Quantitative Analysis of Multiphase Contrast-Enhanced CT Images: A Pilot Study of Preoperative Prediction of Fat-Poor Angiomyolipoma and Renal Cell Carcinoma

Machine learning-based CT texture analysis in the differentiation of testicular masses

Purpose

To evaluate the ability of texture features for distinguishing between benign and malignant testicular masses, and furthermore, for identifying primary testicular lymphoma in malignant tumors and identifying seminoma in testicular germ cell tumors, respectively.

Methods

We retrospectively collected 77 patients with an abdominal and pelvic enhanced computed tomography (CT) examination and a histopathologically confirmed testicular mass from a single center. The ROI of each mass was split into two parts by the largest cross-sectional slice and deemed to be two samples. After all processing steps, three-dimensional texture features were extracted from unenhanced and contrast-enhanced CT images. Excellent reproducibility of texture features was defined as intra-class correlation coefficient ≥0.8 (ICC ≥0.8). All the groups were balanced via the synthetic minority over-sampling technique (SMOTE) method. Dimension reduction was based on pearson correlation coefficient (PCC). Before model building, minimum-redundancy maximum-relevance (mRMR) selection and recursive feature elimination (RFE) were used for further feature selection. At last, three ML classifiers with the highest cross validation with 5-fold were selected: autoencoder (AE), support vector machine(SVM), linear discriminant analysis (LAD). Logistics regression (LR) and LR-LASSO were also constructed to compare with the ML classifiers.

Results

985 texture features with ICC ≥0.8 were extracted for further feature selection process. With the highest AUC of 0.946 (P <0.01), logistics regression was proved to be the best model for the identification of benign or malignant testicular masses. Besides, LR also had the best performance in identifying primary testicular lymphoma in malignant testicular tumors and in identifying seminoma in testicular germ cell tumors, with the AUC of 0.982 (P <0.01) and 0.928 (P <0.01), respectively.

Conclusion

Until now, this is the first study that applied CT texture analysis (CTTA) to assess the heterogeneity of testicular tumors. LR model based on CTTA might be a promising non-invasive tool for the diagnosis and differentiation of testicular masses. The accurate diagnosis of testicular masses would assist urologists in correct preoperative and perioperative decision making.

The classification of benign and malignant lung nodules based on CT radiomics: a systematic review, quality score assessment, and meta-analysis

Radiomics methods are increasingly used to identify benign and malignant lung nodules, and early monitoring is essential in prognosis and treatment strategy formulation. To evaluate the diagnostic performance of computed tomography (CT)-based radiomics for distinguishing between benign and malignant lung nodules by performing a meta-analysis. Between January 2000 and December 2021, we searched the PubMed and Embase electronic databases for studies in English. Studies were included if they demonstrated the sensitivity and specificity of CT-based radiomics for diagnosing benign and malignant lung nodules. The studies were evaluated using the QUADAS-2 and radiomics quality scores (RQS). The inhomogeneity of the data and publishing bias were also evaluated. Some subgroup analyses were performed to investigate the impact of diagnostic efficiency. The Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) Guidelines were followed for this meta-analysis. A total of 20 studies involving 3793 patients were included. The combined sensitivity, specificity, diagnostic odds ratio, and area under the summary receiver operating characteristic curve based on CT radiomics diagnosis of benign and malignant lung nodules were 0.81, 0.86, 27.00, and 0.91, respectively. Deek's funnel plot asymmetry test confirmed no significant publication bias in all studies. Fagan nomograms showed a 40% increase in post-test probability among pretest-positive patients. Current evidence shows that CT-based radiomics has high accuracy in the diagnosis of benign and malignant lung nodules.

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.

Automatic renal mass segmentation and classification on CT images based on 3D U-Net and ResNet algorithms

Purpose

To automatically evaluate renal masses in CT images by using a cascade 3D U-Net- and ResNet-based method to accurately segment and classify focal renal lesions.

Material and Methods

We used an institutional dataset comprising 610 CT image series from 490 patients from August 2009 to August 2021 to train and evaluate the proposed method. We first determined the boundaries of the kidneys on the CT images utilizing a 3D U-Net-based method to be used as a region of interest to search for renal mass. An ensemble learning model based on 3D U-Net was then used to detect and segment the masses, followed by a ResNet algorithm for classification. Our algorithm was evaluated with an external validation dataset and kidney tumor segmentation (KiTS21) challenge dataset.

