The incremental value of histogram analysis in the differentiation between hyperdense cysts and solid renal masses on unenhanced CT images

OBJECTIVE

To investigate the utility of voxel histogram analysis (HA) for differentiating hyperdense renal cysts from small solid masses on unenhanced CT scans.

METHODS

A retrospective analysis of 99 hyperdense cystic lesions and 28 solid malignant lesions was conducted using a radiological database (from 2015 to 2021) and a pathological database (from 2010 to 2020). The study investigated the distribution of voxel attenuation values using percentiles to establish reliable criteria for differentiation after drawing a region of interest (ROI) in the centre of the lesions. The standard of reference was a histopathological diagnosis for malignant masses and contrast-enhanced CT or MRI for cysts.

RESULTS

HA provided higher diagnostic accuracy than the conventional mean attenuation value of 70 Hounsfield Units (HU). For the 75th and 90th percentiles ± 1 standard deviation, accuracies of 77.2% (95% confidence interval 68.9%-84.2%) for the 75th and 68.5% (59.7%-76.4%) for the 90th were found, versus 37.0% (28.6%-46.0%) for the 70 HU threshold criterion. A Gaussian distribution of voxel attenuation values was observed in 88.9% of the lesions, suggesting that it is feasible to calculate these parameters from a single measurement.

CONCLUSION

The study underscores the potential of HA as a valuable tool for characterizing hyperdense cysts on unenhanced CT by using the same ROI for measuring lesion attenuation. HA could offer additional value beyond the 70 HU criterion and possibly influence clinical decisions. Multi-institutional studies are necessary for external validation to confirm its generalizability and more extensive applicability.

ADVANCES IN KNOWLEDGE

(1) A single measurement on unenhanced CT images, using mean attenuation and standard deviation, accurately reflects the voxel distribution of both cystic and solid masses, allowing for the application of histogram analysis. (2) The 75th percentile threshold of 65 HU or higher could potentially increase sensitivity in diagnosing hyperdense cysts, compared to the 70 HU mean attenuation threshold, without compromising specificity.

Radiomics for differential diagnosis of Bosniak II-IV renal masses via CT imaging

RATIONALE AND OBJECTIVES

The management of complex renal cysts is guided by the Bosniak classification system, which may be inadequate for risk stratification of patients to determine the appropriate intervention. Radiomics models based on CT imaging may provide additional useful information.

MATERIALS AND METHODS

A total of 322 patients with Bosniak II-IV cysts were included in the study from January 2010 to December 2019. Contrast-enhanced CT scans were performed on all patients. ITK-snap was used for segmentation, and the PyRadiomics 3.0.1 package was used for feature extraction. The radiomics features were screened via the least absolute shrinkage and selection operator (LASSO) regression method. After feature selection, a logistic regression (LR) model, support vector machine (SVM) model and random forest (RF) model were constructed.

RESULTS

In the present study, 217 benign renal cysts (67.4%) and 105 cystic renal cell carcinomas (32.6%) were identified. According to the Bosniak classification, the sample included 179 (55.6%) Bosniak II cysts, 38 (11.8%) Bosniak IIF cysts, 44 (13.7%) Bosniak III cysts and 61 (18.9%) Bosniak IV cysts. A total of 1334 radiomics features were extracted from both unenhanced and cortical CT scans. After LASSO regression, all the models (LR, SVM and RF) showed satisfactory discrimination and reliability in both unenhanced and cortical CT scans (AUC > 0.950). In the Bosniak IIF-III subgroup analysis, the diagnostic accuracy of the LR model was very low for both the unenhanced and cortical scans. In contrast, the SVM model and RF model showed excellent and stable performance in classifying Bosniak IIF-III cysts. The AUCs of the models were all > 0.85, with a maximum of 0.941. The sensitivity, specificity, accuracy, and AUC of the RF model were 0.889, 0.913, 0.902, and 0.941, respectively.

CONCLUSION

Our data indicate that radiomics models can effectively distinguish between cystic renal cell carcinoma (cRCC) and complex renal cysts (Bosniak II-IV). Radiomics models may still have high diagnostic accuracy even for Bosniak IIF-III cysts that are clinically difficult to distinguish. However, external validation of these findings is still needed.

