A proof of concept for tissue characterization in atypical autosomal dominant polycystic kidney disease patients from diffusion tensor imaging

A proof of concept for tissue characterization in atypical autosomal dominant polycystic kidney disease patients from diffusion tensor imaging | 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 A proof of concept for tissue characterization in atypical autosomal dominant polycystic kidney disease patients from diffusion tensor imaging Linnea Valeri, Irene Capelli, Damiana Lazzaro, Gaetano La Manna, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8734288/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 The potential of magnetic resonance diffusion tensor imaging (MR-DTI) in autosomal dominant polycystic kidney disease (ADPKD) has been poorly investigated. The few studies available are focused on typical ADPKD populations. In this exploratory study we evaluate the value of MR-DTI in atypical ADPKD patients to quantitatively characterize renal tissue, correlating MR-DTI derived parameters with kidney function tests. Methods Ten atypical ADPKD patients have been enrolled in the study. 3D volumes of interest (VOI) corresponding to a “cyst-only VOI” and a “parenchyma-only VOI” for each patient were manually drawn. Several diffusion anisotropy indices were computed, and their values compared between (1) the parenchyma-only VOI and the cyst-only VOI in our population and (2) the corresponding values in healthy kidneys. Results All indexes were able to significantly discriminate between cyst and parenchyma, especially the apparent diffusion coefficient (ADC) (p = 7.3x10-12). Compared to healthy kidneys, in atypical ADPKD patients, parenchymal ADC slightly decreases (2.11 ± 0.0003 vs. 2.15 ± 0.14/2.25 ± 0.1) and parenchymal FA decreases more significantly (0.184 ± 0.098 vs. 0.28 ± 0.05/0.38 ± 0.025). Parenchymal ADC was shown to have the strongest correlation with serum creatinine and estimated glomerular filtration rate (-0.43 and 0.60, respectively). Conclusions MR-DTI has been applied for the first time to ADPKD atypical patients showing its capability of differentiating between cysts and parenchyma. Based on our preliminary findings on a small but rare and poorly studied population, the hypothesis-generated suggests ADC might have the potential to be used as a prognostic index for disease risk stratification. Autosomal Dominant Polycystic Kidney Disease Diffusion Anisotropic indices Diffusion Tensor Imaging Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Polycystic kidney disease (PKD) has two main manifestations, the autosomal dominant (ADPKD) and the autosomal recessive (ARPKD) forms, the latter being less common but more aggressive [ 1 , 2 ]. ADPKD is typically characterized by bilateral and diffuse distribution of cysts that replace kidney tissue and where all cysts contribute similarly to total kidney volume (TKV) (Class 1 patients). ADPKD has been extensively studied and a quantitative classification, mainly used for disease prognosis, has been previously developed [ 1 ]. The Mayo Clinic Imaging Classification is based on TKV, which has been proposed by the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) as the biomarker to assess the risk of chronic kidney disease. Height-adjusted TKV (htTKV) combined with age allows the classification of ADPKD patients into 5 subclasses, characterized by a progressively faster decline in renal function [ 1 ]. Unfortunately, the Mayo Clinic predictive model only applies to ADPKD typical patients (Class 1), excluding ADPKD patients with an atypical manifestation of the disease (Class 2). For Class 2, representing only 5–10% of the ADPKD population, the prognosis method used in clinical practice is qualitative only [ 2 ]. Based on prespecified imaging findings, Class 2 ADPKD patients are subdivided in two classes, A and B. Class A is further divided into four subclasses (unilateral, segmental, asymmetric and lopsided) whilst class B includes patients with kidney atrophy (unilateral or bilateral). To better characterize Class 2 ADPKD very recently Iliuta and al. [ 2 ] reported a systematic study to define the prevalence and clinical characteristics of such patients, adding two more subclasses to class A (mild lopsided and segmental sparing). Bae and al. reported an expanded imaging classification model in which the use of a recalculated htTKV value that excludes prominent exophytic cysts improved the prediction for the estimated glomerular filtration rate (eGFR) trajectory [ 3 ]. Using the recalculated htTKV may improve the classification of ADPKD patients with prominent exophytic cysts, allowing the inclusion of some Class 2 patients in the Mayo predictive model and reclassifying some Class 1 patients [ 3 ]. Despite this recent attempt, a comprehensive characterization of atypical ADPKD is lacking, and more studies are needed to better characterize the ADPKD atypical manifestation. Multiparametric MRI has been recently proposed to assess renal function and severity of polycystic kidney disease in animal models [ 4 , 5 ]. In Class 1 ADPKD population, diffusion-weighted magnetic resonance imaging (MR-DWI) is an emerging technique whose application to ADPKD patients is still very limited. In [ 6 ] it has shown its potential for detecting and monitoring renal disease at an earlier stage than conventional qualitative imaging techniques. Suwabe and al. applied MR-DWI to identify acute cyst haemorrhage and infection [ 7 ]. Only two studies on a limited number of ADPKD patients reported the analysis of magnetic resonance diffusion tensor magnetic resonance imaging (MR-DTI) in Class 1 ADPKD data [ 8 ] and in ARPKD [ 9 ], respectively. Lupica and al. [ 8 ] highlighted MR-DTI as a promising radiological tool that could be used to evaluate early micro-structure alterations, without side effects and contrast agent administration; Serai and al. [ 9 ] confirmed such findings in young adults with ARPKD, based on changes in diffusion anisotropy. No MR-DWI- and MR-DTI-based investigation applied to atypical ADPKD patients is reported in literature. In our study, we aimed to explore the value of MR-DTI in Class 2 ADPKD to characterize renal tissue, correlating MR-DTI derived parameters with kidney function tests. MATERIALS AND METHODS Study Population A prospective and observational study was designed. The study was approved by the local Ethics Committee (306/2021/Oss/AOUBo), and it was performed according to the Declaration of Helsinki. Study participants gave their informed consent prior to participation. A subset of 10 atypical ADPKD patients (4 men and 6 women), with an average age of 56.1 ± 13.7, were enrolled within the MICKY-ADPKD study (306/2021/Oss/AOUBo). Exclusion criteria included: expectancy of life shorter than 2 years, pregnancy, diagnosis of cancer in the last 12 months (with exclusion of the skin basocellular tumour), mental conditions that may avoid the subject to understand the purpose and the consequences of the study, contraindications to MRI abdominal acquisition and participation to other clinical trials in the last three months. According to the qualitative Mayo Clinic classification method for Class 2 ADPKD patients [ 1 , 2 ] the study group was characterized by 4 Lopsided patients, 1 Mild lopsided, 2 Asymmetric, 2 Unilateral, 1 Bilateral atrophy patient. For each patient serum creatinine (S cr ) was measured at baseline evaluation (average on the group of 0.96 ± 0.21 mg/dL) and eGFR was calculated according to the CKD-EPI formula [ 10 ] (average on the group of 73.7 ± 13.2 ml/min/1.73m 2 ). Other clinical parameters as the total kidney volume (TKV) and the height-adjusted total kidney volume (htTKV) were obtained applying previous volumetric approach for ADPKD MRI data segmentation [ 11 , 12 ]. MRI Protocol MRI Protocol MRI was performed by a 1.5T Philips Ingenia MRI scanner. MR-DTI acquisition was performed applying a respiratory-triggered protocol in the coronal view and b-value of 0 s/mm 2 (b 0 ) and 800 s/mm 2 with TR = 2111 ms, TE = 94 ms, flip-angle = 90°, using 6–15 different diffusion gradient directions. The MRI parameters were 3-4.5 mm slice thickness, 0.76–0.92 mm/pixel resolution and square images of 352–560 pixel per side. The total number of images per patient ranged from 30 to 66 depending on the kidney size. Image Processing We manually drew regions of interest, slice by slice, on the MR-DTI b 0 images, obtaining 3 different volumes of interest (VOIs) for each patient (Fig. 1 ): a “total-kidney VOI” (TK-VOI) (excluding pelvis), a “cyst-only VOI” (cyst-VOI) and a “parenchyma-only VOI” (parenchyma-VOI). The parenchyma-only VOI consisted of all the parenchyma, without discrimination between cortex and medulla. The DTI parameters were calculated for each of these VOIs. The quantitative evaluation of diffusion and anisotropy indexes (DAIs) was done on appropriate generated 3D maps. DTI maps were calculated from the diffusion tensor obtained with the H-matrix [ 13 ]. All codes were implemented in Matlab (Matlab, Matworks®). As shown in Fig. 2 , the following DTI parameters were calculated for all subjects: apparent diffusion coefficient (ADC), fractional anisotropy (FA), scaled relative anisotropy (sRA), volume fraction (VF), lattice index (LI) and the ultimate anisotropy indexes considering surface diffusion (surf), volume diffusion (vol) or both (UA surf , UA vol , UA vol,surf ). All indexes are summarized in Table 1 . Table 1 Apparent Diffusion Coefficient (ADC) and intravoxel diffusion and anisotropy indices (DAIs) formulas, expressed in terms of eigenvalues. Index Formulas [ 14 ] Apparent Diffusion Coefficient (ADC) : Scalar value representing the overall diffusivity inside a voxel \(\:\text{A}\text{D}\text{C}=\frac{{{\lambda\:}}_{1}+{{\lambda\:}}_{2}+{{\lambda\:}}_{3}}{3}\) Fractional Anisotropy (FA) : Represents a normalized standard deviation of the diffusivities along the three axes \(\:\text{F}\text{A}=\sqrt{\frac{3[{\left({{\lambda\:}}_{1}-{\text{D}}_{\text{a}\text{v}}\right)}^{2}+{\left({{\lambda\:}}_{2}-{\text{D}}_{\text{a}\text{v}}\right)}^{2}+{\left({{\lambda\:}}_{3}-{\text{D}}_{\text{a}\text{v}}\right)}^{2}]}{2({{\lambda\:}}_{1}^{2}+{{\lambda\:}}_{2}^{2}+{{\lambda\:}}_{3}^{2})}}\) Scaled Relative Anisotropy (sRA) : Ratio between the anisotropic and isotropic components of