Clear Cell Renal Cell Carcinoma Growth Correlates with Baseline Diffusion-weighted MRI in Von Hippel-Lindau Disease

Clear Cell Renal Cell Carcinoma: Characterizing the Phenotype of Von Hippel-Lindau Mutation Using MRI

BACKGROUND

The von Hippel-Lindau (VHL) mutation is an important alteration in clear cell renal cell carcinoma (ccRCC); however, its imaging phenotype remains unclear.

PURPOSE

To investigate whether MRI features can reflect the VHL mutation status.

STUDY TYPE

Retrospective.

FIELD STRENGTH/SEQUENCE

3 T/fast spin echo T2-weighted, spin-echo echo planar diffusion-weighted, gradient recalled echo T1-weighted, gradient recalled echo chemical-shift T1-weighted, and contrast-enhanced gradient recalled echo T1-weighted sequences.

POPULATION

One hundred five patients with ccRCC who underwent preoperative contrast-enhanced MRI and subsequent genomic sequencing: 59 consecutive patients from our institution (38 [64.41%] with VHL mutations) formed a training cohort, and 46 from The Cancer Genome Atlas (TCGA) database (24 [52.17%] with VHL mutations) formed an independent test cohort.

ASSESSMENT

Two radiologists, with 23 and 33 years of experience respectively, jointly evaluated the semantic MRI features of the primary lesion in ccRCCs to propose potential features related to VHL mutations in both cohorts. Three additional readers, with 5, 7, and 10 years of experience respectively, independently reviewed all lesions to assess the interobserver agreement of MRI features. A VHL mutational likelihood score (VHL-MULIS) system was constructed using the training cohort and validated using the independent test cohort.

STATISTICAL TESTS

Fisher's test or chi-square test, t-test or Mann-Whitney U test, logistic regression, Cohen's kappa (κ), area under the receiver operating characteristic curve (AUC). A two-sided P value <0.05 was considered statistically significant.

RESULTS

In both the local and public cohorts, T2-weighted signal intensity and presence of microscopic fat from primary lesions were significantly associated with VHL mutation status. The VHL-MULIS incorporated maximum diameter, T2-weighted signal intensity, and presence of microscopic fat in the training cohort and demonstrated promising diagnostic ability (AUC, 0.82; sensitivity, 0.79; specificity, 0.82) and substantial interobserver agreement (κ, 0.787) in the test cohort.

DATA CONCLUSION

The VHL mutation exhibited a distinct MRI phenotype. Integrating multiple semantic MRI features has potential to reflect the mutation status in patients with ccRCC.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Histogram Analysis of Apparent Diffusion Coefficient Maps Provides Genotypic and Pretreatment Phenotypic Information in Pediatric and Young Adult Rhabdomyosarcoma

INTRODUCTION

We evaluate the role of apparent diffusion coefficient (ADC) histogram metrics in stratifying pediatric and young adult rhabdomyosarcomas.

METHODS

We retrospectively evaluated baseline diffusion-weighted imaging (DWI) from 38 patients with rhabdomyosarcomas (Not otherwise specified: 2; Embryonal: 21; Spindle Cell: 2; Alveolar: 13, mean ± std dev age: 8.1 ± 7.76 years). The diffusion images were obtained on a wide range of 1.5 T and 3 T scanners at multiple sites. FOXO1 fusion status was available for 35 patients, nine of whom harbored the fusion. 13 patients were TNM stage 1, eight had stage 2 disease, nine were stage 3, and eight had stage 4 disease. 23 patients belonged to Clinical Group III and seven to Group IV, while two and five were CG I and II, respectively. Nine patients were classified as low risk, while 21 and five were classified as intermediate and high risk respectively. Histogram parameters of the apparent diffusion coefficient (ADC) map from the entire tumor were obtained based on manual tumor contouring. A two-tailed Mann-Whitney U test was used for all two-group, and the Kruskal-Wallis's test was used for multiple-group comparisons. Bootstrapped receiver operating characteristic (ROC) curves and areas under the curve (AUC) were generated for the statistically significant histogram parameters to differentiate genotypic and phenotypic parameters.