CT-derived textural analysis parameters discriminate high-attenuation renal cysts from solid renal neoplasms

AIM

To assess the utility of textural features on computed tomography (CT) to differentiate high-attenuation cysts from solid renal neoplasms among indeterminate renal lesions detected incidentally on CT.

MATERIALS AND METHODS

Patients were included if they had an indeterminate renal lesion on CT that was subsequently characterised on ultrasound or magnetic resonance imaging (MRI). Up to three lesions per patient were included if they had a size ≥10 mm and density of 20-70 HU on unenhanced CT or any single phase of contrast-enhanced CT. Cases were categorised as benign or most likely benign cysts (Bosniak II and IIF) versus indeterminate (Bosniak III), mixed solid and cystic (Bosniak IV), or solid renal lesions. A random forest model was generated using 95 textural parameters and four clinical parameters for each lesion.

RESULTS

Two hundred and thirty-four patients were included who had a total of 278 lesions. Of these, 193 (69%) were benign or most likely benign cysts and 85 (31%) were indeterminate, mixed cystic and solid, or solid renal lesions. The random forest model had an area under the curve of 0.71 (95% confidence interval [CI]: 0.65, 0.78), with a sensitivity and specificity of 81.2% and 38.9%, respectively.

CONCLUSION

A multivariate model including textural and clinical parameters had moderate overall performance for discriminating benign or likely benign cysts from indeterminate, mixed solid and cystic, or solid renal lesions. This study serves as a proof of concept and may reduce the need for further follow-up by characterising a significant portion of indeterminate lesions on CT as benign.

A pilot radiometabolomics integration study for the characterization of renal oncocytic neoplasia

Differentiating benign renal oncocytic tumors and malignant renal cell carcinoma (RCC) on imaging and histopathology is a critical problem that presents an everyday clinical challenge. This manuscript aims to demonstrate a novel methodology integrating metabolomics with radiomics features (RF) to differentiate between benign oncocytic neoplasia and malignant renal tumors. For this purpose, thirty-three renal tumors (14 renal oncocytic tumors and 19 RCC) were prospectively collected and histopathologically characterised. Matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) was used to extract metabolomics data, while RF were extracted from CT scans of the same tumors. Statistical integration was used to generate multilevel network communities of -omics features. Metabolites and RF critical for the differentiation between the two groups (delta centrality > 0.1) were used for pathway enrichment analysis and machine learning classifier (XGboost) development. Receiver operating characteristics (ROC) curves and areas under the curve (AUC) were used to assess classifier performance. Radiometabolomics analysis demonstrated differential network node configuration between benign and malignant renal tumors. Fourteen nodes (6 RF and 8 metabolites) were crucial in distinguishing between the two groups. The combined radiometabolomics model achieved an AUC of 86.4%, whereas metabolomics-only and radiomics-only classifiers achieved AUC of 72.7% and 68.2%, respectively. Analysis of significant metabolite nodes identified three distinct tumour clusters (malignant, benign, and mixed) and differentially enriched metabolic pathways. In conclusion, radiometabolomics integration has been presented as an approach to evaluate disease entities. In our case study, the method identified RF and metabolites important in differentiating between benign oncocytic neoplasia and malignant renal tumors, highlighting pathways differentially expressed between the two groups. Key metabolites and RF identified by radiometabolomics can be used to improve the identification and differentiation between renal neoplasms.

A CT-based radiomics nomogram for distinguishing between malignant and benign Bosniak IIF masses: a two-centre study

AIM

To establish and assess a computed tomography (CT)-based radiomics nomogram for identifying malignant and benign Bosniak IIF masses.

MATERIALS AND METHODS

In total, 150 patients with Bosniak IIF masses were separated into a training set (n=106) and a test set (n=44) in a ratio of 7:3. A radiomics signature was calculated based on extracted features from the three phases of CT images. A clinical model was constructed based on clinical characteristics and CT features, and a nomogram incorporating the radiomics signature and independent clinical variables was established. The calibration ability, discrimination accuracy, and clinical value of the nomogram model were assessed.

