Radiomic Features of Multi-ROI and Multi-Phase MRI for the Prediction of Microvascular Invasion in Solitary Hepatocellular Carcinoma

ObjectivesTo develop and validate an MR radiomics-based nomogram to predict the presence of MVI in patients with solitary HCC and further evaluate the performance of predictors for MVI in subgroups (HCC ≤ 3 cm and > 3 cm).Materials and MethodsBetween May 2015 and October 2020, 201 patients with solitary HCC were analysed. Radiomic features were extracted from precontrast T1WI, arterial phase, portal venous phase, delayed phase and hepatobiliary phase images in regions of the intratumoral, peritumoral and their combining areas. The mRMR and LASSO algorithms were used to select radiomic features related to MVI. Clinicoradiological factors were selected by using backward stepwise regression with AIC. A nomogram was developed by incorporating the clinicoradiological factors and radiomics signature. In addition, the radiomic features and clinicoradiological factors related to MVI were separately evaluated in the subgroups (HCC ≤ 3 cm and > 3 cm).ResultsHistopathological examinations confirmed MVI in 111 of the 201 patients (55.22%). The radiomics signature showed a favourable discriminatory ability for MVI in the training set (AUC, 0.896) and validation set (AUC, 0.788). The nomogram incorporating peritumoral enhancement, tumour growth type and radiomics signature showed good discrimination in the training (AUC, 0.932) and validation sets (AUC, 0.917) and achieved well-fitted calibration curves. Subgroup analysis showed that tumour growth type was a predictor for MVI in the HCC ≤ 3 cm cohort and peritumoral enhancement in the HCC > 3 cm cohort; radiomic features related to MVI varied between the HCC ≤ 3 cm and HCC > 3 cm cohort. The performance of the radiomics signature improved noticeably in both the HCC ≤ 3 cm (AUC, 0.953) and HCC > 3 cm cohorts (AUC, 0.993) compared to the original training set.ConclusionsThe preoperative nomogram integrating clinicoradiological risk factors and the MR radiomics signature showed favourable predictive efficiency for predicting MVI in patients with solitary HCC. The clinicoradiological factors and radiomic features related to MVI varied between subgroups (HCC ≤ 3 cm and > 3 cm). The performance of radiomics signature for MVI prediction was improved in both the subgroups.

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Case 2.23

Mayo Clinic Body MRI Case Review ◽

10.1093/med/9780199915705.003.0054 ◽

2014 ◽

pp. 107-107

Author(s):

Christine U. Lee ◽

James F. Glockner

Keyword(s):

Liver Disease ◽

Signal Intensity ◽

Phase Image ◽

Hepatobiliary Phase ◽

Portal Venous Phase ◽

Hepatic Lobe ◽

Phase Images ◽

Venous Phase ◽

The Right ◽

Multiple Nodules

55-year-old woman with chronic liver disease Axial fat-suppressed FSE T2-weighted images (Figure 2.23.1) demonstrate a cirrhotic liver with diffuse, innumerable, small low-signal-intensity nodules. Axial arterial, portal venous, and hepatobiliary phase postgadolinium (Eovist) 3D SPGR images (Figure 2.23.2) demonstrate heterogeneous enhancement of the background parenchyma, particularly in the right hepatic lobe. The multiple nodules are initially hypointense on arterial and portal venous phase images but become hyperintense relative to adjacent liver on the hepatobiliary phase image....