the diffusion tensor \(\:\text{s}\text{R}\text{A}=\sqrt{\frac{{\left({{\lambda\:}}_{1}-{\text{D}}_{\text{a}\text{v}}\right)}^{2}+{\left({{\lambda\:}}_{2}-{\text{D}}_{\text{a}\text{v}}\right)}^{2}+{\left({{\lambda\:}}_{3}-{\text{D}}_{\text{a}\text{v}}\right)}^{2}}{\sqrt{6}\:{\text{D}}_{\text{a}\text{v}}}}\) Volume Fraction (VF) : Ratio between the volume of the ellipsoid, representation of the diffusion tensor, and the volume of a diffusion sphere with a radius equal to the average diffusion (ADC) \(\:\text{V}\text{F}=1-\frac{{{\lambda\:}}_{1}{{\lambda\:}}_{2}{{\lambda\:}}_{3}}{{\text{D}}_{\text{a}\text{v}}^{3}}\) Ultimate Anisotropy - Average, Surface (UA surf ) : Represents the ratio between average and surface diffusion \(\:\text{U}{\text{A}}_{\text{s}\text{u}\text{r}\text{f}}=1-\frac{\sqrt{({{\lambda\:}}_{1}{{\lambda\:}}_{2}+{{\lambda\:}}_{2}{{\lambda\:}}_{3}+{{\lambda\:}}_{1}{{\lambda\:}}_{3})/3}}{{\text{D}}_{\text{a}\text{v}}}\) Ultimate Anisotropy - Average, Volume (UA vol ) : Represents the ratio between the average diffusion and the volumetric diffusion \(\:\text{U}{\text{A}}_{\text{v}\text{o}\text{l}}=1-\frac{\sqrt[3]{{({\lambda\:}}_{1}{{\lambda\:}}_{2}{{\lambda\:}}_{3})}}{{\text{D}}_{\text{a}\text{v}}}\) Ultimate Anisotropy - Volume, Surface (UA vol,surf ) : Represents the ratio between volumetric and surface diffusion \(\:\text{U}{\text{A}}_{\text{v}\text{o}\text{l},\text{s}\text{u}\text{r}\text{f}}=1-\frac{\sqrt[3]{{({\lambda\:}}_{1}{{\lambda\:}}_{2}{{\lambda\:}}_{3})}}{\sqrt{({{\lambda\:}}_{1}{{\lambda\:}}_{2}+{{\lambda\:}}_{2}{{\lambda\:}}_{3}+{{\lambda\:}}_{1}{{\lambda\:}}_{3})/3}}\) Lattice Index (LI) : Also exist in form of intervoxel index \(\:LI=\frac{FA+F{A}^{2}}{2}\) where \(\:{\text{D}}_{\text{a}\text{v}}={({\lambda\:}}_{1}{+{\lambda\:}}_{2}+{{\lambda\:}}_{3})/3\) Intravoxel diffusion and anisotropy indices formulas range from zero (isotropic) to one (anisotropic). ADC: Apparent Diffusion Coefficient; FA: Fractional Anisotropy; sRA: Scaled Relative Anisotropy; VF: Volume Fraction; UA surf : Ultimate Anisotropy - Average, Surface; UA vol : Ultimate Anisotropy - Average, Volume; UA vol,surf : Ultimate Anisotropy - Volume, Surface; LI: Lattice Index Statistical Analysis Data were presented as mean ± SD (standard deviation) for normally distributed values (at Kolmogorov–Smirnov test) and median ± IQR (interquartile range) for non-normally distributed values. Differences between the values in the parenchyma-VOI and the cyst-VOI were determined by t-test for all indexes. A comparison between the results in the parenchyma-VOI and the parenchyma in healthy subjects was made considering previous studies. The comparison was only possible for ADC and FA, which were the only indexes that had already been computed in literature for the kidney; among all studies, only those with a b-value equal to 800 s/mm 2 , as in our study, were considered, as the b-value appears to be the most incisive factor on DAIs values [ 14 ]. The Pearson correlation coefficient was used to test correlations between DTI parameters and other biological markers, such as S cr and eGFR. RESULTS DTI maps and DAIs DTI parameters were expressed as median and interquartile range, as distributions were not normal. All the parameters are summarized in Table 2 . All indexes were able to significantly discriminate (p < 0.05) between cyst and parenchyma, especially ADC (p = 7.3 x \(\:1{0}^{-12}\) ). This discrimination is also visible by looking at the DTI maps in Fig. 3 . Kidneys that were more affected by cysts showed less to no cortico-medullary differentiation (CMD), while CMD was visible in kidneys of Unilateral and Mild Lopsided patients (Fig. 3 , 1st and 2nd rows, right panels), which were less affected by cysts. Table 2 Values of diffusion tensor imaging (DTI) parameters in the total kidney (TK), parenchyma and cyst VOIs. TK Parenchyma Cyst p-value ADC (x \(\:1{0}^{-3}\) ) 2.71 ± 0.0004 2.11 ± 0.0003 2.86 ± 0.0003 7.3 x \(\:1{0}^{-12}\) FA 0.127 ± 0.079 0.184 ± 0.098 0.112 ± 0.063 0.00013 sRA (x \(\:1{0}^{-1}\) ) 0.74 ± 0.047 1.08 ± 0.059 0.65 ± 0.037 0.00013 VF (x \(\:1{0}^{-2}\) ) 1.75 ± 0.023 3.62 ± 0.041 1.31 ± 0.016 0.00032 UA surf (x \(\:1{0}^{-3}\) ) 2.93 ± 0.004 6.11 ± 0.007 2.19 ± 0.003 0.00030 UA vol ( x \(\:1{0}^{-2}\) ) 0.584 ± 0.008 1.22 ± 0.014 0.438 ± 0.005 0.00033 UA vol,surf (x \(\:1{0}^{-3}\) ) 2.94 ± 0.004 6.13 ± 0.007 2.19 ± 0.003 0.00035 LI (x \(\:1{0}^{-1}\) ) 0.72 ± 0.051 1.10 ± 0.069 0.62 ± 0.039 0.00016 Values expressed as median ± interquartile range (IQR); p-value is reported between parameters in the parenchyma and the cyst VOIs ADC: Apparent Diffusion Coefficient; FA: Fractional Anisotropy; sRA: Scaled Relative Anisotropy; VF: Volume Fraction; UA surf : Ultimate Anisotropy - Average, Surface; UA vol : Ultimate Anisotropy - Average, Volume; UA vol,surf : Ultimate Anisotropy - Volume, Surface; LI: Lattice Index Comparison with DAIs in healthy kidneys DTI parameters applied in literature on healthy control kidneys are summarized in Table 3 [ 8 , 15 – 18 ]. Comparing the results of Zheng et al. [ 18 ] and Chen et al. [ 17 ] (presenting b-value = 800 as in our study) with the values on the parenchyma-VOI of our study group, ADC slightly decreases (2.11 ± 0.0003 vs. 2.15 ± 0.14 in [ 18 ] and vs. 2.25 ± 0.1 in [ 17 ]) and FA decreases more significantly (0.184 ± 0.098 vs. 0.28 ± 0.05 in [ 18 ] and vs. 0.38 ± 0.025 in [ 17 ]). Table 3 Apparent diffusion coefficient (ADC) and fractional anisotropy (FA) in the parenchyma of healthy controls vs. in the parenchyma of ADPKD atypical patients. Parenchyma in healthy controls Parenchyma in study group Author Gaudiano et al. [ 16 ] Hueper et al. [ 17 ] Lupica et al. [ 8 ] Zheng et al. [ 19 ] Chen et al. [ 18 ] Year 2013 2011 2016 2014 2022 2024 Number of subjects 17 14 6 73 20 10 b-value 0 & 500 0 & 600 0 & 600 0 & 800 0 & 800 0 & 800 Tesla 1.5 T 1.5 T 3 T 3 T 3 T 1.5 T ADC Cortex 2.55 ± 0.18 2.32 ± 0.16 2.28 ± 0.04 2.23 ± 0.14 2.29 ± 0.1 - Medulla 2.25 ± 0.24 2.34 ± 0.19 2.26 ± 0.13 2.06 ± 0.14 2.20 ± 0.09 - TK 2.4 ± 0.21^ 2.33 ± 0.18^ 2.28 ± 0.09^ 2.15 ± 0.14^ 2.25 ± 0.1^ 2.11 ± 0.0003° FA Cortex 0.308 ± 0.071 0.165 ± 0.01 0.19 ± 0.00 0.24 ± 0.05 0.31 ± 0.02 - Medulla 0.389 ± 0.067 0.415 ± 0.046 0.24 ± 0.01 0.32 ± 0.05 0.44 ± 0.03 - TK 0.35 ± 0.069^ 0.29 ± 0.056^ 0.22 ± 0.01^ 0.28 ± 0.05^ 0.38 ± 0.025^ 0.184 ± 0.098° Healthy controls: Apparent Diffusion Coefficient (ADC), Fractional Anisotropy (FA) expressed as mean ± SD Study group: Apparent Diffusion Coefficient (ADC), Fractional Anisotropy (FA) expressed as median ± IQR n.a.: not available ^computed from the mean values between the cortex and the medulla; °evaluated on the parenchyma-VOI Correlations between DAIs and biomarkers The Pearson correlation coefficients between DAIs, evaluated in the three VOIs, and the biomarkers eGFR and S cr , are shown in Table 4 . Among all the computed parameters, the ADC, evaluated in the parenchyma-VOI, was shown to have the best correlation with the biomarkers: positive for eGFR (0.598) and negative for S cr (-0.425). The linear regression for this parameter is shown in Fig. 4 . DISCUSSION and CONCLUSION Although MR-DTI has been widely used in neurological field, its application for morphological and functional evaluation of the kidney in an ADPKD population is limited. Multiparametric MRI including DTI has been proven to be a valid technique for studying the microstructure of renal tissue, which reflects the health conditions and functionality of the kidney [ 4 , 5 , 19 ]. Consequently, diffusion and anisotropy indexes (DAIs) might be used to quantify characteristics of the renal microstructure. MR-DWI and MR-DTI have already been applied to ADPKD typical patients in only two studies on limited population of 8 and 35 Class 1 ADPKD patients [ 8 , 20 ], and this is the first time MR-DTI has been applied specifically to a study group of Class 2 ADPKD patients. Moreover, previous MR-DWI and MR-DTI studies on the kidney have only considered ADC and FA; in this study, to further investigate MR-DTI potential, in addition to ADC and FA, we considered six more DAIs, that have already been applied in the investigation of the brain [ 21 – 23 ]: sRA, VF, LI, UA surf , UA vol , UA vol,surf . In agreement with previous studies [ 8 , 9 ], we found that cyst development seemed to compromise renal microstructure, resulting in a decrease of CMD. A qualitative observation of the FA maps (Fig. 3 ) showed that kidneys with no (or minimum) cyst involvement, of Unilateral and Mild Lopsided patients, preserve CMD; while CMD was not present in patients of other subclasses, with greater bilateral cyst involvement. Differently from previous studies in which the assessment of the MR-DTI derived parameters in PKD was evaluated without separating cortex and medulla [ 9 ] or on the total kidney volume without separating cystic and parenchymal tissue [ 8 ], this study considered the 3D DAIs on three different VOIs: the cyst-VOI (including only the cysts), the parenchyma-VOI (including only the parenchyma tissue) and the TK-VOI including the entire kidney region. The comparison of ADC and FA in TK-VOI showed that ADC and FA resulted respectively higher (2.71 ± 0.0004 vs. 2.15 ± 0.14 in [ 18 ], and vs. 2.25 ± 0.1 in [ 18 ]) and lower (0.127 ± 0.079 vs. 0.28 ± 0.05 in [ 18 ], and vs. 0.38 ± 0.025 in [ 17 ]) in the study group than in healthy subjects. These results confirmed what had already been found by Lupica et al. in ADPKD [ 8 ].While previous studies had evaluated the parameters only on parenchyma ROIs [ 9 ], or on the TK-VOI [ 8 ], including both the cyst and the parenchyma tissue, this study went a step further considering a parenchyma-VOI and a cyst-VOI. This allowed detecting changes specifically addressing the whole parenchyma of ADPKD atypical patients. Parenchyma of the study group showed a lower ADC (2.11 ± 0.0003 vs. 2.15 ± 0.14 in [ 18 ], and vs. 2.25 ± 0.1 in [ 17 ]) and a lower FA (0.184 ± 0.09 vs. 0.28 ± 0.05 in [ 18 ], and vs. 0.38 ± 0.025 in [ 17 ]) compared to healthy patients, resulting to be less diffusive and less anisotropic; confirming the results of Serai et al. for FA in ARPKD [ 9 ]. Lastly, the cysts were characterized by high ADC and low FA compared to parenchyma, as expected by objects filled with isotropic liquid. This evidence suggests that the increase of ADC in the TK-VOI