Differentiating solid, non-macroscopic fat containing, enhancing renal masses using fast Fourier transform analysis of multiphase CT

Varghese, Bino; Chen, Fanglin; Hwang, Darryl; Cen, Steven; Gill, Inderbir S.; Duddalwar, Vinay

doi:10.1259/bjr.20170789

Cited by 11 publications

(17 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because chRCC and RO are relatively rare compared to renal clear cell and renal papillary cell carcinoma, radiomic studies of renal tumors are focused on relatively common renal tumors. Studies on the most frequently occurring renal clear cell carcinoma have focused on different aspects such as preoperative diagnosis [19][20][21][22], tumor grade [23], prognostic evaluation [24], and molecular analysis of the cancer genes [25][26][27]. Yu et al [20] extracted the texture features of four types of renal tumors, including renal clear cell carcinoma, renal papillary cell carcinoma, chRCC, and RO.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, a variety of mathematical techniques have been used in radiomics to quantify image textures, including statistical, Fourier, and wavelet analysis, and have been applied to the study of a variety of tumors. Varghese et al [22] used multi-phase CT fast Fourier transform index to analyze the CT-enhanced solid and fat-deficient renal masses. Good classification results were obtained when distinguishing benign from the malignant renal masses, differentiating RO from chRCC, and RO from lipid-poor angiomyolipoma (AUC > 0.7).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Value of radiomics in differential diagnosis of chromophobe renal cell carcinoma and renal oncocytoma

Huang

Xia³

2019

Abdom Radiol

View full text Add to dashboard Cite

Purpose To explore the value of CT-enhanced quantitative features combined with machine learning for differential diagnosis of renal chromophobe cell carcinoma (chRCC) and renal oncocytoma (RO). Methods Sixty-one cases of renal tumors (chRCC = 44; RO = 17) that were pathologically confirmed at our hospital between 2008 and 2018 were retrospectively analyzed. All patients had undergone preoperative enhanced CT scans including the corticomedullary (CMP), nephrographic (NP), and excretory phases (EP) of contrast enhancement. Volumes of interest (VOIs), including lesions on the images, were manually delineated using the RadCloud platform. A LASSO regression algorithm was used to screen the image features extracted from all VOIs. Five machine learning classifications were trained to distinguish chRCC from RO by using a fivefold cross-validation strategy. The performance of the classifier was mainly evaluated by areas under the receiver operating characteristic (ROC) curve and accuracy. Results In total, 1029 features were extracted from CMP, NP, and EP. The LASSO regression algorithm was used to screen out the four, four, and six best features, respectively, and eight features were selected when CMP and NP were combined. All five classifiers had good diagnostic performance, with area under the curve (AUC) values greater than 0.850, and support vector machine (SVM) classifier showed a diagnostic accuracy of 0.945 (AUC 0.964 ± 0.054; sensitivity 0.999; specificity 0.800), showing the best performance. Conclusions Accurate preoperative differential diagnosis of chRCC and RO can be facilitated by a combination of CTenhanced quantitative features and machine learning.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Value of radiomics in differential diagnosis of chromophobe renal cell carcinoma and renal oncocytoma

Huang

Xia³

2019

Abdom Radiol

View full text Add to dashboard Cite

show abstract

“…A homogeneous enhancement pattern and high unenhanced attenuation on CT are reported to be sensitive and specific for lipid‐poor AML . Moreover, texture analysis has been shown to improve the diagnostic accuracy for lipid‐poor AML . However, no definitive method for correct preoperative diagnosis of lipid‐poor AML has yet been established.…”

Section: Discussionmentioning

confidence: 99%

“…It is well known that AML is the most common benign tumor of the kidney whereas ccRCC is the most common malignant one . Concurrence of RCC and AML is quite rare, and even the latest imaging modalities still have limited efficacy for distinguishing lipid‐poor AML from ccRCC . In general, the probability of synchronous metastasis of RCC increases with tumor size, and the median diameter of RCC with synchronous metastasis is 8.0 cm .…”

