Magnetic Resonance Imaging as a Biomarker for Renal Cell Carcinoma

Incidence and survival patterns of clear cell renal cell carcinoma from 2000 to 2017: A SEER Database Analysis

Background: Clear cell renal cell carcinoma (ccRCC) incidence and death have considerably changed in recent years. Our study aimed to investigate the incidence, survival, and tumor characteristics of ccRCC in the year of diagnosis. Methods: Our study participants were selected from the SEER database (2000-2017). Age-standardized incidence rates were calculated to compare incidence rates across time. In addition, we used Kaplan-Meier curves to calculate overall survival (OS) and Cox proportional hazards models to explore risk factors associated with mortality outcomes in patients with ccRCC. Results: In the SEER analysis from 2000 to 2017, the increasing trend in age-adjusted incidence of ccRCC has remained relatively stable over the years, increasing from 2.63 per 100,000 in 2000 to 8.79 per 100,000 in 2017. The increase in the incidence of patients at a localized stage plays a decisive role in the overall increase in the incidence of ccRCC. Conclusions: In the general population, patients diagnosed between 2009-2017 had a higher survival rate than those diagnosed between 2000-2008, which is consistent with all stages of the tumor. The incidence of ccRCC increases steadily with the year of diagnosis, while overall survival has significantly improved.

Value of Noncontrast-Enhanced Vessel Wall MRI in Longitudinal Venous System Invasion Before Robot-Assisted Radical Nephrectomy

Objectives: To explore the value of vessel wall MRI (VW-MRI) in the preoperative assessment of T3 renal-cell carcinoma (RCC) with varying degrees of longitudinal venous system invasion. Materials and Methods: Patients with RCC with pathological T3 stage between January 2016 and December 2023 were included in this retrospective study. All the patients underwent contrast-enhanced CT (CECT), conventional MRI (con-MRI) or VW-MRI. Images were independently and blindly evaluated at 4-week intervals by three readers. The pathological features reported in the pathological report, combined with clinical data, were used as the reference standards. The incremental value was calculated using net reclassification improvement (NRI) and integrated discrimination improvement. Results: Eighty-two T3 RCC patients (median age, 65 years) were enrolled. The accuracy of T staging in CECT (n = 59), con-MRI (n = 49), and VW-MRI (n = 30) was 69.5%, 71.4%, and 93.3%, respectively. VW-MRI had a statistically incremental value for CECT in the preoperative evaluation of T3a-c stages (T3a: NRI = 0.066, p = 0.04. T3b: NRI = 0.085, p = 0.02. T3c: NRI = 0.178, P = 0.02), especially in renal pelvicaliceal invasion (NRI = 0.154, p = 0.04) and vena cava wall invasion (NRI = 0.263, p = 0.01). Besides, statistically significant preoperative incremental effects were obtained in the assessment of T3a-c stages (T3a: NRI = 0.264, p = 0.01. T3b: NRI = 0.373, p = 0.03. T3c: NRI = 0.202, p = 0.045), renal vein invasion (NRI = 0.630, p = 0.03), and vena cava wall invasion (NRI = 0.185, p = 0.02) when added VW-MRI into con-MRI. VW-MRI changed 24% (4/27) of the previous CECT and con-MRI-based surgical plan. Conclusion: VW-MRI added a preoperative value for evaluating T stage of T3 RCC, especially in the evaluation of renal vein invasion and vena cava wall invasion.

