Identification of benign from malignant small renal tumors: Is there a possible role of T1 mapping?

Identification of benign from malignant small renal tumors: Is there a possible role of T1 mapping? | 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 Identification of benign from malignant small renal tumors: Is there a possible role of T1 mapping? Lianting Zhong, Danlan Lian, Yuqin Ding, Jiefeng Guo, Weifeng Lin, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4867341/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Differentiating benign from malignant small renal tumors can help to guide clinical decision-making. T1 mapping enables quantitative assessment of T1 relaxation time and may help to evaluate tumor properties. This study aimed to investigate the possible utility of T1 mapping for quantificationally distinguishing benign from malignant small solid renal tumors. Methods: The data set used in this retrospective study, consisting of 99 patients with 99 small renal masses (≤4 cm). 78 malignant small renal tumors and 21 benign tumors respectively. Quantitative variables (including pre- and post- T1 mapping) were calculated and compared between different renal tumors. The clinical features and image qualitative characteristics were recorded accordingly. Univariate and multivariate logistic regression models were used to identify independent influencing factors. The diagnostic accuracy of independent influencing factors was represented with the area under the receiver operating characteristic curve (AUC). Results : The pre-contrast T1 mapping (T1) and the ratio of T1 reduction in malignance were higher than those in benign small renal tumors, while post-contrast T1 mapping was lower (all P < 0.025). In the multivariable logistic regression, the patient’s gender (odds ratio (OR) = 4.987, P = 0.008), patient’s age (OR = 2.026, P = 0.020), and T1 (OR = 3.652, P = 0.001) were independent predictors. For the identification of benign renal tumors, the T1 demonstrated moderate diagnostic efficiency with an AUC of 0.697 (0.596-0.785), a sensitivity of 51.28%, and a specificity of 100% ( P < 0.000). The T1+ gender + age model achieved an AUC of 0.832 (0.743-0.899), a sensitivity of 60.26%, and a specificity of 95.26%. Conclusion : Quantitative T1 mapping parameters may provide an added value in noninvasively distinguishing small benign renal tumors from renal cell carcinoma (RCC). Magnetic resonance imaging Identify Renal neoplasm Figures Figure 1 Figure 2 Figure 3 Figure 4 Background The incidence of diagnosed small (≤ 4 cm) renal tumors (SRMs) has been increasing rapidly in the past few decades, due to the widespread utilization of cross-sectional imaging[ 1 ]. However, there has not been an obvious decrease in kidney cancer-specific mortality with the increased number of surgeries and ablations performed for suspected renal masses[ 2 – 4 ]. In a series of 173 patients only 58% of renal tumors 7 cm were[ 5 ]. Therefore, a substantial amount of incidentally discovered SRMs were not malignant[ 6 ], and treatment of SRMs will not benefit from definitive therapy[ 7 , 8 ]. To decrease patient morbidity and healthcare costs related to unnecessary surgery treatments, the discrimination of suspicious SRMs before surgery is crucial for the appropriate treatment planning[ 9 , 10 ]. The percutaneous biopsy has been proposed[ 11 ] in diagnosing benign and malignant, but the invasiveness and complications cannot be ignored[ 12 – 14 ]. Besides, the percutaneous biopsy was not suitable for every patient, especially elderly individuals with multiple underlying conditions[ 15 , 16 ]. SRMs were still indistinguishable from malignant with currently available clinical imaging[ 17 ]. In addition, macroscopic fat is lacking in fat-poor renal classical angiomyolipoma (fp-AML), a most common benign tumor. The lack of macroscopic fat is also presented in renal cell carcinoma (RCC). Thus, fat-based diagnosis provided by conventional imaging is hard to be distinguish fp-AML from RCC. Moreover, some of the morphological and non-quantitative features findings from conventional traditional imaging methods may be non-specific and overlapping, especially for small renal tumors[ 18 ]. Thus, more diagnostic information is expected to be provided. Longitudinal relaxation time was a property determined by a tissue’s molecular composition that is related to water content and mobility. T1 mapping can provide quantitative information about longitudinal relaxation time at each pixel. Previous studies demonstrated that the T1 mapping was feasible for evaluating the severity of fibrosis or inflammation changes in nephropathy[ 19 – 21 ], predicting the histopathological grade of clear cell renal cell carcinoma (ccRCC)[ 22 ], and distinguishing ccRCC from fat-poor angiomyolipoma[ 23 ]. Nevertheless, the efficacy of T1 mapping for the discrimination of SRMs is entirely unexplored. This study aimed to evaluate if the quantitative T1 mapping can be used as a preoperative predictor of benign SMRs, which would be particularly helpful for clinical decisions. Methods Patients This retrospective study obtained permission from our institutional review board and the written informed consent was waived. We reviewed surgical resected SRMs between September 2014 and September 2021. All patients were diagnosed with suspicious renal lesions by previous computer tomography (CT) or ultrasonography examinations and then performed magnetic resonance imaging (MRI) scanning within 1 month before resection for clinical diagnosis. Exclusion criteria are as follows: (a) patients receiving preoperative neoadjuvant therapy, (b) poor imaging quality or incomplete scan protocol, (c) pathologically proven renal cysts or classical angiomyolipoma. Figure 1 shows an overview of the study workflow. MRI protocols All patients with renal lesions were scanned with the same 1.5 T MRI system (Magnetom Area; Siemens Healthineers) using an 18-channel body array coil. Conventional renal MRI includes: (1) axial T1-weighted in-and-out-of-phase imaging with volume interpolated breath-hold examination (VIBE), (2) axial T2-weighted imaging with fat suppression, (3) fat saturation VIBE-T1-weighted imaging. The corticomedullary phase (CMP) and nephrographic phase (NP) were obtained in the 20s and 80s after intravenous injection of the gadopentetate dimeglumine (Magnevist; Bayer Schering Pharma AG), respectively. The T1 mapping was performed with a dual flip-angle 3D gradient-echo VIBE sequence. Pre- and post-contrast-T1 mapping images (T1 and T1e) were obtained before and after 90 s-120 s the intravenous contrast administration. The parameters were as follows: TR = 4.38 ms; TE = 1.93 ms; flip angle, 2° and 12°; FOV, 380–400 × 300–324 mm 2 ; matrix size, 216 × 288; slice thickness, 5 mm. All patients were injected with 0.1 mmol/kg body weight of Gd-DTPA at a rate of approximately 2 mL/s. Quantitative MRI Image Analysis All MRI images were processed at the same Syngo workstation. The regions of interest (ROI) were delineated by two observers with over 3 (XXX) and 10 (XXX) years of experience in diagnostic abdominal radiology respectively, neither of whom had prior knowledge of pathological data. For each tumor, an ROI was drawn on the NP sequence and it was copied to corresponding other MRI sequence. The ROI included the solid components of the lesion and was set as large as possible. We try to minimize selection bias by avoiding renal parenchyma, perirenal fat, as well as intra-tumor heterogeneous components (such as necrosis, cystic degeneration, hemorrhage, calcification, and peritumoral membranes). In predominantly solid SRMs, the ROI would cover the whole tumor. Each ROI was triple-measured and averaged. Finally, the average value measured by the two observers was taken. The mean ROI area of all tumors was 82.3 mm 2 (range 56.4–160.0 mm 2 ). Figure 2 shows the different SRMs images and the ROI. The enhanced ratios of CMP (CMPr) and NP (NPr) and the ratio of T1 reduction (T1r) were calculated as follows: CMPr/ NPr = (CMP/NP- T1WI)/T1WI×100%; T1r = (T1–T1e) / T1×100%. Statistical analysis Statistical analyses were performed with SPSS (version 26.0). The independent T-test and Mann-Whitney U test were used for quantitative analysis, and Fisher exact tests were used for qualitative analysis. Univariate and multivariate logistic regression were summarized with influence factors and 95% confidence intervals (CIs). Those factors with P values < 0.05 in univariate analysis were included in multivariate analysis. The receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic efficacy of different factors, and optimal cutoff values of ROC curves were calculated from the Youden index. The DeLong nonparametric method was used to compare ROC curves. Interobserver agreement was assessed using the intraclass correlation coefficient (ICC). The grades of 0.2, 0.21–0.40, 0.41–0.60, 0.61–0.80, and 0.81-1.00 indicate the slight, fair, moderate, substantial, and perfect ICC value, respectively. P values less than 0.05 were considered statistically significant. Results Population characteristics A total of 99 patients included 63 males (63%) and 36 females (36%) with a mean age of 54.3 ± 11.1 years (range, 27–83 years). The mean size of small renal masses is 2.9 ± 0.7 cm (range, 1.1-4.0 cm) for malignant renal neoplasms and 2.6 ± 0.8 cm (range, 1.2–3.8 cm) for benign renal neoplasms. All patients were given a pathologic diagnosis after partial or radical nephrectomy. The most common tumors were clear cell RCC (n = 48), followed by papillary RCC (n = 16) and chromophobe RCC (n = 14) respectively. Benign histology included 15 minimal fat AMLs and 6 oncocytomas. The conventional clinical and pathological description of all renal masses is summarized in Table 1 . Table 1 Clinical and pathological Characteristics of Enrolled Patients. Variable All lesions Malignance (n = 78) Benign (n = 21) P values Gender 0.006 Male Female 63 (63%) 36 (36%) 55 (56%) 23 (23%) 8 (8%) 13 (13%) Mean age (years) 54.3 ± 11.1 (27–83) 55.8 ± 10.6(27–83) 48.6 ± 11.2 (29–68) 0.007 Surgery type Partial nephrectomy Radical