scholarly journals Radiomics Analysis of Contrast-Enhanced CT for Hepatocellular Carcinoma Grading

2021 ◽  
Vol 11 ◽  
Author(s):  
Wen Chen ◽  
Tao Zhang ◽  
Lin Xu ◽  
Liang Zhao ◽  
Huan Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Area Under The Curve ◽  
Test Group ◽  
Training Dataset ◽  
Support Vector ◽  
Low Grade ◽  
High Grade ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Svm Model

ObjectivesTo investigate the value of contrast-enhanced computer tomography (CT)-based on radiomics in discriminating high-grade and low-grade hepatocellular carcinoma (HCC) before surgery.MethodsThe retrospective study including 161 consecutive subjects with HCC which was approved by the institutional review board, and the patients were divided into a training group (n = 112) and test group (n = 49) from January 2013 to January 2018. The least absolute shrinkage and selection operator (LASSO) was used to select the most valuable features to build a support vector machine (SVM) model. The performance of the predictive model was evaluated using the area under the curve (AUC), accuracy, sensitivity, and specificity.ResultsThe SVM model showed an acceptable ability to differentiate high-grade from low-grade HCC, with an AUC of 0.904 in the training dataset and 0.937 in the test dataset, accuracy (92.2% versus 95.7%), sensitivity(82.5% versus 88.0%), and specificity (92.7% versus 95.8%), respectively.ConclusionThe machine learning-based radiomics reflects a better evaluating performance in differentiating HCC between low-grade and high-grade, which may contribute to personalized treatment.

scholarly journals Predicting the Grade of Prostate Cancer Based on a Biparametric MRI Radiomics Signature

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Li Zhang ◽  
Xia Zhe ◽  
Min Tang ◽  
Jing Zhang ◽  
Jialiang Ren ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Logistic Regression ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Low Grade ◽  
High Grade ◽  
Random Forest Tree

Purpose. This study aimed to investigate the value of biparametric magnetic resonance imaging (bp-MRI)-based radiomics signatures for the preoperative prediction of prostate cancer (PCa) grade compared with visual assessments by radiologists based on the Prostate Imaging Reporting and Data System Version 2.1 (PI-RADS V2.1) scores of multiparametric MRI (mp-MRI). Methods. This retrospective study included 142 consecutive patients with histologically confirmed PCa who were undergoing mp-MRI before surgery. MRI images were scored and evaluated by two independent radiologists using PI-RADS V2.1. The radiomics workflow was divided into five steps: (a) image selection and segmentation, (b) feature extraction, (c) feature selection, (d) model establishment, and (e) model evaluation. Three machine learning algorithms (random forest tree (RF), logistic regression, and support vector machine (SVM)) were constructed to differentiate high-grade from low-grade PCa. Receiver operating characteristic (ROC) analysis was used to compare the machine learning-based analysis of bp-MRI radiomics models with PI-RADS V2.1. Results. In all, 8 stable radiomics features out of 804 extracted features based on T2-weighted imaging (T2WI) and ADC sequences were selected. Radiomics signatures successfully categorized high-grade and low-grade PCa cases ( P < 0.05 ) in both the training and test datasets. The radiomics model-based RF method (area under the curve, AUC: 0.982; 0.918), logistic regression (AUC: 0.886; 0.886), and SVM (AUC: 0.943; 0.913) in both the training and test cohorts had better diagnostic performance than PI-RADS V2.1 (AUC: 0.767; 0.813) when predicting PCa grade. Conclusions. The results of this clinical study indicate that machine learning-based analysis of bp-MRI radiomic models may be helpful for distinguishing high-grade and low-grade PCa that outperformed the PI-RADS V2.1 scores based on mp-MRI. The machine learning algorithm RF model was slightly better.

Potential application of dynamic contrast enhanced ultrasound in predicting microvascular invasion of hepatocellular carcinoma

2021 ◽  
pp. 1-9
Author(s):  
Yi Dong ◽  
Yijie Qiu ◽  
Daohui Yang ◽  
Lingyun Yu ◽  
Dan Zuo ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Area Under The Curve ◽  
Microvascular Invasion ◽  
Contrast Enhanced Ultrasound ◽  
Negative Group ◽  
Clinical Value ◽  
Positive Group ◽  
Quantitative Perfusion ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

