Accuracy of multidetector CT scans in staging of renal carcinoma

A Narrative Review on Robotic Surgery as Treatment for Renal Cell Carcinoma with Inferior Vena Cava Thrombus

Renal cell carcinoma (RCC) is a common diagnosis, of which a notable portion of patients present with an extension into the venous circulation causing an inferior vena cava (IVC) tumor thrombus. Venous extension has significant implications for staging and subsequent treatment planning, with recommendations for more aggressive surgical removal, although associated surgical morbidity and mortality is relatively increased. The methods for surgical removal of RCC with IVC thrombus remain complex, particularly surrounding the use of robot-assisted surgery. Robot assistance for radical nephrectomy in this context is recently emerging. Thrombus level has important implications for surgical technique and prognosis. Other preoperative considerations may include location, laterality, size, and wall invasion. The urology literature on treatment of such tumors is largely limited to case series and institutional studies that describe the feasibility of various surgical options for these complex tumors. Further understanding of the outcomes and patient-specific risk factors would shed increased light on the optimal treatment for such cases. This narrative review provides a thorough overview on the previously reported use of robot-assisted nephrectomy in RCC with IVC thrombus to inform further studies which may optimize outcomes and guide shared decision-making.

Robotic Partial vs Radical Nephrectomy for Clinical T3a Tumors: A Narrative Review

Introduction: T3a renal masses include a diverse group of tumors that invade the perirenal and/or sinus fat, pelvicaliceal system, or renal vein. The majority of cT3a renal masses represent renal cell carcinoma (RCC) and have historically been treated with radical nephrectomy (RN) given their aggressive nature. With the adoption of minimally invasive approaches to renal surgery, the combination of improved observation, pneumoperitoneum, and robotic articulation has allowed urologists to consider partial nephrectomy (PN) for more complex tumors. Herein, we review the existing literature regarding robot-assisted PN (RAPN) and robot-assisted RN (RARN) in the management of T3a renal masses. Methods: A literature search was performed using PubMed for articles evaluating the role of RARN and RAPN for T3a renal masses. Search parameters were limited to English language studies. Applicable studies were abstracted and included in this narrative review. Results: T3a RCC caused by renal sinus fat or venous involvement is associated with ∼50% lower cancer-specific survival than those with perinephric fat invasion alone. CT and MRI can both be used to stage cT3a tumors, however, MRI is more accurate when assessing venous involvement. Upstaging to pT3a RCC during RAPN does not confer a worse prognosis than pT3a tumors treated with RARN; however, patients who undergo RAPN for T3a RCC with venous involvement have relatively higher rates of recurrence and metastasis. Intraoperative tools including drop-in ultrasound, near-infrared fluorescence, and 3D virtual models improve the ability to perform RAPN for T3a tumors. In well-selected cases, warm ischemia times remain reasonable. Conclusions: cT3a renal masses represent a diverse group of tumors. Depending on substratification of cT3a, RARN or RAPN can be employed for treatment of such masses.

ACR Appropriateness Criteria® Staging of Renal Cell Carcinoma: 2022 Update

Renal cell carcinoma is a complex group of highly heterogenous renal tumors demonstrating variable biological behavior. Pretreatment imaging of renal cell carcinoma involves accurate assessment of the primary tumor, presence of nodal, and distant metastases. CT and MRI are the key imaging modalities used in the staging of renal cell carcinoma. Important imaging features that impact treatment include tumor extension into renal sinus and perinephric fat, involvement of pelvicalyceal system, infiltration into adrenal gland, involvement of renal vein and inferior vena cava, as well as the presence of metastatic adenopathy and distant metastases. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Integrating Surgery in the Multidisciplinary Care of Advanced Renal Cell Carcinoma

The role of surgery for patients with locally advanced and metastatic renal cell carcinoma (RCC) is not precisely defined in our contemporary era of systemic therapies. Research in this field is focused on the role of regional lymphadenectomy, along with indications and timing of cytoreductive nephrectomy and metastasectomy. As our understanding of the molecular and immunological basis of RCC continues to develop along with the advent of novel systemic therapies, prospective clinical trials will be critical in defining how surgery should be integrated into the treatment paradigm of advanced RCC.

Renal cell carcinoma with inferior vena cava thrombus: did we make progress in oncologic outcomes and complications?

PURPOSE OF REVIEW

Surgery for renal cell carcinoma (RCC) with inferior vena cava tumor thrombus (TT) remains one of the most complex surgeries performed with high rates of associated complications and perioperative mortality. Surgical techniques and neoadjuvant therapies have an important role to play in improving outcomes. This review provides a narrative analysis of recent literature on patients with RCC and TT.

