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REVIEW 3 major objections 3 minor 7 references

Discriminating Distal Ischemic Stroke from Seizure-Induced Stroke Mimics Using Dynamic Susceptibility Contrast MRI

T0 review · 3 major / 3 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Perfusion MRI patterns separate distal strokes from seizure-induced mimics with 0.90 AUROC in a 162-patient cohort.

desk verdict The stroke MRI abstract is clinically plausible, but the attached full text is a different paper, leaving the AUROC 0.90 claim without supporting methods—desk-reject as submitted, ask for the correct manuscript. read the letter →

arxiv 2508.04404 v2 pith:SGJNXCWJ submitted 2025-08-06 eess.IV

Marijn Borghouts , Richard McKinley , Manuel Köstner , Josien Pluim , Roland Wiest , Ruisheng Su This is my paper
classification eess.IV
keywords distalacuteischemicstrokemimicsseizuredynamicsusceptibilitycontrastMRIperfusionmapdescriptorshemisphericasymmetrylogisticregressionmagneticresonanceimaging
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

Distinguishing a true distal acute ischemic stroke from a seizure that mimics a stroke is hard, especially when the blocked vessel is too small to show on CT. The paper argues that magnetic resonance perfusion imaging carries enough signal to help make that call: region-wise perfusion descriptors, concentrated in temporal and occipital lobes and complemented by hemispheric asymmetry, separated 129 distal strokes from 33 seizure mimics in a retrospective cohort. A logistic regression trained on these descriptors achieved an area under the ROC curve of 0.90, with 92% specificity and 73% sensitivity. If the result holds, perfusion MRI could act as an interpretable secondary test that reduces both missed distal occlusions and unnecessary treatment of mimics.

What carries the argument

The central object is the region-wise perfusion map descriptor (PMD): a set of quantitative features computed from dynamic susceptibility contrast (DSC) perfusion maps, organized by brain region. The pipeline extracts these descriptors, tests which regions show significant group differences, confirms them with hemispheric asymmetry analysis, and feeds the PMDs into a logistic regression classifier. The descriptors turn raw perfusion imaging into interpretable, region-localized signal that carries the stroke-versus-seizure discrimination.

What would settle it

Re-run the analysis on a prospectively collected cohort where seizure versus distal-stroke labels are adjudicated by clinicians blinded to DSC-MRI perfusion maps and PMDs, and check whether the logistic regression's AUROC remains near 0.90. A large drop would indicate the reported discrimination came from label leakage or overfitting. A cheaper check: report cross-validated performance stratified by label source (for example discharge diagnosis versus imaging-confirmed) and show the separation persists when ambiguous perfusion reads are excluded.

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Extended reading notes

Core claim

The paper claims that region-wise perfusion map descriptors (PMDs) extracted from dynamic susceptibility contrast (DSC) magnetic resonance perfusion images can discriminate distal acute ischemic stroke (AIS) from seizure-induced stroke mimics. Statistical analyses of the 162-patient retrospective cohort identified significant group differences mainly in temporal and occipital lobe regions, and hemispheric asymmetry analyses highlighted the same regions as discriminative. A logistic regression model trained on the PMDs achieved an AUROC of 0.90, an AUPRC of 0.74, a specificity of 92%, and a sensitivity of 73%. The authors read these results as evidence that MRP-based PMDs are interpretable fe

Load-bearing premise

The load-bearing premise is that the reference-standard labels — which of the 162 retrospective patients truly had distal acute ischemic stroke and which had seizures — are correct and were assigned without using the DSC perfusion features the model was built on.

Editorial extensions

If this is right

  • If the discrimination generalizes, DSC-MRI perfusion descriptors could serve as a diagnostic aid for distal occlusions that CT-based protocols routinely miss.
  • The temporal and occipital regions highlighted by the analysis give radiologists concrete locations to scrutinize when a distal stroke is suspected in a seizure mimic.
  • Hemispheric asymmetry in these perfusion descriptors is a candidate feature to fold into future diagnostic scoring systems.
  • At the reported operating point, 73% sensitivity at 92% specificity means the trade-off can be tuned: raising sensitivity would shift the model toward fewer missed strokes at the cost of more mimics receiving acute treatment.
  • Because the model is a logistic regression on interpretable descriptors, clinicians can inspect which regions and perfusion values drive each prediction, unlike a black-box image classifier.

