Identification of a fundamental cryoinjury mechanism in MSCs and its mitigation through cell-cycle synchronization prior to freezing

Clinical development of cellular therapies, including mesenchymal stem/stromal cell (MSC) treatments, has been hindered by ineffective cryopreservation methods that result in substantial loss of post-thaw cell viability and function. Proposed solutions to generate high potency MSC for clinical testing include priming cells with potent cytokines such as interferon gamma (IFNγ) prior to cryopreservation, which has been shown to enhance post-thaw function, or briefly culturing to allow recovery from cryopreservation injury prior to administering to patients. However, both solutions have disadvantages: cryorecovery increases the complexity of manufacturing and distribution logistics, while the pleiotropic effects of IFNγ may have uncharacterized and unintended consequences on MSC function. To determine specific cellular functions impacted by cryoinjury, we first evaluated cell cycle status. It was discovered that S phase MSC are exquisitely sensitive to cryoinjury, demonstrating heightened levels of delayed apoptosis post-thaw and reduced immunomodulatory function. Blocking cell cycle progression at G0/G1 by growth factor deprivation (commonly known as serum starvation) greatly reduced post-thaw dysfunction of MSC by preventing apoptosis induced by double-stranded breaks in labile replicating DNA that form during the cryopreservation and thawing processes. Viability, clonal growth and T cell suppression function were preserved at pre-cryopreservation levels and were no different than cells prior to freezing or frozen after priming with IFNγ. Thus, we have developed a robust and effective strategy to enhance post-thaw recovery of therapeutic MSC.

Activation of the Hedgehog signaling pathway leads to fibrosis in aortic valves

BACKGROUND

Fibrosis is a pathological wound healing process characterized by excessive extracellular matrix deposition, which interferes with normal organ function and contributes to ~ 45% of human mortality. Fibrosis develops in response to chronic injury in nearly all organs, but the a cascade of events leading to fibrosis remains unclear. While hedgehog (Hh) signaling activation has been associated with fibrosis in the lung, kidney, and skin, it is unknown whether hedgehog signaling activation is the cause or the consequence of fibrosis. We hypothesize that activation of hedgehog signaling is sufficient to drive fibrosis in mouse models.

RESULTS

In this study, we provide direct evidence to show that activation of Hh signaling via expression of activated smoothened, SmoM2, is sufficient to induce fibrosis in the vasculature and aortic valves. We showed that activated SmoM2 -induced fibrosis is associated with abnormal function of aortic valves and heart. The relevance of this mouse model to human health is reflected in our findings that elevated GLI expression is detected in 6 out of 11 aortic valves from patients with fibrotic aortic valves.

CONCLUSIONS

Our data show that activating hedgehog signaling is sufficient to drive fibrosis in mice, and this mouse model is relevant to human aortic valve stenosis.

Characterization and Function of Cryopreserved Bone Marrow from Deceased Organ Donors: A Potential Viable Alternative Graft Source