RESULTS

Alveolar rhabdomyosarcomas had a statistically significant lower 10th Percentile (586.54 ± 164.52, mean ± std dev, values are in ×10-6mm2/s) than embryonal rhabdomyosarcomas (966.51 ± 481.33) with an AUC of 0.85 (95%CI. 0.73-0.95) for differentiating the two. The 10th percentile was also significantly different between FOXO1 fusion-positive (553.87 ± 187.64) and negative (898.07 ± 449.38) rhabdomyosarcomas with an AUC of 0.83 (95% CI 0.71-0.94). Alveolar rhabdomyosarcomas also had statistically significant lower Mean, Median, and Root Mean Squared ADC histogram values than embryonal rhabdomyosarcomas. Four, five, and seven of the 18 histogram parameters evaluated demonstrated a statistically significant increase with higher TNM stage, clinical group, assignment, and pretreatment risk stratification, respectively. For example, Entropy had an AUC of 0.8 (95% CI. 0.67-0.92) for differentiating TNM stage 1 from ≥ stage 2 and 0.9 (95% CI. 0.8-0.98) for differentiating low from intermediate or high-risk stratification.

CONCLUSION

Our findings demonstrate the potential of ADC histogram metrics to predict clinically relevant variables for rhabdomyosarcoma, including FOXO1 fusion status, histopathology, Clinical Group, TNM staging, and risk stratification.

CT-derived radiomics predict the growth rate of renal tumours in von Hippel-Lindau syndrome

AIM

To predict renal tumour growth patterns in von Hippel-Lindau syndrome by utilising radiomic features to assist in developing personalised surveillance plans leading to better patient outcomes.

MATERIALS AND METHODS

The study evaluated 78 renal tumours in 55 patients with histopathologically-confirmed clear cell renal cell carcinomas (ccRCCs), which were segmented and radiomics were extracted. Volumetric doubling time (VDT) classified the tumours into fast-growing (VDT <365 days) or slow-growing (VDT ≥365 days). Volumetric and diametric growth analyses were compared between the groups. Multiple logistic regression and random forest classifiers were used to select the best features and models based on their correlation and predictability of VDT.

RESULTS

Fifty-five patients (mean age 42.2 ± 12.2 years, 27 men) with a mean time difference of 3.8 ± 2 years between the baseline and preoperative scans were studied. Twenty-five tumours were fast-growing (low VDT, i.e., <365 days), and 53 tumours were slow-growing (high VDT, i.e., ≥365 days). The median volumetric and diametric growth rates were 1.71 cm3/year and 0.31 cm/year. The best feature using univariate analysis was wavelet-HLL_glcm_ldmn (area under the receiver operating characteristic [ROC] curve [AUC] of 0.80, p<0.0001), and with the random forest classifier, it was log-sigma-0-5-mm-3D_glszm_ZonePercentage (AUC: 79). The AUC of the ROC curves using multiple logistic regression was 0.74, and with the random forest classifier was 0.73.

CONCLUSION

Radiomic features correlated with VDT and were able to predict the growth pattern of renal tumours in patients with VHL syndrome.

Kidney scoring surveillance: predictive machine learning models for clear cell renal cell carcinoma growth using MRI

INTRODUCTION

Classification of clear cell renal cell carcinoma (ccRCC) growth rates in patients with Von Hippel-Lindau (VHL) syndrome has several ramifications for tumor monitoring and surgical planning. Using two separate machine-learning algorithms, we sought to produce models to predict ccRCC growth rate classes based on qualitative MRI-derived characteristics.

MATERIAL AND METHODS

We used a prospectively maintained database of patients with VHL who underwent surgical resection for ccRCC between January 2015 and June 2022. We employed a threshold growth rate of 0.5 cm per year to categorize ccRCC tumors into two distinct groups-'slow-growing' and 'fast-growing'. Utilizing a questionnaire of qualitative imaging features, two radiologists assessed each lesion on different MRI sequences. Two machine-learning models, a stacked ensemble technique and a decision tree algorithm, were used to predict the tumor growth rate classes. Positive predictive value (PPV), sensitivity, and F1-score were used to evaluate the performance of the models.

RESULTS

This study comprises 55 patients with VHL with 128 ccRCC tumors. Patients' median age was 48 years, and 28 patients were males. Each patient had an average of two tumors, with a median size of 2.1 cm and a median growth rate of 0.35 cm/year. The overall performance of the stacked and DT model had 0.77 ± 0.05 and 0.71 ± 0.06 accuracies, respectively. The best stacked model achieved a PPV of 0.92, a sensitivity of 0.91, and an F1-score of 0.90.

CONCLUSION

This study provides valuable insight into the potential of machine-learning analysis for the determination of renal tumor growth rate in patients with VHL. This finding could be utilized as an assistive tool for the individualized screening and follow-up of this population.