RECENT FINDINGS

Several imaging techniques are emerging that may improve diagnostic staging of tumor thrombus level. Robotic approaches to surgical resection for all thrombi levels is feasible and safe, while longer term outcomes for higher level thrombi continues to mature. Early data on neoadjuvant immunotherapy and radiotherapy have shown improvements in complication rates and intermediate term oncologic outcomes.

SUMMARY

Data suggests that neoadjuvant therapies and minimally invasive techniques may improve outcomes in patients undergoing surgical resection for RCC with tumor thrombus. Larger multiinstitutional series are needed to confirm the benefit of these techniques as well as the durable long term oncologic outcomes.

A preliminary radiomics model for predicting perirenal fat invasion on renal cell carcinoma with contrast-enhanced CT images

OBJECTIVE

The aim is to develop a radiomics model based on contrast-enhanced CT scans for preoperative prediction of perirenal fat invasion (PFI) in patients with renal cell carcinoma (RCC).

METHODS

The CT data of 131 patients with pathology-confirmed PFI status (64 positives) were retrospectively collected and randomly assigned to the training and test datasets. The kidneys and the masses were annotated by semi-automatic segmentation. Eight types of regions of interest (ROI) were chosen for the training of the radiomics models. The areas under the curves (AUCs) from the receiver operating characteristic (ROC) curve analysis were used to analyze the diagnostic performance. Eight types of models with different ROIs have been developed. The models with the highest AUC in the test dataset were used for construction of the corresponding final model, and comparison with radiologists' diagnosis.

RESULTS

The AUCs of the models for each ROI was 0.783-0.926, and there was no statistically significant difference between them (P > 0.05). Model 4 was using the ROI of the outer half-ring which extended along the edge of the mass at the outer edge of the kidney into the perirenal fat space with a thickness of 3 mm. It yielded the highest AUC (0.926) and its diagnostic accuracy was higher than the radiologists' diagnosis.

CONCLUSION

We have developed and validated a radiomics model for prediction of PFI on RCC with contrast-enhanced CT scans. The model proved to be more accurate than the radiologists' diagnosis.

Renal cell carcinoma T staging: Diagnostic accuracy of preoperative contrast-enhanced computed tomography

Renal cell carcinoma (RCC) accounts for 1-2% of all malignancies and is the most common renal tumor in adults. Imaging studies are used for diagnosis and staging. Tumor-Node-Metastasis staging strongly affects prognosis and management, while contrast-enhanced computed tomography (CECT) is regarded as a standard imaging technique for local and distant staging. The present study aimed to evaluate the accuracy of CECT for the preoperative staging of RCC by using surgical and pathological staging as the reference methods. This single-center prospective study was conducted between October 2019 and November 2021. The preoperative abdominal CT scans of patients suspected of having RCC were reviewed. Imaging data were collected, including tumor side and size, and perinephric fat invasion. Intraoperative notes were recorded, including the operation type, perinephric fat invasion, renal vein (RV) or inferior vena cava (IVC) tumor extension, and surrounding organ invasion. pathological data were collected on tumor size, RCC type, presence of clear margins, presence of renal capsule or perinephric fat invasion, renal sinus or pelvicalyceal system (PCS) invasion, segmental or main RV extension, and the involvement of Gerota's fascia and nearby organs. Preoperative CECT revealed that 42 out of 59 tumors had a greater maximum diameter than the pathological specimen, with an overall disparity of 0.25 cm. The specificity of CT for the detection of tumor invasion of the perinephric and renal sinus fat and PCS was 95%, and the sensitivity ranged from 80 to 88%. CT had an 83% sensitivity and a 95 specificity in detecting T4 stage cancer, with a 100% specificity for adrenal invasion. The concordance between radiographic and histological results for RV and IVC involvement was high, with specificities of 94 and 98%, and sensitivities of 80 and 100%, respectively. Overall accuracy for correct T staging was 80%. In conclusion, CECT is accurate in the local T staging of RCC, with high sensitivity and specificity for estimating tumor size and detecting extension to nearby structures and venous invasion.

Role of diffusion-weighted MRI for prediction of regional lymph node positivity in radiologically organ-confined renal tumour: a prospective study

Background

Lymph node metastasis is one of the major factors that decide the prognosis of renal cell carcinoma. Presently, lymphadenectomy is only accepted as the most precise and dependable staging method to detect lymph node invasion; still, its therapeutic value for renal cell carcinoma is controversial. Diffusion-weighted magnetic resonance imaging along with its apparent diffusion coefficient value has already shown great value as a non-invasive modality to detect early microstructural changes in various human tumours. The present study is done to know the role of DWMRI in determining regional lymph node positivity in radiologically organ-confined renal cell carcinoma.