Despite the readily available graft sources for allogeneic hematopoietic cell transplantation (alloHCT), a significant unmet need remains in the timely provision of suitable unrelated donor grafts. This shortage is related to the rarity of certain HLA alleles in the donor pool, nonclearance of donors owing to infectious disease or general health status, and prolonged graft procurement and processing times. An alternative hematopoietic progenitor cell (HPC) graft source obtained from the vertebral bodies (VBs) of deceased organ donors could alleviate many of the obstacles associated with using grafts from healthy living donors or umbilical cord blood (UCB). Deceased organ donor-derived bone marrow (BM) can be preemptively screened, cryogenically banked for on-demand use, and made available in adequate cell doses for HCT. We have developed a good manufacturing practice (GMP)-compliant process to recover and cryogenically bank VB-derived HPCs from deceased organ donor (OD) BM. Here we present results from an analysis of HPCs from BM obtained from 250 deceased donors to identify any substantial difference in composition or quality compared with HPCs from BM aspirated from the iliac crests of healthy living donors. BM from deceased donor VBs was processed in a central GMP facility and packaged for cryopreservation in 5% DMSO/2.5% human serum albumin. BM aspirated from living donor iliac crests was obtained and used for comparison. A portion of each specimen was analyzed before and after cryopreservation by flow cytometry and colony-forming unit potential. Bone marrow chimerism potential was assessed in irradiated immunocompromised NSG mice. Analysis of variance with Bonferroni correction for multiple comparisons was used to determine how cryopreservation affects BM cells and to evaluate indicators of successful engraftment of BM cells into irradiated murine models. The t test (with 95% confidence intervals [CIs]) was used to compare cells from deceased donors and living donors. A final dataset of complete clinical and matched laboratory data from 226 cryopreserved samples was used in linear regressions to predict outcomes of BM HPC processing. When compared before and after cryopreservation, OD-derived BM HPCs were found to be stable, with CD34+ cells maintaining high viability and function after thawing. The yield from a single donor is sufficient for transplantation of an average of 1.6 patients (range, 1.2 to 7.5). CD34+ cells from OD-derived HPCs from BM productively engrafted sublethally irradiated immunocompromised mouse BM (>44% and >67% chimerism at 8 and 16 weeks, respectively). Flow cytometry and secondary transplantation confirmed that OD HPCs from BM is composed of long-term engrafting CD34+CD38-CD45RA-CD90+CD49f+ HSCs. Linear regression identified no meaningful predictive associations between selected donor-related characteristics and OD BM HPC quality or yield. Collectively, these data demonstrate that cryopreserved BM HPCs from deceased organ donors is potent and functionally equivalent to living donor BM HPCs and is a viable on-demand graft source for clinical HCT. Prospective clinical trials will soon commence in collaboration with the Center for International Blood and Marrow Research to assess the feasibility, safety, and efficacy of Ossium HPCs from BM (ClinicalTrials.gov identifier NCT05068401).

A Multi‐scale, Multi‐region and Attention Mechanism‐based Deep Learning Framework for Prediction of Grading in Hepatocellular Carcinoma

BACKGROUND

Histopathological grading is a significant risk factor for postsurgical recurrence in hepatocellular carcinoma (HCC). Preoperative knowledge of histopathological grading could provide instructive guidance for individualized treatment decision-making in HCC management.

PURPOSE

This study aims to develop and validate a newly proposed deep learning model to predict histopathological grading in HCC with improved accuracy.

METHODS

In this dual-centre study, we retrospectively enrolled 384 HCC patients with complete clinical, pathological and radiological data. Aiming to synthesize radiological information derived from both tumour parenchyma and peritumoral microenvironment regions, a modelling strategy based on a multi-scale and multi-region dense connected convolutional neural network (MSMR-DenseCNNs) was proposed to predict histopathological grading using preoperative contrast enhanced computed tomography (CT) images. Multi-scale inputs were defined as three-scale enlargement of an original minimum bounding box in width and height by given pixels, which correspondingly contained more peritumoral analysis areas with the enlargement. Multi-region inputs were defined as three regions of interest (ROIs) including a squared ROI, a precisely delineated tumour ROI, and a peritumoral tissue ROI. The DenseCNN structure was designed to consist of a shallow feature extraction layer, dense block module, and transition and attention module. The proposed MSMR-DenseCNN was pretrained by the ImageNet dataset to capture basic graphic characteristics from the images and was retrained by the collected retrospective CT images. The predictive ability of the MSMR-DenseCNN models on triphasic images was compared with a conventional radiomics model, radiological model and clinical model.

RESULTS

MSMR-DenseCNN applied to the delayed phase (DP) achieved the highest area under the curve (AUC) of 0.867 in the validation cohort for grading prediction, outperforming those on the arterial phase (AP) and portal venous phase (PVP). Fusion of the results on triphasic images did not increase the predictive ability, which underscored the role of DP for grading prediction. Compared with a single-scale and single-region network, the DP-phase based MSMR-DenseCNN model remarkably raised sensitivity from 67.4% to 75.5% with comparable specificity of 78.6%. MSMR-DenseCNN on DP defeated conventional radiomics, radiological and clinical models, where the AUCs were correspondingly 0.765, 0.695 and 0.612 in the validation cohort.