nephrectomy 78 (78%) 21 (21%) 59 (60%) 19 (19%) 19 (19%) 2 (2%) 0.240 Mean size(cm) 2.8 ± 0.74 (1.1-4.0) 2.9 ± 0.72 (1.1-4.0) 2.6 ± 0.81 (1.2–3.8) 0.318 Tumor subtype Clear cell RCC Papillary RCC Chromophobe RCC - - - 48 (62%) 16 (21%) 14 (18%) - - - Angiomyolipoma - - 15 (71%) Oncocytoma - - 6 (29%) Data are numbers of patients with percentages in parentheses, or means ± standard deviation with range in parentheses. Qualitative radiological parameters The qualitative parameters of SRMs are shown in Table 2 . The T1 and T1r of benign renal tumors were lower than those of malignant ones ( P = 0.001 and P = 0.007, respectively), while the T1e was higher ( P = 0.021). However, the difference in CMPr and NPr values between benign and malignant renal tumors was not statistically significant ( P > 0.05). Bland-Altman shows a small difference in the T1 mapping parameters between benign and malignant lesions (Fig. 3 ). Table 2 Qualitative radiological parameters of different SRMs. Parameter Malignance (n = 78) Benign (n = 21) P values CMPr (%) 198.97 ± 131.02 179.25 ± 114.46 0.532 NPr (%) 231.82 ± 113.90 227.17 ± 110.39 0.868 T1 relaxation time T1 (ms) 2047.16 ± 619.21 1576.91 ± 326.63 0.001 T1e (ms) 244.42 ± 87.57 290.70 ± 40.18 0.021 T1r (%) 85.92 ± 8.71 80.03 ± 8.55 0.007 CMPr, the enhanced ratios of the corticomedullary phase; NPr, the enhanced ratios of the nephrographic phase; T1, native T1 mapping; T1e, enhanced T1 mapping; T1r, the reduced ratio of T1 mapping. The interobserver agreement of the quantitative features was good, with mean of 0.912 (95% CI: 0.872–0.940) for the assessment of the T1, 0.827 (95% CI: 0.752–0.880) for the T1e, 0.888 (95%CI: 0.838–0.923) for the T1r, 0.967 (95% CI: 0.950–0.978) for the CMPr, and 0.905 ((95% CI: 0.854–0.938) for the NPr, respectively. The agreement between observers for the quantitative value is shown in Table 3 . Table 3 Parameters of each small renal tumor for different observers. Parameter Observer 1 Observer 2 ICC (95% CI) CMPr (%) 194.79 ± 127.39 201༎81 ± 116.78 0.967 (0.950–0.978) NPr (%) 590.15 ± 230.83 502.67 ± 244.63 0.905 (0.854–0.938) T1 relaxation time T1(ms) 1960.73 ± 580.63 1934.09 ± 645.58 0.912 (0.872–0.940) T1e(ms) 258.35 ± 84.70 250.12 ± 86.68 0.827 (0.752–0.880) T1r(%) 84.90 ± 8.32 84.26 ± 10.26 0.888 (0.838–0.923) Note: ICC, intraclass correlation coefficient. Independent parameters for characterizing small renal neoplasms Results from logistic regression analysis are shown in Table 4 . Multivariable logistic regression revealed that patient gender (OR 4.987, 95% CI: 1.534–16.214, P = 0.008), mean age (OR 2.026, 95% CI: 1.120–3.666, P = 0.020), and pre-contrast T1 relaxation time (OR 3.652, 95% CI: 1.657–8.047, P = 0.001) were independently predictive of malignant renal tumors from benign ones. Table 4 Univariate and multivariate Logistic regression analyses of malignant and benign renal tumors. Variable Univariate analysis Multivariate analysis OR (95%CI) P OR (95%CI) P Gender (Male/female) 3.886(1.421–10.629) 0.008 4.987(1.534–16.214) 0.008 Mean age (years) 2.015(1.180–3.442) 0.010 2.026(1.120–3.666) 0.020 Mean size (cm) 1.396(0.859–2.269) 0.178 Shape (Round/irregular) 0.595(0.198–1.791) 0.356 Hemorrhage (Y: N) 2.078(0.433–9.965) 0.360 Central scar (Y: N) 0.937(0.180–4.882) 0.938 Angular interface (Y: N) 0.240(0.045–1.289) 0.096 Segmental enhancement (Y: N) 0.651(0.117–3.618) 0.624 T2 signal (Hyper-/Hypointense) 4.900(1.522–15.773) 0.008 T2 signal Heterogeneity (Y: N) 4.568(1.561–13.372) 0.006 ADC signal (low/no) 0.227(0.062–0.836) 0.026 Out-of-phase T1 (Signal dropout/No) 1.400(0.488–4.016) 0.531 Strengthen degree Low reference 0.383 Middle 0.471(0.147–1.508) 0.205 High Strengthen formal Wash-in and wash-out Persistent enhancement Delay enhancement 1.176(0.293–4.723) reference 7.600(0.894–64.624) 1.360(0.492–3.761) 0.819 0.177 0.063 0.554 CMPr 1.179(0.708–1.963) 0.528 NPr 1.043(0.640–1.700) 0.866 T1 relaxation time T1(ms) 2.726(1.413–5.262) 0.003 3.652(1.657–8.047) 0.001 T1e(ms) 0.569(0.347–0.934) 0.026 T1r(%) 1.846(1.141–2.986) 0.012 Data in parentheses are 95% CI. Each variable with P < 0.05 at univariate analysis was entered into the multivariate analysis. ROC curves analyze the diagnostic value of T1 mapping ROC curves of independent characteristics for predicting benign renal tumors were plotted in Fig. 4 . The AUC for distinguishing benign from malignant SRMs using the T1 was 0.697 (0.596–0.785), with 1944.1 ms as the optimal diagnostic threshold; the diagnostic sensitivity and specificity were 51.28% and 100%, respectively. The T1 + gender + age model achieved an AUC of 0.832 (0.743–0.899), a sensitivity of 60.26%, and a specificity of 95.26%. Discussion Preoperative differentiation between benign and malignant SRMs is important for treatment selection[ 24 ]. In this study, we developed a binary logistic regression model to combine T1 mapping, conventional MRI images, and clinical characteristics, demonstrating that the pre-enhanced T1 mapping is an independent predictor. For the identification of benign renal tumors, T1 mapping demonstrated an AUC of 0.697 (0.596–0.785), and achieved 0.832 (0.743–0.899) when combined with the clinical features. Recent studies have focused on the quantitative evaluation of MRI findings associated with the differentiation of renal masses[ 23 , 25 ]. Adams et al.[ 26 ] used T1 mapping to differentiate between low-grade and high-grade ccRCC. The results showed the reduction in T1 value after contrast agent administration in higher grade ccRCC (ISUP grades 3–4) was significantly higher than lower grade ccRCC (ISUP grades 1–2)[ 27 ]. But their study populations were only 27, which might have been more conclusive if more cases were enrolled. Wang et al.[ 28 ] also applied T1 mapping to differentiate different renal tumors, their study included 56 cases of renal tumors: 46 tumors were pathologically proven (including 40 RCCs and 10 AMLs respectively), but 6 AMLs were diagnosed only through MRI. The results show the statistical significance of different T1 mapping based parameters (pre- and post-contrast T1 mapping, the reduction of T1 mapping, and the reduction ratio of T1 mapping) in identifying renal tumors. Those methods have certain defects. In addition to the small sample size, they did not integrate conventional imaging features and clinical characteristics. In comparison, our study combined clinical and imaging data and focused more on the small solitary renal tumors which were indistinguishable using plain MRI. Further, a relatively larger sample size was included in our study, and all tumors were confirmed by surgical pathology. The role of T1 mapping in identifying renal tumors is still in the initial stage of research and is worthy of further exploration. However, what could be the potential explanation for the efficacy of pre-enhanced T1 mapping in the detection of benign renal tumors in our study? It may be explained by the following reasons. Firstly, malignant renal tumors are always more heterogeneous than benign tumors, exhibit higher levels of necrosis, and are more likely to contain cysts. Although we have avoided these areas in our ROIs, it may still be difficult to exclude macroscopic zones[ 29 ]. Secondly, the upregulation of genes and proteins within the extracellular matrix could play a significant role in malignant renal tumors. It is common for poorly differentiated renal tumors to exhibit irregular tumor cells and loose intercellular spaces[ 22 ]. In contrast, fat-poor angiomyolipoma is characterized by spindle cells or epithelioid smooth muscle cells with abnormal thick-walled blood vessels in variable proportions, which would indicate tiny intercellular spaces. This may demonstrate a benign renal tumor with lower T1 relaxation time[ 22 , 30 ]. An interesting observation is that the native T1 mapping was an independent influence factor for diagnosing benign renal tumors, but the enhanced T1 mapping was not. In general, enhanced T1 mapping can improve the accuracy of blood T1 values and can consequently increase the measurement accuracy of extracellular volume fraction. We speculate that these results may be related to the blood supply of both group tumors. Among our groups, the ccRCC group has the highest percentage (61.5%) of malignant tumors, while poor fat angiomyolipoma predominates (71.4%) in benign tumors. They all have a rich blood supply. This outcome may be more conducive to the clinical promotion of T1 mapping. Native T1 mapping can assist in detecting malignant renal tumors in patients with chronic kidney disease, thereby reducing the risk of adverse effects from contrast agents on renal function, as well as decreasing financial burdens[ 31 ]. Moreover, several regular sequences for MRI inspection were analyzed in this study, such as the T2-weighted imaging and the strengthen formal of tumors, which have been proven to be helpful in the differentiation of benign from malignant tumors[ 18 ]. However, these characteristics were not independent influence factors for SRMs in our study. A meta-analysis by Shang et al. shows the sensitivity and specificity of routine MRI for the detection of small malignant masses achieved 0.85 (95% CI 0.79–0.90) and 0.83 (95% CI 0.67–0.92), respectively[ 32 ]. We analyzed and discussed as follows. First, the advantage of a larger sample size for meta-analysis can’t be ruled out. Second, we only used a 1.5T MR scanner, while the 3.0T instrument may provide more information. Third, we excluded renal cysts and typical fat-containing renal lesions and included patients with small solid tumors, which were difficult to make a definite diagnosis in the clinic. Fourth, qualitative assessment based on the radiologist’s decision might be inconsistent, especially when the sign was equivocal on the image[ 33 ]. This study hopes to be able to perform such assessments quantitatively and objectively. This study has some limitations. The first is the retrospective and single-center design of the study, which has certain limitations and may have retrospective bias. Second, we did not conduct experiments to validate the relationship between T1 mapping and