in ADPKD patients is probably due to an increased cyst ADC, raising the overall ADC value, rather than to be attributed to biological processes, as was suggested by Lupica et al. [ 9 ]. The decrease of parenchymal FA quantifies a loss of anisotropy, thus a loss of the microstructural organization, in the ADPKD parenchyma, as had already been qualitatively observed from the FA maps. Unfortunately, for sRA, VF, LI, UA surf , UA vol , UA vol,surf , comparison with other studies is not possible due to the lack of reference values in both healthy or diseased kidneys for these parameters. All these parameters showed significantly lower values in the cysts compared to the parenchymal tissue, emphasizing the presence of a greater isotropic component than an anisotropic one in cystic structure. Being MR-DTI able to quantify characteristics of the renal microstructure, it could therefore be taken into consideration as prognostic biomarkers. The Mayo imaging classification of ADPKD is a widely accepted tool used in clinical practice to predict the progression of the disease [ 1 ]. However, a quantitative risk prediction model (based on htTKV and age) is only available for typical patients, with diffuse and bilateral cyst involvement (Class 1) [ 1 ]. Atypical patients (Class 2), who represent 5–10% of the ADPKD population, are excluded from the risk prediction model, as htTKV does not predict eGFR decline. A recent study [ 3 ] has attempted to expand the model for atypical patients by using a modified htTKV; however, this approach only covers patients with exophytic cysts, that are not the totality of Class 2. An effective biomarker of renal function for atypical patients is still lacking, and further studies are needed to provide a quantitative risk prediction model suitable for all Class 2 patients. In our study, the parameter that correlated the most with the renal function biomarkers was parenchymal ADC, which decreased as renal function worsened (so for increasing S cr and decreasing eGFR). All the results obtained in this study seem to suggest that a decrease in parenchymal ADC is related to a worsening of the risk class and of the overall renal condition. In fact, as discussed before, healthy subjects present a higher parenchymal ADC compared to the study group. Furthermore, from a closer look at the correlation graphics in Fig. 4 , it appears that patients belonging to Class 2B (the higher risk class in the Mayo classification for Class 2), for a given eGFR or S cr , have lower value of the parenchymal ADC than Class 2A patients (the lower risk class in the Mayo classification for Class 2); and in particular unilateral patients (of Class 2A, with unilateral cyst involvement), for a given eGFR or S cr , have the highest values of the parenchymal ADC of the study group. Based on the very preliminary findings of this exploratory study, the correlation of parenchymal ADC with eGFR and S cr , and the coherent stratification of the subclasses within the study group, suggest that parenchymal ADC should be further investigated as a potential prognostic biomarker for Class 2 ADPKD patients. However, the small cohort of patients enrolled in this study limits the generalizability and statistical power of the findings; the fact that the atypical form of ADPKD affects only 5–10% of ADPKD patients explains the limited characterization of this poorly described phenotype and, on note, our hypothesis-generated study paves the way for further studies investigating the use of ADC to improve the prognosis of patients with atypical ADPKD. Abbreviations ADPKD: Autosomal Dominant Polycystic Kidney Disease DTI: Diffusion Tensor Imaging TKV: Total Kidney Volume DAI: Diffusion Anisotropy Index Declarations Ethics approval and consent to participate Patients were enrolled within the MICKY-ADPKD study. The Ethics Committee and the Institutional Review Board approved the MICKY-ADPKD study (306/2021/Oss/AOUBo) (NCT06759142). Competing interests The authors declare that they have no competing interests. Funding This study has received funding by the European Union - NextGenerationEU through the Italian Ministry of University and Research under PNRR - M4C2-I1.3 Project PE_00000019 "HEAL ITALIA" to DEI CUP J33C22002920006. The views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them.” Author Contribution The individual contributions of the authors can be summarized as follows:• substantial contributions to: the conception and design of the work; the acquisition, analysis and interpretation of data; the drafting and the final approval of the version to be published (L.V., I. C., C.C., N.S.);• substantial contributions to: the acquisition and interpretation of data; the revising of the work critically for important intellectual content; the final approval of the version to be published (D.M., G.L.M., F.C., V.A., M.R.)All authors reviewed the manuscript. Data Availability Data and code for the analysis are available under request to be sent to the corresponding author (CC) References Irazabal MV, Rangel LJ, Bergstralh EJ, Osborn SL, Harmon AJ, Sundsbak JL, et al. Imaging Classification of Autosomal Dominant Polycystic Kidney Disease: A Simple Model for Selecting Patients for Clinical Trials. Journal of the American Society of Nephrology. 2015;26(1):160–72. Iliuta IA, Win AZ, Lanktree MB, Lee SH, Pourafkari M, Nasri F, et al. Atypical Polycystic Kidney Disease as defined by Imaging. Scientific Reports. 2023;13:2952. Bae KT, Shi T, Tao C, Yu ASL, Torres VE, Perrone RD, et al. Expanded Imaging Classification of Autosomal Dominant Polycystic Kidney Disease. Journal of the American Society of Nephrology. 2020;31(7):1640–51. Tournebize C, Schleef M, De Mul A, Pacaud S, Derain-Dubourg L, Juillard L, Rouvière O, Lemoine S. Multiparametric MRI: can we assess renal function differently? Clinical Kidney Journal 2025;18(1): Wang F, Yeon Lee Y, Adelnia F, Takahashi K, Harkins KD, He L, Zu Z, Ellinger P, Grundmann M, Harris RC, Takahashi T, Gore JC. Severity of polycystic kidney disease revealed by multiparametric MRI. Magnetic Resonance in Medicine 2023;90(3):1151-1165. Kline TL, Edwards ME, Garg I, Irazabal MV, Korfiatis P, Harris PC, et al. Quantitative MRI of kidneys in renal disease. Abdominal Radiology. 2018;43(3):629–38. Suwabe T, Ubara Y, Ueno T et al. Intracystic magnetic resonance imaging in patients with autosomal dominant polycystic kidney disease: features of severe cyst infection in a case–control study. BMC Nephrology 17 , 170 (2016). https://doi.org/10.1186/s12882-016-0381-9 Lupica R, Mormina E, Lacquaniti A, Trimboli D, Bianchimano B, Marino S, et al. 3 Tesla-Diffusion Tensor Imaging in Autosomal Dominant Polycystic Kidney Disease: The Nephrologist’s Point of View. Nephron. 2016;134(2):73–80. Serai SD, Otero HJ, Calle-Toro JS, Berman JI, Darge K, Hartung EA. Diffusion tensor imaging of the kidney in healthy controls and in children and young adults with autosomal recessive polycystic kidney disease. Abdominal Radiology. 2019;44(5):1867–72. Levey AS, Stevens LA, Schmid CH, Zhang Y (Lucy), Castro AF, Feldman HI, et al. A New Equation to Estimate Glomerular Filtration Rate. Annals Internal Medicine. 2009;150(9):604–12. Mignani R, Corsi C, De Marco M, Caiani EG, Santucci G, Cavagna E, Severi S, Cagnoli L. Assessment of kidney volume in polycystic kidney disease using magnetic resonance imaging without contrast medium, American Journal of Nephrology, 2011;33(2):176-184. doi: 10.1159/000324039. Turco D, Busutti M, Mignani R, Magistroni R, Corsi C. Comparison of total kidney volume quantification methods in autosomal dominant polycystic disease for a comprehensive disease assessment, American Journal of Nephrology, 2017. doi: 10.1159/000466709. Kingsley PB. Introduction to diffusion tensor imaging mathematics: Part III. Tensor calculation, noise, simulations, and optimization. Concepts in Magnetic Resonance. 2006;28A(2):155–79. Chuck NC, Steidle G, Blume I, Fischer MA, Nanz D, Boss A. Diffusion Tensor Imaging of the Kidneys: Influence of b-Value and Number of Encoding Directions on Image Quality and Diffusion Tensor Parameters. Journal of Clinical Imaging Science. 2013;3:53. Gaudiano C, Clementi V, Busato F, Corcioni B, Orrei MG, Ferramosca E, et al. Diffusion tensor imaging and tractography of the kidneys: assessment of chronic parenchymal diseases. European Radiology. 2013;23(6):1678–85. Hueper K, Gutberlet M, Rodt T, Gwinner W, Lehner F, Wacker F, et al. Diffusion tensor imaging and tractography for assessment of renal allograft dysfunction—initial results. European Radiology. 2011;21(11):2427–33. Chen YX, Zhou W, Ye YQ, Zeng L, Wu XF, Ke B, et al. Clinical study on the use of advanced magnetic resonance imaging in lupus nephritis. BMC Medical Imaging. 2022;22(1):210. Zheng Z, Shi H, Zhang J, Zhang Y. Renal Water Molecular Diffusion Characteristics in Healthy Native Kidneys: Assessment with Diffusion Tensor MR Imaging. PLoS ONE. 2014;9(12):e113469. Copur S, Yavuz F, Sag AA, Tuttle KR, Kanbay M. Future of kidney imaging: Functional magnetic resonance imaging and kidney disease progression. Eur J Clin Invest. 2022;2(5):e13765. Caroli A, Villa G, Brambilla P, Trillini M, Sharma K, Sironi S, et al. Diffusion magnetic resonance imaging for kidney cyst volume quantification and non-cystic tissue characterisation in ADPKD. European Radiology. 2023;33 : 6009–6019. Pierpaoli C, Basser PJ. Toward a quantitative assessment of diffusion anisotropy. Magnetic Resonance in Medicine. 1996;36(6):893–906. Uluğ AM, van Zijl PCM. Orientation-independent diffusion imaging without tensor diagonalization: Anisotropy definitions based on physical attributes of the diffusion ellipsoid. Journal of Magnetic Resonance Imaging. 1999;9(6):804–13. Martin J, Endt S, Wetscherek A, Kuder TA, Doerfler A, Uder M, et al. Contrast-to-noise ratio analysis of microscopic diffusion anisotropy indices in q-space trajectory imaging. Zeitschrift für Medizinische Physik. 2020;30(1):4–16. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8734288","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588675200,"identity":"7a25e376-f5ff-41d9-bbac-bcb24f629b6c","order_by":0,"name":"Linnea Valeri","email":"","orcid":"","institution":"University of Bologna","correspondingAuthor":false,"prefix":"","firstName":"Linnea","middleName":"","lastName":"Valeri","suffix":""},{"id":588675202,"identity":"55664ac5-3aec-4d4a-9b6d-494bb3ac60c4","order_by":1,"name":"Irene Capelli","email":"","orcid":"","institution":"IRCCS S. Orsola University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Irene","middleName":"","lastName":"Capelli","suffix":""},{"id":588675203,"identity":"fb768209-b6c4-4769-9e17-379049dab68f","order_by":2,"name":"Damiana Lazzaro","email":"","orcid":"","institution":"University of Bologna","correspondingAuthor":false,"prefix":"","firstName":"Damiana","middleName":"","lastName":"Lazzaro","suffix":""},{"id":588675204,"identity":"036e54a2-0429-4ded-bc3b-735e248ff4f4","order_by":3,"name":"Gaetano La Manna","email":"","orcid":"","institution":"IRCCS S. Orsola University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Gaetano","middleName":"La","lastName":"Manna","suffix":""},{"id":588675205,"identity":"b72d5aba-da5e-4cbf-8406-027f69e0d229","order_by":4,"name":"Francesca Ciurli","email":"","orcid":"","institution":"IRCCS S. Orsola University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Francesca","middleName":"","lastName":"Ciurli","suffix":""},{"id":588675206,"identity":"fdf8f596-acbc-4dcc-a3ef-d9fba6ed7620","order_by":5,"name":"Valeria Aiello","email":"","orcid":"","institution":"IRCCS S. Orsola University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Valeria","middleName":"","lastName":"Aiello","suffix":""},{"id":588675207,"identity":"486d9ac9-7cb7-43bd-85ad-0c486ae323ae","order_by":6,"name":"Matteo Righini","email":"","orcid":"","institution":"IRCCS S. Orsola University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Matteo","middleName":"","lastName":"Righini","suffix":""},{"id":588675208,"identity":"874d0613-6f22-4b85-88b6-cdf50458747b","order_by":7,"name":"Nicola Sciascia","email":"","orcid":"","institution":"S. Orsola-Malpighi University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Nicola","middleName":"","lastName":"Sciascia","suffix":""},{"id":588675209,"identity":"4048ec37-8973-4c15-b01f-f95cfe0b7d56","order_by":8,"name":"Cristiana Corsi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYFACxgYGBgM4T8KOH0geBmIevFoOIGlJlmyAasGjh4HhALIJG4A8ZgY81vDPPtz8+UOBXR5/A/PDxxV/LJiNb+QePFxQwSBjj0OLxLnENokDBsnFEgfYjA3Ptknwmd3ISzg84wxuhxnwMLYB/cKc2HCAh02ysUGC2exGjsFh3ja8Wpo/HDCoT5wP0tLwR4Jx8wzCWhqADjucuAGshU2CcYMEAS0SZxjbJM4YHE/ceBjol8Y2iWSJM28MgH6R4OE5gCPEetgff6j4U50473jzw4cNf+rs+NtzjD8XVNjYszfgsAYOmNGsJ6R+FIyCUTAKRgEeAABnGFPLOZyMwAAAAABJRU5ErkJggg==","orcid":"","institution":"University of Bologna","correspondingAuthor":true,"prefix":"","firstName":"Cristiana","middleName":"","lastName":"Corsi","suffix":""}],"badges":[],"createdAt":"2026-01-29 17:55:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8734288/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8734288/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102747604,"identity":"f1302e1d-a1e4-428c-a044-0f7bdebb7866","added_by":"auto","created_at":"2026-02-16 09:05:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":110782,"visible":true,"origin":"","legend":"\u003cp\u003eExample of the segmentation step to obtain the VOIs in five slices: the TK-VOI was obtained considering the entire kidney, including the cysts and the parenchyma tissue and excluding the pelvis (first row); the cyst-VOI and parenchyma-VOI were obtained from the TK-VOI considering only the cysts (yellow mask, second row) and only the parenchyma (light blue mask, second row), respectively.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8734288/v1/2a005615b16f5052b6c8e430.png"},{"id":102593209,"identity":"38d8c0b7-aced-4724-8d00-90b24568b9d8","added_by":"auto","created_at":"2026-02-13 11:47:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":230570,"visible":true,"origin":"","legend":"\u003cp\u003eExample of all DTI maps calculated on a slice of the TK-VOI, for one patient of the study group.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8734288/v1/0738f9067d56667ae4f00008.png"},{"id":102593206,"identity":"910f7272-6346-4341-9b08-24cbf78f5e27","added_by":"auto","created_at":"2026-02-13 11:47:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":254896,"visible":true,"origin":"","legend":"\u003cp\u003eExample of the ADC and FA maps for one patient of each subclass of the Mayo Clinic classification present in the study group. From a qualitative observation, parenchyma appears to be different from cyst tissue in all DTI maps, but it is especially evident in the ADC map. CMD was only present in kidneys less affected by cysts, as appeared to be in Unilateral or Mild Lopsided patients; as shown in the figure, CMD is visible in the b0 DTI image (where the medulla is characterized by darker shades of grey than the cortex) and especially in the FA map (with higher values for the medulla than the cortex) of the Unilateral and Mild Lopsided subjects; no CMD was detected in the subjects of the other subclasses.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8734288/v1/f2f95ecfd454c2baf662172d.png"},{"id":102593208,"identity":"6f52541f-bf09-481e-8132-d37fe53de92c","added_by":"auto","created_at":"2026-02-13 11:47:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":75081,"visible":true,"origin":"","legend":"\u003cp\u003eLinear regression between ADC evaluated in the parenchyma-VOI, and the biomarkers eGFR and S\u003csub\u003ecr\u003c/sub\u003e. One point in the plot corresponds to the median value of the index, evaluated in the parenchyma-VOI, in one patient.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8734288/v1/aabcd81c4ad30e26fb61c7e8.png"},{"id":103323942,"identity":"c93b4eb7-f3bc-4fb4-b94b-116e98774747","added_by":"auto","created_at":"2026-02-24 12:25:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1484765,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8734288/v1/742daff0-32d0-4cfa-8e9a-c9b74a5e4f3d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A proof of concept for tissue characterization in atypical autosomal dominant polycystic kidney disease patients from diffusion tensor imaging","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003ePolycystic kidney disease (PKD) has two main manifestations, the autosomal dominant (ADPKD) and the autosomal recessive (ARPKD) forms, the latter being less common but more aggressive [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. ADPKD is typically characterized by bilateral and diffuse distribution of cysts that replace kidney tissue and where all cysts contribute similarly to total kidney volume (TKV) (Class 1 patients). ADPKD has been extensively studied and a quantitative classification, mainly used for disease prognosis, has been previously developed [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The Mayo Clinic Imaging Classification is based on TKV, which has been proposed by the Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) as the biomarker to assess the risk of chronic kidney disease. Height-adjusted TKV (htTKV) combined with age allows the classification of ADPKD patients into 5 subclasses, characterized by a progressively faster decline in renal function [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Unfortunately, the Mayo Clinic predictive model only applies to ADPKD typical patients (Class 1), excluding ADPKD patients with an atypical manifestation of the disease (Class 2). For Class 2, representing only 5\u0026ndash;10% of the ADPKD population, the prognosis method used in clinical practice is qualitative only [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Based on prespecified imaging findings, Class 2 ADPKD patients are subdivided in two classes, A and B. Class A is further divided into four subclasses (unilateral, segmental, asymmetric and lopsided) whilst class B includes patients with kidney atrophy (unilateral or bilateral). To better characterize Class 2 ADPKD very recently Iliuta and al. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] reported a systematic study to define the prevalence and clinical characteristics of such patients, adding two more subclasses to class A (mild lopsided and segmental sparing).\u003c/p\u003e \u003cp\u003eBae and al. reported an expanded imaging classification model in which the use of a recalculated htTKV value that excludes prominent exophytic cysts improved the prediction for the estimated glomerular filtration rate (eGFR) trajectory [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Using the recalculated htTKV may improve the classification of ADPKD patients with prominent exophytic cysts, allowing the inclusion of some Class 2 patients in the Mayo predictive model and reclassifying some Class 1 patients [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Despite this recent attempt, a comprehensive characterization of atypical ADPKD is lacking, and more studies are needed to better characterize the ADPKD atypical manifestation.\u003c/p\u003e \u003cp\u003eMultiparametric MRI has been recently proposed to assess renal function and severity of polycystic kidney disease in animal models [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In Class 1 ADPKD population, diffusion-weighted magnetic resonance imaging (MR-DWI) is an emerging technique whose application to ADPKD patients is still very limited. In [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] it has shown its potential for detecting and monitoring renal disease at an earlier stage than conventional qualitative imaging techniques. Suwabe and al. applied MR-DWI to identify acute cyst haemorrhage and infection [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Only two studies on a limited number of ADPKD patients reported the analysis of magnetic resonance diffusion tensor magnetic resonance imaging (MR-DTI) in Class 1 ADPKD data [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and in ARPKD [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], respectively. Lupica and al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] highlighted MR-DTI as a promising radiological tool that could be used to evaluate early micro-structure alterations, without side effects and contrast agent administration; Serai and al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] confirmed such findings in young adults with ARPKD, based on changes in diffusion anisotropy.