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Hepatobiliary phase images of gadoxetic acid-enhanced MRI may improve accuracy of predicting the size of hepatocellular carcinoma at pathology

Acta Radiologica ◽

10.1177/02841851211014194 ◽

2021 ◽

pp. 028418512110141

Author(s):

San-Yuan Dong ◽

Yu-Tao Yang ◽

Wen-Tao Wang ◽

Shuo Zhu ◽

Wei Sun ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Correlation Coefficient ◽

Tumor Size ◽

Pearson Correlation ◽

Gadoxetic Acid ◽

Arterial Phase ◽

Hepatobiliary Phase ◽

Enhanced Mri ◽

Phase Images ◽

Mri Sequences

Background Gadoxetic acid-enhanced magnetic resonance imaging (MRI) has been widely used in clinical practice. However, scientific evidence is lacking for recommending a particular sequence for measuring tumor size. Purpose To retrospectively compare the size of hepatocellular carcinoma (HCC) measured on different gadoxetic acid-enhanced MRI sequences using pathology as a reference. Material and Methods A total of 217 patients with single HCC who underwent gadoxetic acid-enhanced MRI before surgery were included. The size of the HCC was measured by two abdominal radiologists independently on the following sequences: T1-weighted; T2-weighted; b-500 diffusion-weighted imaging (DWI); and arterial, portal venous, transitional, and hepatobiliary phases. Tumor size measured on MRI was compared with pathological size by using Pearson correlation coefficient, independent-sample t test, and Bland–Altman plot. Agreement between two readers was evaluated with intraclass correlation coefficient (ICC). Results Correlation between the MR images and pathology was high for both readers (0.899–0.955). Absolute error between MRI and pathologic assessment was lowest on hepatobiliary phase images for both readers (reader 1, 2.8±4.2 mm; reader 2, 3.2±3.4 mm) and highest on arterial phase images for reader 1 (4.9±4.4 mm) and DWI phase images for reader 2 (5.1±4.9 mm). Absolute errors were significantly different for hepatobiliary phase compared with other sequences for both readers (reader 1, P≤0.012; reader 2, P≤0.037). Inter-reader agreements for all sequence measurements were strong (0.971–0.997). Conclusion The performance of gadoxetic acid-enhanced MRI sequences varied with HCC size, and the hepatobiliary phase may be optimal among these sequences.

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Case 17.17

Mayo Clinic Body MRI Case Review ◽

10.1093/med/9780199915705.003.0435 ◽

2014 ◽

pp. 821-822

Author(s):

Christine U. Lee ◽

James F. Glockner

Keyword(s):

Arterial Phase ◽

Portal Venous Phase ◽

Phase Images ◽

Signal Void ◽

Venous Phase

58-year-old woman with cirrhosis Axial precontrast (Figure 17.17.1) and arterial phase (Figure 17.17.2) and portal venous phase (Figure 17.17.3) postgadolinium water and fat images from a 3D SPGR Dixon acquisition. Notice that the phase and frequency directions have been swapped on the arterial phase acquisition and that there is a large geographic signal void in the middle of the liver on the water image, with the missing anatomy appearing on the corresponding fat image. All artifacts have been corrected on the portal venous phase images....

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Comparison of gadoxetic acid and gadopentetate dimeglumine-enhanced MRI for HCC detection: prospective crossover study at 3 T

Acta Radiologica Open ◽

10.1177/2047981614561285 ◽

2015 ◽

Vol 4 (2) ◽

pp. 204798161456128 ◽

Cited By ~ 8

Author(s):

Cecilia Besa ◽

Suguru Kakite ◽

Nancy Cooper ◽

Marcelo Facciuto ◽

Bachir Taouli

Keyword(s):

