Solid renal masses: what the numbers tell us

Identification of benign from malignant small renal tumors: is there a possible role of T1 mapping?

PURPOSE

Differentiating benign from malignant small renal tumors (SRMs) can help to guide clinical decision-making. T1 mapping enables quantitative assessment of T1 relaxation time and may help to evaluate SRMs properties. This study aimed to investigate the possible utility of T1 mapping for identification of SRMs.

METHODS

The data set used in this retrospective study, consisted of 104 patients with SRMs (≤ 4 cm). 78 malignant and 25 benign ones respectively. Calculated and compared the quantitative variables (including T1 mapping) between different renal tumors. The clinical features qualitative characteristics were subsequently documented. Finally, the logistic regression models were used to identify independent influencing factors. The diagnostic accuracy of independent influencing factors was represented with the area under the receiver operating characteristic curve (AUC).

RESULTS

The pre-contrast T1 mapping (T1) and the ratio of T1 reduction in malignance were higher than those in benign SRMs, while post-contrast T1 mapping was lower (all P < 0.025). In multivariable logistic regression, the tumor necrosis (odds ratio (OR) = 20.636, P = 0.005) and T1 (OR = 2.982, P = 0.002) were independent predictors. For the identification of SRMs, the performance of the model achieving an AUC of 0.793 (95% CI 0.701-0.866) when combining two factors.

CONCLUSION

Quantitative T1 mapping parameters may be a new potential biomarker for noninvasively distinguishing SRMs.

Frequency of benign tumors after partial nephrectomy and the association between malignant tumor findings and preoperative clinical parameters

BACKGROUND

Partial nephrectomy (PN) has become the dominant treatment modality for cT1 renal tumor lesions. Tumors suspected of malignant potential are indicated for surgery, but some are histologically classified as benign lesions after surgery. This study aims to analyze the number of benign findings after PN according to definitive histology and to evaluate whether there is an association between malignant tumor findings and individual factors.

METHODS

The retrospective study included 555 patients who underwent open or robotic-assisted PN for a tumor in our clinic from January 2013 to December 2020. The cohort was divided into groups according to definitive tumor histology (malignant tumors vs. benign lesions). The association of factors (age, sex, tumor size, R.E.N.A.L.) with the malignant potential of the tumor was further evaluated.

RESULTS

In total, 462 tumors were malignant (83%) and 93 benign (17%). Of the malignant tumors, 66% were clear-cell RCC (renal cell carcinoma), 12% papillary RCC, and 6% chromophobe RCC. The most common benign tumor was oncocytoma in 10% of patients, angiomyolipoma in 2%, and papillary adenoma in 1%. In univariate analysis, there was a higher risk of malignant tumor in males (OR 2.13, 95% CI 1.36-3.36, p = 0.001), a higher risk of malignancy in tumors larger than 20 mm (OR 2.32, 95% CI 1.43-3.74, p < 0.001), and a higher risk of malignancy in tumors evaluated by R.E.N.A.L. as tumors of intermediate or high complexity (OR 2.8, 95% CI 1.76-4.47, p < 0.001). In contrast, there was no association between older age and the risk of malignant renal tumor (p = 0.878).

CONCLUSIONS

In this group, 17% of tumors had benign histology. Male sex, tumor size greater than 20 mm, and intermediate or high R.E.N.A.L. complexity were statistically significant predictors of malignant tumor findings.

Contrast-enhanced US in Renal Transplant Complications: Overview and Imaging Features

Renal transplant is the first-line treatment of end-stage renal disease. The increasing number of transplants performed every year has led to a larger population of transplant patients. Complications may arise during the perioperative and postoperative periods, and imaging plays a key role in this scenario. Contrast-enhanced US (CEUS) is a safe tool that adds additional value to US. Contrast agents are usually administered intravenously, but urinary tract anatomy and complications such as stenosis or leak can be studied using intracavitary administration of contrast agents. Assessment of the graft and iliac vessels with CEUS is particularly helpful in identifying vascular and parenchymal complications, such as arterial or venous thrombosis and stenosis, acute tubular injury, or cortical necrosis, which can lead to graft loss. Furthermore, infectious and malignant graft involvement can be accurately studied with CEUS, which can help in detection of renal abscesses and in the differentiation between benign and malignant disease. CEUS is also useful in interventional procedures, helping to guide percutaneous aspiration of collections with better delimitation of the graft boundaries and to guide renal graft biopsies by avoiding avascular areas. Potential postprocedural vascular complications, such as pseudoaneurysm, arteriovenous fistula, or active bleeding, are identified with CEUS. In addition, newer quantification tools such as CEUS perfusion are promising, but further studies are needed to approve its use for clinical purposes. ©RSNA, 2024 Supplemental material is available for this article.

Evaluation of a multiparametric renal CT algorithm for diagnosis of clear-cell renal cell carcinoma among small (≤ 4 cm) solid renal masses

OBJECTIVE

To evaluate a recently proposed CT-based algorithm for diagnosis of clear-cell renal cell carcinoma (ccRCC) among small (≤ 4 cm) solid renal masses diagnosed by renal mass biopsy.

METHODS

This retrospective study included 51 small renal masses in 51 patients with renal-mass CT and biopsy between 2014 and 2021. Three radiologists independently evaluated corticomedullary phase CT for the following: heterogeneity and attenuation ratio (mass:renal cortex), which were used to inform the CT score (1-5). CT score ≥ 4 was considered positive for ccRCC. Diagnostic accuracy was calculated for each reader and overall using fixed effects logistic regression modelling.

RESULTS

There were 51% (26/51) ccRCC and 49% (25/51) other masses. For diagnosis of ccRCC, area under curve (AUC), sensitivity, specificity, and positive predictive value (PPV) were 0.69 (95% confidence interval 0.61-0.76), 78% (68-86%), 59% (46-71%), and 67% (54-79%), respectively. CT score ≤ 2 had a negative predictive value 97% (92-99%) to exclude diagnosis of ccRCC. For diagnosis of papillary renal cell carcinoma (pRCC), CT score ≤ 2, AUC, sensitivity, specificity, and PPV were 0.89 (0.81-0.98), 81% (58-94%), 98% (93-99%), and 85% (62-97%), respectively. Pooled inter-observer agreement for CT scoring was moderate (Fleiss weighted kappa = 0.52).

CONCLUSION

The CT scoring system for prediction of ccRCC was sensitive with a high negative predictive value and moderate agreement. The CT score is highly specific for diagnosis of pRCC.

CLINICAL RELEVANCE STATEMENT

The CT score algorithm may help guide renal mass biopsy decisions in clinical practice, with high sensitivity to identify clear-cell tumors for biopsy to establish diagnosis and grade and high specificity to avoid biopsy in papillary tumors.

KEY POINTS

• A CT score ≥ 4 had high sensitivity and negative predictive value for diagnosis of clear-cell renal cell carcinoma (RCC) among solid ≤ 4-cm renal masses. • A CT score ≤ 2 was highly specific for diagnosis of papillary RCC among solid ≤ 4-cm renal masses. • Inter-observer agreement for CT score was moderate.

Clinical T1/2 renal cell carcinoma: multiparametric dynamic contrast-enhanced MRI features-based model for the prediction of individual adverse pathology

BACKGROUND

The detection of renal cell carcinoma (RCC) has been rising due to the enhanced utilization of cross-sectional imaging and incidentally discovered lesions with adverse pathology demonstrate potential for metastasis. The purpose of our study was to determine the clinical and multiparametric dynamic contrast-enhanced magnetic resonance imaging (CEMRI) associated independent predictors of adverse pathology for cT1/2 RCC and develop the predictive model.

METHODS

We recruited 105 cT1/2 RCC patients between 2018 and 2022, all of whom underwent preoperative CEMRI and had complete clinicopathological data. Adverse pathology was defined as RCC patients with nuclear grade III-IV; pT3a upstage; type II papillary RCC, collecting duct or renal medullary carcinoma, unclassified RCC; sarcomatoid/rhabdoid features. The qualitative and quantitative CEMRI parameters were independently reviewed by two radiologists. Univariate and multivariate binary logistic regression analyses were utilized to determine the independent predictors of adverse pathology for cT1/2 RCC and construct the predictive model. The receiver operating characteristic (ROC) curve, confusion matrix, calibration plot, and decision curve analysis (DCA) were conducted to compare the diagnostic performance of different predictive models. The individual risk scores and linear predicted probabilities were calculated for risk stratification, and the Kaplan-Meier curve and log-rank tests were used for survival analysis.

RESULTS

Overall, 45 patients were pathologically confirmed as RCC with adverse pathology. Clinical characteristics, including gender, and CEMRI parameters, including RENAL score, tumor margin irregularity, necrosis, and tumor apparent diffusion coefficient (ADC) value were identified as independent predictors of adverse pathology for cT1/2 RCC. The clinical-CEMRI predictive model yielded an area under the curve (AUC) of the ROC curve of 0.907, which outperformed the clinical model or CEMRI signature model alone. Good calibration, better clinical usefulness, excellent risk stratification ability of adverse pathology and prognosis were also achieved for the clinical-CEMRI predictive model.

CONCLUSIONS

The proposed clinical-CEMRI predictive model offers the potential for preoperative prediction of adverse pathology for cT1/2 RCC. With the ability to forecast adverse pathology, the predictive model could significantly benefit patients and clinicians alike by providing enhanced guidance for treatment planning and decision-making.

