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Clear cell renal cell carcinoma detection by multimodal photoacoustic tomography.

ABSTRACT: There is a need for accurate and rapid detection of renal cancer in clinic. Here, we integrated photoacoustic tomography (PAT) with ultrasound imaging in a single system, which achieved tissue imaging depth about 3 mm and imaging speed about 3.5 cm²/min. We used the wavelength at 1197 nm to map lipid distribution in normal renal tissues and clear cell renal cell carcinoma (ccRCC) tissues collected from 19 patients undergone nephrectomy. Our results indicated that the photoacoustic signal from lipids was significantly higher in ccRCC tissues than that in normal tissues. Moreover, based on the quantification of lipid area ratio, we were able to differentiate normal and ccRCC with 100 % sensitivity, 80 % specificity, and area under receiver operating characteristic curve of 0.95. Our findings demonstrate that multimodal PAT can differentiate normal and ccRCC by integrating the morphologic information from ultrasound and lipid amount information from vibrational PAT.

SUBMITTER: Li L

PROVIDER: S-EPMC7683266 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Clear cell renal cell carcinoma detection by multimodal photoacoustic tomography.

Li Lin L Wu Hanbo H Hu Shuai S Yu Yanfei Y Chen Zhicong Z Wang Pu P Zhou Liqun L Li Rui R Yao Lin L Yue Shuhua S

Photoacoustics 20201110

There is a need for accurate and rapid detection of renal cancer in clinic. Here, we integrated photoacoustic tomography (PAT) with ultrasound imaging in a single system, which achieved tissue imaging depth about 3 mm and imaging speed about 3.5 cm<sup>2</sup>/min. We used the wavelength at 1197 nm to map lipid distribution in normal renal tissues and clear cell renal cell carcinoma (ccRCC) tissues collected from 19 patients undergone nephrectomy. Our results indicated that the photoacoustic sig ...[more]

PMID: 33251109

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Project description:ObjectivesTo predict CTLA4 expression levels and prognosis of clear cell renal cell carcinoma (ccRCC) by constructing a computed tomography-based radiomics model and establishing a nomogram using clinicopathologic factors.MethodsThe clinicopathologic parameters and genomic data were extracted from 493 ccRCC cases of the Cancer Genome Atlas (TCGA)-KIRC database. Univariate and multivariate Cox regression and Kaplan-Meier analysis were performed for prognosis analysis. Cibersortx was applied to evaluate the immune cell composition. Radiomic features were extracted from the TCGA/the Cancer Imaging Archive (TCIA) (n = 102) datasets. The support vector machine (SVM) was employed to establish the radiomics signature for predicting CTLA4 expression. Receiver operating characteristic curve (ROC), decision curve analysis (DCA), and precision-recall curve were utilized to assess the predictive performance of the radiomics signature. Correlations between radiomics score (RS) and selected features were also evaluated. An RS-based nomogram was constructed to predict prognosis.ResultsCTLA4 was significantly overexpressed in ccRCC tissues and was related to lower overall survival. A higher CTLA4 expression was independently linked to the poor prognosis (HR = 1.458, 95% CI 1.13-1.881, p = 0.004). The radiomics model for the prediction of CTLA4 expression levels (AUC = 0.769 in the training set, AUC = 0.724 in the validation set) was established using seven radiomic features. A significant elevation in infiltrating M2 macrophages was observed in the RS high group (p < 0.001). The predictive efficiencies of the RS-based nomogram measured by AUC were 0.826 at 12 months, 0.805 at 36 months, and 0.76 at 60 months.ConclusionsCTLA4 mRNA expression status in ccRCC could be predicted noninvasively using a radiomics model based on nephrographic phase contrast-enhanced CT images. The nomogram established by combining RS and clinicopathologic factors could predict overall survival for ccRCC patients. Our findings may help stratify prognosis of ccRCC patients and identify those who may respond best to ICI-based treatments.

Dataset Information

Clear cell renal cell carcinoma detection by multimodal photoacoustic tomography.

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Clear cell renal cell carcinoma detection by multimodal photoacoustic tomography.

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