OBJECTIVE: To investigate the clinical value of dynamic contrast enhanced ultrasound (D-CEUS) in predicting the microvascular invasion (MVI) of hepatocellular carcinoma (HCC). PATIENTS AND METHODS: In this retrospective study, 16 patients with surgery and histopathologically proved HCC lesions were included. Patients were classified according to the presence of MVI: MVI positive group (n = 6) and MVI negative group (n = 10). Contrast enhanced ultrasound (CEUS) examinations were performed within a week before surgery. Dynamic analysis was performed by VueBox ® software (Bracco, Italy). Three regions of interests (ROIs) were set in the center of HCC lesions, at the margin of HCC lesions and in the surrounding liver parenchyma accordingly. Time intensity curves (TICs) were generated and quantitative perfusion parameters including WiR (wash-in rate), WoR (wash-out rate), WiAUC (wash-in area under the curve), WoAUC (wash-out area under the curve) and WiPi (wash-in perfusion index) were obtained and analyzed. RESULTS: All of HCC lesions showed arterial hyperenhancement (100 %) and at the late phase as hypoenhancement (75 %) in CEUS. Among all CEUS quantitative parameters, the WiAUC and WoAUC were higher in MVI positive group than in MVI negative group in the center HCC lesions (P <  0.05), WiAUC, WoAUC and WiPI were higher in MVI positive group than in MVI negative group at the margin of HCC lesions. WiR and WoR were significant higher in MVI positive group. CONCLUSIONS: D-CEUS with quantitative perfusion analysis has potential clinical value in predicting the existence of MVI in HCC lesions.

scholarly journals BIMG-04. MAPPING HETEROGENEITY OF HIGH-GRADE GLIOMA METABOLISM USING HIGH RESOLUTION 7T MRSI

2021 ◽  
Vol 3 (Supplement_1) ◽  
pp. i1-i1
Author(s):  
Gilbert Hangel ◽  
Cornelius Cadrien ◽  
Philipp Lazen ◽  
Sukrit Sharma ◽  
Julia Furtner ◽  
...  
Keyword(s):  
High Resolution ◽  
Imaging Techniques ◽  
Basis Set ◽  
Metabolic Imaging ◽  
Tumor Segmentation ◽  
Low Grade ◽  
High Grade ◽  
Isotropic Resolution ◽  
Contrast Enhanced ◽  
Definition Of

Abstract OBJECTIVES Neurosurgical resection in gliomas depends on the precise preoperative definition of the tumor and its margins to realize a safe maximum resection that translates into a better patient outcome. New metabolic imaging techniques could improve this delineation as well as designate targets for biopsies. We validated the performance of our fast high-resolution whole-brain 3D-magnetic resonance spectroscopic imaging (MRSI) method at 7T in high-grade gliomas (HGGs) as first step to this regard. METHODS We measured 23 patients with HGGs at 7T with MRSI covering the whole cerebrum with 3.4mm isotropic resolution in 15 min. Quantification used a basis-set of 17 neurochemical components. They were evaluated for their reliability/quality and compared to neuroradiologically segmented tumor regions-of-interest (necrosis, contrast-enhanced, non-contrast-enhanced+edema, peritumoral) and histopathology (e.g., grade, IDH-status). RESULTS We found 18/23 measurements to be usable and ten neurochemicals quantified with acceptable quality. The most common denominators were increases of glutamine, glycine, and total choline as well as decreases of N-acetyl-aspartate and total creatine over most tumor regions. Other metabolites like taurine and serine showed mixed behavior. We further found that heterogeneity in the metabolic images often continued into the peritumoral region. While 2-hydroxy-glutarate could not be satisfyingly quantified, we found a tendency for a decrease of glutamate in IDH1-mutant HGGs. DISCUSSION Our findings corresponded well to clinical tumor segmentation but were more heterogeneous and often extended into the peritumoral region. Our results corresponded to previous knowledge, but with previously not feasible resolution. Apart from glycine/glutamine and their role in glioma progression, more research on the connection of glutamate and others to specific mutations is necessary. The addition of low-grade gliomas and statistical ROI analysis in a larger cohort will be the next important steps to define the benefits of our 7T MRSI approach for the definition of spatial metabolic tumor profiles.

Prediction of the number of metastatic axillary lymph nodes in breast cancer by radiomic signature based on dynamic contrast-enhanced MRI

2021 ◽  
pp. 028418512110258
Author(s):  
Lan Li ◽  
Tao Yu ◽  
Jianqing Sun ◽  
Shixi Jiang ◽  
Daihong Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lymph Nodes ◽  
Area Under The Curve ◽  
Test Group ◽  
Predictive Performance ◽  
Axillary Lymph Nodes ◽  
Axillary Lymph ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced ◽  
Delayed Phase

