Radiomics Analysis of Contrast-Enhanced CT Predicts Survival in Clear Cell Renal Cell Carcinoma.

PURPOSE: To develop and validate the radiomics nomogram that combines clinical factors and radiomics features to estimate overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC), and assess the incremental value of radiomics for OS estimation. MATERIALS AND METHODS: One hundred ninety-four ccRCC cases were included in the training cohort and 188 ccRCC patients from another hospital as the test cohort. Three-dimensional region-of-interest segmentation was manually segmented on multiphasic contrast-enhanced abdominal CT images. Radiomics score (Rad-score) was calculated from a formula generated via least absolute shrinkage and selection operator (LASSO) Cox regression, after which the association between the Rad-score and OS was explored. The radiomics nomogram (clinical factors + Rad-score) was developed to demonstrate the incremental value of the Rad-score to the clinical nomogram for individualized OS estimation, which was then evaluated in relation to calibration and discrimination. RESULTS: Rad-score, calculated using a linear combination of the 11 screened features multiplied by their respective LASSO Cox coefficients, was significantly associated with OS. Calibration curves showed good agreement between the OS predicted by the nomograms and observed outcomes. The radiomics nomogram presented higher discrimination capability compared to clinical nomogram in the training (C-index: 0.884; 95% CI: 0.808-0.940 vs. 0.803; 95% CI: 0.705-0.899, P < 0.05) and test cohorts (C-index: 0.859; 95% CI: 0.800-0.921 vs. 0.846; 95% CI: 0.777-0.915, P < 0.05). CONCLUSIONS: The radiomics nomogram may be used for predicting OS in patients with ccRCC, and radiomics is useful to assist quantitative and personalized treatment.

Improved Cellular Delivery of Antisense Oligonucleotide for miRNA-21 Imaging In Vivo Using Cell-Penetrating Peptide-Based Nanoprobes.

Most oligonucleotides fail to enter a cell and cannot escape from endosomes after endocytosis because of their negative charge and large molecular weight. More efficient cellular delivery of oligonucleotides should be developed for the widespread implementation of antisense imaging. The purpose of this study was to construct a novel antisense nanoprobe, 99mTc-labeled anti-miRNA oligonucleotides/cell-penetrating peptide PepFect6 (99mTc-AMO/PF6), and to evaluate its efficacy for imaging the miRNA-21 expression in A549 lung adenocarcinoma xenografts. Naked AMO and commercial Lipofectamine 2000-based nanoparticles (AMO/LIP) were used for comparison. The cellular delivery efficiency of AMO/PF6 was first investigated by laser confocal scanning microscopy using Cy5.5-labeled probes and further validated by in vivo fluorescence imaging. Then, the probes were labeled with 99mTc via hydrazinonicotinamide (HYNIC). The cytotoxicity assay, cellular uptake, and retention kinetics of the probes were evaluated in vitro. The biodistribution of the probes was investigated in A549 lung cancer xenografts, and SPECT imaging was performed in vivo. AMO/PF6 showed lower cytotoxicity than AMO/LIP (P < 0.05) but showed no significant difference with naked AMO. Fluorescence microscopy demonstrated more extensive and scattered signal distribution inside the A549 cells by AMO/PF6 than AMO/LIP. The labeling efficiency of 99mTc-AMO/PF6 was 72.6 ± 1.42%, and the specific activity was 11.6 ± 0.13 MBq/ng. The cellular uptake of 99mTc-PF6/AMO peaked at 12 h, with the uptake of 11.24 ± 0.12 mol/cell × 10-16, and the cellular retention of 99mTc-AMO/PF6 was 3.92 ± 0.15 mol/cell × 10-16 at 12 h after interrupted incubation. AMO/PF6 showed higher cellular uptake and retention than naked AMO and AMO/LIP. The biodistribution study showed that the tumor had the highest radioactivity accumulation, with the uptake ratio of tumor/muscle (T/M) increasing from 14.59 ± 0.67 to 21.76 ± 0.98 between 1 and 6 h after injection, followed by the uptake in the kidneys and the liver. The results of in vivo fluorescence and SPECT imaging were consistent with the results of the biodistribution. The tumor was visualized at 6 h after injection of AMO/PF6 with the highest T/M ratio among these probes (P < 0.05). PF6 improves cellular delivery of antisense oligonucleotides via noncovalent nanoparticles. 99mTc-AMO/PF6 shows favorable imaging properties and is promising for miRNAs imaging in vivo.

Additional value of metabolic parameters to PET/CT-based radiomics nomogram in predicting lymphovascular invasion and outcome in lung adenocarcinoma.

