Preoperative Ultrasound Radiomics Signatures for Noninvasive Evaluation of Biological Characteristics of Intrahepatic Cholangiocarcinoma

Peng, Yung-Hsing; Zhou, Chuan‐Yang; Lin, Peng; Wen, Dong‐Yue; Wang, Xiaodong; Zhong, Xiaozhu; Pan, Denghua; Que, Qiao; Li, Xin; Chen, Ling; He, Yun; Yang, Hong

doi:10.1016/j.acra.2019.07.029

Cited by 48 publications

(43 citation statements)

References 37 publications

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“…So far, only a few studies have reported the relationship between the radiological features and the biological features of cholangiocarcinoma lesions. Researchers discovered that certain texture parameters correlate significantly with microvascular invasion, perineural invasion, differentiation, Ki-67, vascular endothelial growth factor, and cytokeratin 7 based on ultrasonography medical images [40]. They proposed radiomics signatures that have moderate efficiency in predicting the biological behaviors of cholangiocarcinoma noninvasively [40].…”

Section: Principal Findingsmentioning

confidence: 99%

“…Researchers discovered that certain texture parameters correlate significantly with microvascular invasion, perineural invasion, differentiation, Ki-67, vascular endothelial growth factor, and cytokeratin 7 based on ultrasonography medical images [40]. They proposed radiomics signatures that have moderate efficiency in predicting the biological behaviors of cholangiocarcinoma noninvasively [40]. Gu-Wei Ji et al [41] regarded a radiomics model based on arterial phase CT scans as a valuable diagnostic tool to forecast LNM of ICC.…”

Section: Principal Findingsmentioning

confidence: 99%

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A Novel Approach to Assessing Differentiation Degree and Lymph Node Metastasis of Extrahepatic Cholangiocarcinoma: Prediction Using a Radiomics-Based Particle Swarm Optimization and Support Vector Machine Model

Yao¹,

Huang²,

Yang³

et al. 2020

JMIR Med Inform

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Background Radiomics can improve the accuracy of traditional image diagnosis to evaluate extrahepatic cholangiocarcinoma (ECC); however, this is limited by variations across radiologists, subjective evaluation, and restricted data. A radiomics-based particle swarm optimization and support vector machine (PSO-SVM) model may provide a more accurate auxiliary diagnosis for assessing differentiation degree (DD) and lymph node metastasis (LNM) of ECC. Objective The objective of our study is to develop a PSO-SVM radiomics model for predicting DD and LNM of ECC. Methods For this retrospective study, the magnetic resonance imaging (MRI) data of 110 patients with ECC who were diagnosed from January 2011 to October 2019 were used to construct a radiomics prediction model. Radiomics features were extracted from T1-precontrast weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) using MaZda software (version 4.6; Institute of Electronics, Technical University of Lodz). We performed dimension reduction to obtain 30 optimal features of each sequence, respectively. A PSO-SVM radiomics model was developed to predict DD and LNM of ECC by incorporating radiomics features and apparent diffusion coefficient (ADC) values. We randomly divided the 110 cases into a training group (88/110, 80%) and a testing group (22/110, 20%). The performance of the model was evaluated by analyzing the area under the receiver operating characteristic curve (AUC). Results A radiomics model based on PSO-SVM was developed by using 110 patients with ECC. This model produced average AUCs of 0.8905 and 0.8461, respectively, for DD in the training and testing groups of patients with ECC. The average AUCs of the LNM in the training and testing groups of patients with ECC were 0.9036 and 0.8889, respectively. For the 110 patients, this model has high predictive performance. The average accuracy values of the training group and testing group for DD of ECC were 82.6% and 80.9%, respectively; the average accuracy values of the training group and testing group for LNM of ECC were 83.6% and 81.2%, respectively. Conclusions The MRI-based PSO-SVM radiomics model might be useful for auxiliary clinical diagnosis and decision-making, which has a good potential for clinical application for DD and LNM of ECC.

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Section: Principal Findingsmentioning

confidence: 99%

Section: Principal Findingsmentioning

confidence: 99%

A Novel Approach to Assessing Differentiation Degree and Lymph Node Metastasis of Extrahepatic Cholangiocarcinoma: Prediction Using a Radiomics-Based Particle Swarm Optimization and Support Vector Machine Model

Yao¹,

Huang²,

Yang³

et al. 2020

JMIR Med Inform

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“…Radiomics nomogram based on radiomics features and clinical characteristics was shown to predict early recurrence of iCCA after resection preoperatively [33]. Radiomics signatures based on ultrasound (US) medicine images have been proven to predict the biological behaviors of iCCA and had moderate e ciency [34]. In a study by Ji et al [35], a radiomics model was established for predicting LNs metastasis of iCCA.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of magnetic resonance imaging-based nomograms for patients with perihilar cholangiocarcinoma after radical resection: Preoperative prediction of survival