Methods

In this prospective study, we measured the ADC value of renal mass and regional lymph node in patient of RCC. ADC value < 1.25 is taken as cut-off to determine lymph node involvement. A malignant lymph node was confirmed by histopathology postoperatively. After that, we analysed the data retrospectively and studied the association between cut-off ADC value and lymph node positivity.

Results

Total 44 patients of RCC were evaluated in the study. Out of 44 patients, lymph node was found to be malignant on histopathology in 25 (56.8%) patients, and of these, 23 patients had ADC value < 1.25. This association was statistically significant (p < 0.05). The findings of DW MRI were accurate in 72.7% of patients with sensitivity of 63.1%, specificity of 80% and positive predictive value of 70.5%.

Conclusions

Lymph node with ADC value < 1.25 × 10–3 mm2/s has higher probabilities of harbouring malignant cell, so ADC value of DWMRI can be used as a sensitive and specific parameter to differentiate malignant lymph node from benign lymph node. However, our futuristic observation needs to be validated by multi-institutional large sample cohort.

Value of Clinical Information on Radiology Reports in Oncological Imaging

Radiological reporting errors have a direct negative impact on patient treatment. The purpose of this study was to investigate the contribution of clinical information (CI) in radiological reporting of oncological imaging and the dependence on the radiologists’ experience level (EL). Sixty-four patients with several types of carcinomas and twenty patients without tumors were enrolled. Computed tomography datasets acquired in primary or follow-up staging were independently analyzed by three radiologists (R) with different EL (R1: 15 years; R2: 10 years, R3: 1 year). Reading was initially performed without and 3 months later with CI. Overall, diagnostic accuracy and sensitivity for primary tumor detection increased significantly when receiving CI from 77% to 87%; p = 0.01 and 73% to 83%; p = 0.01, respectively. All radiologists benefitted from CI; R1: 85% vs. 92%, p = 0.15; R2: 77% vs. 83%, p = 0.33; R3: 70% vs. 86%, p = 0.02. Overall, diagnostic accuracy and sensitivity for detecting lymphogenous metastases increased from 80% to 85% (p = 0.13) and 42% to 56% (p = 0.13), for detection of hematogenous metastases from 85% to 86% (p = 0.61) and 46% to 60% (p = 0.15). Specificity remained stable (>90%). Thus, CI in oncological imaging seems to be essential for correct radiological reporting, especially for residents, and should be available for the radiologist whenever possible.

A deep learning-based precision volume calculation approach for kidney and tumor segmentation on computed tomography images

Previously, doctors interpreted computed tomography (CT) images based on their experience in diagnosing kidney diseases. However, with the rapid increase in CT images, such interpretations were required considerable time and effort, producing inconsistent results. Several novel neural network models were proposed to automatically identify kidney or tumor areas in CT images for solving this problem. In most of these models, only the neural network structure was modified to improve accuracy. However, data pre-processing was also a crucial step in improving the results. This study systematically discussed the necessary pre-processing methods before processing medical images in a neural network model. The experimental results were shown that the proposed pre-processing methods or models significantly improve the accuracy rate compared with the case without data pre-processing. Specifically, the dice score was improved from 0.9436 to 0.9648 for kidney segmentation and 0.7294 for all types of tumor detections. The performance was suitable for clinical applications with lower computational resources based on the proposed medical image processing methods and deep learning models. The cost efficiency and effectiveness were also achieved for automatic kidney volume calculation and tumor detection accurately.

Predictive features of pre-operative computed tomography and magnetic resonance imaging for advanced disease in renal cell carcinoma

OBJECTIVE

We evaluated predictive features of pre-operative computed tomography and magnetic resonance imaging for advanced disease in renal cell carcinoma.

MATERIALS AND METHODS

92 patients with pathologically confirmed diagnosis of renal cell carcinoma were included in our study. Patients were divided into two groups according to preoperative imaging as computed tomography (CT) (55 patients) and magnetic resonance imaging (MRI) (37 patients). Within the imaging groups, the patients were divided into two groups according to pathological tumor stage: 1-2 (pT1-2) versus ≥ pT3a. It was evaluated whether there was a difference between the two groups in terms of the presence of pre-operative imaging (CT and MRI) features. Predictive value of these features for ≥ pT3a disease was evaluated both for CT and MRI.