Deep Learning for Medical Image-Based Cancer Diagnosis

(1) Background: The application of deep learning technology to realize cancer diagnosis based on medical images is one of the research hotspots in the field of artificial intelligence and computer vision. Due to the rapid development of deep learning methods, cancer diagnosis requires very high accuracy and timeliness as well as the inherent particularity and complexity of medical imaging. A comprehensive review of relevant studies is necessary to help readers better understand the current research status and ideas. (2) Methods: Five radiological images, including X-ray, ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), positron emission computed tomography (PET), and histopathological images, are reviewed in this paper. The basic architecture of deep learning and classical pretrained models are comprehensively reviewed. In particular, advanced neural networks emerging in recent years, including transfer learning, ensemble learning (EL), graph neural network, and vision transformer (ViT), are introduced. Five overfitting prevention methods are summarized: batch normalization, dropout, weight initialization, and data augmentation. The application of deep learning technology in medical image-based cancer analysis is sorted out. (3) Results: Deep learning has achieved great success in medical image-based cancer diagnosis, showing good results in image classification, image reconstruction, image detection, image segmentation, image registration, and image synthesis. However, the lack of high-quality labeled datasets limits the role of deep learning and faces challenges in rare cancer diagnosis, multi-modal image fusion, model explainability, and generalization. (4) Conclusions: There is a need for more public standard databases for cancer. The pre-training model based on deep neural networks has the potential to be improved, and special attention should be paid to the research of multimodal data fusion and supervised paradigm. Technologies such as ViT, ensemble learning, and few-shot learning will bring surprises to cancer diagnosis based on medical images.

Similarities and controversies in imaging of pediatric renal tumors: A SIOP-RTSG and COG collaboration

Malignant renal tumors are rare in children, and Wilms tumors (WTs) are the most common subtype. Imaging plays an essential role in the diagnosis, staging, and follow-up of these patients. Initial workup for staging is mainly performed by cross-sectional imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI). Imaging approach within the two core international groups, the Children's Oncology Group (COG, North America) and the International Society of Pediatric Oncology - Renal Tumor Study Group (SIOP-RTSG, Europe), differs. Whereas abdominal ultrasound (US) is used for the initial diagnosis of a suspected pediatric renal tumor globally, COG protocols support the use of CT or MRI for locoregional staging, contrary to the preference for MRI over CT for abdominopelvic evaluation within the SIOP-RTSG. The purpose of this manuscript is to summarize current imaging approaches, highlighting differences and similarities within these core international groups, while focusing on future innovative efforts and collaboration within the HARMONICA initiative.

In Vivo Renal Lipid Quantification by Accelerated Magnetic Resonance Spectroscopic Imaging at 3T: Feasibility and Reliability Study

A reliable and practical renal-lipid quantification and imaging method is needed. Here, the feasibility of an accelerated MRSI method to map renal fat fractions (FF) at 3T and its repeatability were investigated. A 2D density-weighted concentric-ring-trajectory MRSI was used for accelerating the acquisition of 48 × 48 voxels (each of 0.25 mL spatial resolution) without respiratory navigation implementations. The data were collected over 512 complex-FID timepoints with a 1250 Hz spectral bandwidth. The MRSI sequence was designed with a metabolite-cycling technique for lipid–water separation. The in vivo repeatability performance of the sequence was assessed by conducting a test–reposition–retest study within healthy subjects. The coefficient of variation (CV) in the estimated FF from the test–retest measurements showed a high degree of repeatability of MRSI-FF (CV = 4.3 ± 2.5%). Additionally, the matching level of the spectral signature within the same anatomical region was also investigated, and their intrasubject repeatability was also high, with a small standard deviation (8.1 ± 6.4%). The MRSI acquisition duration was ~3 min only. The proposed MRSI technique can be a reliable technique to quantify and map renal metabolites within a clinically acceptable scan time at 3T that supports the future application of this technique for the non-invasive characterization of heterogeneous renal diseases and tumors.

Diagnostic performance of MRI, SPECT, and PET in detecting renal cell carcinoma: a systematic review and meta-analysis

Background

Renal cell carcinoma (RCC) is one of the most common malignancies worldwide. Noninvasive imaging techniques, such as magnetic resonance imaging (MRI), single photon emission computed tomography (SPECT), and positron emission tomography (PET), have been involved in increasing evolution to detect RCC. This meta-analysis aims to compare to compare the performance of MRI, SPECT, and PET in the detection of RCC in humans, and to provide evidence for decision-making in terms of further research and clinical settings.