\u003c/p\u003e \u003cp\u003eNo MR-DWI- and MR-DTI-based investigation applied to atypical ADPKD patients is reported in literature. In our study, we aimed to explore the value of MR-DTI in Class 2 ADPKD to characterize renal tissue, correlating MR-DTI derived parameters with kidney function tests.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eA prospective and observational study was designed. The study was approved by the local Ethics Committee (306/2021/Oss/AOUBo), and it was performed according to the Declaration of Helsinki. Study participants gave their informed consent prior to participation.\u003c/p\u003e \u003cp\u003eA subset of 10 atypical ADPKD patients (4 men and 6 women), with an average age of 56.1\u0026thinsp;\u0026plusmn;\u0026thinsp;13.7, were enrolled within the MICKY-ADPKD study (306/2021/Oss/AOUBo). Exclusion criteria included: expectancy of life shorter than 2 years, pregnancy, diagnosis of cancer in the last 12 months (with exclusion of the skin basocellular tumour), mental conditions that may avoid the subject to understand the purpose and the consequences of the study, contraindications to MRI abdominal acquisition and participation to other clinical trials in the last three months.\u003c/p\u003e \u003cp\u003eAccording to the qualitative Mayo Clinic classification method for Class 2 ADPKD patients [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] the study group was characterized by 4 Lopsided patients, 1 Mild lopsided, 2 Asymmetric, 2 Unilateral, 1 Bilateral atrophy patient. For each patient serum creatinine (S\u003csub\u003ecr\u003c/sub\u003e) was measured at baseline evaluation (average on the group of 0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21 mg/dL) and eGFR was calculated according to the CKD-EPI formula [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] (average on the group of 73.7\u0026thinsp;\u0026plusmn;\u0026thinsp;13.2 ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eOther clinical parameters as the total kidney volume (TKV) and the height-adjusted total kidney volume (htTKV) were obtained applying previous volumetric approach for ADPKD MRI data segmentation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMRI Protocol\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eMRI Protocol\u003c/div\u003e \u003cp\u003eMRI was performed by a 1.5T Philips Ingenia MRI scanner. MR-DTI acquisition was performed applying a respiratory-triggered protocol in the coronal view and b-value of 0 s/mm\u003csup\u003e2\u003c/sup\u003e (b\u003csub\u003e0\u003c/sub\u003e) and 800 s/mm\u003csup\u003e2\u003c/sup\u003e with TR\u0026thinsp;=\u0026thinsp;2111 ms, TE\u0026thinsp;=\u0026thinsp;94 ms, flip-angle\u0026thinsp;=\u0026thinsp;90\u0026deg;, using 6\u0026ndash;15 different diffusion gradient directions. The MRI parameters were 3-4.5 mm slice thickness, 0.76\u0026ndash;0.92 mm/pixel resolution and square images of 352\u0026ndash;560 pixel per side. The total number of images per patient ranged from 30 to 66 depending on the kidney size.\u003c/p\u003e\n\u003ch3\u003eImage Processing\u003c/h3\u003e\n\u003cp\u003eWe manually drew regions of interest, slice by slice, on the MR-DTI b\u003csub\u003e0\u003c/sub\u003e images, obtaining 3 different volumes of interest (VOIs) for each patient (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e): a \u0026ldquo;total-kidney VOI\u0026rdquo; (TK-VOI) (excluding pelvis), a \u0026ldquo;cyst-only VOI\u0026rdquo; (cyst-VOI) and a \u0026ldquo;parenchyma-only VOI\u0026rdquo; (parenchyma-VOI). The parenchyma-only VOI consisted of all the parenchyma, without discrimination between cortex and medulla. The DTI parameters were calculated for each of these VOIs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe quantitative evaluation of diffusion and anisotropy indexes (DAIs) was done on appropriate generated 3D maps. DTI maps were calculated from the diffusion tensor obtained with the H-matrix [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. All codes were implemented in Matlab (Matlab, Matworks\u0026reg;). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the following DTI parameters were calculated for all subjects: apparent diffusion coefficient (ADC), fractional anisotropy (FA), scaled relative anisotropy (sRA), volume fraction (VF), lattice index (LI) and the ultimate anisotropy indexes considering surface diffusion (surf), volume diffusion (vol) or both (UA\u003csub\u003esurf\u003c/sub\u003e, UA\u003csub\u003evol\u003c/sub\u003e, UA\u003csub\u003evol,surf\u003c/sub\u003e). All indexes are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\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\u003eApparent Diffusion Coefficient (ADC) and intravoxel diffusion and anisotropy indices (DAIs) formulas, expressed in terms of eigenvalues.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormulas [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eApparent Diffusion Coefficient (ADC)\u003c/em\u003e:\u003c/p\u003e \u003cp\u003eScalar value representing the overall diffusivity inside a voxel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{A}\\text{D}\\text{C}=\\frac{{{\\lambda\\:}}_{1}+{{\\lambda\\:}}_{2}+{{\\lambda\\:}}_{3}}{3}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFractional Anisotropy (FA)\u003c/em\u003e:\u003c/p\u003e \u003cp\u003eRepresents a normalized standard deviation of the diffusivities along the three axes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{F}\\text{A}=\\sqrt{\\frac{3[{\\left({{\\lambda\\:}}_{1}-{\\text{D}}_{\\text{a}\\text{v}}\\right)}^{2}+{\\left({{\\lambda\\:}}_{2}-{\\text{D}}_{\\text{a}\\text{v}}\\right)}^{2}+{\\left({{\\lambda\\:}}_{3}-{\\text{D}}_{\\text{a}\\text{v}}\\right)}^{2}]}{2({{\\lambda\\:}}_{1}^{2}+{{\\lambda\\:}}_{2}^{2}+{{\\lambda\\:}}_{3}^{2})}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eScaled\u0026nbsp;Relative\u0026nbsp;Anisotropy (sRA)\u003c/em\u003e:\u003c/p\u003e \u003cp\u003eRatio between the anisotropic and isotropic components of the diffusion tensor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{s}\\text{R}\\text{A}=\\sqrt{\\frac{{\\left({{\\lambda\\:}}_{1}-{\\text{D}}_{\\text{a}\\text{v}}\\right)}^{2}+{\\left({{\\lambda\\:}}_{2}-{\\text{D}}_{\\text{a}\\text{v}}\\right)}^{2}+{\\left({{\\lambda\\:}}_{3}-{\\text{D}}_{\\text{a}\\text{v}}\\right)}^{2}}{\\sqrt{6}\\:{\\text{D}}_{\\text{a}\\text{v}}}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eVolume Fraction (VF)\u003c/em\u003e:\u003c/p\u003e \u003cp\u003eRatio between the volume of the ellipsoid, representation of the diffusion tensor, and the volume of a diffusion sphere with a radius equal to the average diffusion (ADC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{V}\\text{F}=1-\\frac{{{\\lambda\\:}}_{1}{{\\lambda\\:}}_{2}{{\\lambda\\:}}_{3}}{{\\text{D}}_{\\text{a}\\text{v}}^{3}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eUltimate Anisotropy - Average, Surface (UA\u003c/em\u003e\u003csub\u003e\u003cem\u003esurf\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e)\u003c/em\u003e:\u003c/p\u003e \u003cp\u003eRepresents the ratio between average and surface diffusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{U}{\\text{A}}_{\\text{s}\\text{u}\\text{r}\\text{f}}=1-\\frac{\\sqrt{({{\\lambda\\:}}_{1}{{\\lambda\\:}}_{2}+{{\\lambda\\:}}_{2}{{\\lambda\\:}}_{3}+{{\\lambda\\:}}_{1}{{\\lambda\\:}}_{3})/3}}{{\\text{D}}_{\\text{a}\\text{v}}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eUltimate Anisotropy - Average, Volume (UA\u003c/em\u003e\u003csub\u003e\u003cem\u003evol\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e)\u003c/em\u003e:\u003c/p\u003e \u003cp\u003eRepresents the ratio between the average diffusion and the volumetric diffusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{U}{\\text{A}}_{\\text{v}\\text{o}\\text{l}}=1-\\frac{\\sqrt[3]{{({\\lambda\\:}}_{1}{{\\lambda\\:}}_{2}{{\\lambda\\:}}_{3})}}{{\\text{D}}_{\\text{a}\\text{v}}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eUltimate Anisotropy - Volume, Surface (UA\u003c/em\u003e\u003csub\u003e\u003cem\u003evol,surf\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e)\u003c/em\u003e:\u003c/p\u003e \u003cp\u003eRepresents the ratio between volumetric and surface diffusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{U}{\\text{A}}_{\\text{v}\\text{o}\\text{l},\\text{s}\\text{u}\\text{r}\\text{f}}=1-\\frac{\\sqrt[3]{{({\\lambda\\:}}_{1}{{\\lambda\\:}}_{2}{{\\lambda\\:}}_{3})}}{\\sqrt{({{\\lambda\\:}}_{1}{{\\lambda\\:}}_{2}+{{\\lambda\\:}}_{2}{{\\lambda\\:}}_{3}+{{\\lambda\\:}}_{1}{{\\lambda\\:}}_{3})/3}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLattice Index (LI)\u003c/em\u003e:\u003c/p\u003e \u003cp\u003eAlso exist in form of intervoxel index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:LI=\\frac{FA+F{A}^{2}}{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{D}}_{\\text{a}\\text{v}}={({\\lambda\\:}}_{1}{+{\\lambda\\:}}_{2}+{{\\lambda\\:}}_{3})/3\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eIntravoxel diffusion and anisotropy indices formulas range from zero (isotropic) to one (anisotropic).\u003c/p\u003e \u003cp\u003eADC: Apparent Diffusion Coefficient; FA: Fractional Anisotropy; sRA: Scaled\u0026nbsp;Relative\u0026nbsp;Anisotropy; VF: Volume Fraction; UA\u003csub\u003esurf\u003c/sub\u003e: Ultimate Anisotropy - Average, Surface; UA\u003csub\u003evol\u003c/sub\u003e: Ultimate Anisotropy - Average, Volume; UA\u003csub\u003evol,surf\u003c/sub\u003e: Ultimate Anisotropy - Volume, Surface; LI: Lattice Index\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (standard deviation) for normally distributed values (at Kolmogorov\u0026ndash;Smirnov test) and median\u0026thinsp;\u0026plusmn;\u0026thinsp;IQR (interquartile range) for non-normally distributed values. Differences between the values in the parenchyma-VOI and the cyst-VOI were determined by t-test for all indexes. A comparison between the results in the parenchyma-VOI and the parenchyma in healthy subjects was made considering previous studies. The comparison was only possible for ADC and FA, which were the only indexes that had already been computed in literature for the kidney; among all studies, only those with a b-value equal to 800 s/mm\u003csup\u003e2\u003c/sup\u003e, as in our study, were considered, as the b-value appears to be the most incisive factor on DAIs values [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The Pearson correlation coefficient was used to test correlations between DTI parameters and other biological markers, such as S\u003csub\u003ecr\u003c/sub\u003e and eGFR.