Scientific Status Quo of Small Renal Lesions: Diagnostic Assessment and Radiomics

Background: Small renal masses (SRMs) are defined as contrast-enhanced renal lesions less than or equal to 4 cm in maximal diameter, which can be compatible with stage T1a renal cell carcinomas (RCCs). Currently, 50-61% of all renal tumors are found incidentally. Methods: The characteristics of the lesion influence the choice of the type of management, which include several methods SRM of management, including nephrectomy, partial nephrectomy, ablation, observation, and also stereotactic body radiotherapy. Typical imaging methods available for differentiating benign from malignant renal lesions include ultrasound (US), contrast-enhanced ultrasound (CEUS), computed tomography (CT), and magnetic resonance imaging (MRI). Results: Although ultrasound is the first imaging technique used to detect small renal lesions, it has several limitations. CT is the main and most widely used imaging technique for SRM characterization. The main advantages of MRI compared to CT are the better contrast resolution and tissue characterization, the use of functional imaging sequences, the possibility of performing the examination in patients allergic to iodine-containing contrast medium, and the absence of exposure to ionizing radiation. For a correct evaluation during imaging follow-up, it is necessary to use a reliable method for the assessment of renal lesions, represented by the Bosniak classification system. This classification was initially developed based on contrast-enhanced CT imaging findings, and the 2019 revision proposed the inclusion of MRI features; however, the latest classification has not yet received widespread validation. Conclusions: The use of radiomics in the evaluation of renal masses is an emerging and increasingly central field with several applications such as characterizing renal masses, distinguishing RCC subtypes, monitoring response to targeted therapeutic agents, and prognosis in a metastatic context.

Computed tomography for visible haematuria - a single nephrographic phase is sufficient for detecting renal cell carcinoma

OBJECTIVES

No previous studies have compared two computed tomography (CT) protocols in patients presenting with visible haematuria, and most patients undergo a multiphase CT in order to detect upper tract malignancies. We aimed to prospectively compare the diagnostic performance of single- and four-phase CT for detecting renal cell carcinoma (RCC) in patients with visible haematuria.

MATERIALS & METHODS

'A Prospective Trial for Examining Hematuria using Computed Tomography' (PROTEHCT) was a single-centre prospective paired diagnostic study in patients referred for CT due to painless visible haematuria between September 2019 and June 2021. All patients underwent four-phase CT (control) from which a single nephrographic phase dual energy CT (experimental) was extracted. Both were independently assessed for RCC by randomised radiologists. Histologically verified RCC defined a positive reference standard. Follow-up ascertainment of RCC diagnosis was completed in May 2022. Descriptive statistics were used to calculate the accuracies. Inter-reader agreement was assessed by kappa statistics.

RESULTS

A total of 308 patients (median age, 68 years [interquartile range 53-77, range 18-96], 250 males) were included for analysis. RCC was diagnosed in seven (2.3%) patients during a median follow-up time of 19 months (interquartile range: 15-25). For the control and experimental CT, sensitivity was 100% versus 100%, specificity was 97% versus 98% and accuracy 97% versus 97%. The positive predictive value was 44% versus 50%, and the negative predictive value was 100% versus 100%. The agreement between the control and experimental CT was 98% (k = 0.79).

CONCLUSION

A single nephrographic phase dual energy CT is sufficient for detecting RCC in patients with visible haematuria.

Simultaneous surgical management of renal cancer with atrial thrombotic extension and severe chronic coronary artery disease: a case report

BACKGROUND

Renal cell carcinoma accounts for 2-3% of all malignant cancers in adults and is characterized by the potential development of venous tumor thrombus.

CASE PRESENTATION

We present a rare case of a 62-year-old Caucasian man who arrived in the emergency department for monosymptomatic hematuria. Further investigation revealed a right renal cell carcinoma with 16 cm intravascular extension through the renal vein into the inferior vena cava and right atrium associated with significant coronary artery disease based on the computed tomography scan and coronary angiography. To the best of our knowledge, after an extensive literature review, only one similar case has been reported with involvement of the contralateral kidney. Therefore, there are no applicable management recommendations. After performing coronary artery bypass graft surgery, we proceeded with an open right radical nephrectomy and inferior vena cava and right atrium thrombectomy under cardiopulmonary bypass and while the patient's heart was still beating. The postoperative course went without complications, and the patient was discharged from the hospital on the 10th postoperative day.

CONCLUSIONS

Radical nephrectomy and thrombectomy with reconstruction of the inferior vena cava combined with coronary artery bypass graft can be performed safely and effectively in selected patients with renal cell carcinoma and significant coronary artery disease. Multidisciplinary teamwork and careful patient selection are essential for optimal outcomes.

MR Virtual Biopsy of Solid Renal Masses: An Algorithmic Approach

Between 1983 and 2002, the incidence of solid renal tumors increased from 7.1 to 10.8 cases per 100,000. This is in large part due to the increase in the volume of ultrasound and cross-sectional imaging, although a majority of solid renal tumors are still found incidentally. Ultrasound and computed tomography (CT) have been the mainstay of renal mass screening and diagnosis but recent advances in magnetic resonance (MR) technology have made this the optimal choice when diagnosing and staging renal tumors. Our purpose in writing this review is to survey the modern MR imaging approach to benign and malignant solid renal tumors, consolidate the various imaging findings into an easy-to-read reference, and provide an imaging-based, algorithmic approach to renal mass characterization for clinicians. MR is at the forefront of renal mass characterization, surpassing ultrasound and CT in its ability to describe multiple tissue parameters and predict tumor biology. Cutting-edge MR protocols and the integration of diagnostic algorithms can improve patient outcomes, allowing the imager to narrow the differential and better guide oncologic and surgical management.

Deep-learning-based ensemble method for fully automated detection of renal masses on magnetic resonance images

Purpose

Accurate detection of small renal masses (SRM) is a fundamental step for automated classification of benign and malignant or indolent and aggressive renal tumors. Magnetic resonance image (MRI) may outperform computed tomography (CT) for SRM subtype differentiation due to improved tissue characterization, but is less explored compared to CT. The objective of this study is to autonomously detect SRM on contrast-enhanced magnetic resonance images (CE-MRI).

Approach

In this paper, we described a novel, fully automated methodology for accurate detection and localization of SRM on CE-MRI. We first determine the kidney boundaries using a U-Net convolutional neural network. We then search for SRM within the localized kidney regions using a mixture-of-experts ensemble model based on the U-Net architecture. Our dataset contained CE-MRI scans of 118 patients with different solid kidney tumor subtypes including renal cell carcinomas, oncocytomas, and fat-poor renal angiomyolipoma. We evaluated the proposed model on the entire CE-MRI dataset using 5-fold cross validation.

Results

The developed algorithm reported a Dice similarity coefficient of 91.20 ± 5.41 % (mean ± standard deviation) for kidney segmentation from 118 volumes consisting of 25,025 slices. Our proposed ensemble model for SRM detection yielded a recall and precision of 86.2% and 83.3% on the entire CE-MRI dataset, respectively.

Conclusions

We described a deep-learning-based method for fully automated SRM detection using CE-MR images, which has not been studied previously. The results are clinically important as SRM localization is a pre-step for fully automated diagnosis of SRM subtypes.

Differentiation of benign from malignant solid renal lesions using CT-based radiomics and machine learning: comparison with radiologist interpretation

PURPOSE

To assess the performance of a machine learning model trained with contrast-enhanced CT-based radiomics features in distinguishing benign from malignant solid renal masses and to compare model performance with three abdominal radiologists.

METHODS

Patients who underwent intra-operative ultrasound during a partial nephrectomy were identified within our institutional database, and those who had pre-operative contrast-enhanced CT examinations were selected. The renal masses were segmented from the CT images and radiomics features were derived from the segmentations. The pathology of each mass was identified; masses were labeled as either benign [oncocytoma or angiomyolipoma (AML)] or malignant [clear cell, papillary, or chromophobe renal cell carcinoma (RCC)] depending on the pathology. The data were parsed into a 70/30 train/test split and a random forest machine learning model was developed to distinguish benign from malignant lesions. Three radiologists assessed the cohort of masses and labeled cases as benign or malignant.

RESULTS

148 masses were identified from the cohort, including 50 benign lesions (23 AMLs, 27 oncocytomas) and 98 malignant lesions (23 clear cell RCC, 44 papillary RCC, and 31 chromophobe RCCs). The machine learning algorithm yielded an overall accuracy of 0.82 for distinguishing benign from malignant lesions, with an area under the receiver operating curve of 0.80. In comparison, the three radiologists had significantly lower accuracies (p = 0.02) ranging from 0.67 to 0.75.

CONCLUSION

A machine learning model trained with CT-based radiomics features can provide superior accuracy for distinguishing benign from malignant solid renal masses compared to abdominal radiologists.

Etiologies, Gross Appearance, Histopathological Patterns, Prognosis, and Best Treatments for Subtypes of Renal Carcinoma: An Educational Review

Of all primary renal neoplasms, 80-85% are renal cell carcinomas (RCCs), which develop in the renal cortex. There are more than 10 histological and molecular subtypes of the disease, the most frequent of which is clear cell RCC, which also causes most cancer-related deaths. Other renal neoplasms, including urothelial carcinoma, Wilms' tumor, and renal sarcoma, each affect a particular age group and have specific gross and histological features. Due to the genetic susceptibility of each of these malignancies, early mutation discovery is necessary for the early detection of a tumor. Furthermore, it is crucial to avoid environmental factors leading to each type. This study provides relatively detailed and essential information regarding each subtype of renal carcinoma.

Diagnostic Performance and Interreader Agreement of the MRI Clear Cell Likelihood Score for Characterization of cT1a and cT1b Solid Renal Masses: An External Validation Study

BACKGROUND. The clear cell likelihood score (ccLS) has been proposed for the noninvasive differentiation of clear cell renal cell carcinoma (ccRCC) from other renal neoplasms on multiparametric MRI (mpMRI), though further external validation remains needed. OBJECTIVE. The purpose of our study was to evaluate the diagnostic performance and interreader agreement of the ccLS version 2.0 (v2.0) for characterizing solid renal masses as ccRCC. METHODS. This retrospective study included 102 patients (67 men, 35 women; mean age, 56.9 ± 12.8 [SD] years) who underwent mpMRI between January 2013 and February 2018, showing a total of 108 (≥ 25% enhancing tissue) solid renal masses measuring 7 cm or smaller (83 cT1a [≤ 4 cm] and 25 cT1b [> 4 cm and ≤ 7 cm]), all with a histologic diagnosis. Three abdominal radiologists independently reviewed the MRI examinations using ccLS v2.0. Median reader sensitivity, specificity, and accuracy were computed for predicting ccRCC by ccLS of 4 or greater, and individual reader AUCs were derived. The percentage of masses that were ccRCC was calculated, stratified by ccLS. Interobserver agreement was assessed by the Fleiss kappa statistic. RESULTS. The sample included 45 ccRCCs (34 cT1a, 11 cT1b), 30 papillary renal cell carcinomas (RCCs), 13 chromophobe RCCs, 14 oncocytomas, and six fat-poor angiomyolipomas. Median reader sensitivity, specificity, and accuracy for predicting ccRCC by ccLS of 4 or greater were 85%, 82%, and 83% among cT1a masses and 82%, 100%, and 92% among cT1b masses. The three readers' AUCs for predicting ccRCC by ccLS for cT1a masses were 0.90, 0.84, and 0.89 and for cT1b masses were 0.99, 0.97, and 0.92. Across readers, the percentage of masses that were ccRCC among cT1a masses was 0%, 0%, 20%, 68%, and 93% for ccLS of 1, 2, 3, 4, and 5, respectively; among cT1b masses, the percentage of masses that were ccRCC was 0%, 0%, 32%, 90%, and 100% for ccLS of 1, 2, 3, 4, and 5, respectively. Interobserver agreement among cT1a and cT1b masses for ccLS of 4 or greater was 0.82 and 0.83 and for ccLS of 1-5 overall was 0.65 and 0.62, respectively. CONCLUSION. This study provides external validation of the ccLS, finding overall high measures of diagnostic performance and interreader agreement. CLINICAL IMPACT. The ccLS provides a standardized approach to the noninvasive diagnosis of ccRCC by MRI.

