Update on MRI of Cystic Renal Masses Including Bosniak Version 2019

Bosniak Classification of Cystic Renal Masses: Looking Back, Looking Forward

RATIONALE AND OBJECTIVES

According to the 2019 update of the Bosniak classification, the main imaging features that need to be evaluated to achieve a correct characterization of renal cystic masses include the thickness of walls and septa, the number of septa, the appearance of walls and septa, the attenuation/intensity on non-contrast CT/MRI and the presence of unequivocally perceived or measurable enhancement of walls and septa. Despite the improvement deriving from a quantitative evaluation of imaging features, certain limitations seem to persist and some possible scenarios that can be encountered in clinical practice are still missing.

MATERIALS AND METHODS

A deep analysis of the 2019 update of the Bosniak classification was performed.

RESULTS

The most notable potential flaws concern: (1) the quantitative measurement of the walls and septa; (2) the fact that walls and septa > 2 mm are always referred to as "enhancing", not considering the alternative scenario; (3) the description of some class II masses partially overlaps with each other and with the definition of class I masses and (4) the morphological variations of cystic masses over time is not considered.

CONCLUSION

The present paper analyzes in detail the limitations of the 2019 Bosniak classification to improve this important tool and facilitate its use in daily radiological practice.

Satisfaction of search awareness in trauma radiology: Malignant renal lesion on a trauma thoracolumbar spine CT

Fast-paced trauma imaging can result in misses relating to the nonosseous structures included in the field of view. We report a case of a Bosniak type III renal cyst, later found to be clear cell renal cell carcinoma, incidentally noted on post-traumatic CT of the thoracic and lumbar spine. This case includes a discussion of the circumstances which could result in a radiologist missing this finding, the idea of satisfaction of search, the importance of maintaining a thorough search pattern, and the management and communication of incidental findings.

Cystic Clear Cell Renal Cell Carcinoma: A Morphological and Molecular Reappraisal

A wide variety of renal neoplasms can have cystic areas. These can occur for different reasons: some tumors have an intrinsic cystic architecture, while others exhibit pseudocystic degeneration of necrotic foci or they have cystically dilated renal tubules constrained by stromal neoplastic cells. Clear cell renal cell carcinoma (CCRCC), either solid or cystic, is the most frequent type of renal cancer. While pseudocysts are found in high-grade aggressive CCRCC, cystic growth is associated with low-grade indolent cases. The latter also form through a cyst-dependent molecular pathway, and they are more frequent in patients suffering from VHL disease. The differential diagnosis of multilocular cystic renal neoplasm of low malignant potential and clear cell papillary renal cell tumor can be especially hard and requires a focused macroscopical and microscopical pathological analysis. As every class of renal tumor includes cystic forms, knowledge of the criteria required for a differential diagnosis is mandatory.

Cystic Renal Masses: Old and New Paradigms

Cystic renal masses describe a spectrum of lesions with benign and/or malignant features. Cystic renal masses are most often identified incidentally with the Bosniak classification system stratifying their malignant potential. Solid enhancing components most often represent clear cell renal cell carcinoma yet display an indolent natural history relative to pure solid renal masses. This has led to an increased adoption of active surveillance as a management strategy in those who are poor surgical candidates. This article provides a contemporary overview of historical and emerging clinical paradigms in the diagnosis and management of this distinct clinical entity.

Imaging Recommendations for Diagnosis, Staging, and Management of Renal Tumors

Renal cell carcinomas accounts for 2% of all the cancers globally. Most of the renal tumors are detected incidentally. Ultrasound remains the main screening modality to evaluate the renal masses. A multi -phase contrast enhanced computer tomography is must for characterizing the renal lesions. Imaging plays an important role in staging, treatment planning and follow up of renal cancers. In this review , we discuss the imaging guidelines for the management of renal tumors.

Bosniak classification version 2019: a prospective comparison of CT and MRI

OBJECTIVE

To assess the diagnostic accuracy and agreement of CT and MRI in terms of the Bosniak classification version 2019 (BCv2019).

MATERIALS AND METHODS

A prospective multi-institutional study enrolled 63 patients with 67 complicated cystic renal masses (CRMs) discovered during ultrasound examination. All patients underwent CT and MRI scans and histopathology. Three radiologists independently assessed CRMs using BCv2019 and assigned Bosniak class to each CRM using CT and MRI. The final analysis included 60 histopathologically confirmed CRMs (41 were malignant and 19 were benign).

