Update on Renal Mass Biopsy

[Application of Raman-based technologies in the detection of urological tumors]

Urinary system tumors affect a huge number of individuals, and are frequently recurrent and progressing following surgery, necessitating lifelong surveillance. As a result, early and precise diagnosis of urinary system cancers is important for prevention and therapy. Histopathology is now the golden stan-dard for the diagnosis, but it is invasive, time-consuming, and inconvenient for initial diagnosis and re-gular follow-up assessment. Endoscopy can directly witness the tumor's structure, but intrusive detection is likely to cause harm to the patient's organs, and it is apt to create other hazards in frequently examined patients. Imaging is a valuable non-invasive and quick assessment tool; however, it can be difficult to define the type of lesions and has limited sensitivity for early tumor detection. The conventional approaches for detecting tumors have their own set of limitations. Thus, detection methods that combine non-invasive detection, label-free detection, high sensitivity and high specificity are urgently needed to aid clinical diagnosis. Optical diagnostics and imaging are increasingly being employed in healthcare settings in a variety of sectors. Raman scattering can assess changes in molecular signatures in cancer cells or tissues based on the interaction with vibrational modes of common molecular bonds. Due to the advantages of label-free, strong chemical selectivity, and high sensitivity, Raman scattering, especially coherent Raman scattering microscopy imaging with high spatial resolution, has been widely used in biomedical research. And quantity studies have shown that it has a good application in the detection and diagnosis of bladder can-cer, renal clear cell carcinoma, prostate cancer, and other cancers. In this paper, several nonlinear imaging techniques based on Raman scattering technology are briefly described, including Raman spectroscopy, coherent anti-Stokes Raman scattering, stimulated Raman scattering, and surface-enhanced Raman spectroscopy. And we will discuss the application of these techniques for detecting urologic malignancy. Future research directions are predicted using the advantages and limitations of the aforesaid methodologies in the research. For clinical practice, Raman scattering technology is intended to enable more accurate, rapid, and non-invasive in early diagnosis, intraoperative margins, and pathological grading basis for clinical practice.

Utilization of Renal Mass Biopsy for T1 Renal Lesions across Michigan: Results from MUSIC-KIDNEY, A Statewide Quality Improvement Collaborative

BACKGROUND

Renal mass biopsy (RMB) has had limited and varied utilization to guide management of renal masses (RM).

OBJECTIVE

To evaluate utilization of RMB for newly diagnosed cT1 RMs across diverse practice types and assess associations of outcomes with RMB.

DESIGN SETTING AND PARTICIPANTS

MUSIC-KIDNEY commenced data collection in September 2017 for all newly presenting patients with a cT1 RM at 14 diverse practices. Patients were assessed at ≥120 d after initial evaluation.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Demographics and outcomes were compared for patients undergoing RMB versus no RMB. Clinical and demographic characteristics were summarized by RMB status using a χ2 test for categorical variables and Student t test for continuous variables. A mixed-effects logistic regression model was constructed to identify associations with RMB receipt.

RESULTS AND LIMITATIONS

RMB was performed in 15.5% (n = 282) of 1808 patients with a cT1 RM. Practice level rates varied from 0% to 100% (p = 0.001), with only five of 14 practices using RMB in >20% of patients. On multivariate analysis, predictors of RMB included greater comorbidity (Charlson comorbidity index ≥2 vs 0: odds ratio [OR] 1.44; p = 0.025) and solid lesion type (cystic vs solid: OR 0.17; p = 0.001; indeterminate vs solid: OR 0.58; p = 0.01). RMB patients were less likely to have benign pathology at intervention (5.0% vs 13.5%; p = 0.01). No radical nephrectomies were performed for patients with benign histology at RMB. The limitations include short follow-up and inclusion of practices with low numbers of RMBs.

CONCLUSIONS

Utilization of RMB varied widely across practices. Factors associated with RMB include comorbidities and lesion type. Patients undergoing RMB were less likely to have benign histology at intervention.

PATIENT SUMMARY

Current use of biopsy for kidney tumors is low and varies across our collaborative. Biopsy was performed in patients with greater comorbidity (more additional medical conditions) and for solid kidney tumors. Pretreatment biopsy is associated with lower nonmalignant pathology detected at treatment.