Image Quality ◽

Contrast Agents ◽

Contrast Ratio ◽

Gadopentetate Dimeglumine ◽

Gadoxetic Acid ◽

Arterial Phase ◽

Hepatobiliary Phase ◽

Significant Difference ◽

Phase Images ◽

Lesion Conspicuity

Background Gadoxetic acid and gadopentetate dimeglumine are gadolinium-based contrast agents (GBCAs) with an established role in HCC detection and characterization. Purpose To compare gadopentetate dimeglumine and gadoxetic acid-enhanced magnetic resonance imaging (MRI) for image quality and hepatocellular carcinoma (HCC) detection/conspicuity. Material and Methods In this IRB approved cross-over pilot prospective study, 12 patients (all men; mean age, 56 years) with chronic liver disease at risk of HCC underwent two repeat MRI examinations using gadopentetate dimeglumine and gadoxetic acid (mean interval between studies, 5 days). Two independent observers analyzed images for image quality and HCC detection/conspicuity. Per-lesion sensitivity, positive predictive value, quantitative enhancement, and lesion-to-liver contrast ratio were calculated for both contrast agents. Results There was no significant difference in image quality scores between both GBCAs ( P = 0.3). A total of 20 HCCs were identified with reference standard in 12 patients (mean size 2.6 cm, range, 1.0–5.0 cm). Higher sensitivity was seen for observer 1 for gadoxetic acid-set in comparison with gadopentetate dimeglumine-set (sensitivity increased from 85.7% to 92.8%), while no difference was noted for observer 2 (sensitivity of 78.5%). Lesion conspicuity was significantly higher on hepatobiliary phase (HBP) images compared to arterial phase images with both GBCAs for both observers ( P < 0.05). Lesion-to-liver contrast ratios were significantly higher for HBP compared to all dynamic phases for both agents ( P < 0.05). Conclusion Our initial experience suggests that gadoxetic acid-set was superior to gadopentetate dimeglumine-set in terms of HCC detection for one observer, with improved lesion conspicuity and liver-to-lesion contrast on HBP images.

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A New Diagnostic Criterion with Gadoxetic Acid-Enhanced MRI May Improve the Diagnostic Performance for Hepatocellular Carcinoma

Liver Cancer ◽

10.1159/000505696 ◽

2020 ◽

Vol 9 (4) ◽

pp. 414-425

Author(s):

Yi Wei ◽

Zheng Ye ◽

Yuan Yuan ◽

Zixing Huang ◽

Xiaocheng Wei ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Sensitivity And Specificity ◽

Diagnostic Performance ◽

Diagnostic Sensitivity ◽

Arterial Phase ◽

Hepatobiliary Phase ◽

Cross Sectional ◽

Portal Venous Phase ◽

Diagnostic Sensitivity And Specificity ◽

Diagnostic Criterion

Background: To prospectively establish and validate new diagnostic criterion (DC) for liver-specific contrast agents and further compared the diagnostic sensitivity and specificity with conventional DC. Methods: Institutional Review Board approved and written informed consent were obtained for this prospective study. Two board-certified reviewers established the reference standard as hepatocellular carcinoma (HCC), non-HCC lesions by using marks on all cross-sectional MR images. Another 2 abdominal radiologists independently performed the marked lesion observations using 5 different DCs, including DC-1: arterial phase hyperenhancement (APHE) and portal venous phase washout; DC-2: APHE and hepatobiliary phase (HBP) hypointensity; DC-3: APHE and diffusion-weighted imaging (DWI) hyperintensity; DC-4: HBP hypointensity and DWI hyperintensity; DC-5: HBP hypointensity, DWI hyperintensity and excluded these markedly T2 hyperintensity. Diagnostic performance of sensitivity, specificity, and accuracy for each imaging DC was calculated, per-lesion diagnostic sensitivity and specificity of imaging criteria were compared by using McNemars test. Results: A total of 215 patients were included (mean age 53.82 ± 11.24 years; range 24–82 years) with 265 hepatic nodules (175 HCCs and 90 non-HCCs). The DC-4 (93.71%; 164/175) and DC-5 (92.57%; 162/175) yielded the highest diagnostic sensitivity and was better than DC-1 (72.57%; 127/175), DC-2 (82.86%; 145/175), and DC-3 (82.29%; 144/175) (all p < 0.001). The specificity of DC-1 (94.44%; 85/90) was significantly higher than that with DC-2 (83.33%; 75/90), DC-3 (84.44%; 76/90), DC-4 (74.44%; 67/90), and DC-5 (82.22%; 74/90) (all p < 0.05). Additionally, the DC-4 and DC-5 achieved the highest area under curve value of 0.841 (95% CI 0.783–0.899) and 0.874 (95% CI 0.822–0.925). Conclusions: The combined use of HBP hypointensity and DWI hyperintensity as a new DC for HCC enables a high diagnostic sensitivity and comparable specificity.