Section: Introductionmentioning

confidence: 99%

Multiple angiomyolipomas mimicking metastases of concurrent clear cell renal cell carcinoma

et al. 2019

View full text Add to dashboard Cite

Abbreviations & Acronyms AML = angiomyolipoma ccRCC = clear cell renal cell carcinoma CRP = C-reactive protein CT = computed tomography HMB = human melanoma black LDH = lactate dehydrogenase LN = lymph node TSC = tuberous sclerosis complex aSMA = a-smooth muscle actin Correspondence: TakahiroIntroduction: Concurrence of clear cell renal cell carcinoma and angiomyolipoma is quite rare. We report a case of large localized clear cell renal cell carcinoma with concurrent multiple angiomyolipomas mimicking lymph node metastases. Case presentation: A 60-year-old woman presented with general malaise, weight loss, and intermittent fever. Computed tomography scan demonstrated an 8-cm mass in the left kidney, enlarged para-aortic lymph nodes, and small renal nodules adjacent to the main tumor. She was diagnosed preoperatively as having clear cell renal cell carcinoma (cT3a) with multiple para-aortic lymph node metastases, and underwent laparoscopic radical nephrectomy and dissection of the para-aortic lymph nodes. Pathologically, the main tumor was diagnosed as clear cell renal cell carcinoma. By contrast, both the para-aortic lymph nodes and nodules were diagnosed as lipid-poor angiomyolipomas. Conclusion: With the expanding first-line use of molecular targeted therapy for metastatic renal cell carcinoma, nephrectomy may be avoided by overdiagnosis. Upfront nephrectomy can avoid overdiagnosis and undertreatment of nonmetastatic renal cell carcinoma. Keynote messageConcurrence of ccRCC and AML is quite rare. With the expanding first-line use of molecular targeted therapy for metastatic RCC, nephrectomy may be avoided by overdiagnosis. Upfront nephrectomy can avoid overdiagnosis and undertreatment of nonmetastatic RCC.

show abstract

“…We evaluated eight different types of texture quantification techniques on each ROI image with 235 different texture metrics. These techniques have been described in the literature [19][20][21][22][23] (Supplementary S1: Details of CTTA metrics).…”

Section: E | Ctta Metricsmentioning

confidence: 99%

Reliability of CT‐based texture features: Phantom study

Varghese

Hwang

Cen

et al. 2019

J Applied Clin Med Phys

View full text Add to dashboard Cite

Objective: To determine the intra-, inter-and test-retest variability of CT-based texture analysis (CTTA) metrics. Materials and methods: In this study, we conducted a series of CT imaging experiments using a texture phantom to evaluate the performance of a CTTA panel on routine abdominal imaging protocols. The phantom comprises of three different regions with various textures found in tumors. The phantom was scanned on two CT scanners viz. the Philips Brilliance 64 CT and Toshiba Aquilion Prime 160 CT scanners. The intra-scanner variability of the CTTA metrics was evaluated across imaging parameters such as slice thickness, field of view, post-reconstruction filtering, tube voltage, and tube current. For each scanner and scanning parameter combination, we evaluated the performance of eight different types of texture quantification techniques on a predetermined region of interest (ROI) within the phantom image using 235 different texture metrics. We conducted the repeatability (test-retest) and robustness (intra-scanner) test on both the scanners and the reproducibility test was conducted by comparing the inter-scanner differences in the repeatability and robustness to identify reliable CTTA metrics. Reliable metrics are those metrics that are repeatable, reproducible and robust. Results: As expected, the robustness, repeatability and reproducibility of CTTA metrics are variably sensitive to various scanner and scanning parameters. Entropy of Fast Fourier Transform-based texture metrics was overall most reliable across the two scanners and scanning conditions. Post-processing techniques that reduce image noise while preserving the underlying edges associated with true anatomy or pathology bring about significant differences in radiomic reliability compared to when they were not used. Conclusion: Following large-scale validation, identification of reliable CTTA metrics can aid in conducting large-scale multicenter CTTA analysis using sample sets acquired using different imaging protocols, scanners etc.

show abstract

Differentiating solid, non-macroscopic fat containing, enhancing renal masses using fast Fourier transform analysis of multiphase CT

Cited by 11 publications

References 44 publications

Value of radiomics in differential diagnosis of chromophobe renal cell carcinoma and renal oncocytoma

Value of radiomics in differential diagnosis of chromophobe renal cell carcinoma and renal oncocytoma

Multiple angiomyolipomas mimicking metastases of concurrent clear cell renal cell carcinoma

Reliability of CT‐based texture features: Phantom study

Contact Info

Product

Resources

About