Results

The algorithm achieved a Dice similarity coefficient (DSC) of 0.99 for bilateral kidney boundary segmentation in the test set. The average DSC for renal mass delineation using the 3D U-Net was 0.75 and 0.83. Our method detected renal masses with recalls of 84.54% and 75.90%. The classification accuracy in the test set was 86.05% for masses (<5 mm) and 91.97% for masses (≥5 mm).

Conclusion

We developed a deep learning-based method for fully automated segmentation and classification of renal masses in CT images. Testing of this algorithm showed that it has the capability of accurately localizing and classifying renal masses.

Alkaline phosphatase combines with CT factors for differentiating small (≤ 4 cm) fat-poor angiomyolipoma from renal cell carcinoma: a multiple quantitative tool

PURPOSE

This study aimed to evaluate the diagnostic value of serum and CT factors to establish a convenient diagnostic method for differentiating small (≤ 4 cm) fat-poor angiomyolipoma (AML) from renal cell carcinoma (RCC).

MATERIALS AND METHODS

This study analyzed the preoperative serum laboratory data and CT data of 32 fat-poor AML patients and 133 RCC patients. The CT attenuation value of tumor (AVT), relative enhancement ratio (RER), and heterogeneous degree of tumor were detected using region of interest on precontrast phase (PCP) and the corticomedullary phase. Multivariate regression was performed to filter the main factors. The main factors were selected to establish the prediction models. The area under the curve (AUC) was measured to evaluate the diagnostic efficacy.

RESULTS

Fat-poor AML was more common found in younger (47.91 ± 2.09 years vs 53.63 ± 1.17 years, P = 0.02) and female (70.68 vs 28.13%, P < 0.001) patients. Alkaline phosphatase (ALP) was higher in RCC patients (81.80 ± 1.75 vs 63.25 ± 2.95 U/L, P < 0.01). For CT factors, fat-poor AML was higher in PCP_AVT (40.30 ± 1.49 vs 32.98 ± 0.69Hu, P < 0.01) but lower in RER (67.17 ± 3.17 vs 84.64 ± 2.73, P < 0.01). Gender, ALP, PCP_AVT and RER was found valuable for the differentiation. When compared with laboratory-based or CT-based diagnostic models, the combination model integrating gender, ALP, PCP_AVT and RER shows the best diagnostic performance (AUC = 0.922).

CONCLUSION

ALP was found higher in RCC patients. Female patients with ALP < 70.50U/L, PCP_AVT > 35.97Hu and RER < 82.66 are more likely to be diagnose as fat-poor AML.

Histogram peritumoral enhanced features on MRI arterial phase with extracellular contrast agent can improve prediction of microvascular invasion of hepatocellular carcinoma

BACKGROUND

Preoperative microvascular invasion (MVI) prediction plays an important role in therapeutic decision-making of hepatocellular carcinoma (HCC). This study aimed to investigate the value of histogram based on the arterial phase (AP) of magnetic resonance imaging (MRI) with extracellular contrast agent compared with radiological features for predicting MVI of solitary HCC.

METHODS

In total, 113 patients with pathologically proven solitary HCC were retrospectively enrolled who received surgical resection and underwent preoperative abdominal MRI. The patients were divided into the ≤3 cm [small HCC (sHCC)] cohort and the >3 cm cohort. Based on pathological analysis of surgical specimens, the patients were classified into MVI negative (MVI-) and MVI positive (MVI+) groups. Peritumoral and intratumoral histogram features [mean, median, standard deviation (Std), coefficient of variation (CV), skewness, kurtosis] were acquired on AP subtraction images and radiological features [size, capsule, corona enhancement, corona enhancement thickness (CET), CET group]. Receiver operating characteristic (ROC) curve was constructed to assess predictive capability. Subgroup analysis of patients with a visible corona enhancement based on the CET cut-off value was performed.

RESULTS

None of the features extracted from the intratumor area were significantly different between the MVI+ and MVI- groups in both cohorts. Histogram defined peritumoral (peri-) mean, median, kurtosis, and radiological features including CET and CET group were associated with MVI in sHCCs. Peri-mean, median, Std and radiological features including incomplete capsule, CET, and CET group were associated with MVI in HCC >3 cm. In multivariate logistic regression analysis, the CET group and peri-mean were independent predictors for HCC >3 cm with an area under the curve (AUC) of 0.741. Peri-mean was an independent predictor for sHCC (AUC =0.798). Subgroup analysis of the corona enhancement using 8 mm as a cut-off value showed 100% sensitivity and negative predictive value (NPV).

