Assessment of Semiquantitative Parameters of Dynamic Contrast-Enhanced Perfusion MR Imaging in Differentiation of Subtypes of Renal Cell Carcinoma

Can radiomic analysis of a single-phase dual-energy CT improve the diagnostic accuracy of differentiating enhancing from non-enhancing small renal lesions?

BACKGROUND

The value of dual-energy computed tomography (DECT)-based radiomics in renal lesions is unknown.

PURPOSE

To develop DECT-based radiomic models and assess their incremental values in comparison to conventional measurements for differentiating enhancing from non-enhancing small renal lesions.

MATERIAL AND METHODS

A total of 349 patients with 519 small renal lesions (390 non-enhancing, 129 enhancing) who underwent contrast-enhanced nephrographic phase DECT examinations between June 2013 and January 2020 on multiple DECT platforms were retrospectively recruited. Cohort A included all lesions, while cohort B included Bosniak II-IV and solid enhancing renal lesions. Radiomic models were built with features selected by the least absolute shrinkage and selection operator regression (LASSO). ROC analyses were performed to compare the diagnostic accuracy among conventional and radiomic models for predicting enhancing renal lesions.

RESULTS

The individual iodine concentration (IC), normalized IC, mean attenuation on 75-keV images, radiomic model of iodine images, 75-keV images and a combined model integrating all the above-mentioned features all demonstrated high AUCs for predicting renal lesion enhancement in cohort A (AUCs = 0.934-0.979) as well as in the test dataset (AUCs = 0.892-0.962) of cohort B (P values with Bonferroni correction >0.003). The AUC (0.864) of mean attenuation on 75-keV images was significantly lower than those of other models (all P values ≤0.001) except the radiomic model of 75-keV images (P = 0.038) in the training dataset of cohort B.

CONCLUSION

No incremental value was found by adding radiomic and machine learning analyses to iodine images for differentiating enhancing from non-enhancing renal lesions.

Dynamic contrast-enhanced (DCE) imaging: state of the art and applications in whole-body imaging

Dynamic contrast-enhanced (DCE) imaging is a non-invasive technique used for the evaluation of tissue vascularity features through imaging series acquisition after contrast medium administration. Over the years, the study technique and protocols have evolved, seeing a growing application of this method across different imaging modalities for the study of almost all body districts. The main and most consolidated current applications concern MRI imaging for the study of tumors, but an increasing number of studies are evaluating the use of this technique also for inflammatory pathologies and functional studies. Furthermore, the recent advent of artificial intelligence techniques is opening up a vast scenario for the analysis of quantitative information deriving from DCE. The purpose of this article is to provide a comprehensive update on the techniques, protocols, and clinical applications - both established and emerging - of DCE in whole-body imaging.

The role of diffusion-weighted MRI and contrast-enhanced MRI for differentiation between solid renal masses and renal cell carcinoma subtypes

PURPOSE

To assess the value of diffusion-weighted magnetic resonance imaging (DW-MRI) and contrast-enhanced MRI (CE-MRI) for differentiation between benign and malignant solid renal masses, renal cell carcinoma (RCC) subtypes, oncocytomas, and lipid-poor angiomyolipomas (LP-AML).

METHODS

Minimum or lowest 'apparent diffusion coefficient' (ADC₁) and representative ADC values (ADC₂) of 112 renal masses (n: 46 benign renal mass, n: 66 malignant renal mass) were measured on DW-MRI images (b 50, 400, 800 s/mm²). Signal intensity (SI) measurements were performed in normal renal parenchyma and most avid enhanced area of the renal masses at precontrast, corticomedullary, and nephrographic phases on CE-MRI. Contrast enhancement rate (CER) and contrast enhancement index (CEI) values of renal masses were compared between benign-malignant renal masses and RCC subtypes, oncocytomas, and LP-AMLs.