Methods

Electronic databases including PubMed, Web of Science, Embase, and Cochrane Library were systemically searched. The keywords such as “magnetic resonance imaging”, “MRI”, “single-photon emission computed tomography”, “SPECT”, “positron emission tomography”, “PET”, “renal cell carcinoma” were used for the search. Studies concerning MRI, SPECT, and PET for the detection of RCC were included. Pooled sensitivity, specificity, and the area under the summary receiver operating characteristic (SROC) curve (AUC), etc. were calculated.

Results

A total of 44 articles were finally detected for inclusion in this study. The pooled sensitivities of MRI, ¹⁸F-FDG PET and ¹⁸F-FDG PET/CT were 0.80, 0.83, and 0.89, respectively. Their respective overall specificities were 0.90, 0.86, and 0.88. The pooled sensitivity and specificity of MRI studies at 1.5 T were 0.86 and 0.94, respectively. With respect to prospective PET studies, the pooled sensitivity, specificity and AUC were 0.90, 0.93 and 0.97, respectively. In the detection of primary RCC, PET studies manifested a pooled sensitivity, specificity, and AUC of 0.77, 0.80, and 0.84, respectively. The pooled sensitivity, specificity, and AUC of PET/CT studies in detecting primary RCC were 0.80, 0.85, and 0.89.

Conclusion

Our study manifests that MRI and PET/CT present better diagnostic value for the detection of RCC in comparison with PET. MRI is superior in the diagnosis of primary RCC.

Adaptive Magnetic Resonance-Guided Stereotactic Body Radiotherapy: The Next Step in the Treatment of Renal Cell Carcinoma

Adaptive MR-guided radiotherapy (MRgRT) is a new treatment paradigm and its role as a non-invasive treatment option for renal cell carcinoma is evolving. The early clinical experience to date shows that real-time plan adaptation based on the daily MRI anatomy can lead to improved target coverage and normal tissue sparing. Continued technological innovations will further mitigate the challenges of organ motion and enable more advanced treatment adaptation, and potentially lead to enhanced oncologic outcomes and preservation of renal function. Future applications look promising to make a positive clinical impact and further the personalization of radiotherapy in the management of renal cell carcinoma.

Intravoxel incoherent motion magnetic resonance imaging: basic principles and clinical applications

The purpose of this article was to show basic principles, acquisition, advantages, disadvantages, and clinical applications of intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI). IVIM MRI as a method was introduced in the late 1980s, but recently it started attracting more interest thanks to its applications in many fields, particularly in oncology and neuroradiology. This imaging technique has been developed with the objective of obtaining not only a functional analysis of different organs but also different types of lesions. Among many accessible tools in diagnostic imaging, IVIM MRI aroused the interest of many researchers in terms of studying its applicability in the evaluation of abdominal organs and diseases. The major conclusion of this article is that IVIM MRI seems to be a very auspicious method to investigate the human body, and that nowadays the most promising clinical application for IVIM perfusion MRI is oncology. However, due to lack of standardisation of image acquisition and analysis, further studies are needed to validate this method in clinical practice.

Imaging for Metastatic Renal Cell Carcinoma

Patients with renal cell carcinoma may develop metastases after radical nephrectomy, and therefore monitoring with imaging for recurrent or metastatic disease is critical. Imaging varies with specific suspected site of disease. Computed tomography/MRI of the abdomen and pelvis are mainstay modalities. Osseous and central nervous system imaging is reserved for symptomatic patients. Radiologic reporting is evolving to reflect effects of systemic therapy on lesion morphology. Nuclear medicine studies compliment routine imaging as newer agents are evaluated for more accurate tumor staging. Imaging research aims to fill gaps in treatment selection and monitoring of treatment response in metastatic renal cell carcinoma.

Differential diagnosis of <3 cm renal tumors by ultrasonography: a rapid, quantitative, elastography self-corrected contrast-enhanced ultrasound imaging mode beyond screening

OBJECTIVES

To assess the combined diagnostic strategy of contrast-enhanced ultrasound (CEUS) and acoustic radiation force impulse (ARFI) in the precise differential diagnosis of clear cell renal cell carcinoma (CCRCC) and urothelium carcinoma of the renal pelvis (UCRP) with other small renal tumors (SRTs) ＜3 cm in size.