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eDTI maps and DAIs\u003c/h2\u003e\n \u003cp\u003eDTI parameters were expressed as median and interquartile range, as distributions were not normal. All the parameters are summarized in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. All indexes were able to significantly discriminate (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between cyst and parenchyma, especially ADC (p\u0026thinsp;=\u0026thinsp;7.3 x \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:1{0}^{-12}\\)\u003c/span\u003e\u003c/span\u003e). This discrimination is also visible by looking at the DTI maps in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Kidneys that were more affected by cysts showed less to no cortico-medullary differentiation (CMD), while CMD was visible in kidneys of Unilateral and Mild Lopsided patients (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, 1st and 2nd rows, right panels), which were less affected by cysts.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eValues of diffusion tensor imaging (DTI) parameters in the total kidney (TK), parenchyma and cyst VOIs.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth style=\"text-align: left;\"\u003e\n \u003cp\u003eTK\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"text-align: left;\"\u003e\n \u003cp\u003eParenchyma\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"text-align: left;\"\u003e\n \u003cp\u003eCyst\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"text-align: left;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADC\u003c/strong\u003e (x\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:1{0}^{-3}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e7.3 x \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:1{0}^{-12}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.127\u0026thinsp;\u0026plusmn;\u0026thinsp;0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.184\u0026thinsp;\u0026plusmn;\u0026thinsp;0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.112\u0026thinsp;\u0026plusmn;\u0026thinsp;0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.00013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e\u003cstrong\u003esRA\u003c/strong\u003e (x\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:1{0}^{-1}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.00013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVF\u003c/strong\u003e (x\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:1{0}^{-2}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e1.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e3.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.00032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUA\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003esurf\u003c/strong\u003e\u003c/sub\u003e (x\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:1{0}^{-3}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e6.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.00030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUA\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003evol\u003c/strong\u003e\u003c/sub\u003e ( x\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:1{0}^{-2}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.584\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.438\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.00033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUA\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003evol,surf\u003c/strong\u003e\u003c/sub\u003e (x\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:1{0}^{-3}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e6.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.00035\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLI\u003c/strong\u003e (x\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:1{0}^{-1}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e1.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.00016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" style=\"text-align: left;\"\u003e\n \u003cp\u003eValues expressed as median\u0026thinsp;\u0026plusmn;\u0026thinsp;interquartile range (IQR);\u003c/p\u003e\n \u003cp\u003ep-value is reported between parameters in the parenchyma and the cyst VOIs\u003c/p\u003e\n \u003cp\u003eADC: Apparent Diffusion Coefficient; FA: Fractional Anisotropy; sRA: Scaled\u0026nbsp;Relative\u0026nbsp;Anisotropy; VF: Volume Fraction; UA\u003csub\u003esurf\u003c/sub\u003e: Ultimate Anisotropy - Average, Surface; UA\u003csub\u003evol\u003c/sub\u003e: Ultimate Anisotropy - Average, Volume; UA\u003csub\u003evol,surf\u003c/sub\u003e: Ultimate Anisotropy - Volume, Surface; LI: Lattice Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eComparison with DAIs in healthy kidneys\u003c/h3\u003e\n\u003cp\u003eDTI parameters applied in literature on healthy control kidneys are summarized in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. Comparing the results of Zheng et al. [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e] and Chen et al. [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e] (presenting b-value\u0026thinsp;=\u0026thinsp;800 as in our study) with the values on the parenchyma-VOI of our study group, ADC slightly decreases (2.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0003 vs. 2.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14 in [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e] and vs. 2.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 in [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]) and FA decreases more significantly (0.184\u0026thinsp;\u0026plusmn;\u0026thinsp;0.098 vs. 0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 in [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e] and vs. 0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.025 in [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]).\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eApparent diffusion coefficient (ADC) and fractional anisotropy (FA) in the parenchyma of healthy controls vs. in the parenchyma of ADPKD atypical patients.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" style=\"text-align: left;\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"5\" style=\"text-align: left;\"\u003e\n \u003cp\u003e\u003cem\u003eParenchyma in healthy controls\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" style=\"text-align: left;\"\u003e\n \u003cp\u003e\u003cem\u003eParenchyma in study group\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth colspan=\"2\" style=\"text-align: left;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAuthor\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"text-align: left;\"\u003e\n \u003cp\u003e\u003cem\u003eGaudiano\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eet al.\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"text-align: left;\"\u003e\n \u003cp\u003e\u003cem\u003eHueper\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eet al.\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"text-align: left;\"\u003e\n \u003cp\u003e\u003cem\u003eLupica\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eet al.\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"text-align: left;\"\u003e\n \u003cp\u003e\u003cem\u003eZheng\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eet al.\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth style=\"text-align: left;\"\u003e\n \u003cp\u003e\u003cem\u003eChen\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eet al.\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"text-align: left;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"text-align: left;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of subjects\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"text-align: left;\"\u003e\n \u003cp\u003e\u003cstrong\u003eb-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0 \u0026amp; 500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0 \u0026amp; 600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0 \u0026amp; 600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0 \u0026amp; 800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0 \u0026amp; 800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0 \u0026amp; 800\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"text-align: left;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTesla\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e1.5 T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e1.5 T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e3 T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e3 T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e3 T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e1.5 T\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"text-align: left;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCortex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedulla\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21^\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18^\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09^\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14^\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1^\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e2.