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The utility of a dual-phase, dual-energy CT protocol in patients presenting with overt gastrointestinal bleeding

Acta Radiologica Open ◽

10.1177/20584601211030658 ◽

2021 ◽

Vol 10 (7) ◽

pp. 205846012110306

Author(s):

Payam Mohammadinejad ◽

Lukasz Kwapisz ◽

Jeff L Fidler ◽

Shannon P Sheedy ◽

Jay P Heiken ◽

...

Keyword(s):

Dual Energy ◽

Arterial Phase ◽

Gi Bleeding ◽

Two Phase ◽

Portal Venous Phase ◽

Portal Phase ◽

Ct Protocol ◽

Phase Images ◽

Venous Phase ◽

Gi Bleed

Background Due to their easy accessibility, CT scans have been increasingly used for investigation of gastrointestinal (GI) bleeding. Purpose To estimate the performance of a dual-phase, dual-energy (DE) GI bleed CT protocol in patients with overt GI bleeding in clinical practice and examine the added value of portal phase and DE images. Materials and Methods Consecutive patients with GI bleeding underwent a two-phase DE GI bleed CT protocol. Two gastroenterologists established the reference standard. Performance was estimated using clinical CT reports. Three GI radiologists rated confidence in GI bleeding in a subset of 62 examinations, evaluating first mixed kV arterial images, then after examining additional portal venous phase images, and finally after additional DE images (virtual non-contrast and virtual monoenergetic 50 keV images). Results 52 of 176 patients (29.5%) had GI bleeding by the reference standard. The overall sensitivity, specificity, and positive and negative predictive values of the CT GI bleed protocol for detecting GI bleeding were 65.4%, 89.5%, 72.3%, and 86.0%, respectively. In patients with GI bleeding, diagnostic confidence of readers increased after adding portal phase images to arterial phase images ( p = 0.002), without additional benefit from dual energy images. In patients without GI bleeding, confidence in luminal extravasation appropriately decreased after adding portal phase, and subsequently DE images ( p = 0.006, p = 0.018). Conclusion A two-phase DE GI bleed CT protocol had high specificity and negative predictive value in clinical practice. Portal venous phase images improved diagnostic confidence in comparison to arterial phase images alone. Dual-energy images further improved radiologist confidence in the absence of bleeding.

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MRI Features for Predicting Microvascular Invasion of Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis

Liver Cancer ◽

10.1159/000513704 ◽

2021 ◽

Vol 10 (2) ◽

pp. 94-106

Author(s):