Development of a Multiparametric Renal CT Algorithm for Diagnosis of Clear Cell Renal Cell Carcinoma Among Small (≤ 4 cm) Solid Renal Masses

BACKGROUND. The MRI clear cell likelihood score predicts the likelihood that a renal mass is clear cell renal cell carcinoma (ccRCC). A CT-based algorithm has not yet been established. OBJECTIVE. The purpose of our study was to develop and evaluate a CT-based algorithm for diagnosing ccRCC among small (≤ 4 cm) solid renal masses. METHODS. This retrospective study included 148 patients (73 men, 75 women; mean age, 58 ± 12 [SD] years) with 148 small (≤ 4 cm) solid (> 25% enhancing tissue) renal masses that underwent renal mass CT (unenhanced, corticomedullary, and nephrographic phases) before resection between January 2016 and December 2019. Two radiologists independently evaluated CT examinations and recorded calcification, mass attenuation in all phases, mass-to-cortex corticomedullary attenuation ratio, and heterogeneity score (score on a 5-point Likert scale, assessed in corticomedullary phase). Features associated with ccRCC were identified by multivariable logistic regression analysis and then used to create a five-tiered CT score for diagnosing ccRCC. RESULTS. The masses comprised 53% (78/148) ccRCC and 47% (70/148) other histologic diagnoses. The mass-to-cortex corticomedullary attenuation ratio was higher for ccRCC than for other diagnoses (reader 1: 0.84 ± 0.68 vs 0.68 ± 0.65, p = .02; reader 2: 0.75 ± 0.29 vs 0.59 ± 0.25, p = .02). The heterogeneity score was higher for ccRCC than other diagnoses (reader 1: 4.0 ± 1.1 vs 1.5 ± 1.6, p < .001; reader 2: 4.4 ± 0.9 vs 3.3 ± 1.5, p < .001). Other features showed no difference. A five-tiered diagnostic algorithm including the mass-to-cortex corticomedullary attenuation ratio and heterogeneity score had interobserver agreement of 0.71 (weighted κ) and achieved an AUC for diagnosing ccRCC of 0.75 (95% CI, 0.68-0.82) for reader 1 and 0.72 (95% CI, 0.66-0.82) for reader 2. A CT score of 4 or greater achieved sensitivity, specificity, and PPV of 71% (95% CI, 59-80%), 79% (95% CI, 67-87%), and 79% (95% CI, 67-87%) for reader 1 and 42% (95% CI, 31-54%), 81% (95% CI, 70-90%), and 72% (95% CI, 56-84%) for reader 2. A CT score of 2 or less had NPV of 85% (95% CI, 69-95%) for reader 1 and 88% (95% CI, 69-97%) for reader 2. CONCLUSION. A five-tiered renal CT algorithm, including the mass-to-cortex corticomedullary attenuation ratio and heterogeneity score, had substantial interobserver agreement, moderate AUC and PPV, and high NPV for diagnosing ccRCC. CLINICAL IMPACT. The CT algorithm, if validated, may represent a useful clinical tool for diagnosing ccRCC.

Utility of Fluorine18 Fluoro-2-deoxy-D-glucose Positron Emission Tomography/Computed Tomography in Metabolic Characterization of Solid Renal Mass Lesion and Localization of Extra Renal Lesions in the Body - A Prospective Study from the Tertiary Care Center in South India

Purpose of the Study

Renal mass lesions in majority of the cases are due to malignant etiology and about one-third of them are reported with metastatic lesions at the time of presentation. Thus proper investigational workup is needed for staging and thereby treatment planning. The current fluorine18 fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (F18-FDG PET/CT) study was designed to characterize renal mass lesions metabolically and identifying other metabolically active lesions in the body suggesting metastatic disease.

Materials and Methods

A total of 24 patients (males - 18 and females - 6) with a mean age of 53.8 ± 12.3 years were recruited in this study for dual time-point PET/CT scan. All patients with renal mass lesions underwent contrast-enhanced CT prior to PET/CT. Metabolic parameters such as maximum standardized uptake value (max.SUV) with a cut off ≥2.5 and retention index (RI) of ≥10% were used to label the lesion as malignant and remaining less than cutoff as benign. The final diagnosis of lesion on imaging was confirmed with a histopathological examination (HPE).

Results

Using max.SUV cut off value, 17/24 renal mass lesions were characterized as malignant and remaining 7/24 renal lesions of benign etiology. PET/CT showed sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 80%, 75%, 94.1%, 42.8%, and 79.1%, respectively, by considering HPE as a gold standard. Nine patients were diagnosed with distant site involvement suggestive of metastases.

Conclusion

F18-FDG PET/CT can efficiently characterize solid renal mass lesion as benign and malignant using metabolic parameters such as max.SUV and RI. In addition, whole-body survey identified distant site involvement in 25% of the patients, thus contributing change in management.

Differentiation of benign from malignant solid renal lesions with MRI-based radiomics and machine learning

BACKGROUND

Solid renal masses are often indeterminate for benignity versus malignancy on magnetic resonance imaging. Such masses are typically evaluated with either percutaneous biopsy or surgical resection. Percutaneous biopsy can be non-diagnostic and some surgically resected lesions are inadvertently benign.

PURPOSE

To assess the performance of ten machine learning (ML) algorithms trained with MRI-based radiomics features in distinguishing benign from malignant solid renal masses.

METHODS

Patients with solid renal masses identified on pre-intervention MRI were curated from our institutional database. Masses with a definitive diagnosis via imaging (for angiomyolipomas) or via biopsy or surgical resection (for oncocytomas or renal cell carcinomas) were selected. Each mass was segmented for both T2- and post-contrast T1-weighted images. Radiomics features were derived from the segmented masses for each imaging sequence. Ten ML algorithms were trained with the radiomics features gleaned from each MR sequence, as well as the combination of MR sequences.

RESULTS

In total, 182 renal masses in 160 patients were included in the study. The support vector machine algorithm trained on radiomics features from T2-weighted images performed superiorly, with an accuracy of 0.80 and an area under the curve (AUC) of 0.79. Linear discriminant analysis (accuracy = 0.84 and AUC = 0.77) and logistic regression (accuracy = 0.78 and AUC = 0.78) algorithms trained on T2-based radiomics features performed similarly. ML algorithms trained on radiomics features from post-contrast T1-weighted images or the combination of radiomics features from T2- and post-contrast T1-weighted images yielded lower performance.

CONCLUSION

Machine learning models trained with radiomics features derived from T2-weighted images can provide high accuracy for distinguishing benign from malignant solid renal masses.

CLINICAL IMPACT

Machine learning models derived from MRI-based radiomics features may improve the clinical management of solid renal masses and have the potential to reduce the frequency with which benign solid renal masses are biopsied or surgically resected.

Mucinous tubular and spindle cell carcinomas of the kidney (MTSCC-Ks): CT and MR imaging characteristics

PURPOSE

To strengthen the recognition of mucinous tubular and spindle cell carcinomas of the kidney (MTSCC-Ks) by analyzing CT and MR imaging findings of MTSCC-Ks.

MATERIALS AND METHODS

This study retrospectively enrolled ten patients with pathologically confirmed MTSCC-Ks from 2007 to 2020. The main observed imaging characteristics included growth pattern, signal characteristics on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI), hemorrhage, necrosis, cystic degeneration, lipid component, pseudocapsule and the enhancement pattern. Apparent diffusion coefficient (ADC) value of MTSCC-Ks and normal renal cortex were measured, respectively. All imaging features were evaluated in consensus by two genitourinary radiologists.

RESULTS

All patients (53.1 ± 6.5 years, male to female, 3:7) presented with a solitary renal tumor with the mean diameter of 3.5 ± 0.4 cm. All lesions showed iso- or slight hypoattenuation on non-contrast CT with no hemorrhage but cystic degeneration (10%) and necrosis (10%). On T2WI, all lesions showed predominantly slight hypointensity with focal hyperintensity. The ADC value of MTSCC-Ks was 0.845 ± 0.017 × 10^-3 mm²/s, and ADC_tumor-to-ADC_{renal cortex} value was 0.376 ± 0.084. Pseudocapsules existed in all MTSCC-Ks on MRI. There were seven lesions showed heterogeneous enhancement, while three lesions showed homogeneous enhancement. Among them, six MTSCC-Ks showed slight multiple patchy enhancement (60%) in the corticomedullary phase, while the remaining MTSCC-Ks showed homogeneously slight enhancement (30%) or slightly stratified enhancement (10%). All MTSCC-Ks exhibited slow and progressive enhancement in the late phases.

CONCLUSION

Iso- or slight hypoattenuation on CT, slight hypointensity with focal hyperintensity on T2WI, marked diffusion restriction on DWI and ADC map, slight multiple patchy enhancement in the corticomedullary phase, and slow and progressive enhancement in the late phases are the imaging features of MTSCC-Ks, which may facilitate the diagnosis of MTSCC-Ks.