METHODS

The elastography self-corrected CEUS (ESC) mode was established to perform the quantitative differential diagnosis of SRTs (<3 cm). The kidney shear wave velocity (SWV) value recorded by ARFI showed substantial variability in patients with CCRCC (high elasticity value) and UCRP (low elasticity value) compared with other renal masses, thus providing critical self-correction information for the ultrasound differential diagnosis of SRTs.

RESULTS

In this work, the ESC observations and the corresponding ESC criteria show a remarkable 94.6% accuracy in reference to the gold standards, thus allowing the quantitative, early triple distinction of CCRCC with UCRP and other SRTs in patients with suspicious SRTs.

CONCLUSIONS

This ARFI self-corrected CEUS diagnostic strategy is far beyond a screening method and may have the potential to identify a window of therapeutic opportunity in which emerging therapies might be applied to patients with CCRCC and UCRP, reducing overtreatment and medical costs.

ADVANCES IN KNOWLEDGE

In our study, a new rapid and non-invasive elastography self-corrected CEUS (ESC) ultrasound imaging mode was developed, which was useful in the triple distinction of CCRCC, UCRP, and other SRTs with 94.6% accuracy. ESC is a promising method in the differential diagnosis of SRTs with accuracy and practicability far beyond a single screening model.

Multiparametric MRI Radiomic Model for Preoperative Predicting WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma

BACKGROUND

Nuclear grade is of importance for treatment selection and prognosis in patients with clear cell renal cell carcinoma (ccRCC).

PURPOSE

To develop and validate an MRI-based radiomic model for preoperative predicting WHO/ISUP nuclear grade in ccRCC.

STUDY TYPE

Retrospective.

POPULATION

In all, 379 patients with histologically confirmed ccRCC. Training cohort (n = 252) and validation cohort (n = 127) were randomly assigned.

FIELD STRENGTH/SEQUENCE

Pretreatment 3.0T renal MRI. Imaging sequences were fat-suppressed T₂ WI, contrast-enhanced T₁ WI, and diffusion weighted imaging.

ASSESSMENT

Three prediction models were developed using selected radiomic features, radiomic and clinicoradiologic characteristics, and a model containing only clinicoradiologic characteristics. Receiver operating characteristic (ROC) curves and area under the curve (AUC) were used to assess the predictive performance of these models in predicting high-grade ccRCC.

STATISTICAL TESTS

The least absolute shrinkage and selection operator (LASSO) and minimum redundancy maximum relevance (mRMR) method were used for the selection of radiomic features and clinicoradiologic characteristics, respectively. Multivariable logistic regression analysis was used to develop the radiomic signature of radiomic features and clinicoradiologic model of clinicoradiologic characteristics.

RESULTS

The radiomic signature showed good performance in discriminating high-grade (grades 3 and 4) from low-grade (grades 1 and 2) ccRCC, with sensitivity, specificity, and AUC of 77.3%, 80.0%, and 0.842, respectively, in the validation cohort. The radiomic model, combining radiomic signature and clinicoradiologic characteristics, displayed good predictive ability for high-grade with sensitivity, specificity, and accuracy of 63.6%, 93.3%, and 88.2%, respectively, in the validation cohort. The radiomic model showed a significantly better performance than the clinicoradiologic model (P < 0.05).

DATA CONCLUSION

Multiparametric MRI-based radiomic model can predict WHO/ISUP grade in patients with ccRCC with satisfying performance, and thus could help the physician to improve treatment decisions.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY STAGE: 2.

Current management of small renal masses

One of the consequences of the growing use of diagnostic imaging techniques is the notable growth in the detection of small renal masses presumably corresponding to localized tumors that are potentially curable with surgical treatment. When faced with the finding of a small renal mass, radiologists must determine whether it is benign or malignant, and if it is malignant, what subtype it belong to, and whether it should be managed with surgical treatment, with ablative techniques, or with watchful waiting with active surveillance. Small renal masses are now a clinical entity that require management different from the approaches used for classical renal cell carcinomas. In this scenario, radiologists are key because they are involved in all aspects of the management of these tumors, including in their diagnosis, treatment, and follow-up.