RESULTS

There was no significant difference between ADC₁, ADC₂ values, and SI of benign and malignant renal masses (p = 0.721, p = 0.255, p = 0.872). Mean ADC₁ and ADC₂ values of clear cell RCCs were significantly higher than nonclear cell RCCs (p = 0.005 p = 0.002). Mean CER value of clear cell RCCs was significantly higher than nonclear cell RCCs in nephrographic phase (p = 0.003). Mean CEI values of clear cell RCCs were significantly higher than nonclear cell RCCs in the corticomedullary and nephrographic phase (p = 0.027 vs. 0.008). LP-AMLs were differentiated from other renal masses with wash-out phenomenon.

CONCLUSION

Combined usage of ADC, SI, CER, and CEI values may be useful for discrimination between RCC subtypes, oncocytomas, and lipid-poor AMLs.

Predicting common solid renal tumors using machine learning models of classification of radiologist-assessed magnetic resonance characteristics

PURPOSE

Solid renal masses (SRM) are difficult to differentiate based on standard MR features. The purpose of this study was to assess MR imaging features of SRM to evaluate performance of ensemble methods of classifying SRM subtypes.

MATERIALS AND METHODS

MR images of SRM (n = 330) were retrospectively evaluated for standard and multiparametric (mp) features. Models of MR features for predicting malignant and benign lesions as well as subtyping SRM were developed using a training dataset and performance was evaluated in a test data-set using recursive partitioning (RP), gradient booting machine (GBM), and random forest (RF) methods.

RESULTS

In the test dataset, GBM and RF models demonstrated an accuracy of 86% (95% CI 75% to 93%) for predicting benign versus malignant SRM compared to 83% (95% CI 71% to 91%) for the RP model. RF had the greatest accuracy in predicting SRM subtypes, 81.2% (95% CI 69.5% to 89.9%) compared with GBM 73.4% (95% CI 60.9% to 83.7%) or RP 70.3% (95% CI 57.6% to 81.1%). Marginal homogeneity was reduced by the RF model compared with the RP model (P < 0.001), but not the GBM model (P = 0.135). All models had high sensitivity and specificity for clear cell and papillary renal cell carcinomas (RCC), but performed less well in differentiating chromophobe RCC, oncocytomas, and fat-poor angiomyolipomas.

CONCLUSION

Ensemble methods for prediction of SRM from radiologist-assessed image characteristics have high accuracy for distinguishing benign and malignant lesions. SRM subtype classification is limited by the ability to categorize chromophobe RCCs, oncocytomas, and fat-poor angiomyolipomas.

PSMA expression on neovasculature of solid tumors

The use of prostate specific membrane antigen (PSMA) binding agents, labelled with diagnostic and therapeutic radio-isotopes is opening the potential for a new era of personalized management of prostate carcinoma. A wide variety of immunohistochemistry studies have shown PSMA also to be upregulated on the endothelial cells of the neovasculature of a wide variety of other solid tumors where it may facilitate endothelial cell sprouting and invasion through its regulation of lytic proteases that have the ability to cleave the extracellular matrix. Similar to the introduction of PSMA-targeting theranostics in prostate carcinoma, overexpression of PSMA on newly formed tumor vessels may serve as a target for imaging and subsequent treatment of cancer through the use of agents that are capable of blocking PSMA in its function or through PSMA-mediated delivery of chemotherapeutics or radiation agents. In this review, the available data on PSMA expression on tumor neovasculature in human solid tumors assessed by using immunohistochemistry are discussed.

Renal perfusion imaging by MRI

Renal perfusion can be quantitatively assessed by multiple magnetic resonance imaging (MRI) methods, including dynamic contrast enhanced (DCE), arterial spin labeling (ASL), and diffusion-weighted imaging with intravoxel incoherent motion (IVIM) analysis. In this review we summarize the advances in the field of renal-perfusion MRI over the past 5 years. The review starts with a brief introduction of relevant MRI methods, followed by a discussion of recent technical developments. In the main section of the review, we examine the clinical and preclinical applications for three disease populations: chronic kidney disease, renal transplant, and renal tumors. The DCE method has been routinely used for assessing renal tumors but not other renal diseases. As a noncontrast alternative, ASL was extensively explored in both preclinical and clinical applications and showed much promise. Protocol standardization for the methods is desperately needed, and then large-scale clinical trials for the methods can be initiated prior to their broad clinical use. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:369-379.