Seung Baek Hong ◽

Sang Hyun Choi ◽

So Yeon Kim ◽

Ju Hyun Shim ◽

Seung Soo Lee ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Tumor Size ◽

Meta Analysis ◽

Diagnostic Odds Ratio ◽

Microvascular Invasion ◽

Hepatobiliary Phase ◽

Phase Imaging ◽

Arterial Enhancement ◽

Mri Features ◽

Peritumoral Enhancement

Purpose: Microvascular invasion (MVI) is an important prognostic factor in patients with hepatocellular carcinoma (HCC). However, the reported results of magnetic resonance imaging (MRI) features for predicting MVI of HCC are variable and conflicting. Therefore, this meta-analysis aimed to identify the significant MRI features for MVI of HCC and to determine their diagnostic value. Methods: Original studies reporting the diagnostic performance of MRI for predicting MVI of HCC were identified in MEDLINE and EMBASE up until January 15, 2020. Study quality was assessed using QUADAS-2. A bivariate random-effects model was used to calculate the meta-analytic pooled diagnostic odds ratio (DOR) and 95% confidence interval (CI) for each MRI feature for diagnosing MVI in HCC. The meta-analytic pooled sensitivity and specificity were calculated for the significant MRI features. Results: Among 235 screened articles, we found 36 studies including 4,274 HCCs. Of the 15 available MRI features, 7 were significantly associated with MVI: larger tumor size (>5 cm) (DOR = 5.2, 95% CI [3.0–9.0]), rim arterial enhancement (4.2, 95% CI [1.7–10.6]), arterial peritumoral enhancement (4.4, 95% CI [2.8–6.9]), peritumoral hypointensity on hepatobiliary phase imaging (HBP) (8.2, 95% CI [4.4–15.2]), nonsmooth tumor margin (3.2, 95% CI [2.2–4.4]), multifocality (7.1, 95% CI [2.6–19.5]), and hypointensity on T1-weighted imaging (T1WI) (4.9, 95% CI [2.5–9.6]). Both peritumoral hypointensity on HBP and multifocality showed very high meta-analytic pooled specificities for diagnosing MVI (91.1% [85.4–94.8%] and 93.3% [74.5–98.5%], respectively). Conclusions: Seven MRI features including larger tumor size, rim arterial enhancement, arterial peritumoral enhancement, peritumoral hypointensity on HBP, nonsmooth margin, multifocality, and hypointensity on T1WI were significant predictors for MVI of HCC. These MRI features predictive of MVI can be useful in the management of HCC.

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A radiomic nomogram based on arterial phase of CT for differential diagnosis of ovarian cancer

Abdominal Radiology ◽

10.1007/s00261-021-03120-w ◽

2021 ◽

Author(s):

Yumin Hu ◽

Qiaoyou Weng ◽

Haihong Xia ◽

Tao Chen ◽

Chunli Kong ◽

...

Keyword(s):

Artificial Intelligence ◽

Ovarian Cancer ◽

Clinical Data ◽

Arterial Phase ◽

Multivariable Logistic Regression Analysis ◽

Clinical Factors ◽

Good Discrimination ◽

Training Cohort ◽

Ovarian Cancers ◽

Radiomics Signature

Abstract Purpose To develop and validate a radiomic nomogram based on arterial phase of CT to discriminate the primary ovarian cancers (POCs) and secondary ovarian cancers (SOCs). Methods A total of 110 ovarian cancer patients in our hospital were reviewed from January 2010 to December 2018. Radiomic features based on the arterial phase of CT were extracted by Artificial Intelligence Kit software (A.K. software). The least absolute shrinkage and selection operation regression (LASSO) was employed to select features and construct the radiomics score (Rad-score) for further radiomics signature calculation. Multivariable logistic regression analysis was used to develop the predicting model. The predictive nomogram model was composed of rad-score and clinical data. Nomogram discrimination and calibration were evaluated. Results Two radiomic features were selected to build the radiomics signature. The radiomics nomogram that incorporated 2 radiomics signature and 2 clinical factors (CA125 and CEA) showed good discrimination in training cohort (AUC 0.854), yielding the sensitivity of 78.8% and specificity of 90.7%, which outperformed the prediction model based on radiomics signature or clinical data alone. A visualized differential nomogram based on the radiomic score, CEA, and CA125 level was established. The calibration curve demonstrated the clinical usefulness of the proposed nomogram. Conclusion The presented nomogram, which incorporated radiomic features of arterial phase of CT with clinical features, could be useful for differentiating the primary and secondary ovarian cancers.

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Preoperative prediction for lauren type of gastric cancer: A radiomics nomogram analysis based on CT images and clinical features

Journal of X-Ray Science and Technology ◽

10.3233/xst-210888 ◽

2021 ◽

pp. 1-12

Author(s):

Zongqiong Sun ◽

Linfang Jin ◽

Shuai Zhang ◽

Shaofeng Duan ◽

Wei Xing ◽

...

