003c/sup\u003e observer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e30.61\u003c/p\u003e\n \u003cp\u003e34.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e90.41\u003c/p\u003e\n \u003cp\u003e86.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e66.00\u003c/p\u003e\n \u003cp\u003e66.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e68.18\u003c/p\u003e\n \u003cp\u003e62.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e66.39\u003c/p\u003e\n \u003cp\u003e65.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADC\u003cbr\u003e 1\u003csup\u003est\u003c/sup\u003e observer\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003csup\u003end\u003c/sup\u003e observer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e79.59\u003c/p\u003e\n \u003cp\u003e89.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e47.95\u003c/p\u003e\n \u003cp\u003e42.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e77.78\u003c/p\u003e\n \u003cp\u003e86.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e50.65\u003c/p\u003e\n \u003cp\u003e51.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e60.66\u003c/p\u003e\n \u003cp\u003e61.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003cp\u003e0.699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKS\u003cbr\u003e 1\u003csup\u003est\u003c/sup\u003e observer\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003csup\u003end\u003c/sup\u003e observer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e91.84\u003c/p\u003e\n \u003cp\u003e85.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e90.41\u003c/p\u003e\n \u003cp\u003e79.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e94.29\u003c/p\u003e\n \u003cp\u003e89.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e86.54\u003c/p\u003e\n \u003cp\u003e73.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e90.98\u003c/p\u003e\n \u003cp\u003e81.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.907\u003c/p\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKS1\u003cbr\u003e 1\u003csup\u003est\u003c/sup\u003e observer\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003csup\u003end\u003c/sup\u003e observer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e91.84\u003c/p\u003e\n \u003cp\u003e89.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e83.56\u003c/p\u003e\n \u003cp\u003e72.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e93.85\u003c/p\u003e\n \u003cp\u003e91.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e78.95\u003c/p\u003e\n \u003cp\u003e68.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e86.89\u003c/p\u003e\n \u003cp\u003e79.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.916\u003c/p\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKS2\u003cbr\u003e 1\u003csup\u003est\u003c/sup\u003e observer\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003csup\u003end\u003c/sup\u003e observer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e91.84\u003c/p\u003e\n \u003cp\u003e85.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e91.78\u003c/p\u003e\n \u003cp\u003e80.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e94.37\u003c/p\u003e\n \u003cp\u003e89.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e88.24\u003c/p\u003e\n \u003cp\u003e75.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e91.80\u003c/p\u003e\n \u003cp\u003e82.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.915\u003c/p\u003e\n \u003cp\u003e0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKS3\u003cbr\u003e 1\u003csup\u003est\u003c/sup\u003e observer\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003csup\u003end\u003c/sup\u003e observer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e91.84\u003c/p\u003e\n \u003cp\u003e89.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e89.04\u003c/p\u003e\n \u003cp\u003e78.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e94.20\u003c/p\u003e\n \u003cp\u003e91.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e84.91\u003c/p\u003e\n \u003cp\u003e73.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e90.16\u003c/p\u003e\n \u003cp\u003e82.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003cp\u003e0.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNPV: negative predictive value, PPV: positive predictive value, ADC: apparent diffusion coefficient, KS: Kaiser score.\u003c/p\u003e\n\u003cp\u003eTable (5): Interobserver kappa agreement and intra class correlation coefficient according to type of variables\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"738\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 308px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003csup\u003end\u003c/sup\u003e Observer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 430px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1st Observer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ek\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eICC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKS score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0.830 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.766-0.880) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKS (benign versus malignant)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADC value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0.644 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.528-0.736) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADC (benign versus malignant)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKS1 score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0.822 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.755- 0.872) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKS1 score (benign versus malignant)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKS2 score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0.807 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.735-0.861) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKS2 score (benign versus malignant)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKS3 score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0.812 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e(0.742-0.865) \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKS3 score (benign versus malignant)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMicrocalcifications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eK: kappa, ICC: intraclass correlation coefficient, CI: confidence interval, KS: Kaiser score, ADC: Apparent diffusion coefficient \u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Kaiser Score, microcalcifications, breast lesions, BI-RADS 4, MRI, mammogram","lastPublishedDoi":"10.21203/rs.3.rs-7612130/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7612130/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eBreast cancer is considered the most commonly diagnosed cancer in the world and is responsible for a high rate of deaths among women. The malignancy risk dramatically increases in Breast Imaging Reporting and Data System (BI-RADS) 4 and 5 lesions. Therefore, this study aimed to evaluate the reliability of adding the apparent diffusion coefficient (ADC) and suspicious microcalcifications (when present) in combination with the Kaiser Score (KS) in improving the accuracy of the evaluation of magnetic resonance imaging (MRI) BI-RADS 4 lesions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA total of 115 patients with 122 breast lesions categorized as BI-RADS 4 on MRI were included in the study. All patients had an MRI and a mammogram. Two observers analyzed images and calculated ADC, KS, KS1, KS2, and KS3. The diagnostic performance was calculated using receiver operating characteristic (ROC) analysis as well as interobserver agreement.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThis study involved 122 breast lesions (mean age: 48.1 years\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3). The sensitivity for KS, KS1, KS2, and KS3 ranged from 85% to 91.84%, with area under the curve (AUC) values of 0.907, 0.916, 0.915, and 0.913, and accuracy rates of 90.98%, 86.89%, 91.8%, and 90.16%, respectively, for the first observer, denoting high and significant sensitivity (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Interobserver agreement was substantial (0.614 and 0.785) for ADC and KS, and perfect (0.822, 0.820, 0.817, and 0.852) for microcalcifications, KS1, KS2, and KS3, respectively, with the highest value for KS3 (0.85). The intraclass correlation coefficient (ICC) was moderate (0.644) for ADC and good (0.830, 0.822, 0.807, and 0.812) for KS, KS1, KS2, and KS3, respectively.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study highlighted the value of the KS as a structured diagnostic tool in evaluating BI-RADS 4 breast lesions, particularly when combined with ADC and microcalcifications. KS3, which integrates all three parameters, provided the highest sensitivity and interobserver agreement. The findings uniquely demonstrate that microcalcifications contributed more to sensitivity than ADC when added to the KS framework, while adding ADC improved specificity and accuracy. These results support the use of multiparametric composite scoring to enhance MRI interpretation, reduce unnecessary biopsies, and improve diagnostic confidence in daily practice.\u003c/p\u003e","manuscriptTitle":"Title: Diagnostic performance of combining apparent diffusion coefficient and microcalcifications to Kaiser Score in evaluation of BI-RADS 4 breast lesions.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-05 06:29:33","doi":"10.21203/rs.3.rs-7612130/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":"9d3c358f-1537-4518-af77-12735fe6c849","owner":[],"postedDate":"November 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T16:03:00+00:00","versionOfRecord":{"articleIdentity":"rs-7612130","link":"https://doi.org/10.1186/s43055-026-01689-0","journal":{"identity":"egyptian-journal-of-radiology-and-nuclear-medicine","isVorOnly":false,"title":"Egyptian Journal of Radiology and Nuclear Medicine"},"publishedOn":"2026-01-31 15:59:02","publishedOnDateReadable":"January 31st, 2026"},"versionCreatedAt":"2025-11-05 06:29:33","video":"","vorDoi":"10.1186/s43055-026-01689-0","vorDoiUrl":"https://doi.org/10.1186/s43055-026-01689-0","workflowStages":[]},"version":"v1","identity":"rs-7612130","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7612130","identity":"rs-7612130","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}