Objectives
To investigate the predictive value of quantifiable imaging and inflammatory biomarkers in patients with hepatocellular carcinoma (HCC) for the clinical outcome after drug-eluting bead ...transarterial chemoembolization (DEB-TACE) measured as volumetric tumor response and progression-free survival (PFS).
Methods
This retrospective study included 46 patients with treatment-naïve HCC who received DEB-TACE. Laboratory work-up prior to treatment included complete and differential blood count, liver function, and alpha-fetoprotein levels. Neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) were correlated with radiomic features extracted from pretreatment contrast-enhanced magnetic resonance imaging (MRI) and with tumor response according to quantitative European Association for the Study of the Liver (qEASL) criteria and progression-free survival (PFS) after DEB-TACE. Radiomic features included single nodular tumor growth measured as sphericity, dynamic contrast uptake behavior, arterial hyperenhancement, and homogeneity of contrast uptake. Statistics included univariate and multivariate linear regression, Cox regression, and Kaplan–Meier analysis.
Results
Accounting for laboratory and clinical parameters, high baseline NLR and PLR were predictive of poorer tumor response (
p
= 0.014 and
p
= 0.004) and shorter PFS (
p
= 0.002 and
p
< 0.001). When compared to baseline imaging, high NLR and PLR correlated with non-spherical tumor growth (
p
= 0.001 and
p
< 0.001).
Conclusions
This study establishes the prognostic value of quantitative inflammatory biomarkers associated with aggressive non-spherical tumor growth and predictive of poorer tumor response and shorter PFS after DEB-TACE.
Key Points
• In treatment-naïve hepatocellular carcinoma (HCC), high baseline platelet-to-lymphocyte ratio (PLR) and neutrophil-to-lymphocyte ratio (NLR) are associated with non-nodular tumor growth measured as low tumor sphericity.
• High PLR and NLR are predictive of poorer volumetric enhancement-based tumor response and PFS after DEB-TACE in HCC.
• This set of readily available, quantitative immunologic biomarkers can easily be implemented in clinical guidelines providing a paradigm to guide and monitor the personalized application of loco-regional therapies in HCC.
Objectives
To develop and validate a proof-of-concept convolutional neural network (CNN)–based deep learning system (DLS) that classifies common hepatic lesions on multi-phasic MRI.
Methods
A custom ...CNN was engineered by iteratively optimizing the network architecture and training cases, finally consisting of three convolutional layers with associated rectified linear units, two maximum pooling layers, and two fully connected layers. Four hundred ninety-four hepatic lesions with typical imaging features from six categories were utilized, divided into training (
n
= 434) and test (
n
= 60) sets. Established augmentation techniques were used to generate 43,400 training samples. An Adam optimizer was used for training. Monte Carlo cross-validation was performed. After model engineering was finalized, classification accuracy for the final CNN was compared with two board-certified radiologists on an identical unseen test set.
Results
The DLS demonstrated a 92% accuracy, a 92% sensitivity (Sn), and a 98% specificity (Sp). Test set performance in a single run of random unseen cases showed an average 90% Sn and 98% Sp. The average Sn/Sp on these same cases for radiologists was 82.5%/96.5%. Results showed a 90% Sn for classifying hepatocellular carcinoma (HCC) compared to 60%/70% for radiologists. For HCC classification, the true positive and false positive rates were 93.5% and 1.6%, respectively, with a receiver operating characteristic area under the curve of 0.992. Computation time per lesion was 5.6 ms.
Conclusion
This preliminary deep learning study demonstrated feasibility for classifying lesions with typical imaging features from six common hepatic lesion types, motivating future studies with larger multi-institutional datasets and more complex imaging appearances.
Key Points
• Deep learning demonstrates high performance in the classification of liver lesions on volumetric multi-phasic MRI, showing potential as an eventual decision-support tool for radiologists.
• Demonstrating a classification runtime of a few milliseconds per lesion, a deep learning system could be incorporated into the clinical workflow in a time-efficient manner.
Purpose
Liver Imaging Reporting and Data System (LI-RADS) uses multiphasic contrast-enhanced imaging for hepatocellular carcinoma (HCC) diagnosis. The goal of this feasibility study was to establish ...a proof-of-principle concept towards automating the application of LI-RADS, using a deep learning algorithm trained to segment the liver and delineate HCCs on MRI automatically.
Methods
In this retrospective single-center study, multiphasic contrast-enhanced MRIs using T1-weighted breath-hold sequences acquired from 2010 to 2018 were used to train a deep convolutional neural network (DCNN) with a U-Net architecture. The U-Net was trained (using 70% of all data), validated (15%) and tested (15%) on 174 patients with 231 lesions. Manual 3D segmentations of the liver and HCC were ground truth. The dice similarity coefficient (DSC) was measured between manual and DCNN methods. Postprocessing using a random forest (RF) classifier employing radiomic features and thresholding (TR) of the mean neural activation was used to reduce the average false positive rate (AFPR).
Results
73 and 75% of HCCs were detected on validation and test sets, respectively, using > 0.2 DSC criterion between individual lesions and their corresponding segmentations. Validation set AFPRs were 2.81, 0.77, 0.85 for U-Net, U-Net + RF, and U-Net + TR, respectively. Combining both RF and TR with the U-Net improved the AFPR to 0.62 and 0.75 for the validation and test sets, respectively. Mean DSC between automatically detected lesions using the DCNN + RF + TR and corresponding manual segmentations was 0.64/0.68 (validation/test), and 0.91/0.91 for liver segmentations.