The kidney imaging surveillance scoring system (KISSS): using qualitative MRI features to predict growth rate of renal tumors in patients with von-Hippel Lindau (VHL) syndrome

OBJECTIVE

To determine the reliability of an MRI-based qualitative kidney imaging surveillance scoring system (KISSS) and assess which imaging features predict growth rate (GR) of renal tumors in patients with VHL.

MATERIALS AND METHODS

We identified 55 patients with VHL with 128 renal tumors who underwent intervention from 2015 to 2020 at the National Cancer Institute. All patients had 2 preoperative MRIs at least 3 months apart. Two fellowship-trained radiologists scored each tumor on location and MR-sequence-specific imaging parameters from the earlier MRI. Weighted kappa was used to determine the degree of agreement between radiologists for each parameter. GR was calculated as the difference in maximum tumor dimension over time (cm/year). Differences in mean growth rate (MGR) within categories of each imaging variable were assessed by ANOVA.

RESULTS

Apart from tumor margin and renal sinus, reliability was at least moderate (K > 0.40) for imaging parameters. Median initial tumor size was 2.1 cm, with average follow-up of 1.2 years. Tumor MGR was 0.42 cm/year. T2 hypointense, mixed/predominantly solid, and high restricted diffusion tumors grew faster. When comparing different combinations of these variables, the model with the lowest mean error among both radiologists utilized only solid/cystic and restricted diffusion features.

CONCLUSIONS

We demonstrate a novel MR-based scoring system (KISSS) that has good precision with minimal training and can be applied to other qualitative radiology studies. A subset of imaging variables (T2 intensity; restricted diffusion; and solid/cystic) were independently associated with growth rate in VHL renal tumors, with the combination of the latter two most optimal. Additional validation, including in sporadic RCC population, is warranted.

Von Hippel-Lindau Disease (VHL): Characteristic Lesions with Classic Imaging Findings

Von Hippel-Lindau disease (VHL) is a multisystem cancer syndrome caused by the inactivation of the VHL tumor suppressor gene and involves various organ systems including the central nervous system (CNS), endocrine system, and the kidneys. Tumors seen in patients with VHL disease can be benign or malignant and are usually multifocal, bilateral, and hypervascular in nature. As most lesions associated with VHL are asymptomatic initially, early diagnosis and the institution of an evidence-based surveillance protocol are of paramount importance. Screening, surveillance, and genetic counseling are key aspects in the management of patients diagnosed with VHL disease and often require a multidisciplinary approach and referral to specialized centers. This article will discuss the characteristic lesions seen with VHL disease, their diagnosis, screening protocols and management strategies, as well as an illustrative case to demonstrate the natural progression of the disease with classic imaging findings.

Imaging Recommendations for Diagnosis, Staging, and Management of Hereditary Malignancies

Hereditary cancer syndromes, characterized by genetically distinct neoplasms developing in specific organs in more than one family members, predispose an individual to early onset of distinct site-specific tumors. Early age of onset, multiorgan involvement, multiple and bilateral tumors, advanced disease at presentation, and aggressive tumor histology are few characteristic features of hereditary cancer syndromes. A multidisciplinary approach to hereditary cancers has led to a paradigm shift in the field of preventive oncology and precision medicine. Imaging plays a pivotal role in the screening, testing, and follow-up of individuals and their first- and second-degree relatives with hereditary cancers. In fact, a radiologist is often the first to apprise the clinician about the possibility of an underlying hereditary cancer syndrome based on pathognomonic imaging findings. This article focuses on the imaging spectrum of few common hereditary cancer syndromes with specific mention of the imaging features of associated common and uncommon tumors in each syndrome. The screening and surveillance recommendations for each condition with specific management approaches, in contrast to sporadic cases, have also been described.

An MRI-based radiomics model to predict clear cell renal cell carcinoma growth rate classes in patients with von Hippel-Lindau syndrome

PURPOSE

Upfront knowledge of tumor growth rates of clear cell renal cell carcinoma in von Hippel-Lindau syndrome (VHL) patients can allow for a more personalized approach to either surveillance imaging frequency or surgical planning. In this study, we implement a machine learning algorithm utilizing radiomic features of renal tumors identified on baseline magnetic resonance imaging (MRI) in VHL patients to predict the volumetric growth rate category of these tumors.

