Radiomics and deep learning in liver diseases

Sung, Yu Sub; Park, Bumwoo; Park, Hyo Jung; Lee, Seung Soo

doi:10.1111/jgh.15414

Cited by 11 publications

(4 citation statements)

References 49 publications

(80 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhang et al [ 57 ] constructed an integrated nomogram combining clinical features and DL signatures based on contrast CT to improve overall survival prediction in HCC patients treated with TACE plus sorafenib, with a C -index of 0.730 in the validation set. Based on the successful applications of DL in patients with HCC, we explored the VGGnet-19, which generally performs a more robust and automatic image analysis without export’s intervention [ 58 ], to extend the associations between DL and prognosis of HCC. Additionally, we employed 2D regions of interest to establish the DL signatures, which showed great performance; this finding corroborates with previous studies [ 57 , 59 , 60 ], wherein AUCs of 0.826–0.894 were achieved in the validation set.…”

Section: Discussionmentioning

confidence: 99%

Deep learning nomogram based on Gd-EOB-DTPA MRI for predicting early recurrence in hepatocellular carcinoma after hepatectomy

Meng

Zhang

et al. 2023

Eur Radiol

View full text Add to dashboard Cite

Objectives The accurate prediction of post-hepatectomy early recurrence in patients with hepatocellular carcinoma (HCC) is crucial for decision-making regarding postoperative adjuvant treatment and monitoring. We aimed to explore the feasibility of deep learning (DL) features derived from gadoxetate disodium (Gd-EOB-DTPA) MRI, qualitative features, and clinical variables for predicting early recurrence. Methods In this bicentric study, 285 patients with HCC who underwent Gd-EOB-DTPA MRI before resection were divided into training (n = 195) and validation (n = 90) sets. DL features were extracted from contrast-enhanced MRI images using VGGNet-19. Three feature selection methods and five classification methods were combined for DL signature construction. Subsequently, an mp-MR DL signature fused with multiphase DL signatures of contrast-enhanced images was constructed. Univariate and multivariate logistic regression analyses were used to identify early recurrence risk factors including mp-MR DL signature, microvascular invasion (MVI), and tumor number. A DL nomogram was built by incorporating deep features and significant clinical variables to achieve early recurrence prediction. Results MVI (p = 0.039), tumor number (p = 0.001), and mp-MR DL signature (p < 0.001) were independent risk factors for early recurrence. The DL nomogram outperformed the clinical nomogram in the training set (AUC: 0.949 vs. 0.751; p < 0.001) and validation set (AUC: 0.909 vs. 0.715; p = 0.002). Excellent DL nomogram calibration was achieved in both training and validation sets. Decision curve analysis confirmed the clinical usefulness of DL nomogram. Conclusion The proposed DL nomogram was superior to the clinical nomogram in predicting early recurrence for HCC patients after hepatectomy. Key Points • Deep learning signature based on Gd-EOB-DTPA MRI was the predominant independent predictor of early recurrence for hepatocellular carcinoma (HCC) after hepatectomy. • Deep learning nomogram based on clinical factors and Gd-EOB-DTPA MRI features is promising for predicting early recurrence of HCC. • Deep learning nomogram outperformed the conventional clinical nomogram in predicting early recurrence.

show abstract

Section: Discussionmentioning

confidence: 99%

Deep learning nomogram based on Gd-EOB-DTPA MRI for predicting early recurrence in hepatocellular carcinoma after hepatectomy

Meng

Zhang

et al. 2023

Eur Radiol

View full text Add to dashboard Cite

show abstract

“…Yu Sub Sung et al [ 29 ] proposed radiomics and deep learning in liver disease combination used in various liver imaging fields such as liver fibrosis, prognosis of malign tumors, automated detection and characterisation of liver tumors, abdominal organ segmentation and body composition analysis. This study aimed at anticipate binary classifications of liver disease by comparing among several classifiers, machine learning and deep learning methods and also to calculate confusion matrices for comparing target classes with output classes.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep learning algorithm performance evaluation in detection and classification of liver disease using CT images

