Identifying enhancement-based staging markers on baseline MRI in patients with colorectal cancer liver metastases undergoing intra-arterial tumor therapy.

OBJECTIVES: To determine if three-dimensional whole liver and baseline tumor enhancement features on MRI can serve as staging biomarkers and help predict survival of patients with colorectal cancer liver metastases (CRCLM) more accurately than one-dimensional and non-enhancement-based features. METHODS: This retrospective study included 88 patients with CRCLM, treated with transarterial chemoembolization or Y90 transarterial radioembolization between 2001 and 2014. Semi-automated segmentations of up to three dominant lesions were performed on pre-treatment MRI to calculate total tumor volume (TTV) and total liver volumes (TLV). Quantitative 3D analysis was performed to calculate enhancing tumor volume (ETV), enhancing tumor burden (ETB, calculated as ETV/TLV), enhancing liver volume (ELV), and enhancing liver burden (ELB, calculated as ELV/TLV). Overall and enhancing tumor diameters were also measured. A modified Kaplan-Meier method was used to determine appropriate cutoff values for each metric. The predictive value of each parameter was assessed by Kaplan-Meier survival curves and univariable and multivariable cox proportional hazard models. RESULTS: All methods except whole liver (ELB, ELV) and one-dimensional/non-enhancement-based methods were independent predictors of survival. Multivariable analysis showed a HR of 2.1 (95% CI 1.3-3.4, p = 0.004) for enhancing tumor diameter, HR 1.7 (95% CI 1.1-2.8, p = 0.04) for TTV, HR 2.3 (95% CI 1.4-3.9, p < 0.001) for ETV, and HR 2.4 (95% CI 1.4-4.0, p = 0.001) for ETB. CONCLUSIONS: Tumor enhancement of CRCLM on baseline MRI is strongly associated with patient survival after intra-arterial therapy, suggesting that enhancing tumor volume and enhancing tumor burden are better prognostic indicators than non-enhancement-based and one-dimensional-based markers. KEY POINTS: • Tumor enhancement of colorectal cancer liver metastases on MRI prior to treatment with intra-arterial therapies is strongly associated with patient survival. • Three-dimensional, enhancement-based imaging biomarkers such as enhancing tumor volume and enhancing tumor burden may serve as the basis of a novel prognostic staging system for patients with liver-dominant colorectal cancer metastases.

Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features.

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.

Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI.

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.

Irinotecan-Eluting 75-150-µm Embolics Lobar Chemoembolization in Patients with Colorectal Cancer Liver Metastases: A Prospective Single-Center Phase I Study.

PURPOSE: The primary end point of this trial was to determine the feasibility and safety of transarterial chemoembolization with the use of 75-150-µm drug-eluting embolics loaded with irinotecan (DEE-IRI) for the treatment of metastatic colorectal cancer (CRC) refractory to systemic chemotherapy. MATERIALS AND METHODS: Fourteen patients (mean age 57.9 years) with liver-dominant metastatic disease (14.3% unilobar, 85.7% bilobar), who had failed at least 1 line of chemotherapy, were enrolled and received up to 4 (mean 2.3) cycles of DEE-IRI lobar transarterial chemoembolization. Technical complications and adverse events were recorded, and response was assessed by means of imaging-based criteria. Levels of irinotecan and angiogenesis biomarkers in the serum were measured at multiple time points. RESULTS: Thirty-two DEE-IRI transarterial chemoembolizations were successfully performed, and the full dose (100 mg) was delivered in all cases. The only grade 3-4 toxicity was abdominal pain (29%). One patient had objective response according to the Response Evaluation Criteria in Solid Tumors and World Health Organization, and 3 patients had objective response according to the European Association for the Study of the Liver. The median overall survival was 18.14 months, and the 1-year survival was 65%. The average plasma Cmax of the active metabolite was 41.5 ± 26.1 ng/mL, with average Tmax of 1.3 ± 0.5 hours. The treatment significantly reduced levels of vascular endothelial growth factor receptor 1 (VEGFR1) at 24 hours. CONCLUSIONS: Lobar transarterial chemoembolization with the use of DEE-IRI is a technically feasible and well tolerated palliative treatment for patients with refractory liver-predominant CRC metastatic disease and has acceptable pharmacokinetics. VEGFR1 is a potential biomarker for predicting treatment efficacy and risk of adverse events.

Response assessment methods for patients with hepatic metastasis from rare tumor primaries undergoing transarterial chemoembolization.