Role of MR texture analysis in histological subtyping and grading of renal cell carcinoma: a preliminary study

PURPOSE

The study evaluated the usefulness of magnetic resonance imaging (MRI) texture parameters in differentiating clear cell renal carcinoma (CC-RCC) from non-clear cell carcinoma (NC-RCC) and in the histological grading of CC-RCC.

MATERIALS AND METHODS

After institutional ethical approval, this retrospective study analyzed 33 patients with 34 RCC masses (29 CC-RCC and five NC-RCC; 19 low-grade and 10 high-grade CC-RCC), who underwent MRI between January 2011 and December 2012 on a 1.5-T scanner (Avanto, Siemens, Erlangen, Germany). The MRI protocol included T2-weighted imaging (T2WI), diffusion-weighted imaging [DWI; at b 0, 500 and 1000 s/mm² with apparent diffusion coefficient (ADC) maps] and T1-weighted pre and postcontrast [corticomedullary (CM) and nephrographic (NG) phase] acquisition. MR texture analysis (MRTA) was performed using the TexRAD research software (Feedback Medical Ltd., Cambridge, UK) by a single reader who placed free-hand polygonal region of interest (ROI) on the slice showing the maximum viable tumor. Filtration histogram-based texture analysis was used to generate six first-order statistical parameters [mean intensity, standard deviation (SD), mean of positive pixels (MPP), entropy, skewness and kurtosis] at five spatial scaling factors (SSF) as well as on the unfiltered image. Mann-Whitney test was used to compare the texture parameters of CC-RCC versus NC-RCC, and high-grade versus low-grade CC-RCC. P value < 0.05 was considered significant. A 3-step feature selection was used to obtain the best texture metrics for each MRI sequence and included the receiver-operating characteristic (ROC) curve analysis and Pearson's correlation test.

RESULTS

The best performing texture parameters in differentiating CC-RCC from NC-RCC for each sequence included (area under the curve in parentheses): entropy at SSF 4 (0.807) on T2WI, SD at SSF 4 (0.814) on DWI b500, SD at SSF 6 (0.879) on DWI b1000, mean at SSF 0 (0.848) on ADC, skewness at SSF 2 (0.854) on T1WI and skewness at SSF 3 (0.908) on CM phase. In differentiating high from low-grade CC-RCC, the best parameters were: entropy at SSF 6 (0.823) on DWI b1000, mean at SSF 3 (0.889) on CM phase and MPP at SSF 5 (0.870) on NG phase.

CONCLUSION

Several MR texture parameters showed excellent diagnostic performance (AUC > 0.8) in differentiating CC-RCC from NC-RCC, and high-grade from low-grade CC-RCC. MRTA could serve as a useful non-invasive tool for this purpose.

Update on Gadolinium-Based Contrast Agent-Enhanced Imaging in the Genitourinary System

OBJECTIVE. The purpose of this article is to review gadolinium-based contrast agent (GBCA)-enhanced MRI applications in the genitourinary system. CONCLUSION. Nephrogenic systemic fibrosis is rare or nonexistent with standard dosing of group II GBCAs. Gadolinium retention, cost, and examination times are emerging considerations affecting GBCA use. GBCA is unnecessary to diagnose adrenal adenomas, simple cysts, and some Bosniak category II cysts; however, it is required to determine solid or septal renal mass enhancement. Biparametric prostate MRI requires high-quality and reproducible DWI; therefore, dynamic contrast-enhanced MRI remains valuable in selected prostate MRI examinations.

Diffusion weighted and dynamic contrast enhanced MRI as an imaging biomarker for stereotactic ablative body radiotherapy (SABR) of primary renal cell carcinoma

Purpose

To explore the utility of diffusion and perfusion changes in primary renal cell carcinoma (RCC) after stereotactic ablative body radiotherapy (SABR) as an early biomarker of treatment response, using diffusion weighted (DWI) and dynamic contrast enhanced (DCE) MRI.