RESULTS

Twelve features derived from CT images were applied to establish the radiomics signature. The performance levels of three machine-learning models were improved by adding the synthetic minority oversampling technique algorithm. The optimised machine learning model was a combination of the minimum redundancy maximum relevance-least absolute shrinkage and selection operator feature screening method + logistic regression classifier + synthetic minority oversampling technique algorithm, which demonstrated excellent identification ability on the test set (area under the curve [AUC], 0.970; 95% confidence interval [CI], 0.940-1.000). The nomogram model displayed outstanding discrimination ability on the test set (AUC, 0.972; 95% CI, 0.942-1.000).

CONCLUSIONS

The CT-based radiomics nomogram was useful for discriminating between malignant and benign Bosniak IIF masses, which improved the precision of preoperative diagnosis.

Role of AI and Radiomic Markers in Early Diagnosis of Renal Cancer and Clinical Outcome Prediction: A Brief Review

Globally, renal cancer (RC) is the 10th most common cancer among men and women. The new era of artificial intelligence (AI) and radiomics have allowed the development of AI-based computer-aided diagnostic/prediction (AI-based CAD/CAP) systems, which have shown promise for the diagnosis of RC (i.e., subtyping, grading, and staging) and prediction of clinical outcomes at an early stage. This will absolutely help reduce diagnosis time, enhance diagnostic abilities, reduce invasiveness, and provide guidance for appropriate management procedures to avoid the burden of unresponsive treatment plans. This survey mainly has three primary aims. The first aim is to highlight the most recent technical diagnostic studies developed in the last decade, with their findings and limitations, that have taken the advantages of AI and radiomic markers derived from either computed tomography (CT) or magnetic resonance (MR) images to develop AI-based CAD systems for accurate diagnosis of renal tumors at an early stage. The second aim is to highlight the few studies that have utilized AI and radiomic markers, with their findings and limitations, to predict patients' clinical outcome/treatment response, including possible recurrence after treatment, overall survival, and progression-free survival in patients with renal tumors. The promising findings of the aforementioned studies motivated us to highlight the optimal AI-based radiomic makers that are correlated with the diagnosis of renal tumors and prediction/assessment of patients' clinical outcomes. Finally, we conclude with a discussion and possible future avenues for improving diagnostic and treatment prediction performance.

Differentiating Benign From Malignant Cystic Renal Masses: A Feasibility Study of Computed Tomography Texture-Based Machine Learning Algorithms

OBJECTIVE

The Bosniak classification attempts to predict the likelihood of renal cell carcinoma (RCC) among cystic renal masses but is subject to interobserver variability and often requires multiphase imaging. Artificial intelligence may provide a more objective assessment. We applied computed tomography texture-based machine learning algorithms to differentiate benign from malignant cystic renal masses.

METHODS

This is an institutional review board-approved, Health Insurance Portability and Accountability Act-compliant retrospective study of 147 patients (mean age, 62.4 years; range, 28-89 years; 94 men) with 144 cystic renal masses (93 benign, 51 RCC); 69 were pathology proven (51 RCC, 18 benign), and 75 were considered benign based on more than 4 years of stability at follow-up imaging. Using a single image from a contrast-enhanced abdominal computed tomography scan, mean, SD, mean value of positive pixels, entropy, skewness, and kurtosis radiomics features were extracted. Random forest, multivariate logistic regression, and support vector machine models were used to classify each mass as benign or malignant with 10-fold cross validation. Receiver operating characteristic curves assessed algorithm performance in the aggregated test data.

RESULTS

For the detection of malignancy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve were 0.61, 0.87, 0.72, 0.80, and 0.79 for the random forest model; 0.59, 0.87, 0.71, 0.79, and 0.80 for the logistic regression model; and 0.55, 0.86, 0.68, 0.78, and 0.76 for the support vector machine model.

CONCLUSION

Computed tomography texture-based machine learning algorithms show promise in differentiating benign from malignant cystic renal masses. Once validated, these may serve as an adjunct to radiologists' assessments.

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.

Natural history of simple renal cysts: longitudinal CT-based evaluation

PURPOSE

Simple renal cysts are common benign lesions that arise from the renal parenchyma. Cyst growth can lead to confusion as well as concern from patients and referring providers about the need for imaging follow-up or additional evaluation. The purpose of this study was to evaluate the natural history of simple renal cysts and determine the best metric to characterize cyst evolution.