Salvaging low contrast abdominal CT studies using noise-optimised virtual monoenergetic image reconstruction

Objectives

To assess the impact of noise-optimised virtual monoenergetic imaging (VMI+) on image quality and diagnostic evaluation in abdominal dual-energy CT scans with impaired portal-venous contrast.

Methods

We screened 11,746 patients who underwent portal-venous abdominal dual-energy CT for cancer staging between 08/2014 and 11/2019 and identified those with poor portal-venous contrast.Standard linearly-blended image series and VMI+ image series at 40, 50, and 60 keV were reconstructed. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of abdominal organs and vascular structures were calculated. Image noise, image contrast and overall image quality were rated by three radiologists using 5-point Likert scale.

Results

452 of 11,746 (4%) exams were poorly opacified. We excluded 190 cases due to incomplete datasets or multiple exams of the same patient with a final study group of 262. Highest CNR values in all abdominal organs (liver, 6.4 ± 3.0; kidney, 17.4 ± 7.5; spleen, 8.0 ± 3.5) and vascular structures (aorta, 16.0 ± 7.3; intrahepatic vein, 11.3 ± 4.7; portal vein, 15.5 ± 6.7) were measured at 40 keV VMI+ with significantly superior values compared to all other series. In subjective analysis, highest image contrast was seen at 40 keV VMI+ (4.8 ± 0.4), whereas overall image quality peaked at 50 keV VMI+ (4.2 ± 0.5) with significantly superior results compared to all other series (p < 0.001).

Conclusions

Image reconstruction using VMI+ algorithm at 50 keV significantly improves image contrast and image quality of originally poorly opacified abdominal CT scans and reduces the number of non-diagnostic scans.

Advances in knowledge

We validated the impact of VMI+ reconstructions in poorly attenuated DECT studies of the abdomen in a big data cohort.

Deep Learning Algorithm for Fully Automated Detection of Small (≤4 cm) Renal Cell Carcinoma in Contrast-Enhanced Computed Tomography Using a Multicenter Database

OBJECTIVES

Renal cell carcinoma (RCC) is often found incidentally in asymptomatic individuals undergoing abdominal computed tomography (CT) examinations. The purpose of our study is to develop a deep learning-based algorithm for fully automated detection of small (≤4 cm) RCCs in contrast-enhanced CT images using a multicenter database and to evaluate its performance.

MATERIALS AND METHODS

For the algorithmic detection of RCC, we retrospectively selected contrast-enhanced CT images of patients with histologically confirmed single RCC with a tumor diameter of 4 cm or less between January 2005 and May 2020 from 7 centers in the Japan Medical Image Database. A total of 453 patients from 6 centers were selected as dataset A, and 132 patients from 1 center were selected as dataset B. Dataset A was used for training and internal validation. Dataset B was used only for external validation. Nephrogenic phase images of multiphase CT or single-phase postcontrast CT images were used. Our algorithm consisted of 2-step segmentation models, kidney segmentation and tumor segmentation. For internal validation with dataset A, 10-fold cross-validation was applied. For external validation, the models trained with dataset A were tested on dataset B. The detection performance of the models was evaluated using accuracy, sensitivity, specificity, and the area under the curve (AUC).

RESULTS

The mean ± SD diameters of RCCs in dataset A and dataset B were 2.67 ± 0.77 cm and 2.64 ± 0.78 cm, respectively. Our algorithm yielded an accuracy, sensitivity, and specificity of 88.3%, 84.3%, and 92.3%, respectively, with dataset A and 87.5%, 84.8%, and 90.2%, respectively, with dataset B. The AUC of the algorithm with dataset A and dataset B was 0.930 and 0.933, respectively.

CONCLUSIONS

The proposed deep learning-based algorithm achieved high accuracy, sensitivity, specificity, and AUC for the detection of small RCCs with both internal and external validations, suggesting that this algorithm could contribute to the early detection of small RCCs.

Novel Imaging Methods for Renal Mass Characterization: A Collaborative Review

CONTEXT

The incidental detection of localized renal masses has been rising steadily, but a significant proportion of these tumors are benign or indolent and, in most cases, do not require treatment. At the present time, a majority of patients with an incidentally detected renal tumor undergo treatment for the presumption of cancer, leading to a significant number of unnecessary surgical interventions that can result in complications including loss of renal function. Thus, there exists a clinical need for improved tools to aid in the pretreatment characterization of renal tumors to inform patient management.

OBJECTIVE

To systematically review the evidence on noninvasive, imaging-based tools for solid renal mass characterization.

EVIDENCE ACQUISITION

The MEDLINE database was systematically searched for relevant studies on novel imaging techniques and interpretative tools for the characterization of solid renal masses, published in the past 10 yr.

EVIDENCE SYNTHESIS

Over the past decade, several novel imaging tools have offered promise for the improved characterization of indeterminate renal masses. Technologies of particular note include multiparametric magnetic resonance imaging of the kidney, molecular imaging with targeted radiopharmaceutical agents, and use of radiomics as well as artificial intelligence to enhance the interpretation of imaging studies. Among these, 99mTc-sestamibi single photon emission computed tomography/computed tomography (CT) for the identification of benign renal oncocytomas and hybrid oncocytic chromophobe tumors, and positron emission tomography/CT imaging with radiolabeled girentuximab for the identification of clear cell renal cell carcinoma, are likely to be closest to implementation in clinical practice.

CONCLUSIONS

A number of novel imaging tools stand poised to aid in the noninvasive characterization of indeterminate renal masses. In the future, these tools may aid in patient management by providing a comprehensive virtual biopsy, complete with information on tumor histology, underlying molecular abnormalities, and ultimately disease prognosis.

PATIENT SUMMARY

Not all renal tumors require treatment, as a significant proportion are either benign or have limited metastatic potential. Several innovative imaging tools have shown promise for their ability to improve the characterization of renal tumors and provide guidance in terms of patient management.

Small Renal Masses without Gross Fat: What Is the Role of Contrast-Enhanced MDCT?

Increased detection of small renal masses (SRMs) has encouraged research for non-invasive diagnostic tools capable of adequately differentiating malignant vs. benign SRMs and the type of the tumour. Multi-detector computed tomography (MDCT) has been suggested as an alternative to intervention, therefore, it is important to determine both the capabilities and limitations of MDCT for SRM evaluation. In our study, two abdominal radiologists retrospectively blindly assessed MDCT scan images of 98 patients with incidentally detected lipid-poor SRMs that did not present as definitely aggressive lesions on CT. Radiological conclusions were compared to histopathological findings of materials obtained during surgery that were assumed as the gold standard. The probability (odds ratio (OR)) in regression analyses, sensitivity (SE), and specificity (SP) of predetermined SRM characteristics were calculated. Correct differentiation between malignant vs. benign SRMs was detected in 70.4% of cases, with more accurate identification of malignant (73%) in comparison to benign (65.7%) lesions. The radiological conclusions of SRM type matched histopathological findings in 56.1%. Central scarring (OR 10.6, p = 0.001), diameter of lesion (OR 2.4, p = 0.003), and homogeneous accumulation of contrast medium (OR 3.4, p = 0.03) significantly influenced the accuracy of malignant diagnosis. SE and SP of these parameters varied from 20.6% to 91.3% and 22.9% to 74.3%, respectively. In conclusion, MDCT is able to correctly differentiate malignant versus uncharacteristic benign SRMs in more than 2/3 of cases. However, frequency of the correct histopathological SRM type MDCT identification remains low.

Expanding the Role of Ultrasound for the Characterization of Renal Masses

The incidental detection of renal masses has been steadily rising. As a significant proportion of renal masses that are surgically treated are benign or indolent in nature, there is a clear need for better presurgical characterization of renal masses to minimize unnecessary harm. Ultrasound is a widely available and relatively inexpensive real-time imaging technique, and novel ultrasound-based applications can potentially aid in the non-invasive characterization of renal masses. Evidence acquisition: We performed a narrative review on novel ultrasound-based techniques that can aid in the non-invasive characterization of renal masses. Evidence synthesis: Contrast-enhanced ultrasound (CEUS) adds significant diagnostic value, particularly for cystic renal masses, by improving the characterization of fine septations and small nodules, with a sensitivity and specificity comparable to magnetic resonance imaging (MRI). Additionally, the performance of CEUS for the classification of benign versus malignant renal masses is comparable to that of computed tomography (CT) and MRI, although the imaging features of different tumor subtypes overlap significantly. Ultrasound molecular imaging with targeted contrast agents is being investigated in preclinical research as an addition to CEUS. Elastography for the assessment of tissue stiffness and micro-Doppler imaging for the improved detection of intratumoral blood flow without the need for contrast are both being investigated for the characterization of renal masses, though few studies have been conducted and validation is lacking. Conclusions: Several novel ultrasound-based techniques have been investigated for the non-invasive characterization of renal masses. CEUS has several advantages over traditional grayscale ultrasound, including the improved characterization of cystic renal masses and the potential to differentiate benign from malignant renal masses to some extent. Ultrasound molecular imaging offers promise for serial disease monitoring and the longitudinal assessment of treatment response, though this remains in the preclinical stages of development. While elastography and emerging micro-Doppler techniques have shown some encouraging applications, they are currently not ready for widespread clinical use.

Machine learning-based CT radiomics approach for predicting WHO/ISUP nuclear grade of clear cell renal cell carcinoma: an exploratory and comparative study

Purpose

To investigate the predictive performance of machine learning-based CT radiomics for differentiating between low- and high-nuclear grade of clear cell renal cell carcinomas (CCRCCs).

Methods

This retrospective study enrolled 406 patients with pathologically confirmed low- and high-nuclear grade of CCRCCs according to the WHO/ISUP grading system, which were divided into the training and testing cohorts. Radiomics features were extracted from nephrographic-phase CT images using PyRadiomics. A support vector machine (SVM) combined with three feature selection algorithms such as least absolute shrinkage and selection operator (LASSO), recursive feature elimination (RFE), and ReliefF was performed to determine the most suitable classification model, respectively. Clinicoradiological, radiomics, and combined models were constructed using the radiological and clinical characteristics with significant differences between the groups, selected radiomics features, and a combination of both, respectively. Model performance was evaluated by receiver operating characteristic (ROC) curve, calibration curve, and decision curve analyses.