RESULTS

Discordance between CT and MRI findings was noticed in 50% (30/60) CRMs when data were analyzed in terms of the Bosniak classes. Of these, 16 (53.3%) were malignant. Based on consensus reviewing, there was no difference in the sensitivity, specificity, and accuracy of the BCv2019 with MRI and BCv2019 with CT (87.8%; 95% CI = 73.8-95.9% versus 75.6%; 95% CI = 59.7-87.6%; p = 0.09, 84.2%; 95% CI = 60.4-96.6% versus 78.9%; 95% CI = 54.4-93.9%; p = 0.5, and 86.7%; 95% CI = 64.0-86.6% versus 76.7%; 95% CI = 75.4-94.1%; p = 0.1, respectively). The number and thickness of septa and the presence of enhanced nodules accounted for the majority of variations in Bosniak classes between CT and MRI. The inter-reader agreement (IRA) was substantial for determining the Bosniak class in CT and MRI (k = 0.66; 95% CI = 0.54-0.76, k = 0.62; 95% CI = 0.50-0.73, respectively). The inter-modality agreement of the BCv219 between CT and MRI was moderate (κ = 0.58).

CONCLUSION

In terms of BCv2019, CT and MRI are comparable in the classification of CRMs with no significant difference in diagnostic accuracy and reliability.

KEY POINTS

• There is no significant difference in the sensitivity, specificity, and accuracy of the BCv2019 with MRI and BCv2019 with CT. • The number of septa and their thickness and the presence of enhanced nodules accounted for the majority of variations in Bosniak classes between CT and MRI. • The inter-reader agreement was substantial for determining the Bosniak class in CT and MRI and the inter-modality agreement of the BCv219 between CT and MRI was moderate.

Deep learning and radiomic feature-based blending ensemble classifier for malignancy risk prediction in cystic renal lesions

BACKGROUND

The rising prevalence of cystic renal lesions (CRLs) detected by computed tomography necessitates better identification of the malignant cystic renal neoplasms since a significant majority of CRLs are benign renal cysts. Using arterial phase CT scans combined with pathology diagnosis results, a fusion feature-based blending ensemble machine learning model was created to identify malignant renal neoplasms from cystic renal lesions (CRLs). Histopathology results were adopted as diagnosis standard. Pretrained 3D-ResNet50 network was selected for non-handcrafted features extraction and pyradiomics toolbox was selected for handcrafted features extraction. Tenfold cross validated least absolute shrinkage and selection operator regression methods were selected to identify the most discriminative candidate features in the development cohort. Feature's reproducibility was evaluated by intra-class correlation coefficients and inter-class correlation coefficients. Pearson correlation coefficients for normal distribution and Spearman's rank correlation coefficients for non-normal distribution were utilized to remove redundant features. After that, a blending ensemble machine learning model were developed in training cohort. Area under the receiver operator characteristic curve (AUC), accuracy score (ACC), and decision curve analysis (DCA) were employed to evaluate the performance of the final model in testing cohort.

RESULTS

The fusion feature-based machine learning algorithm demonstrated excellent diagnostic performance in external validation dataset (AUC = 0.934, ACC = 0.905). Net benefits presented by DCA are higher than Bosniak-2019 version classification for stratifying patients with CRL to the appropriate surgery procedure.

CONCLUSIONS

Fusion feature-based classifier accurately distinguished malignant and benign CRLs which outperformed the Bosniak-2019 version classification and illustrated improved clinical decision-making utility.

Stratification of malignant renal neoplasms from cystic renal lesions using deep learning and radiomics features based on a stacking ensemble CT machine learning algorithm

Using nephrographic phase CT images combined with pathology diagnosis, we aim to develop and validate a fusion feature-based stacking ensemble machine learning model to distinguish malignant renal neoplasms from cystic renal lesions (CRLs). This retrospective research includes 166 individuals with CRLs for model training and 47 individuals with CRLs in another institution for model testing. Histopathology results are adopted as diagnosis criterion. Nephrographic phase CT scans are selected to build the fusion feature-based machine learning algorithms. The pretrained 3D-ResNet50 CNN model and radiomics methods are selected to extract deep features and radiomics features, respectively. Fivefold cross-validated least absolute shrinkage and selection operator (LASSO) regression methods are adopted to identify the most discriminative candidate features in the development cohort. Intraclass correlation coefficients and interclass correlation coefficients are employed to evaluate feature’s reproducibility. Pearson correlation coefficients for normal distribution features and Spearman’s rank correlation coefficients for non-normal distribution features are used to eliminate redundant features. After that, stacking ensemble machine learning models are developed in the training cohort. The area under the receiver operator characteristic curve (ROC), calibration curve, and decision curve analysis (DCA) are adopted in the testing cohort to evaluate the performance of each model. The stacking ensemble machine learning algorithm reached excellent diagnostic performance in the testing dataset. The calibration plot shows good stability when using the stacking ensemble model. Net benefits presented by DCA are higher than the Bosniak 2019 version classification when employing any machine learning algorithm. The fusion feature-based machine learning algorithm accurately distinguishes malignant renal neoplasms from CRLs, which outperformed the Bosniak 2019 version classification, and proves to be more applicable for clinical decision-making.

