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REVIEW 4 major objections 4 minor 2 references

Enhancing kidney quality assessment: Power Doppler during normothermic machine perfusion

T0 review · 4 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Power Doppler ultrasound during machine perfusion can identify non-functional kidneys from cortical blood flow images.

desk verdict A plausible first use of Power Doppler for kidney quality assessment during NMP, but the headline accuracy numbers are in-sample and the ground-truth labels are unvalidated, so treat the AUCs as suggestive, not established. read the letter →

arxiv 2501.04457 v2 pith:YCGDJTTQ submitted 2025-01-08 physics.med-ph physics.bio-ph

Yitian Fang , Anton V. Nikolaev , Jeroen Essers , Gisela Ambagtsheer , Marian C. Clahsen-van Groningen , Robert C. Minnee , Ron W.F. de Bruin , Gijs van Soest This is my paper · ORCID
classification physics.med-phphysics.bio-ph
keywords powerdopplernormothermicmachineperfusionkidneyviabilityqualityassessmentvascularizationindexflowrenalbloodporcinemodel
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Power Doppler ultrasound, a real-time imaging technique that shows blood moving through tissue, can grade kidney quality during normothermic machine perfusion. The paper reports that in 22 porcine kidneys, the Doppler metrics vascularization index, flow index, and vascularization flow index correlate with creatinine clearance and oxygen consumption, the standard perfusion-based markers of kidney function and viability. At 120 minutes of perfusion, these metrics separate kidneys deemed functional from non-functional with an area under the ROC curve near 0.94 and, at chosen cutoffs, 100% specificity. The authors conclude that Power Doppler can serve as a practical alternative to renal blood flow measurement for pre-transplant kidney assessment, with the advantage of imaging the cortical microcirculation directly. If true, this would give transplant teams a non-invasive, repeatable way to watch organ recovery without repeated blood sampling.

What carries the argument

The central object is Power Doppler ultrasound, an imaging mode in which the ultrasound system assigns pixel intensity proportional to the amplitude of the Doppler signal from moving red blood cells. Three summary metrics are computed from the renal cortex, defined as a 5 mm layer below the kidney surface: the vascularization index (VI), the percentage of colored pixels in the region of interest; the flow index (FI), the mean signal intensity of those colored pixels; and the vascularization flow index (VFI), the product of VI and FI. These indices convert a qualitative perfusion image into numbers that can be tracked over time and compared across kidneys, and it is this quantification that lets the authors treat PD as a functional assay rather than just a picture.

What would settle it

Perfuse a set of kidneys with identical metabolic status but artificially varied pump flow and compare PD indices; if VI, FI, and VFI track pump flow more tightly than they track CrCl and VO2, then PD is a perfusion meter rather than a viability test. The stronger falsifier is a transplant follow-up: if PD metrics taken during NMP fail to predict post-transplant urine output or graft survival, the central claim that PD can replace RBF for kidney-quality assessment is refuted.

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Extended reading notes

Core claim

The paper's claim is that Power Doppler imaging during normothermic machine perfusion captures the same biological signal that clinicians currently read from renal blood flow, and does so earlier and more precisely. Using 22 slaughterhouse pig kidneys perfused for two hours, the authors show that the three quantitative PD metrics differ significantly between kidneys labelled functional (n=7) and non-functional (n=15) by creatinine clearance above 1 ml/min/100g and oxygen consumption above 2.6 ml/min/100g. Pooled over all time points, VI, FI and VFI correlate moderately with CrCl (r≈0.54) and strongly with VO2, with VI reaching r=0.839. At 120 minutes, VI and VFI reach AUC 0.943 and FI 0.924 for intergroup separation, and cutoffs of VI above 17%, FI above 50 a.u., and VFI above 9 a.u. identify non-functional kidneys with 100% specificity and 73% sensitivity, an overall accuracy of 82% versus 77% for renal blood flow. The authors state that PD metrics reflect renal cortical perfusion specifically, which explains why they outperform whole-organ flow.