Results

SVM-ReliefF algorithm outperformed SVM-LASSO and SVM-RFE in distinguishing low- from high-grade CCRCCs. The combined model showed better prediction performance than the clinicoradiological and radiomics models (p < 0.05, DeLong test), which achieved the highest efficacy, with an area under the ROC curve (AUC) value of 0.887 (95% confidence interval [CI] 0.798–0.952), 0.859 (95% CI 0.748–0.935), and 0.828 (95% CI 0.731–0.929) in the training, validation, and testing cohorts, respectively. The calibration and decision curves also indicated the favorable performance of the combined model.

Conclusion

A combined model incorporating the radiomics features and clinicoradiological characteristics can better predict the WHO/ISUP nuclear grade of CCRCC preoperatively, thus providing effective and noninvasive assessment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13244-021-01107-1.

Utility of material-specific fat images derived from rapid-kVp-switch dual-energy renal mass CT for diagnosis of renal angiomyolipoma

BACKGROUND

Renal angiomyolipoma (AML) are benign masses that require detection of macroscopic fat for accurate diagnosis.

PURPOSE

To evaluate fat material-specific images derived from dual-energy computed tomography (DECT) to diagnose renal AML.

MATERIAL AND METHODS

This retrospective case-control study evaluated 25 renal AML and 44 solid renal masses (41 renal cell carcinomas, three other tumors) imaged with rapid-kVp-switch DECT (120 kVp non-contrast-enhanced [NECT], 70-keV corticomedullary [CM], and 120-kVp nephrographic [NG]-phase CECT) during 2017-2018. A radiologist measured attenuation (Hounsfield Units [HU]) on NECT, CM-CECT, NG-CECT, and fat concentration (mg/mL) using fat-water base-pair images.

RESULTS

At NECT, 100% (44/44) non-AML and 4.0% (1/25) AML measured >-15 HU. At CM-CECT and NG-CECT, 24.0% (6/25) and 20.0% (5/25) AML measured >-15 HU (size 6-20 mm). To diagnose AML, area under receiver operating characteristic curve (AUC) using -15 HU was: 0.98 (95% confidence interval [CI] 0.98-1.00) NECT, 0.88 (95% CI 0.79-0.91) CM-CECT, and 0.90 (95% CI 0.82-0.98) NG-CECT. At DECT, fat concentration was higher in AML (163.7 ± 333.9 [-553.0 to 723.5] vs. -2858.1 ± 460.3 [-2421.2 to -206.0] mg/mL, P<0.001). AUC to diagnose AML using ≥-206.0 mg/mL threshold was 0.98 (95% CI 0.95-1.0) with sensitivity/specificity of 92.0%/96.7%. Of AML, 8.0% (2/25) were incorrectly classified; one of these was fat-poor. AUC was higher for fat concentration compared to HU measurements on CM-CECT and NG-CECT (P=0.009-0.050) and similar to NECT (P=0.98).

CONCLUSION

DECT material-specific fat images can help confirm the presence of macroscopic fat in renal AML which may be useful to establish a diagnosis if unenhanced CT is unavailable.

Clinical and radiological findings for small renal masses under active surveillance

OBJECTIVE

To analyze the experience performing active surveillance (AS) of small renal masses (SRMs) in our center and to correlate the evolution of SRMs under AS with clinical and radiological findings.

METHODS

Patients on AS between January 2012 until May 2020 for SRMs in our center have been included. Growth rate (GR) per year was analyzed and correlated with radiographic features. Patients with growth kinetics higher than 5mm/year during follow up were offered active treatment.

RESULTS

73 patients were included in AS: the mean age was 75.7 years, a mean initial tumour size of 21.2 mm, and a mean growth rate of 2.05 mm/year. Around 60 % had an ASA score of 3. The tumor size did not change over time in 43% of cases; in 4% we noticed a regression in size and in 52% of cases growth during follow-up (38% 1-5mm/year and 14% more than 5 mm/year). Delayed active treatment was indicated in 16 (21%) of cases. Treatment applied was as following: 2 radiofrequency ablations, 6 radical and 8 partial nephrectomies. A weak correlation was found between initial size and growth rate (r = 0.38, P = 0.02). No significant association was detected regarding any of the analyzed radiological findings and GR. With a mean follow up time of 33 months none of the patients presented metastatic progression.

CONCLUSION

Active surveillance is a feasible option for management of SRMs in selected patients without jeopardizing oncological safety. In our series, no clinical or radiological characteristics for predicting tumour growth were found.

A review of clinical and MR imaging features of renal lipid-poor angiomyolipomas

BACKGROUND

Lipid-poor angiomyolipomas (lpAMLs) constitute up to 5% of renal angiomyolipomas and are challenging to differentiate from malignant renal lesions on imaging alone. This review aims to identify clinical and MRI features which can be utilized to improve specificity and diagnostic accuracy for detecting lpAMLs in patients being considered for active surveillance rather than intervention.

FINDINGS

Young age, female sex, and small lesion size are associated with lpAMLs in studies evaluating indeterminate renal lesions. The accuracy of criteria using T2-weighted imaging, diffusion-weighted imaging, chemical shift imaging, dynamic contrast enhancement, multiparametric imaging, and radiomics are reviewed. Low T2 signal intensity is a particularly important MRI feature for lpAML. In studies with low T2 signal intensity, homogeneous early enhancement is a typical feature with an arterial-to-delay enhancement ratio > 1.5. Intratumoral hemorrhage with decrease in signal intensity on in-phase chemical shift imaging may be particularly useful for differentiating papillary renal cell carcinomas from lpAMLs in low T2 signal intensity lesions. Combining clinical and multiparametric MRI features can result in near-perfect specificity for lpAML. In select patients, clinical and MRI features can result in a high specificity and diagnostic accuracy for lpAMLs. These lesions can be considered for active surveillance rather than invasive diagnostic and therapeutic procedures such as biopsy or surgery.

A convention-radiomics CT nomogram for differentiating fat-poor angiomyolipoma from clear cell renal cell carcinoma

This study aimed to construct convention-radiomics CT nomogram containing conventional CT characteristics and radiomics signature for distinguishing fat-poor angiomyolipoma (fp-AML) from clear-cell renal cell carcinoma (ccRCC). 29 fp-AML and 110 ccRCC patients were enrolled and underwent CT examinations in this study. The radiomics-only logistic model was constructed with selected radiomics features by the analysis of variance (ANOVA)/Mann–Whitney (MW), correlation analysis, and Least Absolute Shrinkage and Selection Operator (LASSO), and the radiomics score (rad-score) was computed. The convention-radiomics logistic model based on independent conventional CT risk factors and rad-score was constructed for differentiating. Then the relevant nomogram was developed. Receiver operation characteristic (ROC) curves were calculated to quantify the accuracy for distinguishing. The rad-score of ccRCC was smaller than that of fp-AML. The convention-radioimics logistic model was constructed containing variables of enhancement pattern, V_UP, and rad-score. To the entire cohort, the area under the curve (AUC) of convention-radiomics model (0.968 [95% CI 0.923–0.990]) was higher than that of radiomics-only model (0.958 [95% CI 0.910–0.985]). Our study indicated that convention-radiomics CT nomogram including conventional CT risk factors and radiomics signature exhibited better performance in distinguishing fp-AML from ccRCC.

Natural growth pattern of sporadic renal angiomyolipoma

BACKGROUND

Surveillance of sporadic renal angiomyolipomas is a growing issue for physicians and radiologists. Current treatment recommendations favor active surveillance. However, the evidence underlying these is based on small case series, which also typically include angiomyolipomas associated with tuberous sclerosis.

PURPOSE

To evaluate the natural growth pattern of sporadic renal angiomyolipomas in patients without tuberous sclerosis.

MATERIAL AND METHODS

A retrospective review was performed in three separate tertiary referral centers. A keyword search of each institutions PACS history was performed. Inclusion criteria were angiomyolipomas > 1 cm in size, three years of follow-up, and lesions requiring treatment before reaching three years of follow-up. Exclusion criteria included a diagnosis of tuberous sclerosis, pregnancy, prior treatment with embolization without any prior imaging, and lesions which were treated on presentation. Growth of the angiomyolipomas was evaluated on the basis of maximum dimension on initial and follow-up images.

RESULTS

Sixty-three patients were identified in total, with 64 lesions eligible for inclusion. The majority of patients were women (55/63). The mean age at which the angiomyolipomas discovered was 56.4 years. Mean total growth was 0.085 mm and mean follow-up was 65.5 months. At initial measurement, the mean maximum dimension of the lesions in our cohort was 2.08 cm. After follow-up, this was 2.16 cm. The average rate of growth was 0.015 cm per year.

CONCLUSION

Sporadic angiomyolipomas exhibit minimal, if any, natural growth. Current surveillance strategies could be relaxed.

Imaging Characterization of Renal Masses

The detection of a renal mass is a relatively frequent occurrence in the daily practice of any Radiology Department. The diagnostic approaches depend on whether the lesion is cystic or solid. Cystic lesions can be managed using the Bosniak classification, while management of solid lesions depends on whether the lesion is well-defined or infiltrative. The approach to well-defined lesions focuses mainly on the differentiation between renal cancer and benign tumors such as angiomyolipoma (AML) and oncocytoma. Differential diagnosis of infiltrative lesions is wider, including primary and secondary malignancies and inflammatory disease, and knowledge of the patient history is essential. Radiologists may establish a possible differential diagnosis based on the imaging features of the renal masses and the clinical history. The aim of this review is to present the contribution of the different imaging techniques and image guided biopsies in the diagnostic management of cystic and solid renal lesions.

Effect of phase of enhancement on texture analysis in renal masses evaluated with non-contrast-enhanced, corticomedullary, and nephrographic phase-enhanced CT images

OBJECTIVE

To compare texture analysis (TA) features of solid renal masses on renal protocol (non-contrast enhanced [NECT], corticomedullary [CM], nephrographic [NG]) CT.