RESULTS

The cut-off value for the Gerota's fascia thickness in predicting ≥ pT3a disease was calculated as 0.205 cm. Positive predictive value (PPV) for Gerota's fascia thickness was 52.4% (31.0-73.7) and 66.7% (40.0-93.3) for CT and MRI respectively. The PPV value for renal capsule invasion was 75.0% (53.8-96.2) and 90.0% (71.4-108.6) for CT and MRI respectively. PPV of perirenal fat invasion for CT and MRI was 69.2% (44.1-94.3) and 81.8% (59.0-104.6) respectively.

CONCLUSION

Renal capsular invasion and perirenal fat invasion are reliable signs for locally advanced (≥ pT3a) renal cell carcinoma both in CT and MRI. Gerota's fascia thickness has relatively low PPV value for prediction of locally advanced disease. Presence of enlarged collateral vessels, tumor necrosis, perinephric stranding are not reliable signs. For all predictors MRI seems more reliable than CT.

Radiology Imaging Scans for Early Diagnosis of Kidney Tumors: A Review of Data Analytics-Based Machine Learning and Deep Learning Approaches

Plenty of disease types exist in world communities that can be explained by humans’ lifestyles or the economic, social, genetic, and other factors of the country of residence. Recently, most research has focused on studying common diseases in the population to reduce death risks, take the best procedure for treatment, and enhance the healthcare level of the communities. Kidney Disease is one of the common diseases that have affected our societies. Sectionicularly Kidney Tumors (KT) are the 10th most prevalent tumor for men and women worldwide. Overall, the lifetime likelihood of developing a kidney tumor for males is about 1 in 466 (2.02 percent) and it is around 1 in 80 (1.03 percent) for females. Still, more research is needed on new diagnostic, early, and innovative methods regarding finding an appropriate treatment method for KT. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of machine learning can save diagnosis time, improve test accuracy, and reduce costs. Previous studies have shown that deep learning can play a role in dealing with complex tasks, diagnosis and segmentation, and classification of Kidney Tumors, one of the most malignant tumors. The goals of this review article on deep learning in radiology imaging are to summarize what has already been accomplished, determine the techniques used by the researchers in previous years in diagnosing Kidney Tumors through medical imaging, and identify some promising future avenues, whether in terms of applications or technological developments, as well as identifying common problems, describing ways to expand the data set, summarizing the knowledge and best practices, and determining remaining challenges and future directions.

CT-based radiomics stratification of tumor grade and TNM stage of clear cell renal cell carcinoma

OBJECTIVES

To evaluate the utility of CT-based radiomics signatures in discriminating low-grade (grades 1-2) clear cell renal cell carcinomas (ccRCC) from high-grade (grades 3-4) and low TNM stage (stages I-II) ccRCC from high TNM stage (stages III-IV).

METHODS

A total of 587 subjects (mean age 60.2 years ± 12.2; range 22-88.7 years) with ccRCC were included. A total of 255 tumors were high grade and 153 were high stage. For each subject, one dominant tumor was delineated as the region of interest (ROI). Our institutional radiomics pipeline was then used to extract 2824 radiomics features across 12 texture families from the manually segmented volumes of interest. Separate iterations of the machine learning models using all extracted features (full model) as well as only a subset of previously identified robust metrics (robust model) were developed. Variable of importance (VOI) analysis was performed using the out-of-bag Gini index to identify the top 10 radiomics metrics driving each classifier. Model performance was reported using area under the receiver operating curve (AUC).

RESULTS

The highest AUC to distinguish between low- and high-grade ccRCC was 0.70 (95% CI 0.62-0.78) and the highest AUC to distinguish between low- and high-stage ccRCC was 0.80 (95% CI 0.74-0.86). Comparable AUCs of 0.73 (95% CI 0.65-0.8) and 0.77 (95% CI 0.7-0.84) were reported using the robust model for grade and stage classification, respectively. VOI analysis revealed the importance of neighborhood operation-based methods, including GLCM, GLDM, and GLRLM, in driving the performance of the robust models for both grade and stage classification.

CONCLUSION

Post-validation, CT-based radiomics signatures may prove to be useful tools to assess ccRCC grade and stage and could potentially add to current prognostic models. Multiphase CT-based radiomics signatures have potential to serve as a non-invasive stratification schema for distinguishing between low- and high-grade as well as low- and high-stage ccRCC.

