Renal cell carcinoma (RCC) is the 14th most prevalent cancer globally, accounting for more than 400,000 new diagnoses in 2020 (1, 2). Its global incidence has continued to rise in recent years, posing a substantial public-health burden. Clear cell renal cell carcinoma (ccRCC) constitutes the predominant histologic subtype of RCC, representing approximately 80% of all cases (1). Extensive research has established that pathological tumor nuclear grade is closely correlated with prognosis in ccRCC patients (3). The 2016 World Health Organization/International Society of Urological Pathology (WHO/ISUP) nuclear grading system has been widely adopted in clinical practice (4). This system stratifies tumors from grade I to IV, with grades I–II considered low grade and grades III–IV high grade. High-grade ccRCCs generally exhibit greater aggressiveness and a higher propensity for recurrence, resulting in poorer outcomes (3, 5). Consequently, accurate preoperative assessment of tumor nuclear grade is critical for devising individualized treatment strategies.

At present, preoperative nuclear grading primarily depends on percutaneous biopsy. This invasive procedure carries risks such as hemorrhage and needle tract seeding. Moreover, intratumoral heterogeneity and sampling limitations can lead to misclassification of nuclear grade (6, 7). Magnetic resonance imaging (MRI), with its superior soft-tissue contrast, absence of ionizing radiation, and multiparametric capability (8), plays a central role in the diagnostic evaluation and management of ccRCC. Nevertheless, conventional MRI has limitations such as interobserver variability and limited reproducibility. Thus, there is an urgent clinical need to develop a noninvasive, accurate method for preoperative nuclear grading to better inform precision treatment decisions.

Radiomics enables precise quantitative assessment of tumor biological characteristics through the high- throughput extraction and analysis of quantitative imaging features, integrated with artificial intelligence algorithms such as machine learning and deep learning (9). Currently, MRI-based radiomics has demonstrated considerable promise in ccRCC for differential diagnosis (10), nuclear grading (11, 12), and prognostic assessment (13). However, most existing investigations have focused predominantly on the intratumoral region, with relatively fewer studies addressing the peritumoral region. The peritumoral compartment, an integral component of the tumor microenvironment, harbors abundant stromal cells and cytokines that play pivotal roles in tumor initiation and progression (14, 15). Furthermore, accumulating evidence indicates that peritumoral imaging features can materially enhance model performance (16–18). Conventional radiomics approaches typically treat intratumoral and peritumoral regions as homogeneous wholes, thereby overlooking spatial heterogeneity shaped by cellular subpopulations, vascular architecture, and the immune microenvironment. To overcome this limitation, habitat imaging technology has been developed. This technique partitions the tumor into subregions composed of voxels with similar imaging signatures via clustering algorithms (19), thereby enabling fine-grained quantification of intratumoral heterogeneity. Each habitat subregion embodies distinct biological properties, reflecting differences in angiogenesis, cellularity, metabolic activity, and gene expression, and thus reveals the spatial heterogeneity and biological complexity of the tumor (20). Habitat radiomics has shown great promise in studies of glioma (21), oral squamous cell carcinoma (14), and breast cancer (22). However, MRI-based habitat radiomics for predicting the ccRCC WHO/ISUP nuclear grade remains unexplored.

In this study, we aimed to construct a nomogram based on contrast-enhanced T1-weighted images by integrating clinical variables with intratumoral, peritumoral and habitat radiomic features to predict WHO/ISUP nuclear grade in ccRCC. In addition, Shapley Additive Explanations (SHAP) analysis was incorporated to enhance model transparency and interpretability, thereby helping to support clinical decision-making.

