Radiomic fingerprints for knee MR images assessment

Aleksandra Ivanova; Chaozong Liu; Jie Huang; Rikin Hargunani; Shaheer U. Saeed; Simin Ni; Yaxi Chen; Yipeng Hu

REVIEW 4 major objections 4 minor 14 references

Radiomic fingerprints for knee MR images assessment

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The paper claims that a patient-specific radiomic fingerprint—a sparse set of hand-crafted MRI features selected per patient by a neural network and read out by a logistic regression—can match or beat end-to-end deep learning on knee MRI…

desk verdict A real idea—per-patient dynamic radiomic selection—with convincing ablations, but the underspecified pairwise logistic regression and the threshold-dependent accuracy claims need tightening before the results can be trusted. read the letter →

arxiv 2506.20306 v1 pith:YQ2BMLC3 submitted 2025-06-25 cs.CV

Yaxi Chen , Simin Ni , Shaheer U. Saeed , Aleksandra Ivanova , Rikin Hargunani , Jie Huang , Chaozong Liu , Yipeng Hu This is my paper · ORCID

classification cs.CV

keywords kneeMRIradiomicsradiomicfingerprintpatient-specificfeatureselectionclinicalinterpretabilitydeeplearningACLtearmeniscus

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

The paper argues that the weakness of classical radiomics for knee MRI is not the use of hand-crafted features but the use of one fixed feature set for every patient. It proposes a radiomic fingerprint framework in which a neural network examines each MRI and assigns an importance score to each feature in a large hand-crafted radiomic pool, producing a sparse, patient-specific set of features that is then classified by a low-dimensional logistic regression. On a public knee MRI benchmark of 1,370 scans, the authors report diagnostic accuracy and area under the curve comparable to or better than established end-to-end deep learning models for general abnormalities, ACL tears, and meniscus tears. If correct, this means interpretable radiomics can retain a transparent decision process without giving up deep-learning-level accuracy, and the selected features may serve as candidate clinical biomarkers.

What carries the argument

The central mechanism is the radiomic fingerprint: a patient-specific sparse selection from a large pool of hand-crafted radiomic features, produced by a neural network's predicted relevance scores. A 3D residual network takes the full knee ROI and outputs a weight $q_i \in [0,1]$ for each of the $3JK$ features computed over $J$ non-overlapping patches in axial, coronal, and sagittal views. The weighted vector $f \odot q$ is passed to an extended logistic regression that includes first-order terms and second-order pairwise interactions, and both models are trained jointly by minimizing cross-entropy loss; at inference the weights are thresholded at $T$ to form a binary selection mask, yielding the sparse fingerprint $f_s = f \odot q^{(b)}$. The central property is that selection happens per patient, so the effective radiomic space is far larger than any fixed signature, while the final classifier remains a transparent low-dimensional model.

What would settle it

Retrain the same feature-weighting network with the classifier restricted to first-order terms only, and again with all pairwise interactions but with explicit L1 regularization, on the same train/test split, and compare test accuracy plus the overlap of selected features across random seeds. If the interaction-based classifier's accuracy advantage disappears, or if the selected fingerprints are unstable across retraining runs, the claim that per-patient fingerprints are both accurate and interpretable would be falsified.

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Extended reading notes

Core claim

On its own terms, the paper's central claim is that a per-patient radiomic fingerprint—a sparse subset of a large hand-crafted radiomic feature pool, chosen by a deep network for each individual case—can provide both the accuracy of end-to-end deep learning and the interpretability of classical radiomics. The framework extracts features from multiple patches and three MRI views, predicts a weight for every feature with a 3D residual network, weights the features by those predictions, and trains the network jointly with an extended logistic regression classifier, which includes pairwise interactions, via cross-entropy loss. At inference the continuous weights are thresholded into a binary mask, and the resulting sparse fingerprint is passed to the same logistic regression for the final diagnosis. The authors validate this on three diagnostic tasks and report comparable or superior accuracy relative to the deep learning baselines, then analyze specific ACL cases in which the top-ranked features align with radiological findings such as ligament discontinuity, joint effusion, and bone marrow edema.

Load-bearing premise

The method assumes that the extended logistic regression with pairwise interactions can be trained on 1,130 exams without overfitting, even though the paper does not state the number of interactions, any regularization, or the optimization details.

Editorial extensions

If this is right

A fixed population-level radiomic signature is not required for interpretability; selecting features per patient can match or exceed the accuracy of end-to-end deep learning on this task.
The jointly trained network and logistic regression make feature selection task-specific, so the chosen features are optimized for diagnostic accuracy rather than chosen independently of the classifier.
The selected features carry usable clinical meaning: in ACL tear cases top features concentrate near the ligament in the sagittal view, and features like Large Dependence Low Gray Level Emphasis track the extent of tissue disruption.
Ablations show that the full radiomic feature set, a 2×2×2 patch decomposition, per-patient feature selection, and image registration each contribute to the reported performance.
The framework produces a transparent final decision from a sparse patient-specific fingerprint, giving clinicians a feature-by-feature explanation of each prediction.