METHODS

222 simple renal cysts in 182 adults (age = 58.4 ± 6.0 years) were longitudinally evaluated on non-contrast CT examinations over a mean interval of 7.5 ± 2.8 years. Axial long axis, surface area, and volume were evaluated at baseline and follow-up CT examinations. Absolute and percent annualized growth rates were computed between CT studies for each parameter.

RESULTS

At baseline CT examinations, mean (± SD) axial long axis, surface area, and volume measurements were 2.5 ± 1.7 cm, 2.5 ± 4.5 cm², and 17.6 ± 52.5 ml, respectively. On follow-up examinations, measurements were 3.4 ± 2.0 cm, 4.2 ± 5.9 cm², and 34.4 ± 92.3 ml, respectively. Significant differences (p < 0.01) were found between baseline and follow-up values for each parameter. The absolute growth rate of each parameter was + 0.1 ± 0.1 cm/year, + 2.1 ± 3.4 cm²/year, and + 2.0 ± 5.6 ml/year, respectively. The percent annualized growth rate for each parameter was +6.5 ± 7.3%/year, +18 ± 24%/year, and +46 ± 100%/year, respectively. Overall, 86% (190/222) of cysts increased in size over time; most notably 78% (174/222) increased by ≥ 6% in volume per year. None of the simple cysts developed septations or solid components on follow-up examinations.

CONCLUSION

The majority of simple renal cysts increase in size over time, which was not associated with the development of complex features. Surface area and volume are the parameters most indicative of cyst growth or regression over time. In patients with enlarging asymptomatic simple renal cysts, no follow-up imaging is indicated.

CT texture analysis of abdominal lesions – Part II: Tumors of the Kidney and Pancreas

It has been proven in a few early studies that radiomic analysis offers a promising opportunity to detect or differentiate between organ lesions based on their unique texture parameters. Recently, the utilization of CT texture analysis (CTTA) has been receiving significant attention, especially for response evaluation and prognostication of different oncological diagnoses. In this review article, we discuss the unique ability of radiomics and its subfield CTTA to diagnose lesions in the pancreas and kidney. We review studies in which CTTA was used for the classification of histology grades in pancreas and kidney tumors. We also review the role of radiogenomics in the prediction of the molecular and genetic subtypes of pancreatic tumors. Furthermore, we provide a short report on recent advancements of radiomic analysis in predicting prognosis and survival of patients with pancreatic and renal cancers.

Bosniak classification of cystic renal masses, version 2019: interpretation pitfalls and recommendations to avoid misclassification

The purpose of this review is to describe the potential sources of variability or discrepancy in interpretation of cystic renal masses under the Bosniak v2019 classification system. Strategies to avoid these pitfalls and clinical examples of diagnostic approaches are also presented. Potential pitfalls in the application of Bosniak v2019 are divided into three categories: interpretative, technical, and mass related. An organized, comprehensive review of possible discrepancies in interpreting Bosniak v2019 cystic masses is presented with pictorial examples of difficult clinical cases and proposed solutions. The scheme provided can guide readers to consistent, precise application of the classification system. Radiologists should be aware of the possible sources of misinterpretation of cystic renal masses when applying Bosniak v2019. Knowing which features and types of cystic masses are prone to interpretive errors, in addition to the inherent trade-offs between the CT and MR techniques used to characterize them, can help radiologists avoid these pitfalls.

Development and Validation of a Diagnostic Nomogram for the Preoperative Differentiation Between Follicular Thyroid Carcinoma and Follicular Thyroid Adenomas

OBJECTIVE

The aim of the study was to construct and validate a nomogram for differentiating follicular thyroid carcinoma (FTC) from follicular thyroid adenoma (FTA).

METHODS

Two hundred patients with pathologically confirmed thyroid follicular neoplasms were retrospectively analyzed. The patients were randomly divided into a training set (n = 140) and validation set (n = 60). Baseline data including demographics, CT (computed tomography) signs, and radiomic features were analyzed. Predictive models were developed and compared to build a nomogram. The predictive effectiveness of it was evaluated by the area under receiver operating characteristic curve (AUC).