tumor pathophysiological changes. Third, there is significant imaging variation in different types of renal tumor subtypes (such as clear cell RCC, papillary RCC, and chromophobe RCC). Our study is based only on the imaging manifestations between the benign and malignance SRMs. Fourth, there is no unified calculating parameter on T1 mapping. Therefore, a collaborative infrastructure development for multicenter studies is needed, so that the performance of T1 mapping techniques at different magnetic field strengths can be evaluated, and the histopathology of SRMs types can be comprehensively studied. Conclusions Quantitative T1 mapping may be a promising noninvasive approach that can be used to classify benign and malignant small renal tumors. If further validated, T1 mapping may spare patients unnecessary biopsy/surgery and help guide management in clinical settings. Abbreviations AUC The area under the receiver operating characteristic curve T1 The pre-contrast T1 mapping OR Odds ratio RCC Renal cell carcinoma SRMs Small renal tumors fp-AML fat-poor renal classical angiomyolipoma RCC Renal cell carcinoma ccRCC Clear cell renal cell carcinoma CT Computer tomography MRI Magnetic resonance imaging VIBE Volume interpolated breath-hold examination CMP Corticomedullary phase NP Nephrographic phase T1e Post-contrast-T1 mapping ROI The regions of interest CMPr The enhanced ratios of CMP NPr The enhanced ratios of NP T1r The ratio of T1 reduction CIs Confidence intervals ROC The receiver operating characteristic ICC The intraclass correlation coefficient. Declarations Acknowledgements Not applicable. Authors’ contributions Xiao-Bo Qu participated in designing the study. Jian-Jun Zhou conceived and designed the study. Lian-Ting Zhong performed the study, collected data, and wrote the main manuscript text. Dan-Lan Lian labelled the data. Yu-Qin Ding and Jie-Feng Guo created and trained the models. Wei-Feng Lin tested the models, analyzed results and prepared figures. All authors reviewed and approved the manuscript. Funding This study was supported by the National Natural Science Foundation of China (82202285). Availability of data and materials Image data cannot be publicly because of the national legislature on patient data. All imaging data was stored anonymously in our hospital database, and saved by the corresponding author. The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki. We obtained permission from The Ethics Committee of Fudan University Affiliated Zhongshan Hospital in Shanghai (China). Informed consent requirement was waived because of its retrospective nature. Consent for publication Informed consent to publish was obtained from the participants. Competing interests The authors declare no conflict of interest. References Jayson M, Sanders H. Increased incidence of serendipitously discovered renal cell carcinoma. UROLOGY. 1998;51(2):203–5. Wong M, Goggins WB, Yip B, Fung F, Leung C, Fang Y, Wong S, Ng CF. Incidence and mortality of kidney cancer: temporal patterns and global trends in 39 countries. Sci Rep. 2017;7(1):15698. Schieda N, Krishna S, Pedrosa I, Kaffenberger SD, Davenport MS, Silverman SG. Active Surveillance of Renal Masses: The Role of Radiology. Radiology. 2022;302(1):11–24. Jeon HG, Lee SR, Kim KH, Oh YT, Cho NH, Rha KH, Yang SC, Han WK. Benign lesions after partial nephrectomy for presumed renal cell carcinoma in masses 4 cm or less: prevalence and predictors in Korean patients. UROLOGY 2010, 76(3):574–579. Duchene DA, Lotan Y, Cadeddu JA, Sagalowsky AI, Koeneman KS. Histopathology of surgically managed renal tumors: analysis of a contemporary series. UROLOGY. 2003;62(5):827–30. Mao W, Ding Y, Ding X, Wang Y, Fu C, Zeng M, Zhou J. Pathological assessment of chronic kidney disease with DWI: Is there an added value for diffusion kurtosis imaging? J MAGN RESON IMAGING. 2021;54(2):508–17. Johnson DC, Vukina J, Smith AB, Meyer AM, Wheeler SB, Kuo TM, Tan HJ, Woods ME, Raynor MC, Wallen EM, et al. Preoperatively misclassified, surgically removed benign renal masses: a systematic review of surgical series and United States population level burden estimate. J Urol. 2015;193(1):30–5. Frank I, Blute ML, Cheville JC, Lohse CM, Weaver AL, Zincke H. Solid renal tumors: an analysis of pathological features related to tumor size. J Urol. 2003;170(6 Pt 1):2217–20. Jewett MA, Mattar K, Basiuk J, Morash CG, Pautler SE, Siemens DR, Tanguay S, Rendon RA, Gleave ME, Drachenberg DE, et al. Active surveillance of small renal masses: progression patterns of early stage kidney cancer. EUR UROL. 2011;60(1):39–44. Ray S, Cheaib JG, Pierorazio PM. Active Surveillance for Small Renal Masses. Rev Urol. 2020;22(1):9–16. Halverson SJ, Kunju LP, Bhalla R, Gadzinski AJ, Alderman M, Miller DC, Montgomery JS, Weizer AZ, Wu A, Hafez KS, et al. Accuracy of determining small renal mass management with risk stratified biopsies: confirmation by final pathology. J Urol. 2013;189(2):441–6. McEachen JC, Leng S, Atwell TD, Tollefson MK, Friese JL, Wang Z, Murad MH, Schmit GD. Percutaneous Renal Tumor Ablation: Radiation Exposure During Cryoablation and Radiofrequency Ablation. Cardiovasc Intervent Radiol. 2016;39(2):233–8. Volpe A, Mattar K, Finelli A, Kachura JR, Evans AJ, Geddie WR, Jewett MA. Contemporary results of percutaneous biopsy of 100 small renal masses: a single center experience. J Urol. 2008;180(6):2333–7. Jiang SH, Karpe KM, Talaulikar GS. Safety and predictors of complications of renal biopsy in the outpatient setting. CLIN NEPHROL. 2011;76(6):464–9. Patel HD, Johnson MH, Pierorazio PM, Sozio SM, Sharma R, Iyoha E, Bass EB, Allaf ME. Diagnostic Accuracy and Risks of Biopsy in the Diagnosis of a Renal Mass Suspicious for Localized Renal Cell Carcinoma: Systematic Review of the Literature. J Urol. 2016;195(5):1340–7. Pedrosa I, Cadeddu JA. How We Do It: Managing the Indeterminate Renal Mass with the MRI Clear Cell Likelihood Score. Radiology. 2022;302(2):256–69. Kang SK, Huang WC, Pandharipande PV, Chandarana H. Solid renal masses: what the numbers tell us. AJR Am J Roentgenol. 2014;202(6):1196–206. Kay FU, Canvasser NE, Xi Y, Pinho DF, Costa DN, Diaz DLA, Khatri G, Leyendecker JR, Yokoo T, Lay AH et al. Diagnostic Performance and Interreader Agreement of a Standardized MR Imaging Approach in the Prediction of Small Renal Mass Histology. RADIOLOGY 2018, 287(2):543–553. Jiang K, Ponzo TA, Tang H, Mishra PK, Macura SI, Lerman LO. Multiparametric MRI detects longitudinal evolution of folic acid-induced nephropathy in mice. Am J Physiol Ren Physiol. 2018;315(5):F1252–60. Tewes S, Gueler F, Chen R, Gutberlet M, Jang MS, Meier M, Mengel M, Hartung D, Wacker F, Rong S, et al. Functional MRI for characterization of renal perfusion impairment and edema formation due to acute kidney injury in different mouse strains. PLoS ONE. 2017;12(3):e173248. de Bazelaire CM, Duhamel GD, Rofsky NM, Alsop DC. MR imaging relaxation times of abdominal and pelvic tissues measured in vivo at 3.0 T: preliminary results. Radiology. 2004;230(3):652–9. Adams LC, Jurmeister P, Ralla B, Bressem KK, Fahlenkamp UL, Engel G, Siepmann S, Wagner M, Hamm B, Busch J, et al. Assessment of the extracellular volume fraction for the grading of clear cell renal cell carcinoma: first results and histopathological findings. EUR RADIOL. 2019;29(11):5832–43. Wang S, Li J, Zhu D, Hua T, Zhao B. Contrast-enhanced magnetic resonance (MR) T1 mapping with low-dose gadolinium-diethylenetriamine pentaacetic acid (Gd-DTPA) is promising in identifying clear cell renal cell carcinoma histopathological grade and differentiating fat-poor angiomyolipoma. Quant Imaging Med Surg. 2020;10(5):988–98. Rossi SH, Klatte T, Usher-Smith J, Stewart GD. Epidemiology and screening for renal cancer. WORLD J UROL. 2018;36(9):1341–53. Liu H, Cao H, Chen L, Fang L, Liu Y, Zhan J, Diao X, Chen Y. The quantitative evaluation of contrast-enhanced ultrasound in the differentiation of small renal cell carcinoma subtypes and angiomyolipoma. Quant Imaging Med Surg. 2022;12(1):106–18. Moch H, Amin MB, Berney DM, Comperat EM, Gill AJ, Hartmann A, Menon S, Raspollini MR, Rubin MA, Srigley JR et al. The 2022 World Health Organization Classification of Tumours of the Urinary System and Male Genital Organs-Part A: Renal, Penile, and Testicular Tumours. EUR UROL 2022, 82(5):458–468. Adams LC, Jurmeister P, Ralla B, Bressem KK, Fahlenkamp UL, Engel G, Siepmann S, Wagner M, Hamm B, Busch J, et al. Assessment of the extracellular volume fraction for the grading of clear cell renal cell carcinoma: first results and histopathological findings. EUR RADIOL. 2019;29(11):5832–43. Wang S, Li J, Zhu D, Hua T, Zhao B. Contrast-enhanced magnetic resonance (MR) T1 mapping with low-dose gadolinium-diethylenetriamine pentaacetic acid (Gd-DTPA) is promising in identifying clear cell renal cell carcinoma histopathological grade and differentiating fat-poor angiomyolipoma. QUANT IMAG MED SURG. 2020;10(5):988–98. Delahunt B, McKenney JK, Lohse CM, Leibovich BC, Thompson RH, Boorjian SA, Cheville JC. A novel grading system for clear cell renal cell carcinoma incorporating tumor necrosis. AM J SURG PATHOL. 2013;37(3):311–22. Robbers LF, Baars EN, Brouwer WP, Beek AM, Hofman MB, Niessen HW, van Rossum AC, Marcu CB. T1 mapping shows increased extracellular matrix size in the myocardium due to amyloid depositions. Circ Cardiovasc Imaging. 2012;5(3):423–6. Rankin AJ, Mayne K, Allwood-Spiers S, Hall BP, Roditi G, Gillis KA, Mark PB. Will advances in functional renal magnetic resonance imaging translate to the nephrology clinic? Nephrol (Carlton). 2022;27(3):223–30. Shang W, Hong G, Li W. MRI for the detection of small malignant renal masses: a systematic review and meta-analysis. FRONT ONCOL. 2023;13:1194128. Schieda N, Davenport MS, Silverman SG, Bagga B, Barkmeier D, Blank Z, Curci NE, Doshi AM, Downey RT, Edney E et al. Multicenter Evaluation of Multiparametric MRI Clear Cell Likelihood Scores in Solid Indeterminate Small Renal Masses. RADIOLOGY 2022, 303(3):590–599. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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-4867341","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":354373645,"identity":"c87f5bb5-56b2-4b01-b519-182b9802ac20","order_by":0,"name":"Lianting Zhong","email":"","orcid":"","institution":"Zhongshan Hospital (Xiamen), Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Lianting","middleName":"","lastName":"Zhong","suffix":""},{"id":354373646,"identity":"6f28a0b7-89c0-4ce1-8ec2-c012e01b9f00","order_by":1,"name":"Danlan Lian","email":"","orcid":"","institution":"Zhongshan Hospital (Xiamen), Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Danlan","middleName":"","lastName":"Lian","suffix":""},{"id":354373647,"identity":"cdfed444-8bc1-4029-89cb-c4ded7686fb4","order_by":2,"name":"Yuqin Ding","email":"","orcid":"","institution":"Zhongshan Hospital, Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Yuqin","middleName":"","lastName":"Ding","suffix":""},{"id":354373648,"identity":"467529a0-ebb6-46f9-8879-3fc5e057deaa","order_by":3,"name":"Jiefeng Guo","email":"","orcid":"","institution":"Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Jiefeng","middleName":"","lastName":"Guo","suffix":""},{"id":354373649,"identity":"479f3951-45ac-4d87-8a14-bbd18ca5e563","order_by":4,"name":"Weifeng Lin","email":"","orcid":"","institution":"Zhongshan Hospital (Xiamen), Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Weifeng","middleName":"","lastName":"Lin","suffix":""},{"id":354373650,"identity":"6cbeb751-2b26-4c19-abae-dbedb0b98a1a","order_by":5,"name":"Xiaobo Qu","email":"","orcid":"","institution":"Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Xiaobo","middleName":"","lastName":"Qu","suffix":""},{"id":354373651,"identity":"a82e5537-3200-4b29-84a8-070ede8a08ae","order_by":6,"name":"jianjun zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYDACCRA2YGBgbADxKiTk+EnUcsbCWLKBGC1wwNhWkbiBkBb52c3HJCwK7PKYZ+Qe/vhzngTjBgbmh49u4NHCOOdYmoSEQXIx44y8BGPebRLM5gxsxsY5eLQwS+SYAbUwJzbOyDFIZtwmwWbZwMMmjU8LG0RLPVjLwZ9zJHgMDhDQwgPRchikxbCBtwHIJqRFQiIt2ULC4HhiY88bY2aeYxIGks0E/CI/I/ngbYk/1Ykb23OMP/6oqavvZ29++BifFhBgBsWNYQOcS0A5CDB+AFlHhMJRMApGwSgYoQAAVO9DSt/lYUMAAAAASUVORK5CYII=","orcid":"","institution":"Zhongshan Hospital (Xiamen), Fudan University","correspondingAuthor":true,"prefix":"","firstName":"jianjun","middleName":"","lastName":"zhou","suffix":""}],"badges":[],"createdAt":"2024-08-06 09:30:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4867341/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4867341/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66564594,"identity":"7e595d0d-8f9b-45cb-9dd9-d5647ca6a2e9","added_by":"auto","created_at":"2024-10-14 10:37:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":85912,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study population.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4867341/v1/e7ec9d0101e81faf79431f98.png"},{"id":66564595,"identity":"4bfebe0f-5e48-4cab-9bcf-ebade61a4e55","added_by":"auto","created_at":"2024-10-14 10:37:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":393800,"visible":true,"origin":"","legend":"\u003cp\u003eMRI of different renal lesions.\u003c/p\u003e\n\u003cp\u003eThe lesion (ROI) demonstrates the maximum area of the solid component on tumor at each sequence. The arrows indicate the entire tumor. A and F: T1-weighted imaging, B and G: corticomedullary phase, C and H: nephrographic phase, D and I: pre-contrasted T1 mapping, E and H: post-contrasted T1mapping.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA-E\u003c/strong\u003e: a 49-year-old man with pathologically confirmed fat-poor angiomyolipomas (AML) in the left kidney. \u003cstrong\u003e(A)\u003c/strong\u003eThe tumor showed marginally low signal intensity on pre-contrasted T1-weighted imaging. \u003cstrong\u003e(B)\u003c/strong\u003e The lesion demonstrated obvious overall uniform enhancement on the corticomedullary phase and \u003cstrong\u003e(C)\u003c/strong\u003ehypo-intensification in nephrographic phase. The pre-contrasted T1 mapping\u003cstrong\u003e(D)\u003c/strong\u003e and post-contrasted T1 mapping \u003cstrong\u003e(E)\u003c/strong\u003e of the lesion, with a T1 relaxation time of 1733.17 ms and 273.68 ms, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF-J\u003c/strong\u003e: a 66-year-old man with pathologically confirmed papillary RCC in the right kidney.\u003cstrong\u003e (F)\u003c/strong\u003eThe tumor showed low signal intensity on pre-contrasted T1-weighted imaging.\u003cstrong\u003e (G) \u003c/strong\u003eThe lesion demonstrated obvious local enhancement on the corticomedullary phase and \u003cstrong\u003e(H)\u003c/strong\u003e hypo-intensification in nephrographic phase. The pre-contrasted T1 mapping \u003cstrong\u003e(I) \u003c/strong\u003eand post-contrasted T1 mapping\u003cstrong\u003e (J)\u003c/strong\u003e of the lesion, with a T1 relaxation time of 2370.56.48 ms and 184.24 ms, respectively.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4867341/v1/d1df923405c3cc5ef8412cad.png"},{"id":66566026,"identity":"c377b10a-05a3-45a4-8b78-fc724d16f804","added_by":"auto","created_at":"2024-10-14 10:45:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54853,"visible":true,"origin":"","legend":"\u003cp\u003eBland-Altman clarified the small difference in T1, T1e, and T1r between benign and malignant lesions. A. T1; B. T1e; C. T1r.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4867341/v1/d31d54f7afa161cc53d4f7e7.png"},{"id":66564596,"identity":"24b90485-3516-4ba9-bdb3-5ab41f35686a","added_by":"auto","created_at":"2024-10-14 10:37:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":40047,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curve of significant parameter for differentiating benign from malignant renal tumor.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4867341/v1/ca223022c21b27420f9b01fe.png"},{"id":71198138,"identity":"781d1c22-27b9-4fe5-8001-e8d92507c905","added_by":"auto","created_at":"2024-12-12 05:39:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1211422,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4867341/v1/ffb50012-c11b-4939-9c42-7caa0d9b8434.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of benign from malignant small renal tumors: Is there a possible role of T1 mapping?","fulltext":[{"header":"Background","content":"\u003cp\u003eThe incidence of diagnosed small (\u0026le;\u0026thinsp;4 cm) renal tumors (SRMs) has been increasing rapidly in the past few decades, due to the widespread utilization of cross-sectional imaging[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, there has not been an obvious decrease in kidney cancer-specific mortality with the increased number of surgeries and ablations performed for suspected renal masses[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In a series of 173 patients only 58% of renal tumors\u0026thinsp;\u0026lt;\u0026thinsp;4 cm were malignant, however all renal tumors\u0026thinsp;\u0026gt;\u0026thinsp;7 cm were[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, a substantial amount of incidentally discovered SRMs were not malignant[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], and treatment of SRMs will not benefit from definitive therapy[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. To decrease patient morbidity and healthcare costs related to unnecessary surgery treatments, the discrimination of suspicious SRMs before surgery is crucial for the appropriate treatment planning[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe percutaneous biopsy has been proposed[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] in diagnosing benign and malignant, but the invasiveness and complications cannot be ignored[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Besides, the percutaneous biopsy was not suitable for every patient, especially elderly individuals with multiple underlying conditions[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. SRMs were still indistinguishable from malignant with currently available clinical imaging[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In addition, macroscopic fat is lacking in fat-poor renal classical angiomyolipoma (fp-AML), a most common benign tumor. The lack of macroscopic fat is also presented in renal cell carcinoma (RCC). Thus, fat-based diagnosis provided by conventional imaging is hard to be distinguish fp-AML from RCC. Moreover, some of the morphological and non-quantitative features findings from conventional traditional imaging methods may be non-specific and overlapping, especially for small renal tumors[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Thus, more diagnostic information is expected to be provided.\u003c/p\u003e \u003cp\u003eLongitudinal relaxation time was a property determined by a tissue\u0026rsquo;s molecular composition that is related to water content and mobility. T1 mapping can provide quantitative information about longitudinal relaxation time at each pixel. Previous studies demonstrated that the T1 mapping was feasible for evaluating the severity of fibrosis or inflammation changes in nephropathy[\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], predicting the histopathological grade of clear cell renal cell carcinoma (ccRCC)[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and distinguishing ccRCC from fat-poor angiomyolipoma[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Nevertheless, the efficacy of T1 mapping for the discrimination of SRMs is entirely unexplored.\u003c/p\u003e \u003cp\u003eThis study aimed to evaluate if the quantitative T1 mapping can be used as a preoperative predictor of benign SMRs, which would be particularly helpful for clinical decisions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003e This retrospective study obtained permission from our institutional review board and the written informed consent was waived. We reviewed surgical resected SRMs between September 2014 and September 2021. All patients were diagnosed with suspicious renal lesions by previous computer tomography (CT) or ultrasonography examinations and then performed magnetic resonance imaging (MRI) scanning within 1 month before resection for clinical diagnosis. Exclusion criteria are as follows: (a) patients receiving preoperative neoadjuvant therapy, (b) poor imaging quality or incomplete scan protocol, (c) pathologically proven renal cysts or classical angiomyolipoma. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows an overview of the study workflow.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMRI protocols\u003c/h2\u003e \u003cp\u003eAll patients with renal lesions were scanned with the same 1.5 T MRI system (Magnetom Area; Siemens Healthineers) using an 18-channel body array coil. Conventional renal MRI includes: (1) axial T1-weighted in-and-out-of-phase imaging with volume interpolated breath-hold examination (VIBE), (2) axial T2-weighted imaging with fat suppression, (3) fat saturation VIBE-T1-weighted imaging. The corticomedullary phase (CMP) and nephrographic phase (NP) were obtained in the 20s and 80s after intravenous injection of the gadopentetate dimeglumine (Magnevist; Bayer Schering Pharma AG), respectively. The T1 mapping was performed with a dual flip-angle 3D gradient-echo VIBE sequence. Pre- and post-contrast-T1 mapping images (T1 and T1e) were obtained before and after 90 s-120 s the intravenous contrast administration. The parameters were as follows: TR\u0026thinsp;=\u0026thinsp;4.38 ms; TE\u0026thinsp;=\u0026thinsp;1.93 ms; flip angle, 2\u0026deg; and 12\u0026deg;; FOV, 380\u0026ndash;400 \u0026times; 300\u0026ndash;324 mm\u003csup\u003e2\u003c/sup\u003e; matrix size, 216 \u0026times; 288; slice thickness, 5 mm. All patients were injected with 0.1 mmol/kg body weight of Gd-DTPA at a rate of approximately 2 mL/s.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative MRI Image Analysis\u003c/h2\u003e \u003cp\u003eAll MRI images were processed at the same Syngo workstation. The regions of interest (ROI) were delineated by two observers with over 3 (XXX) and 10 (XXX) years of experience in diagnostic abdominal radiology respectively, neither of whom had prior knowledge of pathological data. For each tumor, an ROI was drawn on the NP sequence and it was copied to corresponding other MRI sequence. The ROI included the solid components of the lesion and was set as large as possible. We try to minimize selection bias by avoiding renal parenchyma, perirenal fat, as well as intra-tumor heterogeneous components (such as necrosis, cystic degeneration, hemorrhage, calcification, and peritumoral membranes). In predominantly solid SRMs, the ROI would cover the whole tumor. Each ROI was triple-measured and averaged. Finally, the average value measured by the two observers was taken. The mean ROI area of all tumors was 82.3 mm\u003csup\u003e2\u003c/sup\u003e (range 56.4\u0026ndash;160.0 mm\u003csup\u003e2\u003c/sup\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the different SRMs images and the ROI.\u003c/p\u003e \u003cp\u003eThe enhanced ratios of CMP (CMPr) and NP (NPr) and the ratio of T1 reduction (T1r) were calculated as follows:\u003c/p\u003e \u003cp\u003eCMPr/ NPr = (CMP/NP- T1WI)/T1WI\u0026times;100%;\u003c/p\u003e \u003cp\u003eT1r = (T1\u0026ndash;T1e) / T1\u0026times;100%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed with SPSS (version 26.0). The independent T-test and Mann-Whitney U test were used for quantitative analysis, and Fisher exact tests were used for qualitative analysis. Univariate and multivariate logistic regression were summarized with influence factors and 95% confidence intervals (CIs). Those factors with \u003cem\u003eP\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate analysis were included in multivariate analysis. The receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic efficacy of different factors, and optimal cutoff values of ROC curves were calculated from the Youden index. The DeLong nonparametric method was used to compare ROC curves. Interobserver agreement was assessed using the intraclass correlation coefficient (ICC). The grades of 0.2, 0.21\u0026ndash;0.40, 0.41\u0026ndash;0.60, 0.61\u0026ndash;0.80, and 0.81-1.00 indicate the slight, fair, moderate, substantial, and perfect ICC value, respectively. \u003cem\u003eP\u003c/em\u003e values less than 0.05 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePopulation characteristics\u003c/h2\u003e \u003cp\u003eA total of 99 patients included 63 males (63%) and 36 females (36%) with a mean age of 54.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1 years (range, 27\u0026ndash;83 years). The mean size of small renal masses is 2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 cm (range, 1.1-4.0 cm) for malignant renal neoplasms and 2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 cm (range, 1.2\u0026ndash;3.8 cm) for benign renal neoplasms. All patients were given a pathologic diagnosis after partial or radical nephrectomy. The most common tumors were clear cell RCC (n\u0026thinsp;=\u0026thinsp;48), followed by papillary RCC (n\u0026thinsp;=\u0026thinsp;16) and chromophobe RCC (n\u0026thinsp;=\u0026thinsp;14) respectively. Benign histology included 15 minimal fat AMLs and 6 oncocytomas. The conventional clinical and pathological description of all renal masses is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical and pathological Characteristics of Enrolled Patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll lesions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalignance (n\u0026thinsp;=\u0026thinsp;78)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBenign (n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (63%)\u003c/p\u003e \u003cp\u003e36 (36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (56%)\u003c/p\u003e \u003cp\u003e23 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (8%)\u003c/p\u003e \u003cp\u003e13 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean age (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.3\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1 (27\u0026ndash;83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.8\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6(27\u0026ndash;83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2 (29\u0026ndash;68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery type\u003c/p\u003e \u003cp\u003ePartial nephrectomy\u003c/p\u003e \u003cp\u003eRadical nephrectomy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (78%)\u003c/p\u003e \u003cp\u003e21 (21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (60%)\u003c/p\u003e \u003cp\u003e19 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (19%)\u003c/p\u003e \u003cp\u003e2 (2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean size(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74 (1.1-4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72 (1.1-4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81 (1.2\u0026ndash;3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor subtype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClear cell RCC\u003c/p\u003e \u003cp\u003ePapillary RCC\u003c/p\u003e \u003cp\u003eChromophobe RCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (62%)\u003c/p\u003e \u003cp\u003e16 (21%)\u003c/p\u003e \u003cp\u003e14 (18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngiomyolipoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOncocytoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData are numbers of patients with percentages in parentheses, or means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation with range in parentheses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eQualitative radiological parameters\u003c/h2\u003e \u003cp\u003eThe qualitative parameters of SRMs are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The T1 and T1r of benign renal tumors were lower than those of malignant ones (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007, respectively), while the T1e was higher (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021). However, the difference in CMPr and NPr values between benign and malignant renal tumors was not statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Bland-Altman shows a small difference in the T1 mapping parameters between benign and malignant lesions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQualitative radiological parameters of different SRMs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMalignance (n\u0026thinsp;=\u0026thinsp;78)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBenign (n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMPr (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e198.97\u0026thinsp;\u0026plusmn;\u0026thinsp;131.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e179.25\u0026thinsp;\u0026plusmn;\u0026thinsp;114.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPr (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e231.82\u0026thinsp;\u0026plusmn;\u0026thinsp;113.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e227.17\u0026thinsp;\u0026plusmn;\u0026thinsp;110.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1 relaxation time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1 (ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2047.16\u0026thinsp;\u0026plusmn;\u0026thinsp;619.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1576.91\u0026thinsp;\u0026plusmn;\u0026thinsp;326.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1e (ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e244.42\u0026thinsp;\u0026plusmn;\u0026thinsp;87.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e290.70\u0026thinsp;\u0026plusmn;\u0026thinsp;40.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1r (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e85.92\u0026thinsp;\u0026plusmn;\u0026thinsp;8.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e80.03\u0026thinsp;\u0026plusmn;\u0026thinsp;8.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCMPr, the enhanced ratios of the corticomedullary phase; NPr, the enhanced ratios of the nephrographic phase; T1, native T1 mapping; T1e, enhanced T1 mapping; T1r, the reduced ratio of T1 mapping.