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0003\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"text-align: left;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCortex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.308\u0026thinsp;\u0026plusmn;\u0026thinsp;0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.165\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedulla\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.389\u0026thinsp;\u0026plusmn;\u0026thinsp;0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.415\u0026thinsp;\u0026plusmn;\u0026thinsp;0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTK\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.069^\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.056^\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01^\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05^\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.025^\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"text-align: left;\"\u003e\n \u003cp\u003e0.184\u0026thinsp;\u0026plusmn;\u0026thinsp;0.098\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"text-align: left;\"\u003e\n \u003cp\u003eHealthy controls: Apparent Diffusion Coefficient (ADC), Fractional Anisotropy (FA) expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\n \u003cp\u003eStudy group: Apparent Diffusion Coefficient (ADC), Fractional Anisotropy (FA) expressed as median\u0026thinsp;\u0026plusmn;\u0026thinsp;IQR\u003c/p\u003e\n \u003cp\u003en.a.: not available\u003c/p\u003e\n \u003cp\u003e^computed from the mean values between the cortex and the medulla; \u0026deg;evaluated on the parenchyma-VOI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eCorrelations between DAIs and biomarkers\u003c/h3\u003e\n\u003cp\u003eThe Pearson correlation coefficients between DAIs, evaluated in the three VOIs, and the biomarkers eGFR and S\u003csub\u003ecr\u003c/sub\u003e, are shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. Among all the computed parameters, the ADC, evaluated in the parenchyma-VOI, was shown to have the best correlation with the biomarkers: positive for eGFR (0.598) and negative for S\u003csub\u003ecr\u003c/sub\u003e (-0.425). The linear regression for this parameter is shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1770968720.png\" width=\"746\" height=\"665\"\u003e\u003c/p\u003e\n"},{"header":"DISCUSSION and CONCLUSION","content":"\u003cp\u003eAlthough MR-DTI has been widely used in neurological field, its application for morphological and functional evaluation of the kidney in an ADPKD population is limited. Multiparametric MRI including DTI has been proven to be a valid technique for studying the microstructure of renal tissue, which reflects the health conditions and functionality of the kidney [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Consequently, diffusion and anisotropy indexes (DAIs) might be used to quantify characteristics of the renal microstructure. MR-DWI and MR-DTI have already been applied to ADPKD typical patients in only two studies on limited population of 8 and 35 Class 1 ADPKD patients [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and this is the first time MR-DTI has been applied specifically to a study group of Class 2 ADPKD patients. Moreover, previous MR-DWI and MR-DTI studies on the kidney have only considered ADC and FA; in this study, to further investigate MR-DTI potential, in addition to ADC and FA, we considered six more DAIs, that have already been applied in the investigation of the brain [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e–\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]: sRA, VF, LI, UA\u003csub\u003esurf\u003c/sub\u003e, UA\u003csub\u003evol\u003c/sub\u003e, UA\u003csub\u003evol,surf\u003c/sub\u003e.\u003c/p\u003e\u003cp\u003eIn agreement with previous studies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], we found that cyst development seemed to compromise renal microstructure, resulting in a decrease of CMD. A qualitative observation of the FA maps (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) showed that kidneys with no (or minimum) cyst involvement, of Unilateral and Mild Lopsided patients, preserve CMD; while CMD was not present in patients of other subclasses, with greater bilateral cyst involvement.\u003c/p\u003e\u003cp\u003eDifferently from previous studies in which the assessment of the MR-DTI derived parameters in PKD was evaluated without separating cortex and medulla [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] or on the total kidney volume without separating cystic and parenchymal tissue [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], this study considered the 3D DAIs on three different VOIs: the cyst-VOI (including only the cysts), the parenchyma-VOI (including only the parenchyma tissue) and the TK-VOI including the entire kidney region.\u003c/p\u003e\u003cp\u003eThe comparison of ADC and FA in TK-VOI showed that ADC and FA resulted respectively higher (2.71 ± 0.0004 vs. 2.15 ± 0.14 in [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and vs. 2.25 ± 0.1 in [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]) and lower (0.127 ± 0.079 vs. 0.28 ± 0.05 in [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and vs. 0.38 ± 0.025 in [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]) in the study group than in healthy subjects. These results confirmed what had already been found by Lupica et al. in ADPKD [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].While previous studies had evaluated the parameters only on parenchyma ROIs [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], or on the TK-VOI [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], including both the cyst and the parenchyma tissue, this study went a step further considering a parenchyma-VOI and a cyst-VOI. This allowed detecting changes specifically addressing the whole parenchyma of ADPKD atypical patients. Parenchyma of the study group showed a lower ADC (2.11 ± 0.0003 vs. 2.15 ± 0.14 in [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and vs. 2.25 ± 0.1 in [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]) and a lower FA (0.184 ± 0.09 vs. 0.28 ± 0.05 in [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and vs. 0.38 ± 0.025 in [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]) compared to healthy patients, resulting to be less diffusive and less anisotropic; confirming the results of Serai et al. for FA in ARPKD [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Lastly, the cysts were characterized by high ADC and low FA compared to parenchyma, as expected by objects filled with isotropic liquid. This evidence suggests that the increase of ADC in the TK-VOI in ADPKD patients is probably due to an increased cyst ADC, raising the overall ADC value, rather than to be attributed to biological processes, as was suggested by Lupica et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The decrease of parenchymal FA quantifies a loss of anisotropy, thus a loss of the microstructural organization, in the ADPKD parenchyma, as had already been qualitatively observed from the FA maps. Unfortunately, for sRA, VF, LI, UA\u003csub\u003esurf\u003c/sub\u003e, UA\u003csub\u003evol\u003c/sub\u003e, UA\u003csub\u003evol,surf\u003c/sub\u003e, comparison with other studies is not possible due to the lack of reference values in both healthy or diseased kidneys for these parameters. All these parameters showed significantly lower values in the cysts compared to the parenchymal tissue, emphasizing the presence of a greater isotropic component than an anisotropic one in cystic structure.\u003c/p\u003e\u003cp\u003eBeing MR-DTI able to quantify characteristics of the renal microstructure, it could therefore be taken into consideration as prognostic biomarkers. The Mayo imaging classification of ADPKD is a widely accepted tool used in clinical practice to predict the progression of the disease [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, a quantitative risk prediction model (based on htTKV and age) is only available for typical patients, with diffuse and bilateral cyst involvement (Class 1) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Atypical patients (Class 2), who represent 5–10% of the ADPKD population, are excluded from the risk prediction model, as htTKV does not predict eGFR decline. A recent study [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] has attempted to expand the model for atypical patients by using a modified htTKV; however, this approach only covers patients with exophytic cysts, that are not the totality of Class 2. An effective biomarker of renal function for atypical patients is still lacking, and further studies are needed to provide a quantitative risk prediction model suitable for all Class 2 patients. In our study, the parameter that correlated the most with the renal function biomarkers was parenchymal ADC, which decreased as renal function worsened (so for increasing S\u003csub\u003ecr\u003c/sub\u003e and decreasing eGFR). All the results obtained in this study seem to suggest that a decrease in parenchymal ADC is related to a worsening of the risk class and of the overall renal condition. In fact, as discussed before, healthy subjects present a higher parenchymal ADC compared to the study group. Furthermore, from a closer look at the correlation graphics in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, it appears that patients belonging to Class 2B (the higher risk class in the Mayo classification for Class 2), for a given eGFR or S\u003csub\u003ecr\u003c/sub\u003e, have lower value of the parenchymal ADC than Class 2A patients (the lower risk class in the Mayo classification for Class 2); and in particular unilateral patients (of Class 2A, with unilateral cyst involvement), for a given eGFR or S\u003csub\u003ecr\u003c/sub\u003e, have the highest values of the parenchymal ADC of the study group. Based on the very preliminary findings of this exploratory study, the correlation of parenchymal ADC with eGFR and S\u003csub\u003ecr\u003c/sub\u003e, and the coherent stratification of the subclasses within the study group, suggest that parenchymal ADC should be further investigated as a potential prognostic biomarker for Class 2 ADPKD patients.\u003c/p\u003e\u003cp\u003eHowever, the small cohort of patients enrolled in this study limits the generalizability and statistical power of the findings; the fact that the atypical form of ADPKD affects only 5–10% of ADPKD patients explains the limited characterization of this poorly described phenotype and, on note, our hypothesis-generated study paves the way for further studies investigating the use of ADC to improve the prognosis of patients with atypical ADPKD.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eADPKD: Autosomal Dominant Polycystic Kidney Disease\u003c/p\u003e\n\u003cp\u003eDTI: Diffusion Tensor Imaging\u003c/p\u003e\n\u003cp\u003eTKV: Total Kidney Volume\u003c/p\u003e\n\u003cp\u003eDAI: Diffusion Anisotropy Index\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003ePatients were enrolled within the MICKY-ADPKD study. The Ethics Committee and the Institutional Review Board approved the MICKY-ADPKD study (306/2021/Oss/AOUBo) (NCT06759142).