Conclusion
Our DCNN approach can segment the liver and HCCs automatically. This could enable a more workflow efficient and clinically realistic implementation of LI-RADS.
Objectives
To train a deep learning model to differentiate between pathologically proven hepatocellular carcinoma (HCC) and non-HCC lesions including lesions with atypical imaging features on MRI.
...Methods
This IRB-approved retrospective study included 118 patients with 150 lesions (93 (62%) HCC and 57 (38%) non-HCC) pathologically confirmed through biopsies (
n
= 72), resections (
n
= 29), liver transplants (
n
= 46), and autopsies (
n
= 3). Forty-seven percent of HCC lesions showed atypical imaging features (not meeting Liver Imaging Reporting and Data System LI-RADS criteria for definitive HCC/LR5). A 3D convolutional neural network (CNN) was trained on 140 lesions and tested for its ability to classify the 10 remaining lesions (5 HCC/5 non-HCC). Performance of the model was averaged over 150 runs with random sub-sampling to provide class-balanced test sets. A lesion grading system was developed to demonstrate the similarity between atypical HCC and non-HCC lesions prone to misclassification by the CNN.
Results
The CNN demonstrated an overall accuracy of 87.3%. Sensitivities/specificities for HCC and non-HCC lesions were 92.7%/82.0% and 82.0%/92.7%, respectively. The area under the receiver operating curve was 0.912. CNN’s performance was correlated with the lesion grading system, becoming less accurate the more atypical imaging features the lesions showed.
Conclusion
This study provides proof-of-concept for CNN-based classification of both typical- and atypical-appearing HCC lesions on multi-phasic MRI, utilizing pathologically confirmed lesions as “ground truth.”
Key Points
• A CNN trained on atypical appearing pathologically proven HCC lesions not meeting LI-RADS criteria for definitive HCC (LR5) can correctly differentiate HCC lesions from other liver malignancies, potentially expanding the role of image-based diagnosis in primary liver cancer with atypical features.
• The trained CNN demonstrated an overall accuracy of 87.3% and a computational time of < 3 ms which paves the way for clinical application as a decision support instrument.
Objectives
To develop a proof-of-concept “interpretable” deep learning prototype that justifies aspects of its predictions from a pre-trained hepatic lesion classifier.
Methods
A convolutional neural ...network (CNN) was engineered and trained to classify six hepatic tumor entities using 494 lesions on multi-phasic MRI, described in Part 1. A subset of each lesion class was labeled with up to four key imaging features per lesion. A post hoc algorithm inferred the presence of these features in a test set of 60 lesions by analyzing activation patterns of the pre-trained CNN model. Feature maps were generated that highlight regions in the original image that correspond to particular features. Additionally, relevance scores were assigned to each identified feature, denoting the relative contribution of a feature to the predicted lesion classification.
Results
The interpretable deep learning system achieved 76.5% positive predictive value and 82.9% sensitivity in identifying the correct radiological features present in each test lesion. The model misclassified 12% of lesions. Incorrect features were found more often in misclassified lesions than correctly identified lesions (60.4% vs. 85.6%). Feature maps were consistent with original image voxels contributing to each imaging feature. Feature relevance scores tended to reflect the most prominent imaging criteria for each class.
Conclusions
This interpretable deep learning system demonstrates proof of principle for illuminating portions of a pre-trained deep neural network’s decision-making, by analyzing inner layers and automatically describing features contributing to predictions.
Key Points
• An interpretable deep learning system prototype can explain aspects of its decision-making by identifying relevant imaging features and showing where these features are found on an image, facilitating clinical translation.
• By providing feedback on the importance of various radiological features in performing differential diagnosis, interpretable deep learning systems have the potential to interface with standardized reporting systems such as LI-RADS, validating ancillary features and improving clinical practicality.
• An interpretable deep learning system could potentially add quantitative data to radiologic reports and serve radiologists with evidence-based decision support.
This study was designed to assess 3D vs. 1D and 2D quantitative tumor analysis for prediction of overall survival (OS) in patients with Intrahepatic Cholangiocarcinoma (ICC) who underwent ...conventional transarterial chemoembolization (cTACE). 73 ICC patients who underwent cTACE were included in this retrospective analysis between Oct 2001 and Feb 2015. The overall and enhancing tumor diameters and the maximum cross-sectional and enhancing tumor areas were measured on baseline images. 3D quantitative tumor analysis was used to assess total tumor volume (TTV), enhancing tumor volume (ETV), and enhancing tumor burden (ETB) (ratio between ETV and liver volume). Patients were divided into low (LTB) and high tumor burden (HTB) groups. There was a significant separation between survival curves of the LTB and HTB groups using enhancing tumor diameter (p = 0.003), enhancing tumor area (p = 0.03), TTV (p = 0.03), and ETV (p = 0.01). Multivariate analysis showed a hazard ratio of 0.46 (95%CI: 0.27-0.78, p = 0.004) for enhancing tumor diameter, 0.56 (95% CI 0.33-0.96, p = 0.04) for enhancing tumor area, 0.58 (95%CI: 0.34-0.98, p = 0.04) for TTV, and 0.52 (95%CI: 0.30-0.91, p = 0.02) for ETV. TTV and ETV, as well as the largest enhancing tumor diameter and maximum enhancing tumor area, reliably predict the OS of patients with ICC after cTACE and could identify ICC patients who are most likely to benefit from cTACE.
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