Machine Learning-Enabled Renal Cell Carcinoma Status Prediction Using Multiplatform Urine-Based Metabolomics

Renal cell carcinoma (RCC) is diagnosed through expensive cross-sectional imaging, frequently followed by renal mass biopsy, which is not only invasive but also prone to sampling errors. Hence, there is a critical need for a noninvasive diagnostic assay. RCC exhibits altered cellular metabolism combined with the close proximity of the tumor(s) to the urine in the kidney, suggesting that urine metabolomic profiling is an excellent choice for assay development. Here, we acquired liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) data followed by the use of machine learning (ML) to discover candidate metabolomic panels for RCC. The study cohort consisted of 105 RCC patients and 179 controls separated into two subcohorts: the model cohort and the test cohort. Univariate, wrapper, and embedded methods were used to select discriminatory features using the model cohort. Three ML techniques, each with different induction biases, were used for training and hyperparameter tuning. Assessment of RCC status prediction was evaluated using the test cohort with the selected biomarkers and the optimally tuned ML algorithms. A seven-metabolite panel predicted RCC in the test cohort with 88% accuracy, 94% sensitivity, 85% specificity, and 0.98 AUC. Metabolomics Workbench Study IDs are ST001705 and ST001706.

Surveillance for low-risk kidney cancer: a narrative review of contemporary worldwide practices

The management trend of low-risk kidney cancer over the last decade has been from treatment with radical nephrectomy, to use of nephron sparing procedures of partial nephrectomy and ablation, as well as the option of active surveillance (AS). This narrative review aims to summarise the available guidelines related to AS and review the published descriptions of regional practices on the management of low-risk kidney cancer worldwide. A search of PubMed, Google Scholar and Cochrane Library databases for studies published 2010 to June 2020 identified 15 studies, performed between 2000 and 2019, which investigated 13 different cohorts of low-risk kidney cancer patients on AS. Although international guidelines show a level of agreement in their recommendation on how AS is conducted, in terms of patient selection, surveillance strategy and triggers for intervention, cohort studies show distinct differences in worldwide practice of AS. Prospective studies showed general agreement in their predefined selection criteria for entry into AS. Retrospective studies showed that patients who were older, with greater comorbidities, worse performance status and smaller tumours were more likely to be managed with AS. The rate of percutaneous renal mass biopsy varied between studies from 2% to 56%. The surveillance protocol was different across all studies in terms of recommended modality and frequency of imaging. Of the 6 studies which had set indications for intervention, these were broadly in agreement. Despite clear criteria for intervention, patient or surgeon preference was still the reason in 11–71% of cases of delayed intervention across 5 studies. This review shows that AS is being applied in a variety of centres worldwide and that key areas of patient selection criteria and surveillance strategy have large similarities. However, the rate of renal mass biopsy and of delayed intervention varies significantly between studies, suggesting the process of diagnosing malignant SRM and decision making whilst on AS are varying in practice. Further research is needed on the diagnosis and characterisation of incidentally found small renal masses (SRM), using imaging and histology, and the natural history of these SRM in order to develop evidence-based active surveillance protocols.

Normalized Dual-Energy Iodine Ratio Best Differentiates Renal Cell Carcinoma Subtypes Among Quantitative Imaging Biomarkers From Perfusion CT and Dual-Energy CT

OBJECTIVE. The objective of our study was to assess and compare the diagnostic accuracy of perfusion CT (PCT) and dual-energy CT (DECT) in differentiating clear cell renal cell carcinoma (ccRCC) from non-ccRCC. MATERIALS AND METHODS. This retrospective study included 51 patients with 52 renal cell carcinomas (RCCs) (36 ccRCCs and 16 non-ccRCCs) who underwent both PCT and DECT before surgery or biopsy between January 2014 and December 2018. Three independent readers measured blood flow, blood volume (BV), and permeability using PCT and iodine concentration (IC) and iodine ratio using DECT. Interreader agreement was calculated using the intraclass correlation coefficient (ICC). Multivariable logistic regression analysis was performed to assess PCT and DECT models. Size-specific dose estimates of the two methods were compared. RESULTS. BV (ICC, 0.93) and iodine ratio (ICC, 0.85) were the most reproducible parameters. Both PCT and DECT were significant models (p < 0.05, all readers) for differentiating ccRCC from non-ccRCC. There was no significant difference in diagnostic accuracy between PCT and DECT (p > 0.05). BV and iodine ratio were independent predictors of nonccRCC (p < 0.05). However, the mean size-specific dose estimate was 16 times lower with DECT than with PCT (p < 0.001). The AUC of iodine ratio was 0.95, and sensitivity, specificity, and accuracy with an iodine ratio cutoff of 63.72% was 0.90, 0.86, and 0.87, respectively. CONCLUSION. PCT and DECT had comparable and high diagnostic accuracy in differentiating RCC subtypes; however, because of the significantly lower radiation dose of DECT, iodine ratio may be used as the best independent predictor.