KEY POINTS

• Radiomics signatures derived from clinical multiphase CT images were able to stratify low- from high-grade ccRCC, with an AUC of 0.70 (95% CI 0.62-0.78). • Radiomics signatures derived from multiphase CT images yielded discriminative power to stratify low from high TNM stage in ccRCC, with an AUC of 0.80 (95% CI 0.74-0.86). • Models created using only robust radiomics features achieved comparable AUCs of 0.73 (95% CI 0.65-0.80) and 0.77 (95% CI 0.70-0.84) to the model with all radiomics features in classifying ccRCC grade and stage, respectively.

Surgical Tips for Inferior Vena Cava Thrombectomy

PURPOSE OF REVIEW

The aim of this review is to describe the preoperative evaluation, surgical techniques, and postoperative management of patients with renal cell carcinoma (RCC) undergoing radical nephrectomy (RN) and inferior vena cava (IVC) thrombectomy.

RECENT FINDINGS

RN and IVC thrombectomy remains the standard management option in non-metastatic RCC patients with IVC thrombus. A comprehensive preoperative workup, including high-quality imaging, blood works, and appropriate consultations are required for all patients. The aim of the surgery is complete resection of all tumor burden, which requires a skillful surgical team for such a challenging procedure and is inherently associated with a high rate of perioperative morbidity and mortality. Preoperative CT or MRI is essential for surgical planning. The surgical approach is mainly determined by the level of the tumor thrombus. The open approach has been the standard, though minimally invasive and robotic techniques are emerging in selected cases by experienced surgeons.

Renal Cell Carcinoma with Thrombus Extension into the Inferior Vena Cava and the Right Atrium: A Case Report

•

IVC extension in RCC can present as an acute pulmonary embolism.

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Pre-operative assessment of tumor level is valuable for optimizing management.

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Anticoagulation should be considered when bland thrombus accompanies tumor thrombus.

Preoperative CT Radiomics Predicting the SSIGN Risk Groups in Patients With Clear Cell Renal Cell Carcinoma: Development and Multicenter Validation

Objective: The stage, size, grade, and necrosis (SSIGN) score can facilitate the assessment of tumor aggressiveness and the personal management for patients with clear cell renal cell carcinoma (ccRCC). However, this score is only available after the postoperative pathological evaluation. The aim of this study was to develop and validate a CT radiomic signature for the preoperative prediction of SSIGN risk groups in patients with ccRCC in multicenters.

Methods: In total, 330 patients with ccRCC from three centers were classified into the training, external validation 1, and external validation 2 cohorts. Through consistent analysis and the least absolute shrinkage and selection operator, a radiomic signature was developed to predict the SSIGN low-risk group (scores 0–3) and intermediate- to high-risk group (score ≥ 4). An image feature model was developed according to the independent image features, and a fusion model was constructed integrating the radiomic signature and the independent image features. Furthermore, the predictive performance of the above models for the SSIGN risk groups was evaluated with regard to their discrimination, calibration, and clinical usefulness.

Results: A radiomic signature consisting of sixteen relevant features from the nephrographic phase CT images achieved a good calibration (all Hosmer–Lemeshow p > 0.05) and favorable prediction efficacy in the training cohort [area under the curve (AUC): 0.940, 95% confidence interval (CI): 0.884–0.973] and in the external validation cohorts (AUC: 0.876, 95% CI: 0.811–0.942; AUC: 0.928, 95% CI: 0.844–0.975, respectively). The radiomic signature performed better than the image feature model constructed by intra-tumoral vessels (all p < 0.05) and showed similar performance with the fusion model integrating radiomic signature and intra-tumoral vessels (all p > 0.05) in terms of the discrimination in all cohorts. Moreover, the decision curve analysis verified the clinical utility of the radiomic signature in both external cohorts.

Conclusion: Radiomic signature could be used as a promising non-invasive tool to predict SSIGN risk groups and to facilitate preoperative clinical decision-making for patients with ccRCC.

Outcomes and prognosticators of stage 4 renal cell carcinoma with pathological T4 primary lesion using a large, Canadian, multi-institutional database

INTRODUCTION

The primary objective of this study was to evaluate outcomes and prognosticators in patients who underwent radical nephrectomy (RN) or cytoreductive nephrectomy (CN), depending on the clinical stage of disease preoperatively, with a pathological T4 (pT4) renal cell carcinoma (RCC) outcome. There is little data on the outcome of this specific subset of patients.

METHODS

From 2009-2016, we identified patients in the Canadian Kidney Cancer information system (CKCis) who underwent RN or CN and were found to have pT4 RCC. Clinical, operative, and pathological variables were analyzed with univariable and multivariable Cox proportional hazard models to identify factors associated with overall survival (OS). Survival curves were created using Kaplan-Meier methods and compared using the log-rank test.