MATERIALS AND METHODS

A total of 177 consecutive solid renal masses (116 renal cell carcinoma [RCC]; 51 clear cell [cc], 40 papillary, 25 chromophobe, and 61 benign masses; 49 oncocytomas, 12 fat-poor angiomyolipomas) with three-phase CT between 2012 and 2017 were studied. Two blinded radiologists independently assessed tumor heterogeneity (5-point Likert scale) and segmented tumors. TA features (N = 25) were compared between groups and between phases. Accuracy (area under the curve [AUC]) for RCC versus benign and cc-RCC versus other masses was compared.

RESULTS

Subjectively, tumor heterogeneity differed between phases (p < 0.01) and between tumors within the same phase (p = 0.03 [NECT] and p < 0.01 [CM, NG]). Inter-observer agreement was moderate to substantial (intraclass correlation coefficient = 0.55-0.73). TA differed in 92.0% (23/25) features between phases (p < 0.05) except for GLNU and f6. More TA features differed significantly on CM (80.0% [20/25]) compared with NG (40.0% [10/25]) and NECT (16.0% [4/25]) (p < 0.01). For RCC versus benign, AUCs of texture features did not differ comparing CM and NG (p > 0.05), but were higher for 20% (5/25) and 28% (7/25) of features comparing CM and NG with NECT (p < 0.05). For cc-RCC versus other, 36% (9/25) and 40% (10/25) features on CM had higher AUCs compared with NECT and NG images (p < 0.05).

CONCLUSION

Texture analysis of renal masses differs, when evaluated subjectively and quantitatively, by phase of CT enhancement. The corticomedullary phase had the highest discriminatory value when comparing masses and for differentiating cc-RCC from other masses.

KEY POINTS

• Subjectively evaluated renal tumor heterogeneity on CT differs by phase of enhancement. • Quantitative CT texture analysis features in renal tumors differ by phases of enhancement with the corticomedullary phase showing the highest number and most significant differences compared with non-contrast-enhanced and nephrographic phase images. • For diagnosis of clear cell RCC, corticomedullary phase texture analysis features had improved accuracy of classification in approximately 40% of features studied compared with non-contrast-enhanced and nephrographic phase images.

A Solution for Homogeneous Liver Enhancement in Computed Tomography: Results From the COMpLEx Trial

OBJECTIVES

The aim of the study was to reach homogeneous enhancement of the liver, irrespective of total body weight (TBW) or tube voltage. An easy-to-use rule of thumb, the 10-to-10 rule, which pairs a 10 kV reduction in tube voltage with a 10% decrease in contrast media (CM) dose, was evaluated.

MATERIALS AND METHODS

A total of 256 patients scheduled for an abdominal CT in portal venous phase were randomly allocated to 1 of 4 groups. In group 1 (n = 64), a tube voltage of 120 kV and a TBW-adapted CM injection protocol was used: 0.521 g I/kg. In group 2 (n = 63), tube voltage was 90 kV and the TBW-adapted CM dosing factor remained 0.521 g I/kg. In group 3 (n = 63), tube voltage was reduced by 20 kV and CM dosing factor by 20% compared with group 1, in line with the 10-to-10 rule (100 kV; 0.417 g I/kg). In group 4 (n = 66), tube voltage was decreased by 30 kV paired with a 30% decrease in CM dosing factor compared with group 1, in line with the 10-to-10 rule (90 kV; 0.365 g I/kg). Objective image quality was evaluated by measuring attenuation in Hounsfield units (HU), signal-to-noise ratio, and contrast-to-noise ratio in the liver. Overall subjective image quality was assessed by 2 experienced readers by using a 5-point Likert scale. Two-sided P values below 0.05 were considered significant.

RESULTS

Mean attenuation values in groups 1, 3, and 4 were comparable (118.2 ± 10.0, 117.6 ± 13.9, 117.3 ± 21.6 HU, respectively), whereas attenuation in group 2 (141.0 ± 18.2 HU) was significantly higher than all other groups (P < 0.01). No significant difference in attenuation was found between weight categories 80 kg or less and greater than 80 kg within the 4 groups (P ≥ 0.371). No significant differences in subjective image quality were found (P = 0.180).

CONCLUSIONS

The proposed 10-to-10 rule is an easily reproducible method resulting in similar enhancement in portal venous CT of the liver throughout the patient population, irrespective of TBW or tube voltage.

Tailoring Contrast Media Protocols to Varying Tube Voltages in Vascular and Parenchymal CT Imaging: The 10-to-10 Rule

The latest technical developments in CT have created the possibility for individualized scan protocols at variable kV settings. Lowering tube voltages closer to the K-edge of iodine increases attenuation. However, the latter is also influenced by patient characteristics such as total body weight. To maintain a robust contrast enhancement throughout the patient population in both vascular and parenchymal CT scans, one must adapt the contrast media administration protocols to both the selected kV setting and patient body habitus. This article proposes a simple rule of thumb for how to adapt the contrast media protocol to any kV setting: the 10-to-10 rule.

Importance of phase enhancement for machine learning classification of solid renal masses using texture analysis features at multi-phasic CT

OBJECTIVE

To compare machine learning (ML) of texture analysis (TA) features for classification of solid renal masses on non-contrast-enhanced CT (NCCT), corticomedullary (CM) and nephrographic (NG) phase contrast-enhanced (CE) CT.

MATERIALS AND METHODS

With IRB approval, we retrospectively identified 177 consecutive solid renal masses (116 renal cell carcinoma [RCC]; 51 clear cell [cc], 40 papillary, 25 chromophobe and 61 benign tumors; 49 oncocytomas and 12 fat-poor angiomyolipomas) with renal protocol CT between 2012 and 2017. Tumors were independently segmented by two blinded radiologists. Twenty-five 2-dimensional TA features were extracted from each phase. Diagnostic accuracy for 1) RCC versus benign tumor and 2) cc-RCC versus other tumor was assessed using XGBoost.

RESULTS

ML of texture analysis features on different phases achieved mean area under the ROC curve (AUC [SD]), sensitivity/specificity for 1) RCC vs benign = 0.70(0.19), 96%/32% on CM-CECT and 0.71(0.14), 83%/58% on NG-CECT and; 2) cc-RCC vs other = 0.77(0.12), 49%/90% on CM-CECT and 0.71(0.16), 22%/94% on NG-CECT. There was no difference in AUC comparing CECT to NCCT (p = 0.058-0.54) and no improvement when combining data across all three phases compared single-phase assessment (p = 0.39-0.68) for either outcome. AUCs decreased when ML models were trained with one phase and tested on a different phase for both outcomes (RCC;p = 0.045-0.106, cc-RCC; < 0.001).

CONCLUSION

Accuracy of machine learning classification of renal masses using texture analysis features did not depend on phase; however, models trained using one phase performed worse when tested on another phase particularly when associating NCCT and CECT. These findings have implications for large registries which use varying CT protocols to study renal masses.

Mimics and Pitfalls in Renal Imaging

There are several potential pitfalls that radiologists face when interpreting images of the kidneys. Some result from image acquisition and can arise from the imaging equipment or imaging technique, whereas others are patient related. Another category of pitfalls relates to image interpretation. Some difficulties stem from methods to detect enhancement after contrast administration, whereas others are benign entities that can mimic a renal tumor. Finally, interpretation and diagnosis of fat-containing renal masses may be tricky due to the complexities discerning the pattern of fat within a mass and how that translates to an accurate diagnosis.

Protocol Optimization for Renal Mass Detection and Characterization

Renal masses increasingly are found incidentally, largely due to the frequent use of medical imaging. Computed tomography (CT) and MR imaging are mainstays for renal mass characterization, presurgical planning of renal tumors, and surveillance after surgery or systemic therapy for advanced renal cell carcinomas. CT protocols should be tailored to different clinical indications, balancing diagnostic accuracy and radiation exposure. MR imaging protocols should take advantage of the improved soft tissue contrast for renal tumor diagnosis and staging. Optimized imaging protocols enable analysis of imaging features that help narrow the differential diagnoses and guide management in patients with renal masses.

Reporting on Renal Masses, Recommendations for Terminology, and Sample Templates

Given the incidence of small renal masses, from benign cysts to malignancy, most radiologists encounter these lesions multiple times during their career. Radiologists have an opportunity to provide critical data that will further refine the understanding of the impact of these masses on patient outcomes. This article summarizes and describes recent updates and understanding of the critical observations and descriptors of renal masses. The templates and glossary of terms presented in this review article facilitate the radiology reporting of such data elements, giving radiologists the opportunity to improve diagnostic accuracy and influence management of small renal masses.

Ensemble U-net-based method for fully automated detection and segmentation of renal masses on computed tomography images

PURPOSE

Detection and accurate localization of renal masses (RM) are important steps toward future potential classification of benign vs malignant RM. A fully automated algorithm for detection and localization of RM may eliminate the observer variability in the clinical workflow.

METHOD

In this paper, we describe a fully automated methodology for accurate detection and segmentation of RM from contrast-enhanced computed tomography (CECT) images. We first determine the boundaries of the kidneys on the CECT images utilizing a convolutional neural network-based method to be used as a region of interest to search for RM. We then employ a homogenous U-Net-based ensemble learning model to identify and delineate RM. We used an institutional dataset comprised of CECT images in 315 patients to train and evaluate the proposed method. We compared results of our method to those of three-dimensional (3D) U-Net for RM localization and further evaluated our algorithm using the kidney tumor segmentation (KiTS19) challenge dataset.

RESULTS

The developed algorithm reported a Dice similarity coefficient (DSC) of 95.79% ± 5.16% and 96.25 ± 3.37 (mean ± standard deviation) for segmentation accuracy of kidney boundary from 125 and 60 test images from institutional and KiTS19 datasets, respectively. Using our method, RM were detected in 125 and 52 test cases, which corresponds to 100% and 86.67% sensitivity at patient level in institutional and KiTS19 test images. Our ensemble method for RM localization yielded a mean DSC of 88.65% ± 7.31% and 87.91% ± 6.82% on the institutional and KiTS19 test datasets, respectively. The mean DSC for RM delineation from CECT institutional test images using 3D U-Net was 85.95% ± 1.46%.

CONCLUSION

We describe a method for automated localization of RM using CECT images. Our results are important in terms of clinical perspective as fully automated detection of RM is a fundamental step for further diagnosis of cystic vs solid RM and eventually benign vs malignant solid RM, that has not been reported previously.