Keyword(s):

Gastric Cancer ◽

Clinical Features ◽

Calibration Curve ◽

Prediction Models ◽

Ct Images ◽

Clinical Findings ◽

Training Set ◽

Prediction Probability ◽

Radiomics Signature ◽

Testing Set

PURPOSE: To investigate feasibility of predicting Lauren type of gastric cancer based on CT radiomics nomogram before operation. MATERIALS AND METHODS: The clinical data and pre-treatment CT images of 300 gastric cancer patients with Lauren intestinal or diffuse type confirmed by postoperative pathology were retrospectively analyzed, who were randomly divided into training set and testing set with a ratio of 2:1. Clinical features were compared between the two Lauren types in the training set and testing set, respectively. Gastric tumors on CT images were manually segmented using ITK-SNAP software, and radiomic features of the segmented tumors were extracted, filtered and minimized using the least absolute shrinkage and selection operator (LASSO) regression to select optimal features and develop radiomics signature. A nomogram was constructed with radiomic features and clinical characteristics to predict Lauren type of gastric cancer. Clinical model, radiomics signature model, and the nomogram model were compared using the receiver operating characteristic (ROC) curve analysis with area under the curve (AUC). The calibration curve was used to test the agreement between prediction probability and actual clinical findings, and the decision curve was performed to assess the clinical usage of the nomogram model. RESULTS: In clinical features, Lauren type of gastric cancer relate to age and CT-N stage of patients (all p < 0.05). Radiomics signature was developed with the retained 10 radiomic features. The nomogram was constructed with the 2 clinical features and radiomics signature. Among 3 prediction models, performance of the nomogram was the best in predicting Lauren type of gastric cancer, with the respective AUC, accuracy, sensitivity and specificity of 0.864, 78.0%, 90.0%, 70.0%in the testing set. In addition, the calibration curve showed a good agreement between prediction probability and actual clinical findings (p > 0.05). CONCLUSION: The nomogram combining radiomics signature and clinical features is a useful tool with the increased value to predict Lauren type of gastric cancer.

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Radiomics-based model for predicting early recurrence of intrahepatic mass-forming cholangiocarcinoma after curative tumor resection

Scientific Reports ◽

10.1038/s41598-021-97796-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yong Zhu ◽

Yingfan Mao ◽

Jun Chen ◽

Yudong Qiu ◽

Yue Guan ◽

...

Keyword(s):

Regression Analysis ◽

Multivariate Logistic Regression Analysis ◽

Early Recurrence ◽

Prediction Performance ◽

Multivariate Logistic Regression ◽

Training Set ◽

Combined Model ◽

Test Set ◽

Pathological Model ◽

Radiomics Signature

AbstractTo investigate the ability of CT-based radiomics signature for pre-and postoperatively predicting the early recurrence of intrahepatic mass-forming cholangiocarcinoma (IMCC) and develop radiomics-based prediction models. Institutional review board approved this study. Clinicopathological characteristics, contrast-enhanced CT images, and radiomics features of 125 IMCC patients (35 with early recurrence and 90 with non-early recurrence) were retrospectively reviewed. In the training set of 92 patients, preoperative model, pathological model, and combined model were developed by multivariate logistic regression analysis to predict the early recurrence (≤ 6 months) of IMCC, and the prediction performance of different models were compared using the Delong test. The developed models were validated by assessing their prediction performance in test set of 33 patients. Multivariate logistic regression analysis identified solitary, differentiation, energy- arterial phase (AP), inertia-AP, and percentile50th-portal venous phase (PV) to construct combined model for predicting early recurrence of IMCC [the area under the curve (AUC) = 0.917; 95% CI 0.840–0.965]. While the AUC of pathological model and preoperative model were 0.741 (95% CI 0.637–0.828) and 0.844 (95% CI 0.751–0.912), respectively. The AUC of the combined model was significantly higher than that of the preoperative model (p = 0.049) or pathological model (p = 0.002) in training set. In test set, the combined model also showed higher prediction performance. CT-based radiomics signature is a powerful predictor for early recurrence of IMCC. Preoperative model (constructed with homogeneity-AP and standard deviation-AP) and combined model (constructed with solitary, differentiation, energy-AP, inertia-AP, and percentile50th-PV) can improve the accuracy for pre-and postoperatively predicting the early recurrence of IMCC.

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