This study presents the novel development of an MRI-based habitat radiomics model for predicting the WHO/ISUP nuclear grade in ccRCC, complemented by SHAP analysis to enhance interpretability. The results indicate that the Habitat model, by quantitatively capturing intratumoral heterogeneity, demonstrates improved discriminative performance compared with conventional approaches. Moreover, SHAP analysis helped identify key imaging features and dominant subregions potentially associated with nuclear grade. By integrating corticomedullary enhancement level with intratumoral, 2-mm peritumoral, and integrated habitat region features, we developed a combined nomogram model that demonstrated further improvement in predictive performance for nuclear grading and showed potential clinical utility. This study provides new insights into noninvasive nuclear grading for ccRCC and represents an important step toward precision medicine in the field of renal tumors.Previous studies on MRI-based radiomics have made significant progress in predicting nuclear grade in ccRCC (11, 12). Consistent with these studies, the intratumoral model developed in this study also demonstrated good performance for nuclear grading prediction, with AUCs of 0.848 and 0.816 in the training and test sets, respectively. Recent research has increasingly highlighted the importance of the peritumoral region, which represents the interface between the tumor and surrounding normal tissue and contains key biological information (15). The microenvironment of the peritumoral region is complex, consisting of endothelial cells, fibroblasts, immune cells, and extracellular matrix (16, 23). It is not only a critical site for tumor angiogenesis and lymphangiogenesis but also a major locus for immune cell infiltration and inflammatory responses, all of which contribute to tumor progression (15, 24, 25). High-grade ccRCC is highly invasive (26) and often leads to significant changes in adjacent tissue structures and morphology, which can be effectively captured by peritumoral imaging features (18), thus providing valuable information for noninvasive preoperative nuclear grading. Studies by Ma (17) and Li (13) have confirmed that incorporating peritumoral features improves the predictive performance of nuclear grading in ccRCC. They identified the optimal peritumoral regions as 2 mm and 5 mm, respectively. However, the optimal peritumoral range remains a topic of debate. In this study, we evaluated peritumoral ranges from 1 mm to 5 mm and found that the Peri2mm model performed best in both the training set (AUC = 0.884) and the test set (AUC = 0.839), which is consistent with Ma’s findings. This may suggests that the 2-mm peritumoral region offers a favorable balance between capturing microenvironmental features relevant to tumor nuclear grading and minimizing interference from distant normal tissue, thus optimizing both prediction accuracy and robustness. As the first systematic comparison of different peritumoral ranges, this study provides important evidence for determining the optimal peritumoral region for ccRCC nuclear grading prediction.

However, traditional intratumoral or peritumoral radiomics methods struggle to effectively capture the complex spatial heterogeneity within tumors. Emerging habitat imaging techniques, on the other hand, allow for the identification and quantification of subregions with similar imaging characteristics, enabling precise characterization of heterogeneous subregions. This provides a novel perspective for noninvasive analysis of tumor biological behavior and for further investigating the associations between tumor biology, histological features, treatment response, and clinical prognosis. For instance, Yuan et al. (14) developed intra- and peritumoral habitat models via multi-sequence MRI. Their study aimed to predict pathological complete response (pCR) in oral squamous cell carcinoma patients receiving immunotherapy. The study found that intratumoral and peritumoral habitat features could quantitatively represent the extent of CD45+ leukocyte infiltration in the tumor stroma and the expression level of PD-L1 in tumor cells. These immune microenvironment indicators were significantly correlated with pCR, thus validating the powerful capabilities of habitat imaging for noninvasively assessing tumor biological characteristics.

CT-based habitat radiomics has demonstrated value in predicting nuclear grade (27, 28), metastatic risk (29), and survival outcomes (30) in ccRCC, but MRI-based habitat radiomics studies remain largely unexplored. Given MRI’s superior soft-tissue resolution and multiparametric imaging capabilities, its application potential in ccRCC nuclear grading has yet to be thoroughly investigated. In this study, we employed voxel-level feature extraction, calculating 19-dimensional local radiomic features for each voxel. Three radiology-derived tumor habitat subregions were defined using CH-index-guided K-means clustering: Habitat 1 (moderate-to-high enhancement zone), Habitat 2 (non-enhancement zone), and Habitat 3 (transitional zone between Habitat 1 and 2). Habitat 1, which accounted for the largest proportion (43.54%), exhibited outstanding predictive capability compared with other subregions in both the training and test sets (AUCs = 0.860 and 0.851). Radiologically, this subregion corresponds to the area of most marked enhancement and likelyrepresents a core zone with active angiogenesis and high cellularity. Such characteristics may be associated with increased tumor aggressiveness, a higher risk of metastasis, and a poor prognosis. This observation was corroborated by SHAP analysis, which revealed that six of the top ten most important features originated from Habitat 1, further confirming the dominant role of this subregion in model decision-making. Owing to effective integration of features from all three habitat subregions, the Habitat model captured more comprehensive tumor information and achieved a test set AUC of 0.877, demonstrating better performance than the individual habitat subregion models and aligning with the findings of Lv (27) and Chen (28). Habitat features not only demonstrated stronger discriminative power than conventional radiomic descriptors but also produced models with superior overall performance.