Efficacy of raman spectroscopy in the diagnosis of kidney cancer: A systematic review and meta-analysis

OBJECTIVE

To comprehensively analyze the relative effectiveness of Raman spectroscopy (RS) in the diagnosis of suspected kidney cancer.

PATIENTS AND METHODS

We performed a complete systematic review based on studies from PubMed/Medline, EMBASE, Web of Science, Ovid, Web of Knowledge, Cochrane Library and China National Knowledge Infrastructure. We identified 2413 spectra with strict criteria in 6 individual studies published between January 2008 and November 2018 in accordance to Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. We summarized the test performance using random effects models.

RESULTS

General pooled diagnostic sensitivity and specificity of RS to kidney cancer were 0.96 (95% confidence interval [CI] 0.95-0.97) and 0.91 (95% CI 0.89-0.92). The pooled positive likelihood ratio (LR) was 9.57 (95% CI 5.73-15.46) while the negative LR was 0.04 (95% CI 0.02-0.11). The pooled diagnostic odds ratio was 238.06 (95% CI 77.79-728.54). The area under curve of summary receiver operator characteristics was 0.9466.

CONCLUSION

Through this meta-analysis, we found a promisingly high sensitivity and specificity of RS in the diagnosis of suspected kidney masses and tumors. Other parameters like positive LR, negative LR, diagnostic odds ratio and area under curve of the summary receiver operator characteristics curve all helped to illustrate the high efficacy of RS in the diagnosis of kidney cancer.

Predicting the ISUP grade of clear cell renal cell carcinoma with multiparametric MR and multiphase CT radiomics

OBJECTIVE

To investigate externally validated magnetic resonance (MR)-based and computed tomography (CT)-based machine learning (ML) models for grading clear cell renal cell carcinoma (ccRCC).

MATERIALS AND METHODS

Patients with pathologically proven ccRCC in 2009-2018 were retrospectively included for model development and internal validation; patients from another independent institution and The Cancer Imaging Archive dataset were included for external validation. Features were extracted from T1-weighted, T2-weighted, corticomedullary-phase (CMP), and nephrographic-phase (NP) MR as well as precontrast-phase (PCP), CMP, and NP CT. CatBoost was used for ML-model investigation. The reproducibility of texture features was assessed using intraclass correlation coefficient (ICC). Accuracy (ACC) was used for ML-model performance evaluation.

RESULTS

Twenty external and 440 internal cases were included. Among 368 and 276 texture features from MR and CT, 322 and 250 features with good to excellent reproducibility (ICC ≥ 0.75) were included for ML-model development. The best MR- and CT-based ML models satisfactorily distinguished high- from low-grade ccRCCs in internal (MR-ACC = 73% and CT-ACC = 79%) and external (MR-ACC = 74% and CT-ACC = 69%) validation. Compared to single-sequence or single-phase images, the classifiers based on all-sequence MR (71% to 73% in internal and 64% to 74% in external validation) and all-phase CT (77% to 79% in internal and 61% to 69% in external validation) images had significant increases in ACC.

CONCLUSIONS

MR- and CT-based ML models are valuable noninvasive techniques for discriminating high- from low-grade ccRCCs, and multiparameter MR- and multiphase CT-based classifiers are potentially superior to those based on single-sequence or single-phase imaging.

KEY POINTS

• Both the MR- and CT-based machine learning models are reliable predictors for differentiating high- from low-grade ccRCCs. • ML models based on multiparameter MR sequences and multiphase CT images potentially outperform those based on single-sequence or single-phase images in ccRCC grading.

Percutaneous renal biopsy: approach, diagnostic accuracy and risks

PURPOSE OF REVIEW

This article provides a review of recent advances and issues regarding the controversial topic of renal mass biopsy (RMB). The purpose of this review is to provide an update on the current status of renal biopsy based on recently published literature. Here, we particularly focus on articles that have been published within the last 12 months.

RECENT FINDINGS

The main topics covered in this review are the approach, diagnostic accuracy and risks related to RMB.

SUMMARY

Current literature suggests that improvements in both technique and technological advancements of RMB have led to greater diagnostic accuracy and low risks to the patient. Newer technologies are leading toward innovative and harmless ways to diagnose kidney cancer, including liquid and image-based biopsy. However, it appears that the question of whether or not to instate renal biopsy as standard clinical practice has remained a highly debated controversy.