PURPOSE: This study assessed the response to conventional transarterial chemoembolization (cTACE) in patients with liver metastases from rare tumor primaries using one-dimensional (1D) and three-dimensional (3D) quantitative response assessment methods, and investigate the relationship of lipiodol deposition in predicting response. MATERIALS AND METHODS: This retrospective bicentric study included 16 patients with hepatic metastases from rare tumors treated with cTACE between 2002 and 2017. Multi-phasic MR imaging obtained before and after cTACE was used for assessment of response. Response evaluation criteria in solid tumors (RECIST) and modified-RECIST (mRECIST) were utilized for 1D response assessment, and volumetric RECIST (vRECIST) and enhancement-based quantitative European Association for Study of the Liver EASL (qEASL) were used for 3D response assessment. The same day post-cTACE CT scan was analyzed to quantify intratumoral lipiodol deposition (%). RESULTS: The mean and standard deviation (SD) of diameter of treated lesions per targeted area was 7.5 ± 5.4 cm, and the mean and SD of number of metastases in each targeted area was 4.2 ± 4.6. cTACE was technically successful in all patients, without major complications. While RECIST and vRECIST methods did not allocate patients with partial response, mRECIST and qEASL identified patients with partial response. Intratumoral lipiodol deposition significantly predicted treatment response according qEASL (R2 = 0.470, p < 0.01), while no association was shown between lipiodol deposition within treated tumor area and RECIST or mRECIST (p > 0.212). CONCLUSION: 3D quantitative volumetric response analysis can be used for stratification of response to cTACE in patients with hepatic metastases originating from rare primary tumors. Lipiodol deposition could potentially be used as an early surrogate to predict response to cTACE.

Quantitative temporal proteomic analysis of human embryonic stem cell differentiation into oligodendrocyte progenitor cells.

Oligodendrocytes (OLs) are glial cells of the central nervous system, which produce myelin. Cultured OLs provide immense therapeutic opportunities for treating a variety of neurological conditions. One of the most promising sources for such therapies is human embryonic stem cells (ESCs) as well as providing a model to study human OL development. For these purposes, an investigation of proteome level changes is critical for understanding the process of OL differentiation. In this report, an iTRAQ-based quantitative proteomic approach was used to study multiple steps during OL differentiation including neural progenitor cells, glial progenitor cells and oligodendrocyte progenitor cells (OPCs) compared to undifferentiated ESCs. Using a 1% false discovery rate cutoff, ∼3145 proteins were quantitated and several demonstrated progressive stage-specific expression. Proteins such as transferrin, neural cell adhesion molecule 1, apolipoprotein E and wingless-related MMTV integration site 5A showed increased expression from the neural progenitor cell to the OPC stage. Several proteins that have demonstrated evidence or been suspected in OL maturation were also found upregulated in OPCs including fatty acid-binding protein 4, THBS1, bone morphogenetic protein 1, CRYAB, transferrin, tenascin C, COL3A1, TGFBI and EPB41L3. Thus, by providing the first extensive proteomic profiling of human ESC differentiation into OPCs, this study provides many novel proteins that are potentially involved in OL development.

Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning.

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.

Lipiodol as an intra-procedural imaging biomarker for liver tumor response to transarterial chemoembolization: Post-hoc analysis of a prospective clinical trial.

BACKGROUND: The use of the ethiodized oil- Lipiodol in conventional trans-arterial chemoembolization (cTACE) ensures radiopacity to visualize drug delivery in the process of providing selective drug targeting to hepatic cancers and arterial embolization. Lipiodol functions as a carrier of chemo drugs for targeted therapy, as an embolic agent, augmenting the drug effect by efflux into the portal veins as well as a predictor for the tumor response and survival. PURPOSE: To prospectively evaluate the role of 3D quantitative assessment of intra-procedural Lipiodol deposition in liver tumors on CBCT immediately after cTACE as a predictive biomarker for the outcome of cTACE. MATERIALS & METHODS: This was a post-hoc analysis of data from an IRB-approved prospective clinical trial. Thirty-two patients with hepatocellular carcinoma or liver metastases underwent contrast enhanced CBCT obtained immediately after cTACE, unenhanced MDCT at 24 h after cTACE, and follow-up imaging 30-, 90- and 180-days post-procedure. Lipiodol deposition was quantified on CBCT after cTACE and was characterized by 4 ordinal levels: ≤25%, >25-50%, >50-75%, >75%. Tumor response was assessed on follow-up MRI. Lipiodol deposition on imaging, correlation between Lipiodol deposition and tumor response criteria, and correlation between Lipiodol coverage and median overall survival (MOS) were evaluated. RESULTS: Image analysis demonstrated a high degree of agreement between the Lipiodol deposition on CBCT and the 24 h post-TACE CT, with a Bland-Altman plot of Lipiodol deposition on imaging demonstrated a bias of 2.75, with 95%-limits-of-agreement: -16.6 to 22.1%. An inverse relationship between Lipiodol deposition in responders versus non-responders for two-dimensional EASL reached statistical significance at 30 days (p = 0.02) and 90 days (p = 0.05). Comparing the Lipiodol deposition in Modified Response Evaluation Criteria in Solid Tumors (mRECIST) responders versus non-responders showed a statistically significant higher volumetric deposition in responders for European Association for the Study of the Liver (EASL)-30d, EASL-90d, and quantitative EASL-180d. The correlation between the relative Lipiodol deposition and the change in enhancing tumor volume showed a negative association post-cTACE (30-day: p < 0.001; rho = -0.63). A Kaplan-Meier analysis for patients with high vs. low Lipiodol deposition showed a MOS of 46 vs. 33 months (p = 0.05). CONCLUSION: 3D quantification of Lipiodol deposition on intra-procedural CBCT is a predictive biomarker of outcome in patients with primary or metastatic liver cancer undergoing cTACE. There are spatial and volumetric agreements between 3D quantification of Lipiodol deposition on intra-procedural CBCT and 24 h post-cTACE MDCT. The spatial and volumetric agreement between Lipiodol deposition on intra-procedural CBCT and 24 h post-cTACE MDCT could suggest that acquiring MDCT 24 h after cTACE is redundant. Importantly, the demonstrated relationship between levels of tumor coverage with Lipiodol and degree and timeline of tumor response after cTACE underline the role of Lipiodol as an intra-procedural surrogate for tumor response, with potential implications for the prediction of survival.