Load-bearing premise

The paper's classification of kidneys as functional or non-functional is based entirely on creatinine clearance and oxygen consumption thresholds measured during perfusion, not on transplant or post-reperfusion outcomes, so if those thresholds do not reflect true graft viability then the reported Power Doppler accuracy is measuring agreement with an unvalidated label.

Editorial extensions

If this is right

  • Power Doppler could replace continuous renal blood flow probes during normothermic machine perfusion, giving a similar viability signal with less hardware and no in-line flow sensor.
  • Separation between functional and non-functional kidneys was already seen at 30 minutes, suggesting PD could shorten the assessment window before a discard decision.
  • Because PD images the renal cortex, it may detect regional perfusion defects that whole-organ flow misses, providing information about heterogeneity rather than only average perfusion.
  • The proposed cutoffs (VI above 17%, FI above 50 a.u., VFI above 9 a.u. at two hours) could be tested prospectively in clinical NMP as a pre-specified rule for whether to accept a kidney.
  • If PD is integrated into NMP circuits, quality assessment becomes repeatable and non-destructive, allowing the same kidney to be monitored throughout the perfusion period.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable extension would be to acquire PD images during hypothermic machine perfusion as well, since the same vascular index might predict post-reperfusion function without the cost of warming the organ.
  • If PD correlates with VO2 through microvascular density, then PD might also serve as a surrogate for viability in other perfused organs, such as livers or hearts, during normothermic preservation.
  • The 100% specificity cutoffs are tuned to avoid discarding viable kidneys, but a clinical protocol would still need to weigh the 18% false-negative rate against the consequences of transplanting a marginal organ, a trade-off the paper mentions but does not resolve.
  • Because the functional labels come from CrCl and VO2 thresholds, the strongest test of PD would come from a series where kidneys are imaged during NMP and then transplanted; if PD failed to predict delayed graft function, the present AUCs would overstate its accuracy.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. This manuscript evaluates Power Doppler (PD) ultrasound metrics (VI, FI, VFI) as real-time imaging markers of kidney quality during normothermic machine perfusion (NMP) in 22 slaughterhouse porcine kidneys. Kidneys were classified as functional (n=7) or non-functional (n=15) according to perfusion-based thresholds for creatinine clearance (CrCl > 1 ml/min/100g) and oxygen consumption (VO2 > 2.6 ml/min/100g) at 120 minutes. PD metrics were measured at 30, 60, and 120 minutes and compared with CrCl, VO2, and renal blood flow (RBF). The authors report moderate-to-strong correlations between PD metrics and CrCl/VO2, and present ROC analyses with high AUCs at 120 minutes (up to 0.943), proposing cutoffs with 100% specificity for detecting non-functional kidneys. They conclude that PD imaging can be a valuable alternative to RBF during NMP.

Significance. If the claims were substantiated, PD imaging would provide a non-invasive, real-time, and relatively inexpensive tool for pre-transplant kidney quality assessment during NMP, potentially reducing discard rates of marginal kidneys. The study is exploratory and addresses a clinically relevant gap. Strengths include the side-by-side comparison of PD metrics with RBF at multiple time points, the use of blinded histopathology scoring, and the clear description of the perfusion setup. However, the central diagnostic-accuracy claims are currently compromised by in-sample cutoff optimization, an unvalidated composite reference standard, and statistical analyses that pool repeated measurements; these issues mean the reported AUCs, specificities, and correlation p-values are optimistic and may not reflect real-world performance.

