Commentary: Grading of clear cell renal cell carcinoma using diffusion MRI with a multimodal apparent diffusion model

To reflect the reviewer's suggestion, we have strengthened our recommendation that future studies should incorporate stage-adjusted multivariable analyses or within-stage subgroup comparisons, especially in T1 tumors where accurate grading is most clinically impactful.These clarifications are now included in the revised Letter [Line 49-65, Page 3] (2) conduct subgroup analyses within similar stage groups (especially for T1 tumors) to enhance the specificity of the results.We appreciate this insightful suggestion. In the revised Letter, we have added a dedicated explanation contrasting the roles of staging and grading in current renal cancer imaging workflows.We now clarify that while staging can be reliably assessed using standard CT or MRI, grading remains a major unmet clinical need-and this is precisely where MAD-DWI may offer incremental value. These revisions are intended to better highlight MAD-DWI's potential position as a grading-focused adjunct to routine imaging, consistent with the reviewer's suggestion.The enhanced discussion has been incorporated in the revised Letter. In a retrospective cohort of 54 ccRCC cases scanned using a broad b-value protocol (0-3000 s/mm²), the authors decomposed the DWI signal into restricted, hindered, unimpeded diffusion, and flow-related components. High-grade tumors (WHO/ISUP 3-4) demonstrated lower hindered diffusion coefficient (Dh) and higher restricted fraction (fr) and heterogeneity parameter (ah), while ADC also decreased; a combined Dh+fr+ah model achieved an AUC of 0.796 and outperformed ADC (1). Such multi-compartment and non-Gaussian diffusion strategies are consistent with emerging renal oncology imaging trends aimed at capturing microstructural complexity beyond mono-exponential ADC (3)(4)(5).Several methodological and translational issues deserve attention before MAD-DWI can mature into a clinically robust diagnostic strategy. The most significant confounding factor is the imbalance of T-stage between groups: low-grade tumors were predominantly T1, whereas high-grade lesions tended to present with T3/T4 disease. Advanced diffusion parameters (such as Dh, fr, and ah) are known to correlate not only with grade but also with features that evolve with stage progression, including increasing tumor size, necrosis, and microvascular or stromal remodeling (3,4,6). Consequently, without stage-matched validation, the currently observed differences may reflect a composite signature of both tumor stage and grade, making it difficult to disentangle a true "grade signature." Clarifying this distinction is critical for translation because T-stage is already reliably assessable on routine contrast-enhanced CT or MRI, whereas accurate preoperative grading remains the key unmet need. Future studies should prioritize stage-adjusted multivariable modeling or stratified analyses within comparable subsets-especially T1 tumors where nephron-sparing decisions are sensitive to grading-to strengthen the specificity of MAD metrics (3,6).Building on the reviewer's suggestion, we have expanded our commentary to recommend that future MAD-DWI studies include stage-adjusted multivariable analyses and stage-matched subgroup comparisons, particularly within T1 lesions where treatment decisions (e.g., active surveillance vs. nephron-sparing surgery) are most sensitive to accurate grading. Such methodological refinements would more precisely define whether MAD-derived metrics truly capture grade-specific microstructural features independent of tumor stage.ROI representativeness constitutes another bottleneck. The study relied on a single largest solid-slice ROI while excluding necrosis, hemorrhage, and calcification.Although this increases fitting stability, ccRCC is biologically and spatially heterogeneous, and grade often varies across tumor regions. Single-slice sampling risks under-representing whole-lesion risk and may bias estimates toward more uniform viable tissue, particularly in high-grade tumors where necrosis and mixed architecture are common. Multi-regional and whole-tumor radiomics studies consistently show that capturing intratumoral and peritumoral heterogeneity improves correlation with WHO/ISUP grade and prognosis, reinforcing that global lesion characterization is essential for emerging diagnostics (7,8,9). In this context, the observed increase of ah with grade-interpreted as greater structural uniformity in high-grade lesions-may reflect ROI exclusion of heterogeneous necrotic fractions rather than a genuine biological inversion. Three-dimensional segmentation or multi-slice ROIs, followed by voxel-wise histograms or heterogeneity indices, would likely clarify this relationship and better align MAD readouts with the clinical question of entire-tumor aggressiveness (7)(8)(9).Standardization and reproducibility remain decisive for multi-center adoption. MAD fitting depends on broad b-value sampling and multi-parameter optimization, and advanced diffusion models are sensitive to scanner hardware, sequence timing, b-value distributions, motion correction, and fitting constraints (3)(4)(5)(6)10). ADC in the current study was fitted across the full 0-3000 s/mm² range, differing from typical renal DWI practice and limiting comparability with established ccRCC grading literature (1,5,6). We encourage fuller reporting of fitting uncertainty (e.g., confidence intervals, residuals), sensitivity testing using alternative b-value subsets, and inter-/intra-reader ROI reproducibility, alongside harmonization efforts across platforms. Such steps are prerequisites if MAD parameters are to serve as stable thresholds within real-world urological oncology workflows.To further clarify the potential clinical value of MAD-DWI, we now explicitly distinguish between the respective roles of staging and grading in the preoperative assessment of renal masses. Tumor stage, including local extension beyond the kidney and involvement of perinephric structures, is readily identifiable on standard cross-sectional imaging and already guides major treatment pathways. In contrast, reliable noninvasive grading remains elusive, despite its central importance for prognostication, selection of nephron-sparing approaches, and determining eligibility for active surveillance.Therefore, the added value of MAD-based diffusion modeling lies not in improving staging-which existing imaging already accomplishes-but rather in its potential to provide a biologically interpretable, noninvasive grading biomarker. Within real-world workflows, MAD-DWI could complement routine imaging by refining risk