CONCLUSIONS

The MSMR-DenseCNN modelling strategy increased the accuracy for preoperative prediction of grading in HCC, and enlightens similar radiological analysis pipelines in a variety of clinical scenarios in HCC management.

Prognostic Impact of Serum CRP Level in Head and Neck Squamous Cell Carcinoma

Objective

This study evaluated the association of pretreatment serum C-reactive protein (CRP) level with prognosis in patients with head and neck squamous cell carcinoma (HNSCC).

Methods

Within a single-center retrospective study, HNSCC patients receiving treatment between 2014 and 2016 were analyzed regarding the prognostic value of CRP serum levels. X-Tile software was used to determine the optimal cutoff value of serum CRP level. The log-rank test and Kaplan–Meier method were used to assess the effects of CRP level on prognosis in patients with HNSCC. Univariate and multivariate analyses (enter method) using a Cox proportional hazards model were utilized to identify prognostic indicators of progression-free survival (PFS) as the primary outcome and overall survival (OS) as the secondary outcome.

Results

A total of 221 patients with HNSCC were assessed for eligibility, and 208 cases were included in the analysis. The HNSCC patients in the low-group (CRP ≤11.3 mg/L) showed better survival than those in the high-group (CRP > 11.3 mg/L). The univariate and multivariate analyses showed that N1-3 stage and a high serum CRP level (>11.3 mg/L) were unfavorable prognostic factors for PFS and OS in patients with HNSCC.

Conclusion

Serum CRP level is an independent prognostic marker for patients with HNSCC. CRP level could be regarded as a novel prognostic factor for HNSCC patients.

Radioanatomical Study of the Skull Base and Septum in Chinese: Implications for Using the HBF for Endoscopic Skull Base Reconstruction

Objective

Radioanatomy provides surgeons with different choices to prevent the failure of reconstruction caused by improper flap selection and the occurrence of CSF leakage or other severe complications. To establish a radioanatomical model, this study radioanatomically investigated the use of the Hadad-Bassagasteguy nasoseptal flap (HBF) in skull base reconstruction performed via the transethmoidal, transsphenoidal, and transclival approaches to provide preoperative guidance for the selection of approaches for skull base reconstruction and preparation of the HBF.

Methods

The computed tomography images of 40 Chinese adults were selected for the radioanatomical measurement of data related to the HBF and skull base reconstruction via the transethmoidal, transsphenoidal, and transclival approaches. The results were analyzed using radioanatomy combined with SPSS-based analysis.

Results

In the 40 patients, the area of the HBF exceeded that of skull base defects reconstructed via the transethmoidal approach by 10.21 ± 1.97 cm2, and the anterior margin width, posterior margin width, upper margin length, and lower margin lengths of the HBF all exceeded the corresponding values of skull base defects requiring reconstruction by at least 8.4 mm. The area of the HBF exceeded that of reconstructed skull base defects by an average of 10.72 ± 2.04 cm2. The area of the HBF exceeded that of skull base defects reconstructed via the transclival approach by 9.01 ± 2.87 cm2. The difference between the anterior margin width of the HBF and the middle width of skull base defects reconstructed via the transclival approach did not exceed 6 mm in only one case (5.4 mm).

Conclusion

In Chinese adults, the HBF can cover skull base defects reconstructed via the transethmoidal, transsphenoidal, and transclival approaches, permitting its use in skull base reconstruction performed via all three approaches. Radioanatomy can be used for preoperative guidance to plan surgery via the transethmoidal, transsphenoidal, and transclival approaches.