major comments (4)
  1. [Statistical analysis / Results (Cutoff values for intergroup differentiation)] The cutoff values for VI (17%), FI (50 a.u.), and VFI (9 a.u.) were selected on the same 22-kidney dataset used to compute the reported 100% specificity, 73% sensitivity, NPV of 64%, and overall accuracy of 82%. This is in-sample threshold optimization, which systematically overestimates diagnostic performance; the stated values are therefore not reliable estimates of out-of-sample accuracy. The authors should either report optimism-corrected estimates (e.g., via bootstrap or cross-validation) or explicitly label these as exploratory in-sample cutoffs that require external validation before any clinical or discard decisions can be supported.
  2. [Study design / Materials and Methods (renal function classification) and Limitations] The functional/non-functional classification rests entirely on internally defined CrCl and VO2 thresholds measured at 120 minutes of NMP, with no transplant or post-reperfusion outcome to validate these labels. The authors acknowledge in Limitations that they 'did not transplant the kidneys and hence not able to measure the posttransplant renal function.' Consequently, the ROC analyses evaluate PD agreement with an unvalidated surrogate, not with actual graft viability; if the thresholds do not track transplant-relevant viability, the reported discriminative performance is not meaningful. In addition, the classification criteria as written (functional: CrCl>1 AND VO2>2.6; non-functional: CrCl≤1 AND VO2≤2.6) leave an undefined middle category (e.g., CrCl>1 with VO2≤2.6), yet all 22 kidneys were assigned to a group. The authors should report how mixed profiles were handled and how the classification was applied in practice.
  3. [Results / Correlation analysis (Figure 5)] The correlations reported in Figure 5 pool all 66 measurements (22 kidneys × 3 time points) as independent observations. This violates the independence assumption of Spearman correlation and inflates the effective sample size, making the reported p-values (e.g., r=0.839, p<0.0001) overly precise. Because measurements from the same kidney are correlated, the true number of independent kidneys for the association between VI and VO2 is 22, not 66. The authors should use a mixed-effects model or per-kidney summary measures (e.g., averaged or time-matched values) to account for the repeated-measures structure.
  4. [Discussion / Conclusions] The conclusion that PD imaging 'can be a valuable alternative to RBF' is not directly supported by a statistical comparison of PD metrics against RBF in the same predictive framework. Although the 120-minute AUCs appear numerically higher for PD (0.943 vs. 0.886), the 95% confidence intervals in Tables S3 and S4 overlap substantially (e.g., VI: 0.848-1.000; RBF: 0.747-1.000), and no formal test of AUC difference or added predictive value is reported. Without such a comparison, the claim of superiority or equivalence with respect to RBF remains unsubstantiated.
minor comments (4)
  1. [Results / Power Doppler metrics assessment] In the text immediately after Figure 3, the sentence 'The functional group showed a progressive increase in signal intensity and vascularization across the time points' is descriptive; the corresponding quantitative values at 60 and 120 minutes are not shown in Figure 4, which only displays the 30-minute comparisons. Please report all time points or clarify the figure legend.
  2. [Materials and Methods / Power Doppler measurement] The statement that PD acquisition settings were 'chosen manually to optimize the visualization of the renal cortical vasculature' (Table S2) raises reproducibility concerns. Since PD intensity values are compared across kidneys and time points, the authors should specify whether the same settings were used for all acquisitions and whether any gain or depth adjustments were made between measurements.
  3. [Abstract / Results] The abstract reports 'FI and VFI moderately correlated with CrCl (r=0.537... r=0.536...)' but omits the correlation of VI with CrCl (r=0.528) that appears in the main text. For consistency, either include all three metrics or state that all PD metrics were correlated.
  4. [General] There are minor typographical issues, such as 'scatter' in Figure 5 (should be 'scatter') and the parenthetical '(shown?)' in the Results section, which should be corrected.

Circularity Check

1 steps flagged · score 2.0 of 10

PD metrics are measured independently of the functional labels, but the 100% specificity cutoff values are optimized on the same dataset, creating a mild in-sample circularity in the validation protocol.

  1. fitted input called prediction [Materials and Methods, Statistical analysis; Results, 'Cutoff values for intergroup differentiation']
    "Therefore, we defined cutoff values with the highest possible specificity for detecting non-functional kidneys. ... To ensure that no functional kidney is rejected, we analyzed the cutoff values for these metrics to achieve 100% specificity in discriminating non-functional kidneys."