MATERIALS AND METHODS

A total of 73 VHL patients with 173 pathologically confirmed Clear Cell Renal Cell Carcinoma (ccRCCs) underwent MRI at least at two different time points between 2015 and 2021. Each tumor was manually segmented in excretory phase contrast T1 weighed MRI and co-registered on pre-contrast, corticomedullary and nephrographic phases. Radiomic features and volumetric data from each tumor were extracted using the PyRadiomics library in Python (4544 total features). Tumor doubling time (DT) was calculated and patients were divided into two groups: DT <  = 1 year and DT > 1 year. Random forest classifier (RFC) was used to predict the DT category. To measure prediction performance, the cohort was randomly divided into 100 training and test sets (80% and 20%). Model performance was evaluated using area under curve of receiver operating characteristic curve (AUC-ROC), as well as accuracy, F1, precision and recall, reported as percentages with 95% confidence intervals (CIs).

RESULTS

The average age of patients was 47.2 ± 10.3 years. Mean interval between MRIs for each patient was 1.3 years. Tumors included in this study were categorized into 155 Grade 2; 16 Grade 3; and 2 Grade 4. Mean accuracy of RFC model was 79.0% [67.4-90.6] and mean AUC-ROC of 0.795 [0.608-0.988]. The accuracy for predicting DT classes was not different among the MRI sequences (P-value = 0.56).

CONCLUSION

Here we demonstrate the utility of machine learning in accurately predicting the renal tumor growth rate category of VHL patients based on radiomic features extracted from different T1-weighted pre- and post-contrast MRI sequences.

Diffusion-Weighted MRI in the Genitourinary System

Diffusion weighted imaging (DWI) constitutes a major functional parameter performed in Magnetic Resonance Imaging (MRI). The DW sequence is performed by acquiring a set of native images described by their b-values, each b-value representing the strength of the diffusion MR gradients specific to that sequence. By fitting the data with models describing the motion of water in tissue, an apparent diffusion coefficient (ADC) map is built and allows the assessment of water mobility inside the tissue. The high cellularity of tumors restricts the water diffusion and decreases the value of ADC within tumors, which makes them appear hypointense on ADC maps. The role of this sequence now largely exceeds its first clinical apparitions in neuroimaging, whereby the method helped diagnose the early phases of cerebral ischemic stroke. The applications extend to whole-body imaging for both neoplastic and non-neoplastic diseases. This review emphasizes the integration of DWI in the genitourinary system imaging by outlining the sequence's usage in female pelvis, prostate, bladder, penis, testis and kidney MRI. In gynecologic imaging, DWI is an essential sequence for the characterization of cervix tumors and endometrial carcinomas, as well as to differentiate between leiomyosarcoma and benign leiomyoma of the uterus. In ovarian epithelial neoplasms, DWI provides key information for the characterization of solid components in heterogeneous complex ovarian masses. In prostate imaging, DWI became an essential part of multi-parametric Magnetic Resonance Imaging (mpMRI) to detect prostate cancer. The Prostate Imaging-Reporting and Data System (PI-RADS) scoring the probability of significant prostate tumors has significantly contributed to this success. Its contribution has established mpMRI as a mandatory examination for the planning of prostate biopsies and radical prostatectomy. Following a similar approach, DWI was included in multiparametric protocols for the bladder and the testis. In renal imaging, DWI is not able to robustly differentiate between malignant and benign renal tumors but may be helpful to characterize tumor subtypes, including clear-cell and non-clear-cell renal carcinomas or low-fat angiomyolipomas. One of the most promising developments of renal DWI is the estimation of renal fibrosis in chronic kidney disease (CKD) patients. In conclusion, DWI constitutes a major advancement in genitourinary imaging with a central role in decision algorithms in the female pelvis and prostate cancer, now allowing promising applications in renal imaging or in the bladder and testicular mpMRI.

Growth Kinetics and Progression Rate of Bosniak Classification, Version 2019 III and IV Cystic Renal Masses on Imaging Surveillance