CONCLUSIONS

Peritumoral AP enhanced degree on MRI showed an encouraging predictive performance for preoperative prediction of MVI, especially in sHCCs. CET ≤8 mm could be used as a negative predictive marker for MVI.

Radiomics in Renal Cell Carcinoma-A Systematic Review and Meta-Analysis

Simple Summary

Radiomics may answer questions where the conventional interpretation of medical imaging has limitations. The aim of our systematic review and meta-analysis was to assess the (current) status of evidence in the application of radiomics in the field of renal masses. We focused on its role in diagnosis, sub-entity discrimination and treatment response assessment in renal cell carcinoma (RCC) and benign renal masses. Our quantitative synthesis showed promising results in discrimination of tumor dignity, nevertheless, the value added to human assessment remains unclear and should be the focus of future research. Furthermore, the benefit regarding treatment response assessment remains unclear as well, since the existing studies are investigating already abandoned systemic therapies (ST), which no longer represent the current “reference” standard. Open science could enable to establish technical and clinical validity of radiomic signatures prior to the incorporation of radiomics into everyday clinical practice.

Abstract

Radiomics may increase the diagnostic accuracy of medical imaging for localized and metastatic RCC (mRCC). A systematic review and meta-analysis was performed. Doing so, we comprehensively searched literature databases until May 2020. Studies investigating the diagnostic value of radiomics in differentiation of localized renal tumors and assessment of treatment response to ST in mRCC were included and assessed with respect to their quality using the radiomics quality score (RQS). A total of 113 out of 1098 identified studies met the criteria and were included in qualitative synthesis. Median RQS of all studies was 13.9% (5.0 points, IQR 0.25–7.0 points), and RQS increased over time. Thirty studies were included into the quantitative synthesis: For distinguishing angiomyolipoma, oncocytoma or unspecified benign tumors from RCC, the random effects model showed a log odds ratio (OR) of 2.89 (95%-CI 2.40–3.39, p < 0.001), 3.08 (95%-CI 2.09–4.06, p < 0.001) and 3.57 (95%-CI 2.69–4.45, p < 0.001), respectively. For the general discrimination of benign tumors from RCC log OR was 3.17 (95%-CI 2.73–3.62, p < 0.001). Inhomogeneity of the available studies assessing treatment response in mRCC prevented any meaningful meta-analysis. The application of radiomics seems promising for discrimination of renal tumor dignity. Shared data and open science may assist in improving reproducibility of future studies.

MR texture analysis in differentiating renal cell carcinoma from lipid-poor angiomyolipoma and oncocytoma

OBJECTIVES

To assess the utility of magnetic resonance texture analysis (MRTA) in differentiating renal cell carcinoma (RCC) from lipid-poor angiomyolipoma (lpAML) and oncocytoma.

METHODS

After ethical approval, 42 patients with 54 masses (34 RCC, 14 lpAML and six oncocytomas) who underwent MRI on a 1.5 T scanner (Avanto, Siemens, Erlangen, Germany) between January 2011 and December 2012 were retrospectively included in the study. MRTA was performed on the TexRAD research software (Feedback Plc., Cambridge, UK) using free-hand polygonal region of interest (ROI) drawn on the maximum cross-sectional area of the tumor to generate six first-order statistical parameters. The Mann-Whitney U test was used to look for any statically significant difference. The receiver operating characteristic (ROC) curve analysis was done to select the parameter with the highest class separation capacity [area under the curve (AUC)] for each MRI sequence.

RESULTS

Several texture parameters on MRI showed high-class separation capacity (AUC > 0.8) in differentiating RCC from lpAML and oncocytoma. The best performing parameter in differentiating RCC from lpAML was mean of positive pixels (MPP) at SSF 2 (AUC: 0.891) on DWI b500. In differentiating RCC from oncocytoma, the best parameter was mean at SSF 0 (AUC: 0.935) on DWI b1000.

CONCLUSIONS

MRTA could potentially serve as a useful non-invasive tool for differentiating RCC from lpAML and oncocytoma.

ADVANCES IN KNOWLEDGE

There is limited literature addressing the role of MRTA in differentiating RCC from lpAML and oncocytoma. Our study demonstrated several texture parameters which were useful in this regard.