Zhao¹,

Zhang²,

Zhang³

et al. 2020

Preprint

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BackgroundSurvival status prediction for perihilar cholangiocarcinoma (pCCA) patients is essential for postoperative clinical decision making. This study aimed to develop and validate prediction models for overall survival (OS) evaluation in pCCA patients preoperatively.Materials and MethodsA total of 184 patients who had curative resection for pCCA between January 2010 and December 2018 were enrolled. 110 patients were randomly selected for model development while other 74 patients for model testing. Imaging-derived radiomics signatures were developed. Independent preoperative clinical predictors were involved independently or in combination with radiomics signatures to construct different preoperative models through multivariable Cox proportional hazards method. The nomograms were constructed to predict OS, and the performance of which was evaluated by the discrimination ability, time-dependent receiver operating characteristic curve (ROC), calibration curve and decision curve.ResultsThe clinical model (Modelclinic) was constructed based on three independent variables including preoperative CEA, cN stage and invasion of hepatic artery in images. The model with best performance (Modelclinic&AP&PVP) was build based on three independent variables, SignatureAP and SignaturePVP. In training and testing cohorts, the concordance indexes (C-indexes) of Modelclinic were 0.846 (95% CI, 0.735-0.957) and 0.755 (95% CI, 0.540-969), and Modelclinic&AP&PVP manifested favorable performance with C-indexes of 0.962 (95% CI, 0.905-1) and 0.814 (95% CI, 0.569-1), and both of them outperformed TNM staging system (C-indexes, 0.616, 95% CI, 0.522-0.711 and 0.599, 95% CI, 0.490-0.708). Good agreement was observed in the calibration curves, and favorable clinical utility was validated using the decision curve analysis both for Modelclinic and Modelclinic&AP&PVP.ConclusionsTwo preoperative nomograms were constructed to predict 1-, 3- and 5-years survival following surgery for individual pCCA patients. Such methods are easy to be performed which have clinical application potential for decision-making and patients stratification in randomized, controlled trials.

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“…This technology allows machine learning (ML) 46 and radiomics to apply to ultrasound tomography images even in the presence of bone. Machine learning has historically been applied to other ultrasound modalities [47][48][49][50] and other images, but the presence of bone has lead to artifacts that make ML problematic. Our lack of such artifacts makes application of ML more fruitful.…”

Section: Full Wave 3d Inverse Scattering Transmission Ultrasound Tomomentioning

confidence: 99%

Full wave 3D inverse scattering transmission ultrasound tomography in the presence of high contrast

Wiskin

Malik

Borup

et al. 2020

Sci Rep

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We present here a quantitative ultrasound tomographic method yielding a sub-mm resolution, quantitative 3D representation of tissue characteristics in the presence of high contrast media. This result is a generalization of previous work where high impedance contrast was not present and may provide a clinically and laboratory relevant, relatively inexpensive, high resolution imaging method for imaging in the presence of bone. This allows tumor, muscle, tendon, ligament or cartilage disease monitoring for therapy and general laboratory or clinical settings. The method has proven useful in breast imaging and is generalized here to high-resolution quantitative imaging in the presence of bone. The laboratory data are acquired in ~ 12 min and the reconstruction in ~ 24 min—approximately 200 times faster than previously reported simulations in the literature. Such fast reconstructions with real data require careful calibration, adequate data redundancy from a 2D array of 2048 elements and a paraxial approximation. The imaging results show that tissue surrounding the high impedance region is artifact free and has correct speed of sound at sub-mm resolution.

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Preoperative Ultrasound Radiomics Signatures for Noninvasive Evaluation of Biological Characteristics of Intrahepatic Cholangiocarcinoma

Cited by 48 publications

References 37 publications

A Novel Approach to Assessing Differentiation Degree and Lymph Node Metastasis of Extrahepatic Cholangiocarcinoma: Prediction Using a Radiomics-Based Particle Swarm Optimization and Support Vector Machine Model

A Novel Approach to Assessing Differentiation Degree and Lymph Node Metastasis of Extrahepatic Cholangiocarcinoma: Prediction Using a Radiomics-Based Particle Swarm Optimization and Support Vector Machine Model

Development and validation of magnetic resonance imaging-based nomograms for patients with perihilar cholangiocarcinoma after radical resection: Preoperative prediction of survival

Full wave 3D inverse scattering transmission ultrasound tomography in the presence of high contrast

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