Reading between the lines

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

Beyond the paper: replacing the pairwise logistic regression with a strictly first-order classifier, or adding explicit sparsity regularization, would test whether the reported accuracy comes from the interaction terms or from the per-patient selection mechanism itself.
Beyond the paper: computing fingerprints from repeated or slightly perturbed scans of the same knee would test the stability of the selection, a property the paper does not assess but that any biomarker use would require.
Beyond the paper: correlating the top-ranked fingerprint features with independent radiological grading scores, rather than binary labels alone, could turn the observed case-study findings into a formal biomarker validation study.
Beyond the paper: applying the same selection-plus-transparent-classifier design to other anatomies or imaging modalities, or extending the selected pool to include deep features, would show whether the per-patient fingerprint idea generalizes beyond knee MRI.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Short version: the paper has a real idea—per-patient dynamic radiomic selection via a DL weighting network—and the ablations suggest the selection matters. But the central accuracy claim is weaker than it appears, and the classifier that does the heavy lifting is underspecified to the point of not being reproducible as written.

What's genuinely new: instead of one global radiomic signature, they learn a relevance vector per case, threshold it, and feed the sparse fingerprint to a logistic classifier. That is a legitimate extension of their prior healthy-persona work and of attention-style weighting, and the knee MRI experiments are new. The feature analysis (sagittal view dominance, texture features mattering more in non-tear cases) is the kind of interpretability you want to see. Code is released, the ablations cover feature composition, patch size, feature selection, and registration, and the ablation evidence that feature selection helps is convincing.

Soft spots, in proportion. First, the classifier: Sec. 3.1 says 'extended logistic regression' with second-order pairwise interactions over a pool I make as 3 views × 8 patches × 110 features = 2,640 features. That's millions of interaction terms without any stated regularization, optimizer, or interaction-selection scheme, and it's trained on 1,130 samples. Sec. 2 even says the classifier has 'no higher-degree terms.' That contradiction is not cosmetic—it directly bears on whether the reported test accuracy and the interpretability claims are artifacts of overfitting. Second, the headline claim. Table 1 shows your AUC is numerically below MRNet on all three tasks (e.g., 0.85 vs 0.94 for general abnormalities), and the paper's 'comparable or superior' rests on accuracy, which is threshold-dependent with the threshold tuned on validation. No significance tests against MRNet are given. Third, the motivating claim that fixed radiomic signatures are limited is never tested—there's no fixed-signature baseline, and the NoFS ablation is not equivalent to one. These are fixable: report the regularization and interaction construction, add the baseline, run proper significance tests on AUC, and tone down the abstract.

Who's this for? Someone working on interpretable medical imaging, particularly knee MRI. It deserves a serious referee—the idea is sound enough to warrant careful scrutiny, and the issues are addressable in revision. I would not desk-reject it.

Referee Report

4 major / 4 minor

Summary. The manuscript proposes a patient-specific radiomic fingerprint framework for knee MRI diagnosis. A 3D ResNet takes a registered multi-view ROI as input and predicts relevance weights for a large pool of hand-crafted 3D radiomic features extracted from local patches; the weighted features are thresholded into a sparse per-patient fingerprint and passed to an extended logistic regression classifier with second-order interactions. The method is validated on the MRNet dataset for three tasks (general abnormality, ACL tear, meniscus tear), with reported accuracy/AUC that the authors claim is comparable or superior to end-to-end deep learning models. The paper also reports ablations on feature composition, feature selection, patch configuration, and registration, plus qualitative case analyses intended to show clinical interpretability.

Significance. If the quantitative claims hold, the paper would demonstrate a useful middle ground between rigid, interpretable radiomics and accurate but opaque end-to-end deep learning, with the added benefit of per-patient feature-level explanations. The manuscript has concrete strengths: it uses a public dataset (MRNet), provides a code repository, defines a joint training objective for feature weighting and classification, and includes an ablation study of core components. The clinical case analysis provides a plausible narrative for how selected radiomic features correspond to known ACL pathology. However, the central quantitative claims are currently difficult to verify because the classifier is underspecified, a conventional fixed-radiomic-signature baseline is absent, and the reported statistical comparisons do not cover all baselines. These issues are load-bearing for the abstract's 'comparable or superior' claim and for the interpretability-vs-performance trade-off the paper emphasizes.

major comments (4)