This study identified the corticomedullary enhancement level as an independent predictor of ccRCC nuclear grade, further supporting previous reports (12, 31). It is postulated that high-grade ccRCCs grow rapidly and consequently outstrip their blood supply, leading to relative ischemia and pre-necrotic hypoperfusion that ultimately progresses to tissue necrosis. These alterations manifest as reduced overall enhancement, particularly evident in the corticomedullary phase. By integrating clinical variables with radiomic and habitat-derived features, the nomogram demonstrated outstanding discriminative performance in both the training and test sets (AUCs = 0.934 and 0.912, respectively), indicating its potential for stable, noninvasive preoperative grading. This relatively reliable predictive capability may hold significant implications for the clinical management of ccRCC. Accurate preoperative prediction of WHO/ISUP nuclear grade in ccRCC has important clinical implications. For tumors predicted as low grade (I–II), the model may serve as a valuable risk-stratification tool supporting more conservative management strategies, such as active surveillance or prioritization of nephron-sparing surgery, and may help identify patients for whom preoperative biopsy could be deferred or avoided. Conversely, lesions predicted as high grade (III–IV) may warrant earlier surgical intervention, more meticulous planning of the resection extent, and intensified postoperative surveillance. Thus, our findings provide an important, noninvasive, and potentially clinically useful adjunct to pre-treatment decision making for patients with ccRCC. Moreover, the optimal selection of surgical strategy remains a significant clinical challenge in individualized ccRCC management. This includes decisions regarding partial versus radical nephrectomy (32) and the choice of retroperitoneal or transperitoneal access (33). In the future, integrating noninvasive nuclear grade prediction with anatomical characteristics, such as tumor complexity and abdominal conditions, together with patients’ overall clinical status may facilitate the development of an integrated decision-support framework. Such an approach could provide a rational basis for personalized surgical planning and has the potential to improve perioperative management and long-term outcomes.

While this study has notable strengths, it also has certain limitations. (1) The retrospective, single-center design, combined with a limited sample size and an imbalanced distribution of high- and low-grade cases, may introduce selection bias and restrict the generalizability of the findings. Therefore, the present model’s clinical applicability should be interpreted with caution; future validation in larger, prospective, multicenter cohorts with multiparametric imaging is required to further substantiate model performance. (2) The model was developed using a single MRI sequence (contrast-enhanced T1-weighted imaging) and a single phase (the corticomedullary phase), with all data acquired on the same scanner model at a single institution with uniform imaging parameters. This homogeneity may limit the model’s performance when applied across centers and devices. Future work should integrate multi-sequence data (e.g., T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient maps) and multiphase imaging (e.g., nephrographic, excretory phases) to capture a broader spectrum of tumor biology, and should validate the model on multicenter, multi-vendor cohorts to further evaluate and enhance its robustness and generalizability.(3) Manual regions of interest delineation is time-consuming and subject to interobserver variability, highlighting the need for automated tumor segmentation methods. Future work will focus on building a multi-omics tumor heterogeneity model and exploring the deep integration of habitat radiomics with artificial intelligence techniques. (4) The habitat partitioning in this study is a radiologic phenotyping result derived solely from MRI features and has not been validated against histopathology. Therefore, the precise spatial correspondence between each imaging-derived subregion and underlying biological structures (e.g., viable enhancing tissue versus necrotic tissue) remains undetermined and should be confirmed in future studies using image–pathology spatial co-registration techniques.

In conclusion, this study is the first to develop and validate a nomogram model based on MRI habitat radiomics for noninvasive preoperative prediction of the WHO/ISUP nuclear grade in ccRCC. By integrating clinical factors with intratumoral, peritumoral, and integrated habitat region radiomic features, the model demonstrates promising discriminative performance under internal validation. In addition, SHAP-based interpretability analysis was employed to clarify the model’s decision-making process, which may help increase confidence in the model’s predictions. In addition, SHAP-based interpretability analysis was employed to clarify the model’s decision-making process, which may help increase confidence in the model’s predictions This work furnishes preliminary feasibility evidence for noninvasive preoperative nuclear grading in ccRCC using MRI-based habitat radiomics and offers methodological guidance for the clinical translation of habitat radiomics. Nevertheless, the current model has only been preliminarily validated on single-center, single-sequence data; its clinical utility must be confirmed through external validation in larger, prospective, multicenter cohorts incorporating multiparametric imaging.