    The cutoff values are selected from the same 22-kidney dataset using the explicit criterion of maximizing specificity. Reporting that the chosen cutoffs yield 100% specificity is therefore a restatement of the optimization rule, not an independent or out-of-sample performance estimate. A threshold placed above all functional-kidney PD values in this cohort is guaranteed by the selection procedure to give 100% specificity on the training data. This makes the headline '100% specificity and 73% sensitivity' result an in-sample fitted quantity. The Spearman correlations and ROC AUCs are not affected by this particular cutoff selection, so the circularity is confined to the cutoff-validation step rather than the central association claim.

full rationale

The paper's central measurements are not circular: VI, FI, and VFI are computed from Power Doppler data independently of the CrCl/VO2-based functional classification, and the correlations with CrCl and VO2 are direct empirical associations. The functional/non-functional labels are based on externally cited thresholds (references 24, 25), not on PD values, so the ROC analysis is not testing PD against a PD-derived outcome. The only concrete circular element is the post-hoc choice of diagnostic cutoffs to achieve 100% specificity on the same data, which makes the reported specificity at those cutoffs a fitted description rather than a validated prediction. The absence of transplant outcomes means external validity is unproven, but that is a limitation, not a derivation-level circularity. Self-citations (references 13 and 14) are used only for background and methods context, not to justify the PD claim.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The central claims rest on two hand-picked threshold sets: the functional classification cutoffs (CrCl, VO2) and the PD diagnostic cutoffs, plus the ROI depth choice. The main modeling axioms are that the functional markers define true viability and that the slaughterhouse porcine model transfers to human DCD kidneys. No new entities are invented.

free parameters (3)
  • Functional classification thresholds (CrCl, VO2) = CrCl > 1 ml/min/100g; VO2 > 2.6 ml/min/100g
    Used to define the ground-truth groups of functional (n=7) and non-functional (n=15) kidneys. These are hand-chosen cutoffs based on prior NMP studies but are not independently validated against transplant outcome.
  • PD diagnostic cutoffs (VI, FI, VFI) = VI 17%, FI 50 a.u., VFI 9 a.u.
    Chosen from ROC analysis on the same dataset to achieve 100% specificity; the reported sensitivity and accuracy are computed on the same data, so these are fitted values.
  • Renal cortical ROI depth = 5 mm
    The ROI for PD metrics is defined as the cortical layer at 5 mm depth from the kidney surface; no sensitivity analysis is provided for this choice.
assumptions (3)
  • domain assumption CrCl and VO2 at 120 minutes of NMP are valid surrogates for kidney viability and transplantability.
    The entire classification into functional and non-functional groups rests on these thresholds; if they do not reflect clinical graft outcome, the PD discriminative performance is not anchored to a real gold standard. Location: Methods, classification of kidneys.
  • domain assumption Exsanguinated slaughterhouse pig kidneys are a valid model for human DCD donation.
    The authors assume that the injury pattern induced by slaughterhouse exsanguination and transport reproduces human donation-after-circulatory-death injury. Location: Methods, study design.
  • ad hoc to paper Manual PD acquisition settings yield intensity values that are comparable across kidneys and time points.
    The settings in Table S2 were chosen manually to optimize visualization, and FI/VFI are computed from raw intensity scaled to 0-255; comparability across kidneys is assumed. Location: Power Doppler measurement.

