Role of Multiparametric MR Imaging in Malignancies of the Urogenital Tract

Evaluation of renal space occupying lesions with multiparametric MRI and its correlation with histopathology findings- an observational study

The term multiparametric MRI, is a useful tool in reference to an approach that takes advantage of the added value of different MR imaging acquisitions to yield anatomic and pathophysiologic information about renal space occupying lesions and to evaluate patients with different tumors, including genitourinary malignancies. The role of multiparametric MRI is continuously growing because of its ability to detect and characterize renal space occupying lesions as well as to assess response to treatment. An observational study was carried out in 50 patients who presented with renal mass, based on clinical suspicion and prior imaging diagnosis of neoplastic renal space occupying lesion. Total renal space occupying lesions were 50, of which, 38 were males & 12 were females. The age range of the study population was 30-80 years. In our study, Agreement analysis between mpMRI diagnosis and HPE diagnosis of different RCC subtypes was statistically significant. So, multiparametric MRI had a role in differentiating the subtypes of RCC which had fair agreement with HPE. The present study results state that the renal mass lesions has different ADC values for different lesions because of the change in tissue contents and there was a statistically significant difference in ADC values between low and high-stage RCCs. Histologic and radiologic profiles of renal space occupying lesions and diverse subtypes of RCC can be used as biologic indicators of clinical behavior, response to treatment, and prognosis.

Imaging in translational cancer research

This review is aimed at presenting some of the recent developments in translational cancer imaging research, with a focus on novel, recently established, or soon to be established cross-sectional imaging techniques for computed tomography (CT), magnetic resonance imaging (MRI), and positron-emission tomography (PET) imaging, including computational investigations based on machine-learning techniques.

Kidney tumor diffusion-weighted magnetic resonance imaging derived ADC histogram parameters combined with patient characteristics and tumor volume to discriminate oncocytoma from renal cell carcinoma

PURPOSE

To assess the ability to discriminate oncocytoma from RCC based on a model using whole tumor ADC histogram parameters with additional use of tumor volume and patient characteristics.

METHOD

In this prospective study, 39 patients (mean age 65 years, range 28-79; 9/39 (23%) female) with 39 renal tumors (32/39 (82%) RCC and 7/39 (18%) oncocytoma) underwent multiparametric MRI between November 2014 and June 2018. Two regions of interest (ROIs) were drawn to cover both the entire tumor volume and a part of healthy renal cortex. ROI ADC maps were calculated using a mono-exponential model and ADC histogram distribution parameters were calculated. A logistic regression model was created using ADC histogram parameters, radiographic and patient characteristics that were significantly different between oncocytoma and RCC. A ROC curve of the model was constructed and the AUC, sensitivity and specificity were calculated. Furthermore, differences in intra-patient ADC histogram parameters between renal tumor and healthy cortex were calculated. A separate ROC curve was constructed to differentiate oncocytoma from RCC using statistically significant intra-patient parameter differences.

RESULTS

ADC standard deviation (p = 0.008), entropy (p = 0.010), tumor volume (p = 0.012), and patient sex (p = 0.018) were significantly different between RCC and oncocytoma. The regression model of these parameters combined had an ROC-AUC of 0.91 with a sensitivity of 86% and specificity of 84%. Intra-patient difference in ADC 25th percentile (p < 0.01) and entropy (p = 0.030) combined had a ROC-AUC of 0.86 with a sensitivity and specificity of 86%, and 81%, respectively.

CONCLUSION

A model combining ADC standard deviation and entropy with tumor volume and patient sex has the highest diagnostic value for discrimination of oncocytoma. Although less accurate, intra-patient difference in ADC 25th percentile and entropy between renal tumor and healthy cortex can also be used. Although the results of this preliminary study do not yet justify clinical use of the model, it does stimulate further research using whole tumor ADC histogram parameters.

Novel imaging in renal cell carcinoma

PURPOSE OF REVIEW

Renal cell carcinoma is a heterogeneous disease with a spectrum of subtypes and clinical behavior. Quantitative and qualitative imaging biomarkers are sought to correlate with genetic and histologic features and complement pathologic analysis.

RECENT FINDINGS

Texture analysis, radiogenomics, and modality-specific advancements have yielded an array of renal cell carcinoma imaging biomarkers in the research domain. Although many techniques are promising, standardization and validation of these procedures are needed prior to implementation into clinical practice.

SUMMARY

We introduce novel imaging techniques and analytic methods which have been shown to contribute to characterization of renal cell carcinoma and its subtypes, aggressiveness, and responsiveness to therapy, including associated advantages and limitations.

Development of a Patient-specific Tumor Mold Using Magnetic Resonance Imaging and 3-Dimensional Printing Technology for Targeted Tissue Procurement and Radiomics Analysis of Renal Masses

OBJECTIVE

To implement a platform for colocalization of in vivo quantitative multiparametric magnetic resonance imaging features with ex vivo surgical specimens of patients with renal masses using patient-specific 3-dimensional (3D)-printed tumor molds, which may aid in targeted tissue procurement and radiomics and radiogenomic analyses.

MATERIALS AND METHODS

Volumetric segmentation of 6 renal masses was performed with 3D Slicer (http://www.slicer.org) to create a 3D tumor model. A slicing guide template was created with specialized software, which included notches corresponding to the anatomic locations of the magnetic resonance images. The tumor model was subtracted from the slicing guide to create a depression in the slicing guide corresponding to the exact size and shape of the tumor. A customized, tumor-specific, slicing guide was then printed using a 3D printer. After partial nephrectomy, the surgical specimen was bivalved through the preselected magnetic resonance imaging (MRI) plane. A thick slab of the tumor was obtained, fixed, and processed as a whole-mount slide and was correlated to multiparametric MRI findings.

RESULTS

All patients successfully underwent partial nephrectomy and adequate fitting of the tumor specimens within the 3D mold was achieved in all tumors. Distinct in vivo MRI features corresponded to unique pathologic characteristics in the same tumor. The average cost of printing each mold was US$160.7 ± 111.1 (range: US$20.9-$350.7).

CONCLUSION

MRI-based preoperative 3D printing of tumor-specific molds allow for accurate sectioning of the tumor after surgical resection and colocalization of in vivo imaging features with tissue-based analysis in radiomics and radiogenomic studies.