Cost-effectiveness of dual-energy CT versus multiphasic single-energy CT and MRI for characterization of incidental indeterminate renal lesions

PURPOSE

To evaluate the cost-effectiveness of DECT versus multiphasic CT and MRI for characterizing small incidentally detected indeterminate renal lesions using a Markov Monte Carlo decision-analytic model.

BACKGROUND

Incidental renal lesions are commonly encountered due to the increasing utilization of medical imaging and the increasing prevalence of renal lesions with age. Currently recommended imaging modalities to further characterize incidental indeterminate renal lesions have some inherent drawbacks. Single-phase DECT may overcome these limitations, but its cost-effectiveness remains uncertain.

MATERIALS AND METHODS

A decision-analytic (Markov) model was constructed to estimate life expectancy and lifetime costs for otherwise healthy 64-year-old patients with small (≤ 4 cm) incidentally detected, indeterminate renal lesions on routine imaging (e.g., ultrasound or single-phase CT). Three strategies for evaluating renal lesions for enhancement were compared: multiphase SECT (e.g., true unenhanced and nephrographic phase), multiphasic MRI, and single-phase DECT (nephrographic phase in dual-energy mode). The model incorporated modality-specific diagnostic test performance, incidence, and prevalence of incidental renal cell carcinomas (RCCs), effectiveness, costs, and health outcomes. An incremental cost-effectiveness analysis was performed to identify strategy preference at willingness-to-pay (WTP) thresholds of $50,000 and $100,000 per quality-adjusted life-year (QALY) gained. Deterministic and probabilistic sensitivity analysis were performed.

RESULTS

In the base case analysis, expected mean costs per patient undergoing characterization of incidental renal lesions were $2567 for single-phase DECT, $3290 for multiphasic CT, and $3751 for multiphasic MRI. Associated quality-adjusted life-years were the highest for single-phase DECT at 0.962, for multiphasic MRI it was 0.940, and was the lowest for multiphasic CT at 0.925. Because of lower associated costs and higher effectiveness, the single-phase DECT strategy dominated the other two strategies.

CONCLUSIONS

Single-phase DECT is potentially more cost-effective than multiphasic SECT and MRI for evaluating small incidentally detected indeterminate renal lesions.

Automated classification of solid renal masses on contrast-enhanced computed tomography images using convolutional neural network with decision fusion

OBJECTIVES

To develop a deep learning-based method for automated classification of renal cell carcinoma (RCC) from benign solid renal masses using contrast-enhanced computed tomography (CECT) images.

METHODS

This institutional review board-approved retrospective study evaluated CECT in 315 patients with 77 benign (57 oncocytomas, and 20 fat-poor angiomyolipoma) and 238 malignant (RCC: 123 clear cell, 69 papillary, and 46 chromophobe subtypes) tumors identified consecutively between 2015 and 2017. We employed a decision fusion-based model to aggregate slice level predictions determined by convolutional neural network (CNN) via a majority voting system to evaluate renal masses on CECT. The CNN-based model was trained using 7023 slices with renal masses manually extracted from CECT images of 155 patients, cropped automatically around kidneys, and augmented artificially. We also examined the fully automated approach for renal mass evaluation on CECT. Moreover, a 3D CNN was trained and tested using the same datasets and the obtained results were compared with those acquired from slice-wise algorithms.

RESULTS

For differentiation of RCC versus benign solid masses, the semi-automated majority voting-based CNN algorithm achieved accuracy, precision, and recall of 83.75%, 89.05%, and 91.73% using 160 test cases, respectively. Fully automated pipeline yielded accuracy, precision, and recall of 77.36%, 85.92%, and 87.22% on the same test cases, respectively. 3D CNN reported accuracy, precision, and recall of 79.24%, 90.32%, and 84.21% using 160 test cases, respectively.

CONCLUSIONS

A semi-automated majority voting CNN-based methodology enabled accurate classification of RCC from benign neoplasms among solid renal masses on CECT.

KEY POINTS

• Our proposed semi-automated majority voting CNN-based algorithm achieved accuracy of 83.75% for the diagnosis of RCC from benign solid renal masses on CECT images. • A fully automated CNN-based methodology classified solid renal masses with moderate accuracy of 77.36% using the same test images. • Employing 3D CNN-based methodology yielded slightly lower accuracy for renal mass classification compared with the semi- automated 2D CNN-based algorithm (79.24%).

Deep Learning to Distinguish Benign from Malignant Renal Lesions Based on Routine MR Imaging

PURPOSE

With increasing incidence of renal mass, it is important to make a pretreatment differentiation between benign renal mass and malignant tumor. We aimed to develop a deep learning model that distinguishes benign renal tumors from renal cell carcinoma (RCC) by applying a residual convolutional neural network (ResNet) on routine MR imaging.

EXPERIMENTAL DESIGN

Preoperative MR images (T2-weighted and T1-postcontrast sequences) of 1,162 renal lesions definitely diagnosed on pathology or imaging in a multicenter cohort were divided into training, validation, and test sets (70:20:10 split). An ensemble model based on ResNet was built combining clinical variables and T1C and T2WI MR images using a bagging classifier to predict renal tumor pathology. Final model performance was compared with expert interpretation and the most optimized radiomics model.

RESULTS

Among the 1,162 renal lesions, 655 were malignant and 507 were benign. Compared with a baseline zero rule algorithm, the ensemble deep learning model had a statistically significant higher test accuracy (0.70 vs. 0.56, P = 0.004). Compared with all experts averaged, the ensemble deep learning model had higher test accuracy (0.70 vs. 0.60, P = 0.053), sensitivity (0.92 vs. 0.80, P = 0.017), and specificity (0.41 vs. 0.35, P = 0.450). Compared with the radiomics model, the ensemble deep learning model had higher test accuracy (0.70 vs. 0.62, P = 0.081), sensitivity (0.92 vs. 0.79, P = 0.012), and specificity (0.41 vs. 0.39, P = 0.770).

CONCLUSIONS

Deep learning can noninvasively distinguish benign renal tumors from RCC using conventional MR imaging in a multi-institutional dataset with good accuracy, sensitivity, and specificity comparable with experts and radiomics.

Computed Tomography Imaging Characteristics of Histologically Confirmed Papillary Renal Cell Carcinoma-Implications for Ancillary Imaging

Low-attenuation renal lesions on non-contrast computed tomography (CT) are often considered to be benign cysts without need for further imaging. However, the papillary subtype of renal cell carcinoma (RCC) may have similar radiographic characteristics. A single-center retrospective review was therefore performed to identify extirpated papillary RCC (pRCC) specimens with correlation made to preoperative tumor imaging characteristics. A total of 108 pRCC specimens were identified of which 84 (27 type I, 17 type 2, 40 unspecified) had CT imaging available for review. Non-contrast CT was available for 73 tumors with 16 (22%) demonstrating Hounsfield units (HU) measurements fewer than 20 at baseline without differences between papillary subtypes. Mean attenuation following contrast administration was similar between papillary subtypes (45 HU for type 1 pRCC and 49 HU for type 2). This study highlights that pathologically proven pRCC is a heterogeneous entity in terms of density on preoperative CT imaging. A non-contrast CT scan with HU fewer than 20 may not be an adequate evaluation for incidental renal masses, as over 1 in 5 pRCCs demonstrate lower attenuation than this cutoff. Further study is needed to identify the appropriate role of ancillary imaging in the workup of seemingly benign-appearing renal lesions.

Update on Indications for Percutaneous Renal Mass Biopsy in the Era of Advanced CT and MRI

OBJECTIVE. The objective of this article is to review the burgeoning role of percutaneous renal mass biopsy (RMB). CONCLUSION. Percutaneous RMB is safe, accurate, and indicated for an expanded list of clinical scenarios. The chief scenarios among them are to prevent treatment of benign masses and help select patients for active surveillance (AS). Imaging characterization of renal masses has improved; however, management decisions often depend on a histologic diagnosis and an assessment of biologic behavior of renal cancers, both of which are currently best achieved with RMB.

Diagnostic Accuracy of Attenuation Difference and Iodine Concentration Thresholds at Rapid-Kilovoltage-Switching Dual-Energy CT for Detection of Enhancement in Renal Masses

OBJECTIVE. The objective of our study was to evaluate iodine concentration and attenuation change in Hounsfield unit (ΔHU) thresholds to diagnose enhancement in renal masses at rapid-kilovoltage-switching dual-energy CT (DECT). MATERIALS AND METHODS. We evaluated 30 consecutive histologically confirmed solid renal masses (including nine papillary renal cell carcinomas [RCCs]) and 27 benign cysts (17 simple and 10 hemorrhagic or proteinaceous cysts) with DECT December 2016 and May 2018. A blinded radiologist measured iodine concentration (in milligrams per milliliter) and ΔHU (attenuation on enhanced CT - attenuation on unenhanced CT) using 70-keV corticomedullary (CM) phase virtual monochromatic and 120-kVp nephrographic (NG) phase images. The accuracies of previously described enhancement thresholds were compared by ROC curve analysis. RESULTS. An iodine concentration of ≥ 2.0 mg/mL and an iodine concentration of ≥ 1.2 mg/mL achieved sensitivity, specificity, and the area under the ROC curve (AUC) of 73.3%, 100.0%, and 0.87 and 86.7%, 100.0%, and 0.93, respectively. On 70-keV CM phase images, ΔHU ≥ 20 HU and ΔHU ≥ 15 HU yielded sensitivity, specificity, and AUC of 80.0%, 100.0%, and 0.90 and 90.0%, 100.0%, and 0.95, respectively. The numbers of incorrectly classified papillary RCCs were as follows: iodine concentration of ≥ 2.0 mg/mL, 77.8% (7/9; range, 0.7-1.6 mg/mL); iodine concentration of ≥ 1.2 mg/mL, 44.4% (4/9; range, 0.7-0.9 mg/mL); ΔHU ≥ 20 HU on 70-keV CM phase images, 66.7% (6/9; range, 4-17 HU); and ΔHU ≥ 15 HU on 70-keV DECT images, 33.3% (3/9; 4-12 HU). No cyst pseudoenhancement occurred on DECT. For 120-kVp NG phase DECT, ΔHU ≥ 20 HU and ΔHU ≥ 15 HU yielded sensitivity, specificity, and AUC of 93.3%, 96.3%, and 0.95 and 100.0%, 88.9%, and 0.94, respectively. With ΔHU ≥ 20 HU, 22.2% (2/9) (range, 15-18 HU) of papillary RCCs were misclassified and there was one pseudoenhancing cyst. With ΔHU ≥ 15 HU, no papillary RCCs were misclassified but 11.1% (3/27) of cysts showed pseudoenhancement. Only an iodine concentration of ≥ 2.0 mg/mL showed significantly lower accuracy than other measures (p = 0.031-0.045). CONCLUSION. DECT applied in the CM phase performed best using an iodine concentration of ≥ 1.2 mg/mL or a 70-keV ΔHU ≥ 15 HU; these parameters improved sensitivity for the detection of enhancement in renal masses without instances of cyst pseudoenhancement.