[Sec. 3.1, Classification Model] The extended logistic regression with second-order pairwise interactions is underspecified in a way that directly affects the paper's low-dimensionality and generalization claims. With K=110 (the sum of the listed feature families), J=8 patches, and three views, the feature pool has 3JK=2,640 entries; unrestricted pairwise interactions would produce roughly 3.5 million coefficients, compared with 1,130 training samples. The manuscript does not state whether interactions are formed over the full soft-weighted pool, the thresholded binary selection, or a restricted subset, nor does it give any regularization, optimizer, or interaction-selection scheme. The claim that this classifier is 'low-dimensional' is therefore unsupported, and the reported test accuracy could reflect overfitting. This also conflicts with Sec. 2, where the classifier is described as having 'no higher-degree terms.' Please specify the exact number of trainable parameters, the regularization/selection procedure for interactions, and how the model complexity is controlled relative to the training-set size.
[Tables 1 and 2, Quantitative comparison] The abstract and Sec. 3.2 claim 'comparable or superior diagnostic accuracy' relative to state-of-the-art end-to-end DL models, but the reported AUC is lower than MRNet for general abnormality (0.85±0.17 vs 0.94±0.02) and ACL tear (0.92±0.04 vs 0.97±0.02). The only significance tests reported are against ELNet (p values in Sec. 3.2), with no statistical test against MRNet or SKID. Because accuracy, sensitivity, and specificity depend on the threshold T, which is tuned on the validation set, accuracy-based comparisons are not a satisfactory substitute for AUC comparisons. Please report AUC differences with confidence intervals or a statistical test against all baselines, and state the primary metric used for model selection.
[Sec. 3.1 / Table 2, Baseline and NoFS ablation] The paper's motivation is that fixed population-level radiomic signatures are too constrained and underperform, but no conventional radiomic-signature baseline is evaluated; all comparators are end-to-end DL models. The NoFS ablation is not such a baseline, and its results do not support the text's claim that removing the feature-weighting network leads to 'a noticeable decline in performance.' In Table 2, NoFS has higher AUC than Ours for all three tasks (0.89 vs 0.85 for abnormalities, 0.94 vs 0.92 for ACL, 0.85 vs 0.82 for meniscus), while accuracy is comparable (0.88 vs 0.92, 0.92 vs 0.94, 0.83 vs 0.84). Please include a standard radiomic-signature baseline (e.g., features selected on the training set only plus logistic regression, with no per-patient weighting) and reconcile the NoFS numbers with the claimed benefit of adaptive selection.
[Sec. 3.1, Evaluation protocol] The manuscript reports means and standard deviations but does not state the number of training/validation splits, random seeds, or how the 1,130 development cases are divided. The 120-case test set is small for three binary tasks, and several metrics have large standard deviations (e.g., specificity 0.71±0.33 for abnormalities and 0.67±0.34 for meniscus). Please specify the cross-validation or repeated-split protocol, the number of runs, and the exact use of the validation set for threshold T and any hyperparameter selection, so that the reported error bars are interpretable.

minor comments (4)

[Sec. 2] There are typos such as 'brievity' and 'intepretability'; please proofread the manuscript.
[Tables 1 and 2] The column headers in Table 1 and Table 2 are misaligned; the metric values for 'Ours' appear under the 'Reg Type' columns, making the tables difficult to read. Please restructure them so that Acc, Sen, Spe, and AUC are clearly associated with each method.
[Sec. 3.1] The dimension K of the radiomic feature set is not stated explicitly; the reader has to sum the listed feature counts to obtain K=110. Please state K directly, as the feature-pool dimension 3JK is used throughout the method description.
[Sec. 3.1, ROI definition] The ROI is defined as 50% depth, 30% height, and 50% width of the registered volume; please specify whether these percentages are relative to the resized 32×128×128 volume and provide the anatomical landmarks used to define the subvolume, since this affects reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the radiomic fingerprint is learned from labeled training data and evaluated on a held-out test set, with no equation-level reduction of outputs to inputs.

full rationale

The derivation chain is self-contained. Features f are extracted from the ROI; the network G predicts relevance weights q; the weighted vector f⊙q is passed to a logistic classifier R; G and R are optimized jointly by cross-entropy on labeled training data; at inference q is thresholded and the binary fingerprint is scored on a separate 120-case test set. No equation defines the reported accuracy in terms of the fitted weights: the threshold T is tuned on a validation set, and test metrics are external to training. The only self-citation ([5]) is used for positioning ("This study builds on the previous work [5]") and is not load-bearing for any derivation. The internal contradiction between "low-dimensional function without higher-degree terms" (Sec. 2) and "second-order pairwise interactions" (Sec. 3.1) is an underspecification/reproducibility issue, not a circularity. The ablation against NoFS is an internal control rather than a fitted-input prediction. The stated limitation about sensitivity to imaging protocols (Sec. 4) is a generalization caveat, not a circular step. Thus no claim reduces by construction to its inputs.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claims rest on standard ML training plus several domain assumptions about label quality, radiomic feature informativeness, ROI coverage, and registration alignment. The free parameters are mostly hyperparameters and the ROI, tuned on development data; the interaction classifier is underspecified.