Methods

Patients enrolled in a prospective pilot clinical trial received SABR for primary RCC, and had DWI and DCE MRI scheduled at baseline, 14 days and 70 days after SABR. Tumours <5cm diameter received a single fraction of 26 Gy and larger tumours received three fractions of 14 Gy. Apparent diffusion coefficient (ADC) maps were computed from DWI data and parametric and pharmacokinetic maps were fitted to the DCE data. Tumour volumes were contoured and statistics extracted. Spearman’s rank correlation coefficients were computed between MRI parameter changes versus the percentage tumour volume change from CT at 6, 12 and 24 months and the last follow-up relative to baseline CT.

Results

Twelve patients were eligible for DWI analysis, and a subset of ten patients for DCE MRI analysis. DCE MRI from the second follow-up MRI scan showed correlations between the change in percentage voxels with washout contrast enhancement behaviour and the change in tumour volume (ρ = 0.84, p = 0.004 at 12 month CT, ρ = 0.81, p = 0.02 at 24 month CT, and ρ = 0.89, p = 0.001 at last follow-up CT). The change in mean initial rate of enhancement and mean Ktrans at the second follow-up MRI scan were positively correlated with percent tumour volume change at the 12 month CT onwards (ρ = 0.65, p = 0.05 and ρ = 0.66, p = 0.04 at 12 month CT respectively). Changes in ADC kurtosis from histogram analysis at the first follow-up MRI scan also showed positive correlations with the percentage tumour volume change (ρ = 0.66, p = 0.02 at 12 month CT, ρ = 0.69, p = 0.02 at last follow-up CT), but these results are possibly confounded by inflammation.

Conclusion

DWI and DCE MRI parameters show potential as early response biomarkers after SABR for primary RCC. Further prospective validation using larger patient cohorts is warranted.

Upregulation of long noncoding RNA PVT1 predicts unfavorable prognosis in patients with clear cell renal cell carcinoma

BACKGROUND

Renal cell carcinoma (RCC) is one of the most malignant genitourinary diseases worldwide. Long noncoding RNAs (lncRNAs) are a class of noncoding RNAs in the human genome that are involved in RCC initiation and progression.

OBJECTIVE

To investigate the expression of PVT1 in ccRCC and evaluate its correlation with clinicopathologic characteristics and patients' survival.

METHODS

Quantitative real-time PCR was performed to examine PVT1 expression in 129 ccRCC tissue samples and matched adjacent normal tissue samples. The relationship of PVT1 expression with clinicopathologic characteristics and clinical outcome was evaluated.

RESULTS

We identified the lncRNA PVT1, which was upregulated in clear cell renal cell carcinoma (ccRCC) tissues when compared with corresponding controls. Furthermore, PVT1 expression was positively associated with gender, tumor size, pT stage, TNM stage, and Fuhrman grade. Kaplan-Meier survival analysis showed that patients with high PVT1 expression had shorter disease-free survival (DFS) and overall-survival (OS) than those with low PVT1 expression, and multivariate analysis identified PVT1 as an independent prognostic factor in ccRCC.

CONCLUSIONS

PVT1 may be an oncogene as well as may promote metastasis in ccRCC and could serve as a potential biomarker to predict the prognosis of ccRCC patients.

Differentiating solid, non-macroscopic fat containing, enhancing renal masses using fast Fourier transform analysis of multiphase CT

OBJECTIVE

To test the feasibility of two-dimensional fast Fourier transforms (FFT)-based imaging metrics in differentiating solid, non-macroscopic fat containing, enhancing renal masses using contrast-enhanced CT images. We quantify image-based intratumoral textural variations (indicator of tumor heterogeneity) using frequency-based (FFT) imaging metrics.

METHODS

In this Institutional Review Board approved, Health Insurance Portability and Accountability Act -compliant, retrospective case-control study, we evaluated 156 patients with predominantly solid, non-macroscopic fat containing, enhancing renal masses identified between June 2009 and June 2016. 110 cases (70%) were malignant RCC, including clear cell, papillary and chromophobe subtypes and, 46 cases (30%) were benign renal masses: oncocytoma and lipid-poor angiomyolipoma. Whole lesions were manually segmented using Synapse 3D (Fujifilm, CT) and co-registered from the multiphase CT acquisitions for each tumor. Pathological diagnosis of all tumors was obtained following surgical resection. Matlab function, FFT2 was used to perform the image to frequency transformation.