Assessment of the diagnostic value of using serum CA125 and GI-RADS system in the evaluation of adnexal masses

Cancer antigen 125 (CA125) is a valuable tumor marker for ovarian cancer. Gynecology Imaging Reporting and Data System (GI-RADS) is proved to be effective at identifying the adnexal masses. We investigated whether the combination of these two methods can improve the diagnostic accuracy of ovarian cancer.We retrospectively analyzed preoperative data of 325 patients diagnosed with suspected adnexal mass, 196 patients with benign ovarian masses and 129 with malignant ovarian cancer (stage I: 34, II: 16, III: 61, IV: 18). CA125 was analyzed using the ARCHITECT system, GI-RADS was evaluated according to the International Ovarian Tumor Analysis consensus nomenclature and definitions. Sensitivities and specificities were also calculated for GI-RADS, CA125 and the combinations.The sensitivity, specificity and accuracy of CA125, GI-RADS were 75.97%, 79.59%, 78.15%, and 90.70%, 90.82%,90.77%, the combination data were 94.79%, 96.00%,95.53%. The AUC of combined diagnostic methods was the largest and significantly better compared with each method alone, P < .001). For stage I-II malignancy, GI-RADS as a single method was superior to CA125.Combined use of serum CA 125 and GI-RADS system improved the identification of adnexal masses at high risk of malignancy and could be used for clinical decision-making.

Magnetic resonance spectroscopy of the frontal region in patients with metabolic syndrome: correlation with anthropometric measurement

PURPOSE

To demonstrate 1H-MR spectroscopy of the frontal region in patients with metabolic syndrome and to correlate the metabolic ratios with anthropometric measurement.

MATERIAL AND METHODS

A prospective study was conducted upon 20 patients with metabolic syndrome (10 male, 10 female; mean age 52 years) and 20 age- and sex-matched volunteers. Patients were mild-moderate (n = 14) and marked and morbid obesity (n = 6). Patients and volunteers underwent 1H-MR spectroscopy of the frontal region. The Ch/Cr and NAA/Cr ratio were calculated and correlated with anthropometric measurement.

RESULTS

The Cho/Cr and NAA/Cr of patients with Mets (1.03 ± 0.08 and 1.62 ± 0.08) were significantly different (p = 0.001) to those of volunteers (0.78 ± 0 and 1.71 ± 0.61, respectively). The Cho/Cr and NAA/Cr cutoffs used to differentiate patients from volunteers were 0.89 and 1.77 with areas under the curve of 0.992 and 0.867 and accuracy of 97% and 93%, respectively. There was a significant difference in Cho/Cr and NAA/Cr between patients with marked-morbid obesity and moderate-mild obesity (p = 0.001 respectively).

CONCLUSIONS

We concluded that NAA/Cr and Cho/Cr ratios of the frontal region can differentiate patients with metabolic syndrome from volunteers and are well correlated with the anthropometric measurement.

Computed Tomography Assessment of Hepatic Metastases of Breast Cancer with Revised Response Evaluation Criteria in Solid Tumors (RECIST) Criteria (Version 1.1): Inter-Observer Agreement

Background

To assess inter-observer agreement of revised RECIST criteria (version 1.1) for computed tomography assessment of hepatic metastases of breast cancer.

Material/Methods

A prospective study was conducted in 28 female patients with breast cancer and with at least one measurable metastatic lesion in the liver that was treated with 3 cycles of anthracycline-based chemotherapy. All patients underwent computed tomography of the abdomen with 64-row multi- detector CT at baseline and after 3 cycles of chemotherapy for response assessment. Image analysis was performed by 2 observers, based on the RECIST criteria (version 1.1).