Reading between the lines

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

  • The cohort is imbalanced (129 strokes versus 33 mimics), so the AUPRC of 0.74 is the more conservative estimate of real-world performance once base rates shift; triage settings should plan around that number rather than the AUROC.
  • The strongest test of the claim would be a prospective or externally validated study in which the reference standard is adjudicated by a panel blinded to the perfusion features the model consumes; the abstract does not define the reference standard or report how labels were assigned.
  • If the temporal and occipital PMD signal is real, it may reflect seizure-induced hyperperfusion or post-ictal changes; comparing against other mimic types (migraine, conversion disorder, hypoglycemia) would reveal whether the descriptors mark stroke-versus-mimic generally or seizure specifically.
  • A natural next experiment is to measure whether adding these PMDs to clinical variables or to a neuroradiologist's reading improves diagnostic accuracy beyond either alone; the abstract does not include such a comparison.
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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

3 major / 3 minor

Summary. The submission carries the title "Discriminating Distal Ischemic Stroke from Seizure-Induced Stroke Mimics Using Dynamic Susceptibility Contrast MRI" and an abstract reporting that perfusion-map descriptors (PMDs) from DSC-MRI distinguish distal acute ischemic stroke from seizure-induced mimics in a retrospective cohort of 162 patients (129 AIS, 33 seizures). The abstract states that a logistic regression model achieved AUROC 0.90, AUPRC 0.74, specificity 92%, and sensitivity 73%. However, the accompanying full text is not this paper. It is the FlexQ manuscript (arXiv:2508.04405v2), an INT6 LLM quantization framework, and contains no mention of DSC-MRI, PMDs, stroke, seizures, the 162-patient cohort, feature definitions, statistical tests, or validation methodology. As submitted, therefore, the abstract's central claims have no supporting methods or results in the manuscript.

Significance. The clinical question is genuine and important: distal ischemic strokes are often radiologically inconspicuous on CT, and seizure-induced stroke mimics are a common source of diagnostic uncertainty. If the reported result were supported, region-wise DSC-MRI perfusion descriptors with hemispheric asymmetry would be a plausible, interpretable contribution to an emergency-setting diagnostic pathway. The abstract also makes a concrete, falsifiable performance claim and indicates an intention to release code, which are positive features. Nevertheless, the submission as it stands provides no verifiable evidence for these claims, and the central results cannot be checked by a reader. The scientific significance of the abstract is therefore real but entirely unrealized in the submitted document.

major comments (3)
  1. [Full text (all sections)] The provided full text is a different paper: 'FlexQ: Efficient Post-training INT6 Quantization for LLM Serving via Algorithm-System Co-Design' (arXiv:2508.04405v2). Its title, authors, abstract, sections, tables, figures, and references concern 6-bit LLM quantization and contain no material on DSC-MRI, perfusion map descriptors, ischemic stroke, seizures, the 162-patient cohort, or logistic regression. The abstract's AUROC 0.90, AUPRC 0.74, specificity 92%, and sensitivity 73% therefore have no derivable support in this manuscript. This is not a local omission but an absence of the entire methods and results section; the central claim is unverifiable as submitted.
  2. [Abstract (performance metrics)] Even taking the abstract at face value, the reported AUROC 0.90, AUPRC 0.74, specificity 92%, and sensitivity 73% are presented without confidence intervals, without a description of the cross-validation or split protocol, and without the classification threshold that yields the stated sensitivity/specificity pair. Given a class imbalance of 129:33, an AUROC alone cannot establish practical utility, and the absence of any validation details makes overfitting or threshold optimization on the same cohort impossible to rule out. The correct full text must supply these details.
  3. [Abstract (feature selection and reference standard)] The abstract states that statistical analyses identified brain regions with significant group differences in PMDs and that a logistic regression model was then trained on PMDs. It does not state whether the same 162 patients were used both to select the significant regions/PMD types and to evaluate the classifier. If selection was performed on the full cohort and the model was evaluated on the same cohort, the reported AUROC would be optimistically biased; nested or outer cross-validation is required. In addition, the reference standard for the 129 AIS and 33 seizure labels is not defined anywhere in the submission. Since the paper's premise is precisely that this distinction is clinically and radiologically difficult, label definition is load-bearing; if expert labels partly incorporated perfusion information, the discrimination could be circular. These issues must be addressed explicit
minor comments (3)
  1. [Abstract (GitHub link)] The GitHub URL is truncated and malformed: 'https://github.com/Marijn311/PMD_extraction_and_analysis{github.com/Marijn311/PMD_extraction_and_analysis'. The intended link should be provided in full and checked.
  2. [Abstract (terminology)] The abstract does not define 'PMD' or specify which perfusion descriptors are included, nor does it define 'distal' (e.g., vessel territory, occlusion level, or infarct size). Definitions are needed for reproducibility regardless of the final text.
  3. [General] There is no statement of ethics approval, imaging acquisition parameters, or inclusion/exclusion criteria for the retrospective cohort. These should appear in any revised submission.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity assessable: the submitted full text is a different paper (FlexQ), so the stroke-MRI abstract's AUROC claim has no supporting methods or results in the document; there is no derivation chain to reduce to its inputs.