\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study has received funding by the European Union - NextGenerationEU through the Italian Ministry of University and Research under PNRR - M4C2-I1.3 Project PE_00000019 \u0026quot;HEAL ITALIA\u0026quot; to DEI CUP J33C22002920006. The views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the European Commission can be held responsible for them.\u0026rdquo;\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eThe individual contributions of the authors can be summarized as follows:\u0026bull; substantial contributions to: the conception and design of the work; the acquisition, analysis and interpretation of data; the drafting and the final approval of the version to be published (L.V., I. C., C.C., N.S.);\u0026bull; substantial contributions to: the acquisition and interpretation of data; the revising of the work critically for important intellectual content; the final approval of the version to be published (D.M., G.L.M., F.C., V.A., M.R.)All authors reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData and code for the analysis are available under request to be sent to the corresponding author (CC)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eIrazabal MV, Rangel LJ, Bergstralh EJ, Osborn SL, Harmon AJ, Sundsbak JL, et al. Imaging Classification of Autosomal Dominant Polycystic Kidney Disease: A Simple Model for Selecting Patients for Clinical Trials. Journal of the American Society of Nephrology. 2015;26(1):160\u0026ndash;72. \u003c/li\u003e\n\u003cli\u003eIliuta IA, Win AZ, Lanktree MB, Lee SH, Pourafkari M, Nasri F, et al. Atypical Polycystic Kidney Disease as defined by Imaging. Scientific Reports. 2023;13:2952. \u003c/li\u003e\n\u003cli\u003eBae KT, Shi T, Tao C, Yu ASL, Torres VE, Perrone RD, et al. Expanded Imaging Classification of Autosomal Dominant Polycystic Kidney Disease. Journal of the American Society of Nephrology. 2020;31(7):1640\u0026ndash;51. \u003c/li\u003e\n\u003cli\u003eTournebize C, Schleef M, De Mul A, Pacaud S, Derain-Dubourg L, Juillard L, Rouvi\u0026egrave;re O, Lemoine S. Multiparametric MRI: can we assess renal function differently? Clinical Kidney Journal 2025;18(1):\u003c/li\u003e\n\u003cli\u003eWang F, Yeon Lee Y, Adelnia F, Takahashi K, Harkins KD, He L, Zu Z, Ellinger P, Grundmann M, Harris RC, Takahashi T, Gore JC. Severity of polycystic kidney disease revealed by multiparametric MRI. Magnetic Resonance in Medicine 2023;90(3):1151-1165.\u003c/li\u003e\n\u003cli\u003eKline TL, Edwards ME, Garg I, Irazabal MV, Korfiatis P, Harris PC, et al. Quantitative MRI of kidneys in renal disease. Abdominal Radiology. 2018;43(3):629\u0026ndash;38. \u003c/li\u003e\n\u003cli\u003eSuwabe T, Ubara Y, Ueno T \u003cem\u003eet al.\u003c/em\u003e Intracystic magnetic resonance imaging in patients with autosomal dominant polycystic kidney disease: features of severe cyst infection in a case\u0026ndash;control study. BMC Nephrology \u003cstrong\u003e17\u003c/strong\u003e, 170 (2016). https://doi.org/10.1186/s12882-016-0381-9\u003c/li\u003e\n\u003cli\u003eLupica R, Mormina E, Lacquaniti A, Trimboli D, Bianchimano B, Marino S, et al. 3 Tesla-Diffusion Tensor Imaging in Autosomal Dominant Polycystic Kidney Disease: The Nephrologist\u0026rsquo;s Point of View. Nephron. 2016;134(2):73\u0026ndash;80. \u003c/li\u003e\n\u003cli\u003eSerai SD, Otero HJ, Calle-Toro JS, Berman JI, Darge K, Hartung EA. Diffusion tensor imaging of the kidney in healthy controls and in children and young adults with autosomal recessive polycystic kidney disease. Abdominal Radiology. 2019;44(5):1867\u0026ndash;72. \u003c/li\u003e\n\u003cli\u003eLevey AS, Stevens LA, Schmid CH, Zhang Y (Lucy), Castro AF, Feldman HI, et al. A New Equation to Estimate Glomerular Filtration Rate. Annals Internal Medicine. 2009;150(9):604\u0026ndash;12. \u003c/li\u003e\n\u003cli\u003eMignani R, Corsi C, De Marco M, Caiani EG, Santucci G, Cavagna E, Severi S, Cagnoli L. Assessment of kidney volume in polycystic kidney disease using magnetic resonance imaging without contrast medium, American Journal of Nephrology, 2011;33(2):176-184. doi: 10.1159/000324039.\u003c/li\u003e\n\u003cli\u003eTurco D, Busutti M, Mignani R, Magistroni R, Corsi C. Comparison of total kidney volume quantification methods in autosomal dominant polycystic disease for a comprehensive disease assessment, American Journal of Nephrology, 2017. doi: 10.1159/000466709.\u003c/li\u003e\n\u003cli\u003eKingsley PB. Introduction to diffusion tensor imaging mathematics: Part III. Tensor calculation, noise, simulations, and optimization. Concepts in Magnetic Resonance. 2006;28A(2):155\u0026ndash;79. \u003c/li\u003e\n\u003cli\u003eChuck NC, Steidle G, Blume I, Fischer MA, Nanz D, Boss A. Diffusion Tensor Imaging of the Kidneys: Influence of b-Value and Number of Encoding Directions on Image Quality and Diffusion Tensor Parameters. Journal of Clinical Imaging Science. 2013;3:53. \u003c/li\u003e\n\u003cli\u003eGaudiano C, Clementi V, Busato F, Corcioni B, Orrei MG, Ferramosca E, et al. Diffusion tensor imaging and tractography of the kidneys: assessment of chronic parenchymal diseases. European Radiology. 2013;23(6):1678\u0026ndash;85. \u003c/li\u003e\n\u003cli\u003eHueper K, Gutberlet M, Rodt T, Gwinner W, Lehner F, Wacker F, et al. Diffusion tensor imaging and tractography for assessment of renal allograft dysfunction\u0026mdash;initial results. European Radiology. 2011;21(11):2427\u0026ndash;33. \u003c/li\u003e\n\u003cli\u003eChen YX, Zhou W, Ye YQ, Zeng L, Wu XF, Ke B, et al. Clinical study on the use of advanced magnetic resonance imaging in lupus nephritis. BMC Medical Imaging. 2022;22(1):210. \u003c/li\u003e\n\u003cli\u003eZheng Z, Shi H, Zhang J, Zhang Y. Renal Water Molecular Diffusion Characteristics in Healthy Native Kidneys: Assessment with Diffusion Tensor MR Imaging. PLoS ONE. 2014;9(12):e113469. \u003c/li\u003e\n\u003cli\u003eCopur S, Yavuz F, Sag AA, Tuttle KR, Kanbay M. Future of kidney imaging: Functional magnetic resonance imaging and kidney disease progression. Eur J Clin Invest. 2022;2(5):e13765. \u003c/li\u003e\n\u003cli\u003eCaroli A, Villa G, Brambilla P, Trillini M, Sharma K, Sironi S, et al. Diffusion magnetic resonance imaging for kidney cyst volume quantification and non-cystic tissue characterisation in ADPKD. European Radiology. 2023;33\u003cstrong\u003e:\u003c/strong\u003e6009\u0026ndash;6019. \u003c/li\u003e\n\u003cli\u003ePierpaoli C, Basser PJ. Toward a quantitative assessment of diffusion anisotropy. Magnetic Resonance in Medicine. 1996;36(6):893\u0026ndash;906. \u003c/li\u003e\n\u003cli\u003eUluğ AM, van Zijl PCM. Orientation-independent diffusion imaging without tensor diagonalization: Anisotropy definitions based on physical attributes of the diffusion ellipsoid. Journal of Magnetic Resonance Imaging. 1999;9(6):804\u0026ndash;13. \u003c/li\u003e\n\u003cli\u003eMartin J, Endt S, Wetscherek A, Kuder TA, Doerfler A, Uder M, et al. Contrast-to-noise ratio analysis of microscopic diffusion anisotropy indices in q-space trajectory imaging. Zeitschrift f\u0026uuml;r Medizinische Physik. 2020;30(1):4\u0026ndash;16. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Autosomal Dominant Polycystic Kidney Disease, Diffusion Anisotropic indices, Diffusion Tensor Imaging","lastPublishedDoi":"10.21203/rs.3.rs-8734288/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8734288/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe potential of magnetic resonance diffusion tensor imaging (MR-DTI) in autosomal dominant polycystic kidney disease (ADPKD) has been poorly investigated. The few studies available are focused on typical ADPKD populations. In this exploratory study we evaluate the value of MR-DTI in atypical ADPKD patients to quantitatively characterize renal tissue, correlating MR-DTI derived parameters with kidney function tests.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTen atypical ADPKD patients have been enrolled in the study. 3D volumes of interest (VOI) corresponding to a \u0026ldquo;cyst-only VOI\u0026rdquo; and a \u0026ldquo;parenchyma-only VOI\u0026rdquo; for each patient were manually drawn. Several diffusion anisotropy indices were computed, and their values compared between (1) the parenchyma-only VOI and the cyst-only VOI in our population and (2) the corresponding values in healthy kidneys.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAll indexes were able to significantly discriminate between cyst and parenchyma, especially the apparent diffusion coefficient (ADC) (p\u0026thinsp;=\u0026thinsp;7.3x10-12). Compared to healthy kidneys, in atypical ADPKD patients, parenchymal ADC slightly decreases (2.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0003 vs. 2.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14/2.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1) and parenchymal FA decreases more significantly (0.184\u0026thinsp;\u0026plusmn;\u0026thinsp;0.098 vs. 0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05/0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.025). Parenchymal ADC was shown to have the strongest correlation with serum creatinine and estimated glomerular filtration rate (-0.43 and 0.60, respectively).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eMR-DTI has been applied for the first time to ADPKD atypical patients showing its capability of differentiating between cysts and parenchyma. Based on our preliminary findings on a small but rare and poorly studied population, the hypothesis-generated suggests ADC might have the potential to be used as a prognostic index for disease risk stratification.\u003c/p\u003e","manuscriptTitle":"A proof of concept for tissue characterization in atypical autosomal dominant polycystic kidney disease patients from diffusion tensor imaging","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-13 11:47:01","doi":"10.21203/rs.3.rs-8734288/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":"b7145f30-2f2a-4b48-b27d-7862645dd3f6","owner":[],"postedDate":"February 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-24T12:24:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-13 11:47:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8734288","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8734288","identity":"rs-8734288","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}