Manjunath

Ghanshala

Kwadiki

2023

Multimed Tools Appl

View full text Add to dashboard Cite

To diagnose the liver diseases computed tomography images are used. Most of the time even experienced radiologists find it very tough to note the type, size, and severity of the tumor from computed tomography images due to various complexities involved around the liver. In recent years it is very much essential to develop a computer-assisted imaging technique to diagnose liver disease in turn which improves the diagnosis of a doctor. This paper explains a novel deep learning model for detecting a liver disease tumor and its classification. Tumor from computed tomography images has been classified between Metastasis and Cholangiocarcinoma. We demonstrate that our model predominantly performs very well concerning the accuracy, dice similarity coefficient, and specificity parameters compared to well-known existing algorithms, and adapts very well for different datasets. A dice similarity coefficient value of 98.59% indicates the supremacy of the model.

show abstract

“…Accurate prediction of MVI in HCC can facilitate the accurate estimation of patient prognosis, inform the appropriate selection of effective treatment methods ( 5 - 7 ), including anti-recurrence and anti-metastasis therapies (systemic therapy or immunotherapy); guide posttreatment follow-up; and predict the need for additional treatment. Radiomics aims to objectively and quantitatively characterize the structure of tumors and peritumoral tissue ( 8 , 9 ). Doing so can describe tumor heterogeneity and reflect the histopathologic grading and prognosis ( 10 , 11 ).…”

Section: Introductionmentioning

confidence: 99%

Development of a magnetic resonance imaging-derived radiomics model to predict microvascular invasion in patients with hepatocellular carcinoma

Liu¹,

Cheng²,

Liao³

et al. 2023

Quant Imaging Med Surg

View full text Add to dashboard Cite

Background Hepatocellular carcinoma (HCC) with microvascular invasion (MVI) has a poor prognosis, is prone to recurrence and metastasis, and requires more complex surgical techniques. Radiomics is expected to enhance the discriminative performance for identifying HCC, but the current radiomics models are becoming increasingly complex, tedious, and difficult to integrate into clinical practice. The purpose of this study was to investigate whether a simple prediction model using noncontrast-enhanced T2-weighted magnetic resonance imaging (MRI) could preoperatively predict MVI in HCC. Methods A total of 104 patients with pathologically confirmed HCC (training cohort, n=72; test cohort, n=32; ratio, about 7:3) who underwent liver MRI within 2 months prior to surgery were retrospectively included. A total of 851 tumor-specific radiomic features were extracted on T2-weighted imaging (T2WI) for each patient using AK software (Artificial Intelligence Kit Version; V. 3.2.0R, GE Healthcare). Univariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression were used in the training cohort for feature selection. The selected features were incorporated into a multivariate logistic regression model to predict MVI, which was validated in the test cohort. The model’s effectiveness was evaluated using the receiver operating characteristic and calibration curves in the test cohort. Results Eight radiomic features were identified to establish a prediction model. In the training cohort, the area under the curve, accuracy, specificity, sensitivity, and positive and negative predictive values of the model for predicting MVI were 0.867, 72.7%, 84.2%, 64.7%, 72.7%, and 78.6%, respectively; while in the test cohort, they were 0.820, 75%, 70.6%, 73.3%, 75%, and 68.8%, respectively. The calibration curves displayed good consistency between the prediction of MVI by the model and actual pathological results in both the training and validation cohorts. Conclusions A prediction model using radiomic features from single T2WI can predict MVI in HCC. This model has the potential to be a simple and fast method to provide objective information for decision-making during clinical treatment.

show abstract

Radiomics and deep learning in liver diseases

Cited by 11 publications

References 49 publications

Deep learning nomogram based on Gd-EOB-DTPA MRI for predicting early recurrence in hepatocellular carcinoma after hepatectomy

Deep learning nomogram based on Gd-EOB-DTPA MRI for predicting early recurrence in hepatocellular carcinoma after hepatectomy

Deep learning algorithm performance evaluation in detection and classification of liver disease using CT images

Development of a magnetic resonance imaging-derived radiomics model to predict microvascular invasion in patients with hepatocellular carcinoma

Contact Info

Product

Resources

About