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

doi:10.21203/rs.3.rs-4867341/v1

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

https://doi.org/10.21203/rs.3.rs-4867341/v1

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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

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 < 4 cm were malignant, however all 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.

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 mm²; 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 mm² (range 56.4–160.0 mm²). 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.

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
Variable	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%.

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.

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.

AUC

The area under the receiver operating characteristic curve

The pre-contrast T1 mapping

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

Computer tomography

MRI

Magnetic resonance imaging

VIBE

Volume interpolated breath-hold examination

CMP

Corticomedullary phase

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.

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.

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No competing interests reported.

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

Status:

Version 1

Abstract

Figures

Background

Methods

Patients

MRI protocols

Quantitative MRI Image Analysis

Statistical analysis

Results

Population characteristics

Qualitative radiological parameters

Independent parameters for characterizing small renal neoplasms

ROC curves analyze the diagnostic value of T1 mapping

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1