RESULTS

A total of 82 patients were included in the study cohort. Median patient age was 62 years (interquartile range [IQR] 55, 70). Fifty (61%) patients had clear-cell histology and 14 (17%) had sarcomatoid characteristics. Median followup was 12 months (IQR 3, 24). At last followup, eight (10%) patients are alive with no evidence of disease, 27 (33%) are alive with disease, four (5%) were lost to followup, 36 (44%) died of disease, and seven (8%) died of other causes. Tumor histological subtype (clear-cell vs. non-clear-cell) (p=0.0032), larger tumor size (cm) (p=0.012), and Fuhrman grade (G4 vs. G2-G3) (p=0.045) were significantly associated with mortality in a multivariable Cox regression model.

CONCLUSIONS

For patients with pT4 RCC after RN or CN, survival is poor. Sarcomatoid features, non-clear-cell histology, and presence of systemic symptoms were associated with worse OS.

Correlation between radiological and histopathological findings in patients undergoing nephrectomy for presumed renal cell carcinoma on computed tomography scan at Grey's Hospital

Background

The incidence of renal cell carcinoma (RCC) is increasing globally owing to the increased use of cross-sectional imaging. Computed tomography (CT) scan is the modality of choice in the diagnosis and pre-operative assessment of RCC. Nephrectomy is the standard treatment for RCC and pre-surgery biopsy is not routinely practised. The accuracy of CT diagnosis and staging in a South African population has not been established.

Objectives

To determine the accuracy of CT scan in the diagnosis and pre-operative staging of RCC at Grey’s Hospital.

Methods

A retrospective chart review was performed; CT scan reports and histopathological results of adult patients who underwent nephrectomy for presumed RCC on CT scan between January 2010 and December 2016 were compared.

Results

Fifty patients met the inclusion criteria for the study. CT significantly overestimated the size of renal masses by 0.7 cm (p = 0.045) on average. The positive predictive value of CT for RCC was 81%. Cystic tumours and those 4 cm and smaller were more likely to be benign. CT demonstrated good specificity for extra-renal extension, vascular invasion and lymph node involvement, but poor sensitivity.

Conclusion

In our South African study population, CT is accurate at diagnosing RCC, but false-positives do occur. Non-enhancing or poorly enhancing, cystic, fat-containing and small lesions (4 cm or smaller) are more likely to be benign and ultrasound-guided biopsy should be considered to avoid unnecessary surgery. CT assessment of extra-renal extension and vascular invasion is challenging and additional imaging modalities such as magnetic resonance imaging (MRI) venogram, duplex Doppler ultrasound or Positron emission tomography–computed tomography (PET/CT) may be beneficial.

Assessing lymph node status in patients with kidney cancer

Accurate detection of lymph node involvement on pre-operative imaging in patients diagnosed with renal cell carcinoma (RCC) is critical for determination of disease stage, one of the most significant prognostic factors in RCC. The presence of lymph node involvement in RCC doubles a patient’s risk of distant metastasis and significantly reduces their 5-year survival. Currently, lymph node involvement in patients with RCC is evaluated with numerous modalities, with rapid advancements occurring across these modalities. The purpose of this study was to evaluate the advantages and disadvantages of each modality and utilize sensitivities and specificities to determine the highest performing modalities for accurate lymph node involvement in renal cancer. A comprehensive computer-based literature search of full-length original research English language studies of human subjects with biopsy-proven RCC was performed to evaluate publications on the diagnostic performance of color Doppler sonography (CDS), magnetic resonance imaging (MRI), lymphotrophic nanoparticle enhanced MRI (LNMRI), multidetector-row computed tomography (MDCT), F-fluoro-2-deoxyglucose positron emission tomography (FDG-PET), and PET/CT for evaluation of lymph node status in kidney cancers in articles that were published prior to May 2018. Limited studies were available for evaluating CDS performance for determination of lymph node involvement in renal cancer. While CT is the most common modality for nodal staging, due to its availability and relatively low expense, it did not demonstrate the highest performance of the modalities examined for determination of lymph node status in patients with RCC. Of the modalities examined, MRI demonstrated the highest sensitivity (92–95.7%) for detection of lymph node involvement in RCC. Studies of lymph node involvement in RCC using both MRI and CT indicated that using the current diameter criteria (greater than 1 cm) for determination of positive lymph nodes should be re-evaluated as micro-metastases are frequently overlooked. Studies evaluating lymph node involvement with FDG-PET had the highest specificity (100%), indicating FDG-PET is the preferred modality for confirming lymph node involvement and extent of involvement. However, due to the low sensitivity of FDG-PET, clinicians should be skeptical of negative reports of lymph node involvement in RCC patients. Further studies examining determination of lymph node involvement in renal cancer across modalities are greatly needed, current literature suggests utilizing a combination of MRI and FDG-PET may offer the highest accuracy.