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe interobserver agreement of the quantitative features was good, with mean of 0.912 (95% CI: 0.872\u0026ndash;0.940) for the assessment of the T1, 0.827 (95% CI: 0.752\u0026ndash;0.880) for the T1e, 0.888 (95%CI: 0.838\u0026ndash;0.923) for the T1r, 0.967 (95% CI: 0.950\u0026ndash;0.978) for the CMPr, and 0.905 ((95% CI: 0.854\u0026ndash;0.938) for the NPr, respectively. The agreement between observers for the quantitative value is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameters of each small renal tumor for different observers.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObserver 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObserver 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMPr (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e194.79\u0026thinsp;\u0026plusmn;\u0026thinsp;127.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e201༎81\u0026thinsp;\u0026plusmn;\u0026thinsp;116.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.967 (0.950\u0026ndash;0.978)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPr (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e590.15\u0026thinsp;\u0026plusmn;\u0026thinsp;230.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e502.67\u0026thinsp;\u0026plusmn;\u0026thinsp;244.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.905 (0.854\u0026ndash;0.938)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1 relaxation time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1(ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1960.73\u0026thinsp;\u0026plusmn;\u0026thinsp;580.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1934.09\u0026thinsp;\u0026plusmn;\u0026thinsp;645.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.912 (0.872\u0026ndash;0.940)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1e(ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e258.35\u0026thinsp;\u0026plusmn;\u0026thinsp;84.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e250.12\u0026thinsp;\u0026plusmn;\u0026thinsp;86.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.827 (0.752\u0026ndash;0.880)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1r(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e84.90\u0026thinsp;\u0026plusmn;\u0026thinsp;8.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e84.26\u0026thinsp;\u0026plusmn;\u0026thinsp;10.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.888 (0.838\u0026ndash;0.923)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: ICC, intraclass correlation coefficient.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eIndependent parameters for characterizing small renal neoplasms\u003c/h2\u003e \u003cp\u003eResults from logistic regression analysis are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. Multivariable logistic regression revealed that patient gender (OR 4.987, 95% CI: 1.534\u0026ndash;16.214, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008), mean age (OR 2.026, 95% CI: 1.120\u0026ndash;3.666, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020), and pre-contrast T1 relaxation time (OR 3.652, 95% CI: 1.657\u0026ndash;8.047, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) were independently predictive of malignant renal tumors from benign ones.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate Logistic regression analyses of malignant and benign renal tumors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Male/female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.886(1.421\u0026ndash;10.629)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.987(1.534\u0026ndash;16.214)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean age (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.015(1.180\u0026ndash;3.442)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.026(1.120\u0026ndash;3.666)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean size (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.396(0.859\u0026ndash;2.269)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShape (Round/irregular)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.595(0.198\u0026ndash;1.791)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemorrhage (Y: N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.078(0.433\u0026ndash;9.965)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral scar (Y: N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.937(0.180\u0026ndash;4.882)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngular interface (Y: N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.240(0.045\u0026ndash;1.289)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSegmental enhancement (Y: N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.651(0.117\u0026ndash;3.618)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2 signal (Hyper-/Hypointense)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.900(1.522\u0026ndash;15.773)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2 signal Heterogeneity (Y: N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.568(1.561\u0026ndash;13.372)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADC signal (low/no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.227(0.062\u0026ndash;0.836)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOut-of-phase T1\u003c/p\u003e \u003cp\u003e(Signal dropout/No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.400(0.488\u0026ndash;4.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStrengthen degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.471(0.147\u0026ndash;1.508)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003cp\u003eStrengthen formal\u003c/p\u003e \u003cp\u003eWash-in and wash-out\u003c/p\u003e \u003cp\u003ePersistent enhancement\u003c/p\u003e \u003cp\u003eDelay enhancement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.176(0.293\u0026ndash;4.723)\u003c/p\u003e \u003cp\u003ereference\u003c/p\u003e \u003cp\u003e7.600(0.894\u0026ndash;64.624)\u003c/p\u003e \u003cp\u003e1.360(0.492\u0026ndash;3.761)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.819\u003c/p\u003e \u003cp\u003e0.177\u003c/p\u003e \u003cp\u003e0.063\u003c/p\u003e \u003cp\u003e0.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMPr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.179(0.708\u0026ndash;1.963)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNPr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.043(0.640\u0026ndash;1.700)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1 relaxation time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eT1(ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.726(1.413\u0026ndash;5.262)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.652(1.657\u0026ndash;8.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eT1e(ms)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.569(0.347\u0026ndash;0.934)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eT1r(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.846(1.141\u0026ndash;2.986)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData in parentheses are 95% CI. Each variable with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 at univariate analysis was entered into the multivariate analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eROC curves analyze the diagnostic value of T1 mapping\u003c/h2\u003e \u003cp\u003eROC curves of independent characteristics for predicting benign renal tumors were plotted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The AUC for distinguishing benign from malignant SRMs using the T1 was 0.697 (0.596\u0026ndash;0.785), with 1944.1 ms as the optimal diagnostic threshold; the diagnostic sensitivity and specificity were 51.28% and 100%, respectively. The T1\u0026thinsp;+\u0026thinsp;gender\u0026thinsp;+\u0026thinsp;age model achieved an AUC of 0.832 (0.743\u0026ndash;0.899), a sensitivity of 60.26%, and a specificity of 95.26%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePreoperative differentiation between benign and malignant SRMs is important for treatment selection[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In this study, we developed a binary logistic regression model to combine T1 mapping, conventional MRI images, and clinical characteristics, demonstrating that the pre-enhanced T1 mapping is an independent predictor. For the identification of benign renal tumors, T1 mapping demonstrated an AUC of 0.697 (0.596\u0026ndash;0.785), and achieved 0.832 (0.743\u0026ndash;0.899) when combined with the clinical features.\u003c/p\u003e \u003cp\u003eRecent studies have focused on the quantitative evaluation of MRI findings associated with the differentiation of renal masses[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Adams et al.[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] used T1 mapping to differentiate between low-grade and high-grade ccRCC. The results showed the reduction in T1 value after contrast agent administration in higher grade ccRCC (ISUP grades 3\u0026ndash;4) was significantly higher than lower grade ccRCC (ISUP grades 1\u0026ndash;2)[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. But their study populations were only 27, which might have been more conclusive if more cases were enrolled. Wang et al.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] also applied T1 mapping to differentiate different renal tumors, their study included 56 cases of renal tumors: 46 tumors were pathologically proven (including 40 RCCs and 10 AMLs respectively), but 6 AMLs were diagnosed only through MRI. The results show the statistical significance of different T1 mapping based parameters (pre- and post-contrast T1 mapping, the reduction of T1 mapping, and the reduction ratio of T1 mapping) in identifying renal tumors. Those methods have certain defects. In addition to the small sample size, they did not integrate conventional imaging features and clinical characteristics. In comparison, our study combined clinical and imaging data and focused more on the small solitary renal tumors which were indistinguishable using plain MRI. Further, a relatively larger sample size was included in our study, and all tumors were confirmed by surgical pathology. The role of T1 mapping in identifying renal tumors is still in the initial stage of research and is worthy of further exploration.