RESULTS

The CT model, radiomic model and combination model showed excellent discrimination (AUCs [95% confidence interval] = 0.847 [0.766-0.928], 0.863 [0.746-0.932], 0.913 [0.850-0.975]). The nomogram based on the combination model showed remarkable discrimination in the training and validation sets. The calibration curves suggested good consistency between actual observation and prediction.

CONCLUSIONS

This study proposed a nomogram that can accurately and intuitively predict the malignancy potential of follicular thyroid neoplasms.

Stratification of cystic renal masses into benign and potentially malignant: applying machine learning to the bosniak classification

PURPOSE

To create a CT texture-based machine learning algorithm that distinguishes benign from potentially malignant cystic renal masses as defined by the Bosniak Classification version 2019.

METHODS

In this IRB-approved, HIPAA-compliant study, 4,454 adult patients underwent renal mass protocol CT or CT urography from January 2011 to June 2018. Of these, 257 cystic renal masses were included in the final study cohort. Each mass was independently classified using Bosniak version 2019 by three radiologists, resulting in 185 benign (Bosniak I or II) and 72 potentially malignant (Bosniak IIF, III or IV) masses. Six texture features: mean, standard deviation, mean of positive pixels, entropy, skewness, kurtosis were extracted using commercial software TexRAD (Feedback PLC, Cambridge, UK). Random forest (RF), logistic regression (LR), and support vector machine (SVM) machine learning algorithms were implemented to classify cystic renal masses into the two groups and tested with tenfold cross validations.

RESULTS

Higher mean, standard deviation, mean of positive pixels, entropy, skewness were statistically associated with the potentially malignant group (P ≤ 0.0015 each). Sensitivity, specificity, positive predictive value, negative predictive value, and area under curve of RF model was 0.67, 0.91, 0.75, 0.88, 0.88; of LR model was 0.63, 0.93, 0.78, 0.86, 0.90, and of SVM model was 0.56, 0.91, 0.71, 0.84, 0.89, respectively.

CONCLUSION

Three CT texture-based machine learning algorithms demonstrated high discriminatory capability in distinguishing benign from potentially malignant cystic renal masses as defined by the Bosniak Classification version 2019. If validated, CT texture-based machine learning algorithms may help reduce interreader variability when applying the Bosniak classification.

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.

CT texture analysis predicts abdominal aortic aneurysm post-endovascular aortic aneurysm repair progression

The aim of this study is to investigate the role of early postoperative CT texture analysis in aneurysm progression. Ninety-nine patients who had undergone post-endovascular aneurysm repair (EVAR) infra-renal abdominal aortic aneurysm CT serial scans were enrolled from July 2014 to December 2019. The clinical and traditional imaging features were obtained. Aneurysm texture analysis was performed using three methods—the grey-level co-occurrence matrix (GLCM), the grey-level run length matrix (GLRLM), and the grey-level difference method (GLDM). A multilayer perceptron neural network was applied as a classifier, and receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) analysis were employed to illustrate the classification performance. No difference was found in the morphological and clinical features between the expansion (+) and (−) groups. GLCM yielded the best performance with an accuracy of 85.17% and an AUC of 0.90, followed by GLRLM with an accuracy of 87.23% and an AUC of 0.8615, and GLDM with an accuracy of 86.09% and an AUC of 0.8313. All three texture analyses showed superior predictive ability over clinical risk factors (accuracy: 69.41%; AUC: 0.6649), conventional imaging features (accuracy: 69.02%; AUC: 0.6747), and combined (accuracy: 75.29%; AUC: 0.7249). Early post-EVAR arterial phase-derived aneurysm texture analysis is a better predictor of later aneurysm expansion than clinical factors and traditional imaging evaluation combined.

Radiomics and Artificial Intelligence for Renal Mass Characterization

Radiomics allows for high throughput extraction of quantitative data from images. This is an area of active research as groups try to capture and quantify imaging parameters and convert these into descriptive phenotypes of organs or tumors. Texture analysis is one radiomics tool that extracts information about heterogeneity within a given region of interest. This is used with or without associated machine learning classifiers or a deep learning approach is applied to similar types of data. These tools have shown utility in characterizing renal masses, renal cell carcinoma, and assessing response to targeted therapeutic agents in metastatic renal cell carcinoma.