Discrimination of malignant and normal kidney tissue with short wave infrared dispersive Raman spectroscopy

Renal mass biopsy is still controversial due to imperfect accuracy. Raman spectroscopy (RS) demonstrated promise as an in vivo real-time, nondestructive diagnostic tool in many malignancies. Short wave infrared (SWIR) RS has the potential to improve on previous RS systems for renal mass diagnosis. The aim of this study is to evaluate a SWIR RS system in differentiating normal and malignant renal samples. Measurements were acquired using a benchtop RS system with excitation wavelength at 1064 nm and an InGaAs array detector. Processed spectra were classified with a Bayesian machine learning algorithm, sparse multinomial logistic regression. Sensitivity and receiver operating characteristic curve analyses evaluated the classifier accuracy. Accuracy of the classifier was 92.5% with sensitivity and specificity of 95.8% and 88.8%, respectively. For posterior probability of malignant class assignment, the area under the ROC curve is 0.94 (95% confidence interval: 0.89-0.99, P < .001). SWIR RS accurately differentiated normal and malignant kidney tumors. RS has the potential to be used as a diagnostic tool in kidney cancer.

Accuracy and safety of ultrasound-guided percutaneous needle core biopsy of renal masses: A single center experience in China

Our aim is to determine the sufficiency, accuracy, and safety of ultrasound-guided percutaneous needle core biopsy of renal masses in Chinese patients.Patients who had undergone ultrasound-guided needle core renal mass biopsy from June 2012 to June 2016 at West China Hospital, China were retrospectively reviewed. The information obtained included demographics, mass-related parameters, biopsy indications, technique, complications, pathologic results, and follow-up. Concordance of surgical resection pathology and follow-up data were assessed.Renal mass biopsies were performed in 106 patients. Thirty-nine (36.8%) were asymptomatic. The male/female ratio was 60/46, with a median age of 49.5 years. Median mass size was 8.1 cm (range 1.8-20). Biopsy was performed through a 16-gauge needle, with median cores of 2 taken (range 1-5). Only one significant biopsy-related complication (hemorrhage requiring transfusion) was encountered. An adequate tissue sample was obtained in 97.2% (103/106) of biopsies. Eighty-seven biopsies (82.1%) showed malignant neoplasms, 16 (15.1%) yielded benignity, and 3 (2.8%) were nondiagnostic. After biopsy, 46 patients (43.4%) underwent surgery. Compared with the subsequent mass resection pathology, the biopsy diagnoses were identical in 43 cases. The accuracy rate of biopsy distinguishing malignant from benign lesions was 99.1%, and the rate for determining tumor histological type (excluding the nondiagnostic biopsies) was 95.1%. The sensitivity and specificity in detecting malignancy were 98.9% and 100%, respectively.In several situations, there is still a role for biopsy before intervention. Percutaneous needle core biopsy under ultrasonography guidance is highly accurate and safe, and can determine the proper management of undefinable masses.

Differentiation of renal cell carcinoma subtypes with different iodine quantification methods using single-phase contrast-enhanced dual-energy CT: areal vs. volumetric analyses

PURPOSE

To investigate the possibility of iodine quantification during a single nephrographic phase in characterizing renal cell carcinoma (RCC) subtypes and if there is a difference between areal and volumetric iodine quantification methods.

MATERIALS AND METHODS

This retrospective study included 110 patients with 113 histopathologically confirmed RCCs scanned by dual-energy CT at the nephrographic phase before surgeries. For each lesion, an areal measurement of the iodine concentration with maximum enhancement (I _{max enhan}) and the iodine concentration with maximum area among slices (I _{max area}), as well as a volumetric iodine concentration of the whole-tumor (I _volume), were evaluated by two independent radiologists. The diagnostic performances in a single nephrographic phase for characterizing RCC subtypes were evaluated, and three iodine quantification methods were compared with each other.

RESULTS

There were significant differences (clear cell vs. papillary and clear cell vs. chromophobe RCC) and no significant differences (papillary vs. chromophobe RCC) at the nephrographic phase in all three methods. The area under the receiver operating characteristic (ROC) curve (AUC) derived from the I _{max enhan} for discriminating clear cell from papillary RCC was significantly higher than that derived from the I _{max area} (P = 0.0357) and the I _volume (P = 0.0206), and no significant differences existed among the three methods in distinguishing clear cell RCC from chromophobe RCC. The reliability of all three parameters was very high with an interclass correlation coefficient (ICC) exceeding 0.8.

CONCLUSIONS

Iodine quantification in a single nephrographic phase can be used to differentiate RCC subtypes preoperatively, and the areal maximum enhancement iodine quantification would probably be the most appropriate approach.