free parameters (4)

Threshold T per task = Not reported
Inference-time binary selection threshold chosen per task to maximize Youden's index on the validation set (Sec 3.1); used to compute all test metrics.
ROI definition = 50% depth, 30% height, 50% width
Empirically determined by radiologists to cover ACL and meniscus anatomy (Sec 3.1); affects all feature extraction.
Patch configuration = 2x2x2 (8 patches)
Selected from ablation as best balance of performance and explainability (Table 2); other configurations 1x1x1 and 3x3x3 are ablated.
Pairwise interaction terms in logistic regression = Unspecified
The extended logistic regression includes second-order pairwise feature interactions, but the number of interactions, selection, and regularization are not described (Sec 3.1).

assumptions (5)

domain assumption MRNet labels (general abnormality, ACL tear, meniscus tear) are accurate ground truth.
All evaluation and training use these labels as truth; any label noise transfers to model and metrics (Sec 3.1).
domain assumption Hand-crafted radiomic features computed with PyTorchRadiomics capture pathology-relevant image properties.
The method assumes texture, intensity, and shape features are meaningfully related to knee pathology (Sec 3.1).
ad hoc to paper The predefined ROI subvolume (50% depth, 30% height, 50% width) covers the anatomical regions relevant to ACL and meniscus diagnosis.
The ROI is empirically set by radiologists and is not derived from data or literature; if the ROI excludes relevant tissue, fingerprints lose signal (Sec 3.1).
domain assumption Affine registration with NiftyReg aligns the three MR sequences sufficiently for patch-wise feature extraction.
The NoReg ablation shows performance drops without registration, so registration quality is load-bearing (Table 2, Sec 3.1).
domain assumption Joint training of the selector network and the classifier can be optimized to produce useful per-patient relevance scores.
This is the core hypothesis of the method; if the shared optimization fails, there is no fingerprint. The experiments support it on this dataset, but it is assumed generally (Sec 2).

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Cite this review

Pith. "Pith review of Radiomic fingerprints for knee MR images assessment." pith.science (2026). https://pith.science/paper/YQ2BMLC3

@misc{pith2026250620306,
  author       = {Pith},
  title        = {Pith review of: Radiomic fingerprints for knee MR images assessment},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YQ2BMLC3}},
  note         = {Machine review of arXiv:2506.20306}
}

read the original abstract

Accurate interpretation of knee MRI scans relies on expert clinical judgment, often with high variability and limited scalability. Existing radiomic approaches use a fixed set of radiomic features (the signature), selected at the population level and applied uniformly to all patients. While interpretable, these signatures are often too constrained to represent individual pathological variations. As a result, conventional radiomic-based approaches are found to be limited in performance, compared with recent end-to-end deep learning (DL) alternatives without using interpretable radiomic features. We argue that the individual-agnostic nature in current radiomic selection is not central to its intepretability, but is responsible for the poor generalization in our application. Here, we propose a novel radiomic fingerprint framework, in which a radiomic feature set (the fingerprint) is dynamically constructed for each patient, selected by a DL model. Unlike the existing radiomic signatures, our fingerprints are derived on a per-patient basis by predicting the feature relevance in a large radiomic feature pool, and selecting only those that are predictive of clinical conditions for individual patients. The radiomic-selecting model is trained simultaneously with a low-dimensional (considered relatively explainable) logistic regression for downstream classification. We validate our methods across multiple diagnostic tasks including general knee abnormalities, anterior cruciate ligament (ACL) tears, and meniscus tears, demonstrating comparable or superior diagnostic accuracy relative to state-of-the-art end-to-end DL models. More importantly, we show that the interpretability inherent in our approach facilitates meaningful clinical insights and potential biomarker discovery, with detailed discussion, quantitative and qualitative analysis of real-world clinical cases to evidence these advantages.

Figures

Figures reproduced from arXiv: 2506.20306 by the authors.

**Figure 2.** (a) Anatomical location and feature type distribution of the top 20 top [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

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Reference graph

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A review of handcrafted and deep radiomics in neurological diseases: transitioning from oncology to clinical neuroimaging

Lavrova, E., Woodruff, H.C., Khan, H., Salmon, E., Lambin, P., Phillips, C.: A review of handcrafted and deep radiomics in neurological diseases: transitioning from oncology to clinical neuroimaging. arXiv preprint arXiv:2407.13813 (2024)

work page Pith review arXiv 2024