Results

Computed tomography revealed partial response of hepatic metastases in 7 patients (25%) by one observer and in 10 patients (35.7%) by the other observer, with good inter-observer agreement (k=0.75, percent agreement of 89.29%). Stable disease was detected in 19 patients (67.8%) by one observer and in 16 patients (57.1%) by the other observer, with good agreement (k=0.774, percent agreement of 89.29%). Progressive disease was detected in 2 patients (7.2%) by both observers, with perfect agreement (k=1, percent agreement of 100%). The overall inter-observer agreement in the CT-based response assessment of hepatic metastasis between the two observers was good (k=0.793, percent agreement of 89.29%).

Conclusions

We concluded that computed tomography is a reliable and reproducible imaging modality for response assessment of hepatic metastases of breast cancer according to the RECIST criteria (version 1.1).

Statistical clustering of parametric maps from dynamic contrast enhanced MRI and an associated decision tree model for non-invasive tumour grading of T1b solid clear cell renal cell carcinoma

OBJECTIVES

To apply a statistical clustering algorithm to combine information from dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) into a single tumour map to distinguish high-grade from low-grade T1b clear cell renal cell carcinoma (ccRCC).

METHODS

This prospective, Institutional Review Board -approved, Health Insurance Portability and Accountability Act -compliant study included 18 patients with solid T1b ccRCC who underwent pre-surgical DCE MRI. After statistical clustering of the parametric maps of the transfer constant between the intravascular and extravascular space (K ^trans ), rate constant (K _ep ) and initial area under the concentration curve (iAUC) with a fuzzy c-means (FCM) algorithm, each tumour was segmented into three regions (low/medium/high active areas). Percentages of each region and tumour size were compared to tumour grade at histopathology. A decision-tree model was constructed to select the best parameter(s) to predict high-grade ccRCC.

RESULTS

Seven high-grade and 11 low-grade T1b ccRCCs were included. High-grade histology was associated with higher percent high active areas (p = 0.0154) and this was the only feature selected by the decision tree model, which had a diagnostic performance of 78% accuracy, 86% sensitivity, 73% specificity, 67% positive predictive value and 89% negative predictive value.

CONCLUSIONS

The FCM integrates multiple DCE-derived parameter maps and identifies tumour regions with unique pharmacokinetic characteristics. Using this approach, a decision tree model using criteria beyond size to predict tumour grade in T1b ccRCCs is proposed.

KEY POINTS

• Tumour size did not correlate with tumour grade in T1b ccRCC. • Tumour heterogeneity can be analysed using statistical clustering via DCE-MRI parameters. • High-grade ccRCC has a larger percentage of high active area than low-grade ccRCCs. • A decision-tree model offers a simple way to differentiate high/low-grade ccRCCs.

Diagnostic performance of the Gynecology Imaging Reporting and Data System for malignant adnexal masses

OBJECTIVE

To evaluate the Gynecology Imaging Reporting and Data System (GI-RADS) for diagnosis of malignant adnexal masses in a Chinese population.

METHODS

A retrospective study was conducted of patients who underwent evaluation of suspected adnexal masses at a hospital in Tianjin, China, between January 1, 2015, and January 31, 2016. Ultrasonographic diagnosis was based on the GI-RADS classification-a standardized summary of imaging data that estimates the risk of malignancy-and compared with the final pathological diagnosis.

RESULTS

Among 242 patients, thick wall, solid papillary projection, solid area, central blood flow, ascites, and GI-RADS classification were associated with malignancy (P<0.05 for all variables). The 263 masses evaluated were classified as GI-RADS 2 (functional cyst; n=65), GI-RADS 3 (benign neoplasm; n=68), GI-RADS 4 (one or two morphological findings suggestive of malignancy; n=101), and GI-RADS 5 (≥3 morphological findings suggestive of malignancy; n=28). Four malignant cases with false-negative findings were misclassified as GI-RADS 3, whereas 24 benign cases with false-positive findings were misclassified as GI-RADS 4. The sensitivity, specificity, false-positive rate, false-negative rate, accuracy, and Youden index of the GI-RADS classification were 96.4%, 84.3%, 18.5%, 3.0%, 89.3%, and 80.7%, respectively.

CONCLUSION

The GI-RADS classification performed well in the diagnosis of malignant adnexal masses.