Dataset Information

Point Shear Wave Elastography Using Machine Learning to Differentiate Renal Cell Carcinoma and Angiomyolipoma.

ABSTRACT: The question of whether ultrasound point shear wave elastography can differentiate renal cell carcinoma (RCC) from angiomyolipoma (AML) is controversial. This study prospectively enrolled 51 patients with 52 renal tumors (42 RCCs, 10 AMLs). We obtained 10 measurements of shear wave velocity (SWV) in the renal tumor, cortex and medulla. Median SWV was first used to classify RCC versus AML. Next, the prediction accuracy of 4 machine learning algorithms-logistic regression, naïve Bayes, quadratic discriminant analysis and support vector machines (SVMs)-was evaluated, using statistical inputs from the tumor, cortex and combined statistical inputs from tumor, cortex and medulla. After leave-one-out cross validation, models were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC). Tumor median SWV performed poorly (AUC?=?0.62; p?=?0.23). Except logistic regression, all machine learning algorithms reached statistical significance using combined statistical inputs (AUC?=?0.78-0.98; p < 7.1?×?10-3). SVMs demonstrated 94% accuracy (AUC?=?0.98; p?=?3.13?×?10-6) and clearly outperformed median SWV in differentiating RCC from AML (p?=?2.8?×?10-4).

SUBMITTER: Sagreiya H

PROVIDER: S-EPMC6689386 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Point Shear Wave Elastography Using Machine Learning to Differentiate Renal Cell Carcinoma and Angiomyolipoma.

Sagreiya Hersh H Akhbardeh Alireza A Li Dandan D Sigrist Rosa R Chung Benjamin I BI Sonn Geoffrey A GA Tian Lu L Rubin Daniel L DL Willmann Jürgen K JK

Ultrasound in medicine & biology 20190525 8

The question of whether ultrasound point shear wave elastography can differentiate renal cell carcinoma (RCC) from angiomyolipoma (AML) is controversial. This study prospectively enrolled 51 patients with 52 renal tumors (42 RCCs, 10 AMLs). We obtained 10 measurements of shear wave velocity (SWV) in the renal tumor, cortex and medulla. Median SWV was first used to classify RCC versus AML. Next, the prediction accuracy of 4 machine learning algorithms-logistic regression, naïve Bayes, quadratic d ...[more]

PMID: 31133445

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Project description:Objectives:The aim of the study was to compare elastographic means in parathyroid adenomas, using shear wave elastography and strain elastography. Methods:This prospective study examined 20 consecutive patients diagnosed with primary hyperparathyroidism and parathyroid adenoma, confirmed by biochemical assay, technetium-99 sestamibi scintigraphy, and pathology report, after parathyroid surgery. All patients were examined on conventional 2B ultrasound, 2D shear wave elastography, and strain elastography. We determined using 2D shear wave elastography (SWE) the elasticity index (EI) in parathyroid adenoma, thyroid parenchyma, and surrounding muscle and examined using strain elastography the parathyroid adenoma, and determined the strain ratio with the thyroid tissue and muscle tissue. Results:All patients had positive sestamibi scintigraphy and underwent surgery, with confirmation of parathyroid adenoma in all cases. The mean parathormone (PTH) value before surgery was 153.29?pg/ml (36.5, 464.8) and serum calcium concentration was 10.5?mg/dl (9, 11.5). We compared using 2D-SWE and strain elastography parathyroid adenoma with thyroid tissue and with surrounding muscle. The mean EI measured by SWE in parathyroid adenoma was 4.74?±?2.74?kPa and in thyroid parenchyma was 11.718?±?4.206?kPa (mean difference?=?6.978?kPa, p < 0.001), and the mean EI value in muscle tissue was 16.362?±?3.829?kPa (mean difference?=?11.622, p < 0.001). Using ROC analysis, we found that an EI below 7?kPa correctly identifies parathyroid tissue. We evaluated parathyroid adenomas using strain elastography by color mapping and strain ratio as a semiquantitative measurement; however, we could not find any statistical correlation comparing the strain ratio obtained from the parathyroid adenoma with the thyroid tissue (p=0.485). Conclusion:Ultrasound elastography is a helpful tool in identifying parathyroid adenomas. A cutoff value below 7?kPa can be used in 2D-SWE. Color maps in strain elastography without adding strain ratio can be used, parathyroid adenoma being identified as score 1 in the Rago criteria.

Project description:Background & aimsLiver stiffness measurement (LSM) and spleen stiffness measurement (SSM) have been shown to be useful tools for assessing the risk of fibrosis and portal hypertension, respectively. However, data on the accuracy of LSM and SSM measured by point-shear wave elastography (pSWE) in patients affected by primary sclerosing cholangitis (PSC) are still lacking. Thus, we aimed to prospectively assess their performance in a cohort of patients with PSC.MethodsWe determined the correlation between LSM assessed by a pSWE technique (ElastPQ) and by FibroScan-transient elastography (F-TE). Furthermore, we used receiver-operating characteristic curves and area under the curves (AUROC) to evaluate the performance of LSM by ElastPQ for the staging of fibrosis, using F-TE as a reference standard, and the performance of LSM and SSM by ElastPQ in predicting the presence of oesophageal varices (OVs).ResultsOne hundred and fifty-two patients with PSC (93 males [61.2%], mean age 46 ± 16 years) were prospectively recruited. ElastPQ and F-TE LSMs were available for all patients, while ElastPQ SSM was available in 109 (72%) patients of whom 35 underwent upper gastrointestinal endoscopy within 1 year of the ultrasound assessment. ElastPQ LSM showed an excellent correlation with F-TE (p <0.001, Spearman's 0.93; Lin's 0.86) and a good diagnostic accuracy for fibrosis staging along all stages of liver fibrosis (AUROCs 0.96, 0.97, 0.97 and 0.99 for fibrosis stages F≥1, F≥2, F≥3 and F=4, respectively), using F-TE as a surrogate of histological fibrosis. ElastPQ SSM showed a good diagnostic performance in predicting the presence of OVs at endoscopy.ConclusionsLSM and SSM by ElastPQ can be used as accurate tools for liver fibrosis risk assessment and fibrosis staging, as well as for predicting the presence of OVs in the work-up of patients with PSC.Impact and implicationsLiver and spleen stiffness measurement (LSM and SSM, respectively) by ElastPQ point-shear wave elastography in patients with primary sclerosing cholangitis represent reliable and reproducible tools for non-invasively staging the severity of liver disease and stratifying patients according to their risk of developing liver-related outcomes. In particular, LSM shows good accuracy for staging liver fibrosis and therefore detecting those patients at high risk of having compensated advanced chronic liver disease who require close monitoring. SSM seems to be promising to detect the risk of portal hypertension and therefore of oesophageal varices, enabling the triaging of patients who really need to undergo a screening endoscopy.

Dataset Information

Point Shear Wave Elastography Using Machine Learning to Differentiate Renal Cell Carcinoma and Angiomyolipoma.

Publications

Point Shear Wave Elastography Using Machine Learning to Differentiate Renal Cell Carcinoma and Angiomyolipoma.

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