PURPOSE: Lymphovascular invasion (LVI) impairs surgical outcomes in lung adenocarcinoma (LAC) patients. Preoperative prediction of LVI is challenging by using traditional clinical and imaging parameters. The purpose of this study was to investigate the value of the radiomics nomogram integrating clinical factors, CT features, and maximum standardized uptake value (SUVmax) to predict LVI and outcome in LAC and to evaluate the additional value of the SUVmax to the PET/CT-based radiomics nomogram. METHODS: A total of 272 LAC patients (87 LVI-present LACs and 185 LVI-absent LACs) with PET/CT scans were retrospectively enrolled, and 160 patients with SUVmax ≥ 2.5 of them were used for PET radiomics analysis. Clinical data and CT features were analyzed to select independent LVI predictors. The performance of the independent LVI predictors and SUVmax was evaluated. Two-dimensional (2D) and three-dimensional (3D) CT radiomics signatures (RSs) and PET-RS were constructed with the least absolute shrinkage and selection operator algorithm and radiomics scores (Rad-scores) were calculated. The radiomics nomograms, incorporating Rad-score and independent clinical and CT factors, with SUVmax (RNWS) or without SUVmax (RNWOS) were built. The performance of the models was assessed with respect to calibration, discrimination, and clinical usefulness. All the clinical, PET/CT, pathologic, therapeutic, and radiomics parameters were assessed to identify independent predictors of progression-free survival (PFS). RESULTS: CT morphology was the independent LVI predictor. SUVmax provided better discrimination capability compared with CT morphology in the training set (P < 0.001) and test set (P = 0.042). A total of 1409 CT and PET radiomics features were extracted and reduced to 8, 8, and 10 features to build the 2D CT-RS, 3D CT-RS, and the PET-RS, respectively. There was no significant difference in AUC between the 2D-RS and 3D-RS (P > 0.05), and 2D CT-RS showed a relatively higher AUC than 3D CT-RS. The CT-RS, the CT-RNWOS, and the CT-RNWS showed good discrimination in the training set (AUC [area under the curve], 0.799, 0.796, and 0.851, respectively) and the test set (AUC, 0.818, 0.822, and 0.838, respectively). There was significant difference in AUC between the CT-RNWS and CT-RNWOS (P = 0.044) in the training set. Decision curve analysis (DCA) demonstrated the CT-RNWS outperformed the CT-RS and the CT-RNWOS in terms of clinical usefulness. Furthermore, DCA showed the PETCT-RNWS provided the highest net benefit compared with the PET-RNWS and CT-RNWS. PFS was significantly different between the pathologic and RNWS-predicted LVI-present and LVI-absent patients (P < 0.001). Carbohydrate antigen 125 (CA125), carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), pathologic LVI, histologic subtype, and SUVmax were independent predictors of PFS in the 244 CT-RNWS-predicted cohort; and CA125, NSE, pathologic LVI, and SUVmax were the independent predictors of PFS in the 141 PETCT-RNWS-predicted cohort. CONCLUSIONS: The radiomics nomogram, incorporating Rad-score, clinical and PET/CT parameters, shows favorable predictive efficacy for LVI status in LAC. Pathologic LVI and SUVmax are associated with LAC prognosis.

A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma.

OBJECTIVES: To develop and validate a radiomics nomogram for preoperative differentiating renal angiomyolipoma without visible fat (AML.wovf) from homogeneous clear cell renal cell carcinoma (hm-ccRCC). METHODS: Ninety-nine patients with AML.wovf (n = 36) and hm-ccRCC (n = 63) were divided into a training set (n = 80) and a validation set (n = 19). Radiomics features were extracted from corticomedullary phase and nephrographic phase CT images. A radiomics signature was constructed and a radiomics score (Rad-score) was calculated. Demographics and CT findings were assessed to build a clinical factors model. Combined with the Rad-score and independent clinical factors, a radiomics nomogram was constructed. Nomogram performance was assessed with respect to calibration, discrimination, and clinical usefulness. RESULTS: Fourteen features were used to build the radiomics signature. The radiomics signature showed good discrimination in the training set (AUC [area under the curve], 0.879; 95%; confidence interval [CI], 0.793-0.966) and the validation set (AUC, 0.846; 95% CI, 0.643-1.000). The radiomics nomogram showed good calibration and discrimination in the training set (AUC, 0.896; 95% CI, 0.810-0.983) and the validation set (AUC, 0.949; 95% CI, 0.856-1.000) and showed better discrimination capability (p < 0.05) compared with the clinical factor model (AUC, 0.788; 95% CI, 0.683-0.893) in the training set. Decision curve analysis demonstrated the nomogram outperformed the clinical factors model and radiomics signature in terms of clinical usefulness. CONCLUSIONS: The CT-based radiomics nomogram, a noninvasive preoperative prediction tool that incorporates the Rad-score and clinical factors, shows favorable predictive efficacy for differentiating AML.wovf from hm-ccRCC, which might assist clinicians in tailoring precise therapy. KEY POINTS: • Differential diagnosis between AML.wovf and hm-ccRCC is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, demographics, and CT findings facilitates differentiation of AML.wovf from hm-ccRCC with improved diagnostic efficacy. • The CT-based radiomics nomogram might spare unnecessary surgery for AML.wovf.

Contrast-Enhanced CT Texture Analysis for Distinguishing Fat-Poor Renal Angiomyolipoma From Chromophobe Renal Cell Carcinoma.

OBJECTIVE: To evaluate the value of 2-dimensional (2D) and 3-dimensional (3D) computed tomography texture analysis (CTTA) models in distinguishing fat-poor angiomyolipoma (fpAML) from chromophobe renal cell carcinoma (chRCC). METHODS: We retrospectively enrolled 32 fpAMLs and 24 chRCCs. Texture features were extracted from 2D and 3D regions of interest in triphasic CT images. The 2D and 3D CTTA models were constructed with the least absolute shrinkage and selection operator algorithm and texture scores were calculated. The diagnostic performance of the 2D and 3D CTTA models was evaluated with respect to calibration, discrimination, and clinical usefulness. RESULTS: Of the 177 and 183 texture features extracted from 2D and 3D regions of interest, respectively, 5 2D features and 8 3D features were selected to build 2D and 3D CTTA models. The 2D CTTA model (area under the curve [AUC], 0.811; 95% confidence interval [CI], 0.695-0.927) and the 3D CTTA model (AUC, 0.915; 95% CI, 0.838-0.993) showed good discrimination and calibration (P > .05). There was no significant difference in AUC between the 2 models (P = .093). Decision curve analysis showed the 3D model outperformed the 2D model in terms of clinical usefulness. CONCLUSIONS: The CTTA models based on contrast-enhanced CT images had a high value in differentiating fpAML from chRCC.