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Cite this review

Pith. "Pith review of Enhancing kidney quality assessment: Power Doppler during normothermic machine perfusion." pith.science (2026). https://pith.science/paper/YCGDJTTQ

@misc{pith2026250104457,
  author       = {Pith},
  title        = {Pith review of: Enhancing kidney quality assessment: Power Doppler during normothermic machine perfusion},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YCGDJTTQ}},
  note         = {Machine review of arXiv:2501.04457}
}
read the original abstract

Objectives: Marginal donor kidneys are increasingly used for transplantation to overcome organ shortage. This study aims to investigate the additional value of Power Doppler (PD) imaging in kidney quality assessment during normothermic machine perfusion (NMP). Methods: Porcine kidneys (n=22) retrieved from a local slaughterhouse underwent 2 hours of NMP. Based on creatinine clearance (CrCl) and oxygen consumption (VO2), the kidneys were classified as functional (n=7) and non-functional (n=15) kidneys. PD imaging was performed at 30, 60, and 120 minutes, and PD metrics, including vascularization index (VI), flow index (FI), and vascularization flow index (VFI) were calculated. Renal blood flow (RBF), CrCl, and VO2 were measured at the same time points during NMP. The metrics were compared utilizing correlation analysis. Results: FI and VFI moderately correlated with CrCl (r=0.537, p<0.0001; r=0.536, p<0.0001, respectively), while VI strongly correlated with VO2 (r=0.839, p<0.0001). At 120 minutes, PD metrics demonstrated the highest diagnostic accuracy for distinguishing functional from non-functional kidneys, with an area under the curve (AUC) of 0.943 for VI, 0.924 for FI, and 0.943 for VFI. Cutoff values of 17% for VI, 50 a.u. for FI, and 9 a.u. for VFI provided 100% specificity and 73% sensitivity to identify non-functional kidneys, with an overall diagnostic accuracy of 82%. Baseline kidney biopsies showed moderate acute tubular necrosis in both groups with no significant differences. Conclusions: PD metrics strongly correlate with renal viability and effectively differentiate functional from non-functional kidneys. PD imaging can be a valuable alternative to RBF during NMP for kidney assessment.

Figures

Figures reproduced from arXiv: 2501.04457 by the authors.

Figure 1
Figure 1. Schematic of the normothermic machine perfusion (NMP) and ultrasound (US) [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Function and viability parameters measured [PITH_FULL_IMAGE:figures/full_fig_p012_2.png] view at source ↗
Figure 5
Figure 5. Correlation between (A) RBF, (B) VI, (C) FI, (D) VFI and CrCl (left) and VO [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
Figures from the paper (1 more)
Figure 6
Figure 6. Figure 6: Representative periodic-acid-Schiff (PAS) staining of the functional group (left), and non-functional group (right) prior to NMP. 40 × magnification. Discussion This study used a porcine kidney NMP model to investigate the value of PD imaging in assessing kidney qualit…

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Works this paper leans on

2 extracted references · 2 canonical work pages

  1. [11]

    Magnetic resonance imaging assessment of renal flow distribution patterns during ex vivo normothermic machine perfusion in porcine and human kidneys

    Schutter R, Lantinga VA, Hamelink TL, Pool MBF, van Varsseveld OC, Potze JH, et al. Magnetic resonance imaging assessment of renal flow distribution patterns during ex vivo normothermic machine perfusion in porcine and human kidneys. Transpl Int. 2021;34(9):1643-55. 12. Hamelink TL, Ogurlu B, Pamplona CC, Castelein J, Bennedsgaard SS, Qi H, et al. Magneti...

    work page 2021

  2. [28]

    PROviding Better ACcess To ORgans: A comprehensive overview of organ-access initiatives from the ASTS PROACTOR Task Force

    Hobeika MJ, Miller CM, Pruett TL, Gifford KA, Locke JE, Cameron AM, et al. PROviding Better ACcess To ORgans: A comprehensive overview of organ-access initiatives from the ASTS PROACTOR Task Force. Am J Transplant. 2017;17(10):2546-58. 29. Cooper M, Formica R, Friedewald J, Hirose R, O'Connor K, Mohan S, et al. Report of National Kidney Foundation Consens...

    work page 2017

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Reviewed August 10, 2026 · model on record in the stance chip above.