Native T1 mapping for non-invasive quantitative evaluation of renal function and renal fibrosis in patients with chronic kidney disease

Chao-Gang Wei; Ying Zeng; Rui Zhang; Ye Zhu; Jian Tu; Peng Pan; Qing Ma; Lan-Yi Wei; Wen-Lu Zhao; Jun-Kang Shen

doi:10.21037/qims-22-1304

Native T₁ mapping for non-invasive quantitative evaluation of renal function and renal fibrosis in patients with chronic kidney disease

Quant Imaging Med Surg. 2023 Aug 1;13(8):5058-5071. doi: 10.21037/qims-22-1304. Epub 2023 May 22.

Authors

Chao-Gang Wei^#¹, Ying Zeng^#², Rui Zhang^#¹, Ye Zhu², Jian Tu³, Peng Pan¹, Qing Ma¹, Lan-Yi Wei¹, Wen-Lu Zhao¹, Jun-Kang Shen¹

Affiliations

¹ Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
² Department of Nephrology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
³ Department of Pathology, The Second Affiliated Hospital of Soochow University, Suzhou, China.

^# Contributed equally.

Abstract

Background: To investigate the role of native T₁ mapping in the non-invasive quantitative assessment of renal function and renal fibrosis (RF) in chronic kidney disease (CKD) patients.

Methods: A prospective analysis of 71 consecutive patients [no RF (0%): 9 cases; mild RF (<25%): 36 cases; moderate RF (25-50%): 17 cases; severe RF (>50%): 9 cases] who were clinically diagnosed with CKD that was pathologically confirmed and who underwent magnetic resonance imaging (MRI) examination between October 2021 and September 2022 was performed. T₁-C (mean cortical T₁ value), T₁-M (mean medullary T₁ value), ΔT₁ (mean corticomedullary difference) and T₁% (mean corticomedullary ratio) values were compared. Correlations between T₁ parameters and clinical and histopathological values were analyzed. Regression analysis was performed to determine independent predictors of RF. The areas under the receiver operating characteristic curve (AUC) were calculated to assess the diagnostic value of RF.

Results: The T₁-C, ΔT₁ and T₁% values (P<0.05) were significantly different in the CKD group, but T₁-M was not (P>0.05). The ΔT₁ and T₁% values showed significant differences in pairwise comparisons among CKD subgroups (P<0.05) except for CKD 2 and 3. ΔT₁ and T₁% were moderately correlated with the estimated glomerular filtration rate (ΔT₁: r_s=-0.561; T₁%: r=-0.602), serum creatinine (ΔT₁: r_s=0.591; T₁%: r_s=0.563), blood urea nitrogen (ΔT₁: r_s=0.433; T₁%: r_s=0.435) and histopathological score (ΔT₁: r_s=0.630; T₁%: r_s=0.658). ΔT₁ and T₁%, but not T₁-C, were independent predictors of RF (P<0.05). ΔT₁ and T₁% were set as -410.07 ms and 0.8222 with great specificity [ΔT₁: 91.7% (77.5-98.2%); T₁%: 97.2% (85.5-99.9%)] to identify mild RF and moderate-severe RF. The optimal cutoff values for differentiating severe RF from mild-moderate RF were -343.81 ms (ΔT₁) and 0.8359 (T₁%) with high sensitivity [both 100% (66.4-100%)] and specificity [ΔT₁: 90.6% (79.3-96.9%); T₁%: 94.3% (84.3-98.8%)].

Conclusions: ΔT₁ and T₁% overwhelm T₁-C for assessment of renal function and RF in CKD patients. ΔT₁ and T₁% identify patients with <25% and >50% fibrosis, which can guide clinical decision-making and help to avoid biopsy-related bleeding.

Keywords: Prospective study; chronic kidney disease (CKD); native T1 mapping; quantitative assessment; renal fibrosis.