Background: Active surveillance is increasingly used as first-line management for localized renal masses. Triggers for intervention primarily reflect growth kinetics, which are poorly investigated for cystic masses defined by Bosniak classification version 2019 (v2019). Objective: To determine growth kinetics and incidence rates of progression of class III and IV cystic renal masses, as defined by Bosniak classification v2019. Methods: This retrospective study included 105 patients (68 men, 37 women; median age, 67 years) with 112 Bosniak v2019 class III or IV cystic renal masses on baseline renal-mass protocol CT or MRI examinations from January 2005 to September 2021. Mass dimensions were measured. Progression was defined as any of: linear growth rate (LGR) ≥5 mm per year (representing clinical guideline threshold for intervention), volume doubling time <1 year, T category increase, or N1 or M1 disease. Class III and IV masses were compared. Time-to-progression was estimated using Kaplan-Meier curve analysis. Results: At baseline, 58 masses were class III and 54 were class IV. Median follow-up was 406 days. Median LGR was for class III masses 0.0 mm per year [interquartile range (IQR) -1.3 to 1.8] and for class IV masses 2.3 mm per year (IQR 0.0¬¬-5.7) (p<.001). LGR exceeded 5 mm per year in 4 (7%) class 3 masses and 15 (28%) class IV masses (p=.005). Two patients, both with class IV masses, developed distant metastases. Incidence rate of progression was for class III masses 11.0 (95% CI 4.5-22.8) and for class IV masses 73.6 (95% CI 47.8-108.7) per 100,000 person-days of follow-up. Median time-to-progression was undefined for class III mases given small number of progression events and 710 days for class IV masses. Hazard ratio of progression for class IV relative to class III masses was 5.1 (95% CI 2.5-10.8) (p<.001). Conclusion: During active surveillance of cystic masses evaluated using Bosniak classification v2019, class IV masses grew faster and were more likely to progress than class III masses. Clinical Impact: In comparison with current active surveillance guidelines that treat class III and IV masses similarly, future iterations may incorporate relatively more intensive surveillance for class IV masses.

Renal Neoplasms in Young Adults

Renal cell carcinoma (RCC) is usually diagnosed in older adults (the median age of diagnosis is 64 years). Although less common in patients younger than 45 years, RCCs in young adults differ in clinical manifestation, pathologic diagnosis, and prognosis. RCCs in young adults are typically smaller, are more organ confined, and manifest at lower stages of disease. The proportion of clear cell RCC is lower in young adults, while the prevalence of familial renal neoplastic syndromes is much higher, and genetic testing is routinely recommended. In such syndromic manifestations, benign-appearing renal cysts can harbor malignancy. Radiologists need to be familiar with the differences of RCCs in young adults and apply an altered approach to diagnosis, treatment, and surveillance. For sporadic renal neoplasms, biopsy and active surveillance are less often used in young adults than in older adults. RCCs in young adults are overall associated with better disease-specific survival after surgical treatment, and minimally invasive nephron-sparing treatment options are preferred. However, surveillance schedules, need for biopsy, decision for an initial period of active surveillance, type of surgery (enucleation or wide-margin partial nephrectomy), and utilization of ablative therapy depend on the presence and type of underlying familial renal neoplastic syndrome. In this pictorial review, syndromic, nonsyndromic, and newer RCC entities that are common in young adults are presented. Their associated unique epidemiology, characteristic imaging and pathologic traits, and key aspects of surveillance and management of renal neoplasms in young adults are discussed. The vital role of the informed radiologist in the multidisciplinary management of RCCs in young adults is highlighted. Online supplemental material is available for this article. ^©RSNA, 2022.

Preoperative prediction of the stage, size, grade, and necrosis score in clear cell renal cell carcinoma using MRI-based radiomics

PURPOSE

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma. Currently, there is a lack of noninvasive methods to stratify ccRCC prognosis prior to any invasive therapies. The purpose of this study was to preoperatively predict the tumor stage, size, grade, and necrosis (SSIGN) score of ccRCC using MRI-based radiomics.

METHODS

A multicenter cohort of 364 histopathologically confirmed ccRCC patients (272 low [< 4] and 92 high [≥ 4] SSIGN score) with preoperative T2-weighted and T1-contrast-enhanced MRI were retrospectively identified and divided into training (254 patients) and testing sets (110 patients). The performance of a manually optimized radiomics model was assessed by measuring accuracy, sensitivity, specificity, area under receiver operating characteristic curve (AUROC), and area under precision-recall curve (AUPRC) on an independent test set, which was not included in model training. Lastly, its performance was compared to that of a machine learning pipeline, Tree-Based Pipeline Optimization Tool (TPOT).

RESULTS

The manually optimized radiomics model using Random Forest classification and Analysis of Variance feature selection methods achieved an AUROC of 0.89, AUPRC of 0.81, accuracy of 0.89 (95% CI 0.816-0.937), specificity of 0.95 (95% CI 0.875-0.984), and sensitivity of 0.72 (95% CI 0.537-0.852) on the test set. The TPOT using Extra Trees Classifier achieved an AUROC of 0.94, AUPRC of 0.83, accuracy of 0.89 (95% CI 0.816-0.937), specificity of 0.95 (95% CI 0.875-0.984), and sensitivity of 0.72 (95% CI 0.537-0.852) on the test set.

CONCLUSION

Preoperative MR radiomics can accurately predict SSIGN score of ccRCC, suggesting its promise as a prognostic tool that can be used in conjunction with diagnostic markers.