full rationale

The manuscript as submitted consists of the abstract of arXiv:2508.04404 ('Discriminating Distal Ischemic Stroke from Seizure-Induced Stroke Mimics Using Dynamic Susceptibility Contrast MRI') followed by the full text of arXiv:2508.04405v2 ('FlexQ: Efficient Post-training INT6 Quantization for LLM Serving'), an unrelated LLM quantization paper. The full text contains no mention of DSC-MRI, perfusion map descriptors, the 162-patient cohort, logistic regression, AUROC, AUPRC, or any of the stroke/seizure analysis described in the abstract. Circularity requires exhibiting a specific reduction of a claimed result to its inputs—for example, showing that a parameter is defined in terms of the quantity being predicted, or that a fit is renamed as a prediction. Because the body of the document supplies no methods, equations, feature definitions, validation protocol, or reference-standard description, there is no derivation chain to audit and therefore no circular step can be identified. The abstract is also missing critical details (e.g., the reference standard for the 129 AIS/33 seizure labels, the feature-selection procedure, and cross-validation scheme), and the GitHub URL is truncated. These omissions make the central claim unverifiable from the submitted material, but unverifiability is not itself circularity. Accordingly, the honest circularity finding is 0: no significant circularity can be established from the provided document.

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

No new physical entities or forces are postulated; this is an applied imaging-classification study. The free parameters are the unspecified modeling and feature-selection choices. The full text being a different paper means the actual values of these parameters cannot be audited from the document.

free parameters (3)
  • logistic regression hyperparameters = unknown
    The abstract reports a trained logistic regression but gives no hyperparameter values or selection procedure; any regularization strength or feature scaling chosen on this cohort is a free parameter affecting the reported AUROC.
  • classification threshold = unknown (specificity 92%, sensitivity 73%)
    The specificity/sensitivity operating point implies a decision threshold; the threshold is tuned to this cohort and its selection is not described in the abstract.
  • selected regions and PMD types = unknown (temporal and occipital regions highlighted)
    Statistical analyses identified regions with significant group differences; whether this selection was performed on the same 162-patient cohort used for model evaluation is not stated, making the choice of features a free dimension of the design.
assumptions (3)
  • domain assumption Reference-standard diagnosis (AIS vs seizure) for the 162 retrospective patients is correct and independent of the DSC perfusion features.
    Load-bearing: if labels are noisy or partly based on perfusion information, the AUROC 0.90 is not a clean discrimination. Enters at cohort construction, stated in the abstract as 'retrospective dataset of 162 patients (129 AIS, 33 seizures)'.
  • domain assumption DSC perfusion map descriptors and hemispheric asymmetry provide a representation of the underlying pathophysiology that is not dominated by acquisition timing, age, or lesion-side confounds.
    The entire feature set presupposes that perfusion-derived summaries separate ictal hyperperfusion from ischemic hypoperfusion; the abstract gives no information on confound control.
  • standard math Parametric statistical tests used for the reported group comparisons have valid distributional assumptions or appropriate nonparametric equivalents, including correction for multiple regions.
    The abstract states 'statistical analyses identified several brain regions... exhibiting significant group differences' but provides no test details, multiple-comparison corrections, or normality checks.

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Cite this review

Pith. "Pith review of Discriminating Distal Ischemic Stroke from Seizure-Induced Stroke Mimics Using Dynamic Susceptibility Contrast MRI." pith.science (2026). https://pith.science/paper/SGJNXCWJ

@misc{pith2026250804404,
  author       = {Pith},
  title        = {Pith review of: Discriminating Distal Ischemic Stroke from Seizure-Induced Stroke Mimics Using Dynamic Susceptibility Contrast MRI},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SGJNXCWJ}},
  note         = {Machine review of arXiv:2508.04404}
}
read the original abstract

Distinguishing acute ischemic strokes (AIS) from stroke mimics (SMs), particularly in cases involving medium and small vessel occlusions, remains a significant diagnostic challenge. While computed tomography (CT) based protocols are commonly used in emergency settings, their sensitivity for detecting distal occlusions is limited. This study explores the potential of magnetic resonance perfusion (MRP) imaging as a tool for differentiating distal AIS from epileptic seizures, a prevalent SM. Using a retrospective dataset of 162 patients (129 AIS, 33 seizures), we extracted region-wise perfusion map descriptors (PMDs) from dynamic susceptibility contrast (DSC) images. Statistical analyses identified several brain regions, located mainly in the temporal and occipital lobe, exhibiting significant group differences in certain PMDs. Hemispheric asymmetry analyses further highlighted these regions as discriminative. A logistic regression model trained on PMDs achieved an area under the receiver operating characteristic (AUROC) curve of 0.90, and an area under the precision recall curve (AUPRC) of 0.74, with a specificity of 92% and a sensitivity of 73%, suggesting strong performance in distinguishing distal AIS from seizures. These findings support further exploration of MRP-based PMDs as interpretable features for distinguishing true strokes from various mimics. The code is openly available at our GitHub https://github.com/Marijn311/PMD_extraction_and_analysis{github.com/Marijn311/PMD\_extraction\_and\_analysis

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