Efficient differentiation of human embryonic stem cells into oligodendrocyte progenitors for application in a rat contusion model of spinal cord injury.

This study utilized a contusion model of spinal cord injury (SCI) in rats using the standardized NYU-MASCIS impactor, after which oligodendrocyte progenitor cells (OPCs) derived from human embryonic stem cell (ESC) were transplanted into the spinal cord to study their survival and migration route toward the areas of injury. One critical aspect of successful cell-based SCI therapy is the time of injection following injury. OPCs were injected at two clinically relevant times when most damage occurs to the surrounding tissue, 3 and 24 hours following injury. Migration and survivability after eight days was measured postmortem. In-vitro immunofluorescence revealed that most ESC-derived OPCs expressed oligodendrocyte markers, including CNPase, GalC, Olig1, O4, and O1. Results showed that OPCs survived when injected at the center of injury and migrated away from the injection sites after one week. Histological sections revealed integration of ESC-derived OPCs into the spinal cord with contusion injury without disruption to the parenchyma. Cells survived for a minimum of eight days after injury, without tumor or cyst formation. The extent of injury and effect of early cell transplant was measured using behavioral and electrophysiological assessments which demonstrated increased neurological responses in rats transplanted with OPCs compared to controls.

Automated feature quantification of Lipiodol as imaging biomarker to predict therapeutic efficacy of conventional transarterial chemoembolization of liver cancer.

Conventional transarterial chemoembolization (cTACE) is a guideline-approved image-guided therapy option for liver cancer using the radiopaque drug-carrier and micro-embolic agent Lipiodol, which has been previously established as an imaging biomarker for tumor response. To establish automated quantitative and pattern-based image analysis techniques of Lipiodol deposition on 24 h post-cTACE CT as biomarker for treatment response. The density of Lipiodol deposits in 65 liver lesions was automatically quantified using Hounsfield Unit thresholds. Lipiodol deposition within the tumor was automatically assessed for patterns including homogeneity, sparsity, rim, and peripheral deposition. Lipiodol deposition was correlated with enhancing tumor volume (ETV) on baseline and follow-up MRI. ETV on baseline MRI strongly correlated with Lipiodol deposition on 24 h CT (p < 0.0001), with 8.22% ± 14.59 more Lipiodol in viable than necrotic tumor areas. On follow-up, tumor regions with Lipiodol showed higher rates of ETV reduction than areas without Lipiodol (p = 0.0475) and increasing densities of Lipiodol enhanced this effect. Also, homogeneous (p = 0.0006), non-sparse (p < 0.0001), rim deposition within sparse tumors (p = 0.045), and peripheral deposition (p < 0.0001) of Lipiodol showed improved response. This technical innovation study showed that an automated threshold-based volumetric feature characterization of Lipiodol deposits is feasible and enables practical use of Lipiodol as imaging biomarker for therapeutic efficacy after cTACE.

Design and Development of a Miniaturized Percutaneously Deployable Wireless Left Ventricular Assist Device: Early Prototypes and Feasibility Testing.