Background The number of metastatic axillary lymph nodes (ALNs) play a crucial role in the staging, prognosis and therapy of patients with breast cancer. Purpose To predict the number of metastatic ALNs in breast cancer via radiomics. Material and Methods We enrolled 197 patients with breast cancer who underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). A total of 3386 radiomic features were extracted from the early- and delayed-phase subtraction images. To classify the number of metastatic ALNs, logistic regression was used to develop a radiomic signature and nomogram. Results The radiomic signature were constructed to distinguish the N0 group from the N+ (metastatic ALNs ≥ 1) group, which yielded area under the curve (AUC) values of 0.82 and 0.81 in the training and test group, respectively. Based on the radiomic signature and BI-RADS category, a nomogram was further developed and showed excellent predictive performance with AUC values of 0.85 and 0.89 in the training and test groups, respectively. Another radiomic signature was constructed to distinguish the N1 (1–3 ALNs) group from the N2–3 (≥4 metastatic ALNs) group and showed encouraging performance with AUC values of 0.94 and 0.84 in training and test group, respectively. Conclusions We developed a nomogram and a radiomic signature that can be used to predict ALN metastasis and distinguish the N1 from the N2-3 group. Both nomogram and radiomic signature may be potential tools to assist clinicians in assessing ALN metastasis in patients with breast cancer.

scholarly journals Effectiveness of contrast-enhanced ultrasound for detecting the staging and grading of bladder cancer: a systematic review and meta-analysis

2020 ◽  
Author(s):  
Xinyue Ge ◽  
Zhong-Kai Lan ◽  
Jing Chen ◽  
Shang-Yong Zhu
Keyword(s):  
Bladder Cancer ◽  
Diagnostic Accuracy ◽  
Likelihood Ratio ◽  
Positive Likelihood Ratio ◽  
Cochrane Library ◽  
Forest Plot ◽  
Low Grade ◽  
High Grade ◽  
Contrast Enhanced Ultrasound ◽  
Contrast Enhanced

Aim: The study retrospectively analysed the accuracy of preoperative contrast-enhanced ultrasound (CEUS) in differenti-ating stage Ta-T1 or low-grade bladder cancer (BC) from stage T2 or high-grade bladder cancer. Material and methods: We systematically searched the literature indexed in PubMed, Embase, and the Cochrane Library for original diagnostic articles of bladder cancer. The diagnostic accuracy of CEUS was compared with cystoscopy and/or transurethral resection of bladder tumors (TURBT). The bivariate logistic regression model was used for data pooling, couple forest plot, diagnostic odds ratio (DOR) and summary receiver operating characteristic (SROC). Results: Five studies met the selection criteria; the overall number of reported bladder cancers patients were 436. The pooled-sensitivity (P-SEN), pooled-specificity (P-SPE), pooled-positive likelihood ratio (PLR+), pooled-negative likelihood ratio (PLR−), DOR, and area under the SROC curve were 94.0% (95%CI: 85%–98%), 90% (95%CI: 83%–95%), 9.5 (95%CI: 5.1–17.6), 0.06 (95%CI: 0.02–0.17), 147 (95%CI: 35–612) and 97% (95% CI: 95%–98%) respectively. Conclusion: CEUS reaches a high efficiency in discriminating Ta-T1 or low-grade bladder cancer from stage T2 or high-grade bladder cancer. It can be a promising method in patients to distinguish T staging and grading of bladder cancer because of its high sensitivity, specificity and diagnostic accuracy.

scholarly journals Delayed response to proton beam treatment of hepatocellular carcinoma

2020 ◽  
Vol 6 (1) ◽  
pp. 20180125
Author(s):  
Chee-Wai Cheng ◽  
Mitchell Machtay ◽  
Jennifer Dorth ◽  
Olga Sergeeva ◽  
Hangsheng Xia ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Ct Scan ◽  
Proton Beam ◽  
Arterial Phase ◽  
Delayed Response ◽  
Data Set ◽  
Contrast Enhanced Ct ◽  
Contrast Enhanced ◽  
Enhanced Ct ◽  
Imaging Response

Hepatocellular carcinoma (HCC) has become one of the leading causes of cancer death worldwide. There has been anecdotal report regarding the effectiveness of proton beam treatment for HCC. In this pre-clinical investigation, the woodchuck model of viral hepatitis infection-induced HCC was used for proton beam treatment experiment. The radiopaque fiducial markers that are biodegradable were injected around the tumor under ultrasound guidance to facilitate positioning in sequential treatments. An α cradle mode was used to ensure reproducibility of animal positioning on the treatment couch. A CT scan was performed first for contouring by a radiation oncologist. The CT data set with contours was then exported for dose planning. Three fractionations, each 750 CcGyE, were applied every other day with a Mevion S250 passive scattering proton therapy system. Multiphase contrast-enhanced CT scans were performed after the treatment and at later times for follow-ups. 3 weeks post-treatment, shrinking of the HCC nodule was detected and constituted to a partial response (30% reduction along the long axis). By week nine after treatment, the nodule disappeared during the arterial phase of multiphase contrast-enhanced CT scan. Pathological evaluation corroborated with this imaging response. A delayed, but complete imaging response to proton beam treatment applied to HCC was achieved with this unique and clinically relevant animal model of HCC.

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