Systematic Review and Meta-Analysis Investigating the Diagnostic Yield of Dual-Energy CT for Renal Mass Assessment

OBJECTIVE. The objective of our study was to perform a systematic review and meta-analysis to evaluate the diagnostic accuracy of dual-energy CT (DECT) for renal mass evaluation. MATERIALS AND METHODS. In March 2018, we searched MEDLINE, Cochrane Database of Systematic Reviews, Embase, and Web of Science databases. Analytic methods were based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Pooled estimates for sensitivity, specificity, and diagnostic odds ratios were calculated for DECT-based virtual monochromatic imaging (VMI) and iodine quantification techniques as well as for conventional attenuation measurements from renal mass CT protocols. I² was used to evaluate heterogeneity. The methodologic quality of the included studies and potential bias were assessed using items from the Quality Assessment Tool for Diagnostic Accuracy Studies 2 (QUADAS-2). RESULTS. Of the 1043 articles initially identified, 13 were selected for inclusion (969 patients, 1193 renal masses). Cumulative data of sensitivity, specificity, and summary diagnostic odds ratio for VMI were 87% (95% CI, 80-92%; I², 92.0%), 93% (95% CI, 90-96%; I², 18.0%), and 183.4 (95% CI, 30.7-1093.4; I², 61.6%), respectively. Cumulative data of sensitivity, specificity, and summary diagnostic odds ratio for iodine quantification were 99% (95% CI, 97-100%; I², 17.6%), 91% (95% CI, 89-94%; I², 84.2%), and 511.5 (95% CI, 217-1201; I², 0%). No significant differences in AUCs were found when comparing iodine quantification to conventional attenuation measurements (p = 0.79). CONCLUSION. DECT yields high accuracy for renal mass evaluation. Determination of iodine content with the iodine quantification technique shows diagnostic accuracy similar to conventional attenuation measurements from renal mass CT protocols. The iodine quantification technique may be used to characterize incidental renal masses when a dedicated renal mass protocol is not available.

Uncommon malignant renal tumors and atypical presentation of common ones: a guide for radiologists

OBJECTIVE

While the typical imaging features of the more common RCC subtypes have previously been described, they can at times have unusual, but distinguishing features. Rarer renal tumors span a broad range of imaging features, but they may also have characteristic presentations. We review the key imaging features of atypical presentations of malignant renal tumors and uncommon malignant renal tumors.

CONCLUSION

Renal tumors have many different presentation patterns, but knowledge of the distinguishing MR and CT features can help identify both atypical presentation of common malignancies and uncommon renal tumors.

Patient-specific 3D printed and augmented reality kidney and prostate cancer models: impact on patient education

Background

Patient-specific 3D models are being used increasingly in medicine for many applications including surgical planning, procedure rehearsal, trainee education, and patient education. To date, experiences on the use of 3D models to facilitate patient understanding of their disease and surgical plan are limited. The purpose of this study was to investigate in the context of renal and prostate cancer the impact of using 3D printed and augmented reality models for patient education.

Methods

Patients with MRI-visible prostate cancer undergoing either robotic assisted radical prostatectomy or focal ablative therapy or patients with renal masses undergoing partial nephrectomy were prospectively enrolled in this IRB approved study (n = 200). Patients underwent routine clinical imaging protocols and were randomized to receive pre-operative planning with imaging alone or imaging plus a patient-specific 3D model which was either 3D printed, visualized in AR, or viewed in 3D on a 2D computer monitor. 3D uro-oncologic models were created from the medical imaging data. A 5-point Likert scale survey was administered to patients prior to the surgical procedure to determine understanding of the cancer and treatment plan. If randomized to receive a pre-operative 3D model, the survey was completed twice, before and after viewing the 3D model. In addition, the cohort that received 3D models completed additional questions to compare usefulness of the different forms of visualization of the 3D models. Survey responses for each of the 3D model groups were compared using the Mann-Whitney and Wilcoxan rank-sum tests.

Results

All 200 patients completed the survey after reviewing their cases with their surgeons using imaging only. 127 patients completed the 5-point Likert scale survey regarding understanding of disease and surgical procedure twice, once with imaging and again after reviewing imaging plus a 3D model. Patients had a greater understanding using 3D printed models versus imaging for all measures including comprehension of disease, cancer size, cancer location, treatment plan, and the comfort level regarding the treatment plan (range 4.60–4.78/5 vs. 4.06–4.49/5, p < 0.05).

Conclusions

All types of patient-specific 3D models were reported to be valuable for patient education. Out of the three advanced imaging methods, the 3D printed models helped patients to have the greatest understanding of their anatomy, disease, tumor characteristics, and surgical procedure.

Diagnostic Yield and Complication Rate in Percutaneous Needle Biopsy of Renal Hilar Masses With Comparison With Renal Cortical Mass Biopsies in a Cohort of 195 Patients

OBJECTIVE

The objective of this study was to compare diagnostic yield and complication rate in needle biopsy (NB) of renal hilar and cortical masses.

MATERIALS AND METHODS

With institutional review board approval, we retrospectively studied 195 patients (120 men, 75 women; mean age ± SD, 67 ± 13 years old) who underwent ultrasound-guided renal mass NB between January 2013 and December 2017. Operator years of experience, biopsy technique (coaxial or successive), needle gauge (22-gauge fine-needle aspiration, 18-gauge core-needle, or both), number of passes, postprocedural complication, and histopathologic diagnoses were recorded. A radiologist who was blinded to histopathologic diagnoses recorded mass location (upper pole, interpolar region, lower pole) and percentage of hilar involvement. Comparisons were performed using independent t and chi-square tests.

RESULTS

Of the masses biopsied, 5.6% (11/195) were 100% hilar (mean hilar involvement, 20.8% ± 29.8%; range, 0-100%). Mean lesion size was 44 ± 27 mm (range, 12-157 mm). NB diagnosis was established in 84.6% (165/195) of masses, and 15.4% (30/195) of biopsies were inconclusive, with no association with size (p = 0.55) or percentage of hilar involvement (p = 0.756). In the purely hilar masses, diagnosis was established in 72.7% (8/11) compared with 85.3% (157/184) with any cortical involvement (p = 0.265). There was no association between diagnosis and operator years of experience, biopsy technique, needle gauge, or number of passes (p > 0.05). Bleeding occurred after biopsy in 7.7% (15/195) of cases, was associated with percentage of hilar involvement (39.3% ± 44.9% vs 19.3% ± 27.8%; p = 0.012), and was more common in purely hilar masses (36.4% [4/11] vs 5.6% [11/195]; p < 0.001). Complications were not associated with any other feature (p > 0.05).

CONCLUSION

Percutaneous biopsy of renal hilar masses is technically feasible with diagnostic yield similar to that of cortical masses but with postprocedural bleeding more often than what is seen with cortical masses.

Mono, bi- and tri-exponential diffusion MRI modelling for renal solid masses and comparison with histopathological findings

PURPOSE

To compare diffusion tensor imaging (DTI), intravoxel incoherent motion (IVIM), and tri-exponential models of the diffusion magnetic resonance imaging (MRI) signal for the characterization of renal lesions in relationship to histopathological findings.

METHODS

Sixteen patients planned to undergo nephrectomy for kidney tumour were scanned before surgery at 3 T magnetic resonance imaging (MRI), with T2-weighted imaging, DTI and diffusion weighted imaging (DWI) using ten b-values. DTI parameters (mean diffusivity [MD] and fractional anisotropy [FA]) were obtained by iterative weighted linear least squared fitting of the DTI data and bi-, and tri-exponential fit parameters (Dbi, fstar,and Dtri, ffast,finterm) using a nonlinear fit of the multiple b-value DWI data. Average parameters were calculated for regions of interest, selecting the lesions and healthy kidney tissue. Tumour type and specificities were determined after surgery by histological examination. Mean parameter values of healthy tissue and solid lesions were compared using a Wilcoxon-signed ranked test and MANOVA.

RESULTS

Thirteen solid lesions (nine clear cell carcinomas, two papillary renal cell carcinoma, one haemangioma and one oncocytoma) and four cysts were included. The mean MD of solid lesions are significantly (p < 0.05) lower than healthy cortex and medulla, (1.94 ± 0.32*10- 3 mm2/s versus 2.16 ± 0.12*10- 3 mm2/s and 2.21 ± 0.14*10- 3 mm2/s, respectively) whereas ffast is significantly higher (7.30 ± 3.29% versus 4.14 ± 1.92% and 4.57 ± 1.74%) and finterm is significantly lower (18.7 ± 5.02% versus 28.8 ± 5.09% and 26.4 ± 6.65%). Diffusion coefficients were high (≥2.0*10- 3 mm2/s for MD, 1.90*10- 3 mm2/s for Dbi and 1.6*10- 3 mm2/s for Dtri) in cc-RCCs with cystic structures and/or haemorrhaging and low (≤1.80*10- 3 mm2/s for MD, 1.40*10- 3 mm2/s for Dbi and 1.05*10- 3 mm2/s for Dtri) in tumours with necrosis or sarcomatoid differentiation.

CONCLUSION

Parameters derived from a two- or three-component fit of the diffusion signal are sensitive to histopathological features of kidney lesions.