Texture analysis as a radiomic marker for differentiating renal tumors

Yu, HeiShun; Scalera, Jonathan; Khalid, Maria; Touret, Anne-Sophie; Bloch, B; Li, Baojun; Qureshi, Muhammad M.; Soto, Jorge A.; Anderson, Stephan W.

doi:10.1007/s00261-017-1144-1

Cited by 133 publications

(90 citation statements)

References 27 publications

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“…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%

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

Huang

Xia³

2019

Abdom Radiol

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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.

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Section: Discussionmentioning

confidence: 99%

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

Huang

Xia³

2019

Abdom Radiol

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“…Radiomics, a method that processes imaging data according to various mathematical algorithms, allows for extraction of data that cannot be detected by the human observer. These algorithms are promising for the characterization of tumor type and behavior of some cancers [ 9 , 10 ]. Neuro-oncologists have successfully applied radiomics in the survival assessment of glioblastoma [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of High-Risk Atypical Meningiomas According to Semantic and Radiomic Features

Kalasauskas

Kronfeld

Renovanz

et al. 2020

Cancers

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Up to 60% of atypical meningiomas (World Health Organization (WHO) grade II) reoccur within 5 years after resection. However, no clear radiological criteria exist to identify tumors with higher risk of relapse. In this study, we aimed to assess the association of certain radiomic and semantic features of atypical meningiomas in MRI with tumor recurrence. We identified patients operated on primary atypical meningiomas in our department from 2007 to 2017. An analysis of 13 quantitatively defined radiomic and 11 qualitatively defined semantic criteria was performed based on preoperative MRI scans. Imaging characteristics were assessed along with clinical and survival data. The analysis included 76 patients (59% women, mean age 59 years). Complete tumor resection was achieved in 65 (86%) cases, and tumor relapse occurred in 17 (22%) cases. Mean follow-up time was 41.6 (range 3–168) months. Cystic component was significantly associated with tumor recurrence (odds ratio (OR) 21.7, 95% confidence interval (CI) 3.8–124.5) and shorter progression-free survival (33.2 vs. 80.7 months, p < 0.001), whereas radiomic characteristics had no predictive value in univariate analysis. However, multivariate analysis demonstrated significant predictive value of high cluster prominence (hazard ratio (HR) 5.89 (1.03–33.73) and cystic component (HR 20.21 (2.46–166.02)) for tumor recurrence. The combination of radiomic and semantic features might be an effective tool for identifying patients with high-risk atypical meningiomas. The presence of a cystic component in these tumors is associated with a high risk of tumor recurrence.

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“…The emerging concept that biomedical images contain ‘hidden’ information about the underlying tissue biology that can be revealed via quantitative image analysis, an approach known as radiomics, has prompted the application of automated image analysis tools in renal tumours [68]. Early retrospective data have suggested that texture analysis of CT images combined with machine learning could distinguish RCC subtypes with an AUC > 0.90, warranting prospective investigation [69]. Molecular imaging with 99m technetium–sestamibi single photon emission computed tomography/computed tomography ( 99m Tc-MIBI SPECT/CT) has also been used to differentiate oncocytomas and indolent hybrid oncocytic/chromophobe tumours from other more aggressive malignant small renal tumours.…”

Section: Resultsmentioning

confidence: 99%