Accuracy of preoperative CT T staging of renal cell carcinoma: which features predict advanced stage?

AIMS

To characterise CT findings in renal cell carcinoma (RCC), and establish which features are associated with higher clinical T stage disease, and to evaluate patterns of discrepancy between radiological and pathological staging of RCC.

MATERIALS AND METHODS

Preoperative CT studies of 92 patients with 94 pathologically proven RCCs were retrospectively reviewed. CT stage was compared with pathological stage using the American Joint Committee on Cancer (AJCC), 7(th) edition (2010). The presence or absence of tumour necrosis, perinephric fat standing, thickening of Gerota's fascia, collateral vessels were noted, and correlated with pT stage. The sensitivity, specificity, and positive (PPV) and negative predictive values (NPV) for predicting pT stage ≥pT3a were derived separately for different predictors using cross-tabulations.

RESULTS

Twenty-four lesions were pathological stage T1a, 21 were T1b, seven were T2a, 25 were T3a, 11 were T3b, four were T3c, and two were T4. There were no stage T2b. Sixty-three (67%) patients had necrosis, 27 (29%) thickening of Gerota's fascia (1 T1a), 25 had collateral vessels (0 T1a), 28 (30%) had fat stranding of <2 mm, 20 (21%) of 2-5mm and one (1%) of >5 mm. For pT stage ≥pT3a, the presence of perinephric fat stranding had a sensitivity, specificity, PPV and NPV of 74%, 65%, 63%, and 76%, respectively. Presence of tumour necrosis had a sensitivity, specificity, PPV, and NPV of 81%, 44%, 54%, and 72%, respectively. Thickening of Gerota's fascia had a sensitivity, specificity, PPV, and NPV of 52%, 90%, 81% and 70%, respectively; and enlarged collateral vessels had a sensitivity, specificity, PPV, and NPV value of 52%, 94%, 88%, and 71% respectively.

CONCLUSION

The presence of perinephric stranding and tumour necrosis were not reliable signs for pT stage >T3a. Thickening of Gerota's fascia and the presence of collateral vessels in the peri- or paranephric fat had 90% and 94% specificity, with 82% and 88% PPV, respectively, for the presence of tumour stage for pT stage >T3a. These are considered reliable signs of locally advanced renal cancer.

Stage T3a renal cell carcinoma: staging accuracy of CT for sinus fat, perinephric fat or renal vein invasion

OBJECTIVE

To study the accuracy of CT for staging T3a (TNM 2009) renal cell carcinoma (RCC).

METHODS

Unenhanced and nephrographic phase CT studies of 117 patients (male:female = 82:35; age range, 21-86 years) with T1-T3a RCC were independently reviewed by 2 readers. The presence of sinus or perinephric fat, or renal vein invasion and tumour characteristics were noted.

RESULTS

Median (range) tumour size was 5.5 (0.9-19.0) cm; and 46 (39%), 16 (14%) and 55 (47%) tumours were pT1, pT2 and pT3a RCC, respectively. The sensitivity/specificity for sinus fat, perinephric fat and renal vein invasion were 71/79%, 83/76% and 59/93% (Reader 1) and 88/71%, 68/72% and 69/91% (Reader 2) with κ = 0.41, 0.43 and 0.61, respectively. Sinus fat invasion was seen in 47/55 (85%) cases with T3a RCC vs 16/55 (29%) and 33/55 (60%) for perinephric fat and renal vein invasion. Tumour necrosis, irregularity of tumour edge and direct tumour contact with perirenal fascia or sinus fat increased the odds of local invasion [odds ratio (OR), 2.5-3.7; p < 0.05; κ = 0.42-0.61]. Stage T3a tumours were centrally located (OR, 3.9; p = 0.0009).

CONCLUSION

Stage T3a RCC was identified with a sensitivity of 59-88% and specificity of 71-93% (κ = 0.41-0.61). Sinus fat invasion was the most common invasive feature.

ADVANCES IN KNOWLEDGE

Centrally situated renal tumours with an irregular tumour edge, inseparable from sinus structures or the perirenal fascia and CT features of tumour necrosis should alert the reader to the possibility of Stage T3a RCC (OR, 2.5-3.9).