RESULTS

A Wilcoxon rank sum test showed that FFT-based metrics were significantly (p < 0.005) different between 1. benign vs malignant renal masses, 2. oncocytoma vs clear cell renal cell carcinoma and 3. oncocytoma vs lipid-poor angiomyolipoma. Receiver operator characteristics analysis revealed reasonable discrimination (area under the curve >0.7, p < 0.05) within these three groups of comparisons.

CONCLUSION

In combination with other metrics, FFT-metrics may improve patient management and potentially help differentiate other renal tumors. Advances in knowledge: We report for the first time that FFT-based metrics can differentiate between some solid, non-macroscopic fat containing, enhancing renal masses using their contrast-enhanced CT data.

Texture analysis as a radiomic marker for differentiating renal tumors

PURPOSE

To evaluate the utility of texture analysis for the differentiation of renal tumors, including the various renal cell carcinoma subtypes and oncocytoma.

MATERIALS AND METHODS

Following IRB approval, a retrospective analysis was performed, including all patients with pathology-proven renal tumors and an abdominal computed tomography (CT) examination. CT images of the tumors were manually segmented, and texture analysis of the segmented tumors was performed. A support vector machine (SVM) method was also applied to classify tumor types. Texture analysis results were compared to the various tumors and areas under the curve (AUC) were calculated. Similar calculations were performed with the SVM data.

RESULTS

One hundred nineteen patients were included. Excellent discriminators of tumors were identified among the histogram-based features noting features skewness and kurtosis, which demonstrated AUCs of 0.91 and 0.93 (p < 0.0001), respectively, for differentiating clear cell subtype from oncocytoma. Histogram feature median demonstrated an AUC of 0.99 (p < 0.0001) for differentiating papillary subtype from oncocytoma and an AUC of 0.92 for differentiating oncocytoma from other tumors. Machine learning further improved the results achieving very good to excellent discrimination of tumor subtypes. The ability of machine learning to distinguish clear cell subtype from other tumors and papillary subtype from other tumors was excellent with AUCs of 0.91 and 0.92, respectively.

CONCLUSION

Texture analysis is a promising non-invasive tool for distinguishing renal tumors on CT images. These results were further improved upon application of machine learning, and support the further development of texture analysis as a quantitative biomarker for distinguishing various renal tumors.

Renal cell carcinoma: the search for a reliable biomarker

One particular challenge in the treatment of kidney tumors is the range of histologies and tumor phenotypes a renal mass can represent. A kidney tumor can range from benign (e.g., oncocytoma) to a clinically indolent malignancy (e.g., papillary type I, chromophobe) to aggressive disease [e.g., papillary type II or high-grade clear cell renal cell carcinoma (ccRCC)]. Even among various subtypes, kidney cancers are genetically diverse with variable prognoses and treatment response rates. Therefore, the key to proper treatment is the differentiation of these subtypes. Currently, a wide array of diagnostic, prognostic, and predictive biomarkers exist that may help guide the individualized care of kidney cancer patients. This review will discuss the various serum, urine, imaging, and immunohistological biomarkers available in practice.

CD146 Promoter Polymorphism (rs3923594) Is Associated with Recurrence of Clear Cell Renal Cell Carcinoma in Chinese Population

INTRODUCTION

CD146 is a membrane signal receptor in tumor-induced angiogenesis. However, limited studies have focused on the CD146 promoter polymorphisms in clear cell renal cell carcinoma (ccRCC).

PURPOSE

The purpose of this study was to investigate the association between polymorphisms located in the promoter region of the CD146 gene and characteristics of ccRCC in Chinese population. The association between the CD146 promoter polymorphisms and CD146 expression was also investigated in ccRCC.

MATERIALS AND METHODS

A total of 600 samples including 300 ccRCC patients and 300 healthy controls were collected for analysis of the CD146 promoter polymorphisms by direct sequence. The CD146 expressions were measured by qRT-PCR.