\u003c/p\u003e \u003cp\u003eHowever, what could be the potential explanation for the efficacy of pre-enhanced T1 mapping in the detection of benign renal tumors in our study? It may be explained by the following reasons. Firstly, malignant renal tumors are always more heterogeneous than benign tumors, exhibit higher levels of necrosis, and are more likely to contain cysts. Although we have avoided these areas in our ROIs, it may still be difficult to exclude macroscopic zones[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Secondly, the upregulation of genes and proteins within the extracellular matrix could play a significant role in malignant renal tumors. It is common for poorly differentiated renal tumors to exhibit irregular tumor cells and loose intercellular spaces[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In contrast, fat-poor angiomyolipoma is characterized by spindle cells or epithelioid smooth muscle cells with abnormal thick-walled blood vessels in variable proportions, which would indicate tiny intercellular spaces. This may demonstrate a benign renal tumor with lower T1 relaxation time[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAn interesting observation is that the native T1 mapping was an independent influence factor for diagnosing benign renal tumors, but the enhanced T1 mapping was not. In general, enhanced T1 mapping can improve the accuracy of blood T1 values and can consequently increase the measurement accuracy of extracellular volume fraction. We speculate that these results may be related to the blood supply of both group tumors. Among our groups, the ccRCC group has the highest percentage (61.5%) of malignant tumors, while poor fat angiomyolipoma predominates (71.4%) in benign tumors. They all have a rich blood supply. This outcome may be more conducive to the clinical promotion of T1 mapping. Native T1 mapping can assist in detecting malignant renal tumors in patients with chronic kidney disease, thereby reducing the risk of adverse effects from contrast agents on renal function, as well as decreasing financial burdens[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, several regular sequences for MRI inspection were analyzed in this study, such as the T2-weighted imaging and the strengthen formal of tumors, which have been proven to be helpful in the differentiation of benign from malignant tumors[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, these characteristics were not independent influence factors for SRMs in our study. A meta-analysis by Shang et al. shows the sensitivity and specificity of routine MRI for the detection of small malignant masses achieved 0.85 (95% CI 0.79\u0026ndash;0.90) and 0.83 (95% CI 0.67\u0026ndash;0.92), respectively[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. We analyzed and discussed as follows. First, the advantage of a larger sample size for meta-analysis can\u0026rsquo;t be ruled out. Second, we only used a 1.5T MR scanner, while the 3.0T instrument may provide more information. Third, we excluded renal cysts and typical fat-containing renal lesions and included patients with small solid tumors, which were difficult to make a definite diagnosis in the clinic. Fourth, qualitative assessment based on the radiologist\u0026rsquo;s decision might be inconsistent, especially when the sign was equivocal on the image[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This study hopes to be able to perform such assessments quantitatively and objectively.\u003c/p\u003e \u003cp\u003eThis study has some limitations. The first is the retrospective and single-center design of the study, which has certain limitations and may have retrospective bias. Second, we did not conduct experiments to validate the relationship between T1 mapping and tumor pathophysiological changes. Third, there is significant imaging variation in different types of renal tumor subtypes (such as clear cell RCC, papillary RCC, and chromophobe RCC). Our study is based only on the imaging manifestations between the benign and malignance SRMs. Fourth, there is no unified calculating parameter on T1 mapping. Therefore, a collaborative infrastructure development for multicenter studies is needed, so that the performance of T1 mapping techniques at different magnetic field strengths can be evaluated, and the histopathology of SRMs types can be comprehensively studied.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eQuantitative T1 mapping may be a promising noninvasive approach that can be used to classify benign and malignant small renal tumors. If further validated, T1 mapping may spare patients unnecessary biopsy/surgery and help guide management in clinical settings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe area under the receiver operating characteristic curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe pre-contrast T1 mapping\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRenal cell carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSRMs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSmall renal tumors\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003efp-AML\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efat-poor renal classical angiomyolipoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRenal cell carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eccRCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClear cell renal cell carcinoma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComputer tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMagnetic resonance imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVIBE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVolume interpolated breath-hold examination\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCMP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCorticomedullary phase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNephrographic phase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT1e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePost-contrast-T1 mapping\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe regions of interest\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCMPr\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe enhanced ratios of CMP\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNPr\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe enhanced ratios of NP\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eT1r\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe ratio of T1 reduction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCIs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence intervals\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe receiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe intraclass correlation coefficient.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiao-Bo\u0026nbsp;Qu\u0026nbsp;participated in designing the study. Jian-Jun Zhou conceived and designed the study. Lian-Ting Zhong performed the study, collected data, and wrote the main manuscript text.\u0026nbsp;Dan-Lan\u0026nbsp;Lian\u0026nbsp;labelled the data.\u0026nbsp;Yu-Qin\u0026nbsp;Ding\u0026nbsp;and\u0026nbsp;Jie-Feng Guo\u0026nbsp;created and trained the models.\u0026nbsp;Wei-Feng Lin\u0026nbsp;tested the models, analyzed results and prepared figures. All authors reviewed and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (82202285).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImage data cannot be publicly because of the national legislature on patient data.\u0026nbsp;All imaging data was stored anonymously in our hospital database, and saved by the corresponding author. The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki. We obtained permission from The Ethics Committee of Fudan University Affiliated Zhongshan Hospital in Shanghai\u0026nbsp;(China).\u0026nbsp;Informed consent requirement was waived because of its retrospective nature.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent to publish was obtained from the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJayson M, Sanders H. Increased incidence of serendipitously discovered renal cell carcinoma. UROLOGY. 1998;51(2):203\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong M, Goggins WB, Yip B, Fung F, Leung C, Fang Y, Wong S, Ng CF. Incidence and mortality of kidney cancer: temporal patterns and global trends in 39 countries. Sci Rep. 2017;7(1):15698.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchieda N, Krishna S, Pedrosa I, Kaffenberger SD, Davenport MS, Silverman SG. Active Surveillance of Renal Masses: The Role of Radiology. Radiology. 2022;302(1):11\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJeon HG, Lee SR, Kim KH, Oh YT, Cho NH, Rha KH, Yang SC, Han WK. Benign lesions after partial nephrectomy for presumed renal cell carcinoma in masses 4 cm or less: prevalence and predictors in Korean patients. \u003cem\u003eUROLOGY\u003c/em\u003e 2010, 76(3):574\u0026ndash;579.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuchene DA, Lotan Y, Cadeddu JA, Sagalowsky AI, Koeneman KS. Histopathology of surgically managed renal tumors: analysis of a contemporary series. UROLOGY. 2003;62(5):827\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMao W, Ding Y, Ding X, Wang Y, Fu C, Zeng M, Zhou J. Pathological assessment of chronic kidney disease with DWI: Is there an added value for diffusion kurtosis imaging? J MAGN RESON IMAGING. 