Diagnostic performance of multidetector computed tomography in the evaluation of perinephric fat invasion in renal cell carcinoma patients

PURPOSE

This study aimed to evaluate the diagnostic performance of multidetector computed tomography (MDCT) for preoperative evaluation of perinephric fat invasion in patients with renal cell carcinomas (RCCs).

METHODS

A total of 408 consecutive patients with surgically confirmed RCC who underwent MDCT were included in this study. Image analysis was first performed with axial-only CT images. A second analysis was then performed with both axial and coronal CT images. A qualitative analysis was then conducted by 2 reviewers who reached consensus. The reference standard was pathologic evaluation.

RESULTS

The areas under the curve of the receiver operating characteristic analysis were 0.786 and 0.877 for axial-only images and 0.805 and 0.836 for combined images in both readers. The area under the curve of tumor size was 0.833, a similar value to that of the reviewers. In multivariate analysis, tumor size, a linear-nodular or nodular type of fat infiltration, and an irregular tumor margin were independent predicting factors for perinephric fat invasion.

CONCLUSIONS

The MDCT shows relatively high diagnostic performance in detecting perinephric fat invasion of RCC, but suffers from a relatively low positive predictive value. Tumor size, fat infiltration with a nodular appearance, and an irregular tumor margin were predictors for perinephric invasion.

Diagnostic value of multidetector computed tomography for renal sinus fat invasion in renal cell carcinoma patients

OBJECTIVE

Although renal sinus fat invasion has prognostic significance in patients with renal cell carcinomas (RCCs), there are no previous studies about the value of multidetector computed tomography (MDCT) about this issue in the current literature.

MATERIALS AND METHODS

A total of 863 consecutive patients (renal sinus fat invasion in 110 patients (12.7%)) from single institutions with surgically-confirmed renal cell carcinoma who underwent MDCT between 2010 and 2012 were included in this study. The area under the curves (AUCs) of the receiver operating characteristic (ROC) analysis was used to compare diagnostic performance. Reference standard was pathologic examination. Weighted κ statistics were used to measure the level of interobserver agreement. Multivariate logistic regression model was used to find the predictors for renal sinus fat invasion. Image analysis was first performed with axial-only CT images. A second analysis was then performed with both axial and coronal CT images. A qualitative analysis was then conducted by two reviewers who reached consensus regarding tumor size, decreased perfusion, tumor margin, vessel displacement, and lymph node metastasis. The reference standard was pathologic evaluation.

RESULTS

The AUCs of the ROC analysis were 0.881 and 0.922 for axial-only images and 0.889 and 0.902 for combined images in both readers. The AUC of tumor size was 0.884, a similar value to that of the reviewers. In multivariate analysis, tumor size, a linear-nodular or nodular type of fat infiltration, and an irregular tumor margin were independent predicting factors for perinephric fat invasion.

CONCLUSION

MDCT shows relatively high diagnostic performance in detecting perinephric fat invasion of RCC but suffers from a relatively low PPV related to low prevalence of renal sinus fat invasion. Applying tumor size alone we could get similar diagnostic performance to those of radiologists. Tumor size, fat infiltration with a nodular appearance, and an irregular tumor margin were predictors for perinephric invasion.

EADC Values in Diagnosis of Renal Lesions by 3.0 T Diffusion-Weighted Magnetic Resonance Imaging: Compared with the ADC Values

Exponential apparent diffusion coefficient (EADC) is an indicator of diffusion-weighted imaging (DWI) and reflects the pathological changes of tissues quantitatively. However, no study has been investigated in the space-occupying kidney disease using EADC values. This study aims to evaluate the diagnostic role of EADC values at a high magnetic field strength (3.0 T) in kidney neoplastic lesions, compared with that of the ADC values. Ninety patients with suspected renal tumors (including 101 suspected renal lesions) and 20 healthy volunteers were performed MRI scanning. Diffusion-weighted imaging was performed with a single-shot spin-echo echo-planar imaging (SE-EPI) sequence at a diffusion gradient of b = 500 s/mm². We found renal cell carcinoma (RCC) can be distinguished from angiomyolipoma, and clear cell carcinoma can be distinguished from non-clear cell carcinoma by EADC value. There was significant difference in overall EADC values between renal cell carcinoma (0.150 ± 0.059) and angiomyolipoma (0.270 ± 0.108) when b value was 500 s/mm². When receiver operating characteristic (ROC) was higher than 0.192, the sensitivity and specificity of EADC value of renal cell carcinoma were 84.6 and 81.1 %, respectively. In conclusion, EADC map shows the internal structure of the kidney tumor more intuitively than the ADC map dose, and is also in line with the observation habits of the clinicians. EADC can be used as an effective imaging method for tumor diagnosis.