Comparison of Bosniak Classification of cystic renal masses version 2019 assessed by CT and MRI

OBJECTIVE

To compare imaging features in cystic masses imaged with both CT and MRI using Bosniak Classification version 2019 (Bosniak.v2019) and original Bosniak Classification (Bosniak.original).

MATERIALS AND METHODS

This IRB-approved, retrospective, cross-sectional study evaluated sixty-five consecutively identified cystic (≤ 25% enhancing) masses imaged by CT and MRI between 2009 and 2019: 35 with histologic diagnosis and 30 Bosniak.v2019 Class 2 and Class 2F cystic masses verified by an expert radiologist (R1) with minimum 5-year stability. Three radiologists (R2, R3, R4) independently evaluated CT, followed by MRI and assigned Bosniak.original and Bosniak.v2019 class in two sessions separated by ≥ 1 month and assessed the following: septa number, septa/wall thickness, and protrusions. Discrepancies were resolved by consensus with R1.

RESULTS

There was 70.8% agreement (kappa = 0.60, p = 0.0146) in class assigned by CT versus MRI for Bosniak.original and 72.3% agreement (kappa = 0.63, p = 0.006) for Bosniak.v2019. Increased septa number (p < 0.001) and more protrusions (p = 0.034) were identified on MRI, with no differences in septal/wall thickness (p = 0.067, 0.855) or protrusion size (p = 0.467). For both CT and MRI, Bosniak.v2019 improved specificity (79.0% [95% confidence interval 71.0-87.0%] CT, 70% [62.0-77.0%] MRI) compared to Bosniak.original (63.0% [56.0-69.0%] CT, 66.0% [58.0-74.0%] MRI) with maintained sensitivity and higher overall accuracy. Inter-observer agreement was similar-to-slightly higher for Bosniak.v2019 (K = 0.44 CT, 0.39 MRI) versus Bosniak.original (K = 0.35 CT, 0.37 MRI).

CONCLUSION

Class assignment differs in cystic masses evaluated by CT versus MRI for original and v2019 Bosniak Classification with similar-to-slightly higher agreement and improved specificity and higher overall accuracy on both CT and MRI with Bosniak version 2019.

Cross-sectional imaging assessment of renal masses with emphasis on MRI

Magnetic resonance imaging (MRI) is a useful complementary imaging tool for the diagnosis and characterization of renal masses, as it provides both morphologic and functional information. A core MRI protocol for renal imaging should include a T1-weighted sequence with in- and opposed-phase images (or, alternatively with DIXON technique), T2-weighted and diffusion-weighted images as well as a dynamic contrast-enhanced sequence with subtraction images, followed by a delayed post-contrast T1-weighted sequence. The main advantages of MRI over computed tomography include increased sensitivity for contrast enhancement, less sensitivity for detection of calcifications, absence of pseudoenhancement, and lack of radiation exposure. MRI may be applied for renal cystic lesion characterization, differentiation of renal cell carcinoma (RCC) from benign solid renal tumors, RCC histologic grading, staging, post-treatment follow-up, and active surveillance of patients with treated or untreated RCC.

Accuracy of MRI without intracavernosal prostaglandin E1 injection in staging, preoperative evaluation, and operative planning of penile cancer

PURPOSE

To evaluate the performance of non-erectile MRI in staging and preoperative evaluation of penile carcinomas, compared to postoperative histopathology.

METHODS

In this retrospective study, MRI scans of patients who had undergone surgery for penile carcinoma (n = 54) between January 2012 and April 2018 were read by two radiologists; and disagreement was solved in the presence of a third experienced radiologist. Data necessary for preoperative evaluation and staging were collected and compared with final postoperative histology and the type of surgery performed. All MRI had been performed without intracavernosal injection of prostaglandin E1 and with IV Gadolinium, as per local protocol.

RESULTS

54 patients were included in the study (mean age 57.52 ± 12.78). The number of patients with T1, T2, and T3 staging in histopathology were 32, 14, and 8. Moderate interobserver agreement was found for staging, disease-free penile length, and all subsites except urethra, which had weak agreement. Strong agreement of consensus MRI with final histopathological staging was found (49/54, weighted κ = 0.85), with high sensitivity and specificity. Sensitivity and specificity for involvement of corpus spongiosum, corpora cavernosa, and urethra were 95.5% and 93.8%, 87.5% and 97.8%, and 90.9% and 86.1%, respectively. Sensitivity (89.6%) and specificity (100%) of MRI for predicting adequate disease-free penile length were high.

CONCLUSION

There were acceptable interobserver agreement and good diagnostic performance of MRI for staging and preoperative assessment without intracavernosal injection, especially for higher stages and higher degrees of invasion which require more extensive surgery.