The current left ventricular assist devices (LVADs) are limited by a highly invasive implantation procedure in a severely unstable group of advanced heart failure patients. Additionally, the current transcutaneous power drive line acts as a nidus for infection resulting in significant morbidity and mortality. In an effort to decrease this invasiveness and eliminate drive line complications, we have conceived a wireless miniaturized percutaneous LVAD, capable of being delivered endovascularly with a tether-free operation. The system obviates the need for a transcutaneous fluid purge line required in existing temporary devices by utilizing an incorporated magnetically coupled impeller for a complete seal. The objective of this article was to demonstrate early development and proof-of-concept feasibility testing to serve as the groundwork for future formalized device development. Five early prototypes were designed and constructed to iteratively minimize the pump size and improve fluid dynamic performance. Various magnetic coupling configurations were tested. Using SolidWorks and ANSYS software for modeling and simulation, several geometric parameters were varied. HQ curves were constructed from preliminary in vitro testing to characterize the pump performance. Bench top tests showed no-slip magnetic coupling of the impeller to the driveshaft up to the current limit of the motor. The pump power requirements were tested in vitro and were within the appropriate range for powering via a wireless energy transfer system. Our results demonstrate the proof-of-concept feasibility of a novel endovascular cardiac assist device with the potential to eventually offer patients an untethered, minimally invasive support.

Intra-arterial therapies for liver cancer: assessing tumor response.

INTRODUCTION: Intra-arterial therapies (IATs) play an integral role in the management of unresectable hepatocellular carcinoma and liver metastases. The ability to accurately assess tumor response to intra-arterial therapies is crucial for clinical management. Several one- and two-dimensional manual imaging-based response assessment techniques, based both on tumor size or enhancement, have shown to be highly subjective and merely surrogate for the actual tumor as a whole. Areas covered: Given the currently existing literature, we will discuss all available tumor assessment techniques and criteria for liver cancer with a strong emphasis on 3D quantitative imaging biomarkers of tumor response in this review. Expert commentary: The growing role of information technology in medicine has brought about the advent of software-assisted, segmentation-based assessment techniques that address the outstanding issues of a subjective reader and provide for more accurate assessment techniques for the locally treated lesions. Three-dimensional quantitative tumor assessment techniques are superior to one- and two-dimensional measurements. This allows for treatment alterations and more precise targeting, potentially resulting in improved patient outcome.

Particle embolization for the treatment of life-threatening epistaxis in a left ventricular assist device patient.

While epistaxis is self-limiting in most cases, patients who are anticoagulated are at increased risk for life-threatening hemorrhage. The management of epistaxis in patients with a left ventricular assist device is further complicated by an increased risk for hemodynamic instability. Here, we detail the successful treatment of massive epistaxis in a left ventricular assist device patient by endovascular particle embolization. We hope to increase awareness in the cardiovascular community of this minimally invasive, potentially life-saving procedure.

MicroRNA expression profiling of oligodendrocyte differentiation from human embryonic stem cells.

BACKGROUND: Cells of the oligodendrocyte (OL) lineage play a vital role in the production and maintenance of myelin, a multilamellar membrane which allows for saltatory conduction along axons. These cells may provide immense therapeutic potential for lost sensory and motor function in demyelinating conditions, such as spinal cord injury, multiple sclerosis, and transverse myelitis. However, the molecular mechanisms controlling OL differentiation are largely unknown. MicroRNAs (miRNAs) are considered the "micromanagers" of gene expression with suggestive roles in cellular differentiation and maintenance. Although unique patterns of miRNA expression in various cell lineages have been characterized, this is the first report documenting their expression during oligodendrocyte maturation from human embryonic stem (hES) cells. Here, we performed a global miRNA analysis to reveal and identify characteristic patterns in the multiple stages leading to OL maturation from hES cells including those targeting factors involved in myelin production. METHODOLOGY/PRINCIPAL FINDINGS: We isolated cells from 8 stages of OL differentiation. Total RNA was subjected to miRNA profiling and validations preformed using real-time qRT-PCR. A comparison of miRNAs from our cultured OLs and OL progenitors showed significant similarities with published results from equivalent cells found in the rat and mouse central nervous system. Principal component analysis revealed four main clusters of miRNA expression corresponding to early, mid, and late progenitors, and mature OLs. These results were supported by correlation analyses between adjacent stages. Interestingly, the highest differentially-expressed miRNAs demonstrated a similar pattern of expression throughout all stages of differentiation, suggesting that they potentially regulate a common target or set of targets in this process. The predicted targets of these miRNAs include those with known or suspected roles in oligodendrocyte development and myelination including C11Orf9, CLDN11, MYTL1, MBOP, MPZL2, and DDR1. CONCLUSIONS/SIGNIFICANCE: We demonstrate miRNA profiles during distinct stages in oligodendroglial differentiation that may provide key markers of OL maturation. Our results reveal pronounced trends in miRNA expression and their potential mRNA target interactions that could provide valuable insight into the molecular mechanisms of differentiation.