RESULTS

We had not found any significant differences in genotypic and allelic frequencies of CD146 promoter polymorphisms between ccRCC patients and controls. The rs3923594 was associated with stage and metastasis (300 cases) and recurrence (263 cases) of ccRCC in Chinese population. A significant association was also observed between the rs3923594 and CD146 expression (227 cases) in ccRCC.

CONCLUSIONS

CD146 promoter polymorphisms were not associated with the risk of ccRCC in Chinese population. The rs3923594 was an independent predictor of recurrence in Chinese patients with localized ccRCC.

The radiologist's role in the management of papillary renal cell carcinoma

Papillary carcinoma is the second most common renal cell carcinoma. It has a better prognosis than the more frequent clear cell carcinoma, although this does not hold true for advanced cases, because no specific treatment exists. It presents as a circumscribed peripheral tumor (small and homogeneously solid or larger and cystic/hemorrhagic) or as an infiltrating lesion that invades the veins, which has a worse prognosis. Due to their low vascular density, papillary renal cell carcinomas enhance less than other renal tumors, and this facilitates their characterization. On computed tomography, they might not enhance conclusively, and in these cases they are impossible to distinguish from hyperattenuating cysts. Contrast-enhanced ultrasonography and magnetic resonance imaging are more sensitive for detecting vascularization. Other characteristics include a specific vascular pattern, hypointensity on T2-weighted images, restricted water diffusion, and increased signal intensity in opposed phase images. We discuss the genetic, histologic, clinical, and radiological aspects of these tumors in which radiologists play a fundamental role in management.

Precision Oncology: Identifying Predictive Biomarkers for the Treatment of Metastatic Renal Cell Carcinoma

The recent FDA approval of multiple new pharmaceutical agents for metastatic renal cell carcinoma (RCC) has left physicians with several options for first- and second- line therapy. With limited head-to-head comparisons, however, there is a paucity of evidence to recommend the use of one agent over another. To address this knowledge gap, Voss et al. identified serum biomarkers from specimens collected during the RECORD-3 trial, a comparative study of first-line sunitinib versus first-line everolimus. Of the biomarkers identified, the 5 most strongly associated with first-line everolimus progression-free survival (PFS1L) were combined to form a composite biomarker score (CBS). The CBS was significantly associated with everolimus PFS1L in multivariate regression analysis. This study is an example of the additional value offered by a randomized trial with prospective biospecimen collection and a significant step towards identifying predictive biomarkers for the treatment of metastatic RCC. As further comparative trials are performed, it will be essential that biomarkers are appropriately identified and validated in order to further the goal of precision oncology.

Next-generation technologies for spatial proteomics: Integrating ultra-high speed MALDI-TOF and high mass resolution MALDI FTICR imaging mass spectrometry for protein analysis

MALDI imaging mass spectrometry is a powerful analytical tool enabling the visualization of biomolecules in tissue. However, there are unique challenges associated with protein imaging experiments including the need for higher spatial resolution capabilities, improved image acquisition rates, and better molecular specificity. Here we demonstrate the capabilities of ultra-high speed MALDI-TOF and high mass resolution MALDI FTICR IMS platforms as they relate to these challenges. High spatial resolution MALDI-TOF protein images of rat brain tissue and cystic fibrosis lung tissue were acquired at image acquisition rates >25 pixels/s. Structures as small as 50 μm were spatially resolved and proteins associated with host immune response were observed in cystic fibrosis lung tissue. Ultra-high speed MALDI-TOF enables unique applications including megapixel molecular imaging as demonstrated for lipid analysis of cystic fibrosis lung tissue. Additionally, imaging experiments using MALDI FTICR IMS were shown to produce data with high mass accuracy (<5 ppm) and resolving power (∼75 000 at m/z 5000) for proteins up to ∼20 kDa. Analysis of clear cell renal cell carcinoma using MALDI FTICR IMS identified specific proteins localized to healthy tissue regions, within the tumor, and also in areas of increased vascularization around the tumor.