2021;54(2):508\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson DC, Vukina J, Smith AB, Meyer AM, Wheeler SB, Kuo TM, Tan HJ, Woods ME, Raynor MC, Wallen EM, et al. Preoperatively misclassified, surgically removed benign renal masses: a systematic review of surgical series and United States population level burden estimate. J Urol. 2015;193(1):30\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrank I, Blute ML, Cheville JC, Lohse CM, Weaver AL, Zincke H. Solid renal tumors: an analysis of pathological features related to tumor size. J Urol. 2003;170(6 Pt 1):2217\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJewett MA, Mattar K, Basiuk J, Morash CG, Pautler SE, Siemens DR, Tanguay S, Rendon RA, Gleave ME, Drachenberg DE, et al. Active surveillance of small renal masses: progression patterns of early stage kidney cancer. EUR UROL. 2011;60(1):39\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRay S, Cheaib JG, Pierorazio PM. Active Surveillance for Small Renal Masses. Rev Urol. 2020;22(1):9\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHalverson SJ, Kunju LP, Bhalla R, Gadzinski AJ, Alderman M, Miller DC, Montgomery JS, Weizer AZ, Wu A, Hafez KS, et al. Accuracy of determining small renal mass management with risk stratified biopsies: confirmation by final pathology. J Urol. 2013;189(2):441\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcEachen JC, Leng S, Atwell TD, Tollefson MK, Friese JL, Wang Z, Murad MH, Schmit GD. Percutaneous Renal Tumor Ablation: Radiation Exposure During Cryoablation and Radiofrequency Ablation. Cardiovasc Intervent Radiol. 2016;39(2):233\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVolpe A, Mattar K, Finelli A, Kachura JR, Evans AJ, Geddie WR, Jewett MA. Contemporary results of percutaneous biopsy of 100 small renal masses: a single center experience. J Urol. 2008;180(6):2333\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang SH, Karpe KM, Talaulikar GS. Safety and predictors of complications of renal biopsy in the outpatient setting. CLIN NEPHROL. 2011;76(6):464\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel HD, Johnson MH, Pierorazio PM, Sozio SM, Sharma R, Iyoha E, Bass EB, Allaf ME. Diagnostic Accuracy and Risks of Biopsy in the Diagnosis of a Renal Mass Suspicious for Localized Renal Cell Carcinoma: Systematic Review of the Literature. J Urol. 2016;195(5):1340\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePedrosa I, Cadeddu JA. How We Do It: Managing the Indeterminate Renal Mass with the MRI Clear Cell Likelihood Score. Radiology. 2022;302(2):256\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKang SK, Huang WC, Pandharipande PV, Chandarana H. Solid renal masses: what the numbers tell us. AJR Am J Roentgenol. 2014;202(6):1196\u0026ndash;206.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKay FU, Canvasser NE, Xi Y, Pinho DF, Costa DN, Diaz DLA, Khatri G, Leyendecker JR, Yokoo T, Lay AH et al. Diagnostic Performance and Interreader Agreement of a Standardized MR Imaging Approach in the Prediction of Small Renal Mass Histology. \u003cem\u003eRADIOLOGY\u003c/em\u003e 2018, 287(2):543\u0026ndash;553.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang K, Ponzo TA, Tang H, Mishra PK, Macura SI, Lerman LO. Multiparametric MRI detects longitudinal evolution of folic acid-induced nephropathy in mice. Am J Physiol Ren Physiol. 2018;315(5):F1252\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTewes S, Gueler F, Chen R, Gutberlet M, Jang MS, Meier M, Mengel M, Hartung D, Wacker F, Rong S, et al. Functional MRI for characterization of renal perfusion impairment and edema formation due to acute kidney injury in different mouse strains. PLoS ONE. 2017;12(3):e173248.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Bazelaire CM, Duhamel GD, Rofsky NM, Alsop DC. MR imaging relaxation times of abdominal and pelvic tissues measured in vivo at 3.0 T: preliminary results. Radiology. 2004;230(3):652\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdams LC, Jurmeister P, Ralla B, Bressem KK, Fahlenkamp UL, Engel G, Siepmann S, Wagner M, Hamm B, Busch J, et al. Assessment of the extracellular volume fraction for the grading of clear cell renal cell carcinoma: first results and histopathological findings. EUR RADIOL. 2019;29(11):5832\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang S, Li J, Zhu D, Hua T, Zhao B. Contrast-enhanced magnetic resonance (MR) T1 mapping with low-dose gadolinium-diethylenetriamine pentaacetic acid (Gd-DTPA) is promising in identifying clear cell renal cell carcinoma histopathological grade and differentiating fat-poor angiomyolipoma. Quant Imaging Med Surg. 2020;10(5):988\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRossi SH, Klatte T, Usher-Smith J, Stewart GD. Epidemiology and screening for renal cancer. WORLD J UROL. 2018;36(9):1341\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu H, Cao H, Chen L, Fang L, Liu Y, Zhan J, Diao X, Chen Y. The quantitative evaluation of contrast-enhanced ultrasound in the differentiation of small renal cell carcinoma subtypes and angiomyolipoma. Quant Imaging Med Surg. 2022;12(1):106\u0026ndash;18.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoch H, Amin MB, Berney DM, Comperat EM, Gill AJ, Hartmann A, Menon S, Raspollini MR, Rubin MA, Srigley JR et al. The 2022 World Health Organization Classification of Tumours of the Urinary System and Male Genital Organs-Part A: Renal, Penile, and Testicular Tumours. \u003cem\u003eEUR UROL\u003c/em\u003e 2022, 82(5):458\u0026ndash;468.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdams LC, Jurmeister P, Ralla B, Bressem KK, Fahlenkamp UL, Engel G, Siepmann S, Wagner M, Hamm B, Busch J, et al. Assessment of the extracellular volume fraction for the grading of clear cell renal cell carcinoma: first results and histopathological findings. EUR RADIOL. 2019;29(11):5832\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang S, Li J, Zhu D, Hua T, Zhao B. Contrast-enhanced magnetic resonance (MR) T1 mapping with low-dose gadolinium-diethylenetriamine pentaacetic acid (Gd-DTPA) is promising in identifying clear cell renal cell carcinoma histopathological grade and differentiating fat-poor angiomyolipoma. QUANT IMAG MED SURG. 2020;10(5):988\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDelahunt B, McKenney JK, Lohse CM, Leibovich BC, Thompson RH, Boorjian SA, Cheville JC. A novel grading system for clear cell renal cell carcinoma incorporating tumor necrosis. AM J SURG PATHOL. 2013;37(3):311\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobbers LF, Baars EN, Brouwer WP, Beek AM, Hofman MB, Niessen HW, van Rossum AC, Marcu CB. T1 mapping shows increased extracellular matrix size in the myocardium due to amyloid depositions. Circ Cardiovasc Imaging. 2012;5(3):423\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRankin AJ, Mayne K, Allwood-Spiers S, Hall BP, Roditi G, Gillis KA, Mark PB. Will advances in functional renal magnetic resonance imaging translate to the nephrology clinic? Nephrol (Carlton). 2022;27(3):223\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShang W, Hong G, Li W. MRI for the detection of small malignant renal masses: a systematic review and meta-analysis. FRONT ONCOL. 2023;13:1194128.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchieda N, Davenport MS, Silverman SG, Bagga B, Barkmeier D, Blank Z, Curci NE, Doshi AM, Downey RT, Edney E et al. Multicenter Evaluation of Multiparametric MRI Clear Cell Likelihood Scores in Solid Indeterminate Small Renal Masses. \u003cem\u003eRADIOLOGY\u003c/em\u003e 2022, 303(3):590\u0026ndash;599.\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Magnetic resonance imaging, Identify, Renal neoplasm","lastPublishedDoi":"10.21203/rs.3.rs-4867341/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4867341/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eDifferentiating benign from malignant small renal tumors can help to guide clinical decision-making. T1 mapping enables quantitative assessment of T1 relaxation time and may help to evaluate tumor properties. This study aimed to investigate the possible utility of T1 mapping for quantificationally distinguishing benign from malignant small solid renal tumors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The data set used in this retrospective study, consisting of 99 patients with 99 small renal masses (≤4 cm). 78 malignant small renal tumors and 21 benign tumors respectively. Quantitative variables (including pre- and post- T1 mapping) were calculated and compared between different renal tumors. The clinical features and image qualitative characteristics were recorded accordingly. Univariate and multivariate logistic regression models were used to identify independent influencing factors. The diagnostic accuracy of independent influencing factors was represented with the area under the receiver operating characteristic curve (AUC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The pre-contrast T1 mapping (T1) and the ratio of T1 reduction in malignance were higher than those in benign small renal tumors, while post-contrast T1 mapping was lower (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.025). In the multivariable logistic regression, the patient’s gender (odds ratio (OR) = 4.987, \u003cem\u003eP\u003c/em\u003e = 0.008), patient’s age (OR = 2.026, \u003cem\u003eP\u003c/em\u003e = 0.020), and T1 (OR = 3.652, \u003cem\u003eP\u003c/em\u003e = 0.001) were independent predictors. For the identification of benign renal tumors, the T1 demonstrated moderate diagnostic efficiency with an AUC of 0.697 (0.596-0.785), a sensitivity of 51.28%, and a specificity of 100% (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.000). The T1+ gender + age model achieved an AUC of 0.832 (0.743-0.899), a sensitivity of 60.26%, and a specificity of 95.26%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Quantitative T1 mapping parameters may provide an added value in noninvasively distinguishing small benign renal tumors from renal cell carcinoma (RCC).\u003c/p\u003e","manuscriptTitle":"Identification of benign from malignant small renal tumors: Is there a possible role of T1 mapping?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-14 10:37:33","doi":"10.21203/rs.3.rs-4867341/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":"18e3f979-0b40-4dde-9a98-df94e1377547","owner":[],"postedDate":"October 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-12T05:39:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-14 10:37:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4867341","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4867341","identity":"rs-4867341","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}