LncRNA NCK1-AS1 Promotes Malignant Cellular Phenotypes of Laryngeal Squamous Cell Carcinoma via miR-137/NCK1 Axis

Increasing evidence demonstrates that many long noncoding RNAs (lncRNAs) are implicated with the development of laryngeal squamous cell carcinoma (LSCC). As shown by bioinformatics analysis, lncRNA non-catalytic region of tyrosine kinase adaptor protein 1-antisense 1 (NCK1-AS1) is upregulated in tissues of head and neck squamous cell carcinoma. The study aimed to explore the role and mechanism of NCK1-AS1 in LSCC. NCK1-AS1 expression in LSCC cells was evaluated by reverse transcription qPCR. The viability, proliferation, invasion, migration, and apoptosis of LSCC cells with indicated transfection were evaluated by CCK-8 assays, Ethynyl deoxyuridine incorporation assays, Transwell assays, wound healing assays, and TUNEL assays, respectively. Subcellular fractionation assays were performed to evaluate the cellular distribution of NCK1-AS1 and NCK1. NCK1 protein level in LSCC cells with indicated transfection was quantified by western blotting. The binding relation between miR-137 and NCK1-AS1 (or NCK1) were determined using RNA immunoprecipitation assays and luciferase reporter assays. NCK1-AS1 was highly expressed in LSCC cell lines. NCK1-AS1 depletion suppressed LSCC cell viability, proliferation, invasion, and migration while enhancing cell apoptosis. NCK1, an adjacent gene of NCK1-AS1, is also highly expressed in LSCC cells and was positively regulated by NCK1-AS1. Moreover, NCK1-AS1 interact with miR-137 to upregulate NCK1 expression. NCK1 was the downstream target of miR-137 and was negatively correlated to miR-137. In addition, overexpressed NCK1 reversed the suppressive impact of NCK1-AS1 depletion on malignant behaviors of LSCC cells. NCK1-AS1 contributes to LSCC cellular behaviors by upregulating NCK1 via interaction with miR-137.

LncRNA SNHG16 promotes proliferation and migration in laryngeal squamous cell carcinoma via the miR-140-5p/NFAT5/Wnt/β-catenin pathway axis

BACKGROUND

Recent studies demonstrate that long noncoding RNAs (lncRNAs) are involved in the development of various cancers. Many lncRNAs were reported to abnormally express in laryngeal squamous cell carcinoma (LSCC) and play pivotal roles in its development. LncRNA small nucleolar RNA host gene 16 (SNHG16) was previously validated as an oncogene in hepatocellular carcinoma. Nevertheless, the biological role of SNHG16 in LSCC still needs more explorations. The goal of this assay is to explore the function and molecular mechanism of lncRNA SNHG16 in the development of LSCC.

METHODS AND RESULTS

First, RT-qPCR demonstrated the upregulation of SNHG16 in LSCC cells and tissues. Loss-of-function assays determined the inhibitive influence of SNHG16 downregulation on cell viability, growth, and migration in LSCC. Furthermore, SNHG16 bound with miR-140-5p in LSCC. MiR-140-5p overexpression suppressed LSCC cell proliferation and migration. NFAT5 was identified as a direct target of miR-140-5p. Through rescue experiments, overexpression of NFAT5 reversed SNHG16 knockdown-mediated suppression on cell viability, growth, and migration in LSCC. Additionally, NFAT5 overexpression activated while NFAT5 downregulation inhibited the Wnt/β-catenin signaling pathway.

CONCLUSION

LncRNA SNHG16 is upregulated in LSCC and contributes to the development of LSCC via regulating the miR-140-5p/NFAT5/Wnt/β-catenin pathway axis. The SNHG16/miR-140-5p/NFAT5/Wnt/β-catenin pathway axis might provide a novel strategy for LSCC treatment.

Multi-scale patches convolutional neural network predicting the histological grade of hepatocellular carcinoma

Preoperative predicting histological grade of hepatocellular carcinoma (HCC) is a crucial issue for the evaluation of patient prognosis and determining clinical treatment strategies. Previous studies have shown the potential of preoperative medical imaging in HCC grading diagnosis, however, there still remain challenges. In this work, we proposed a multi-scale 2D dense connected convolutional neural network (MS-DenseNet) for the classification of grade. This architecture consisted of three CNN branches to extract features of CT image patches in different scale. Then the outputs for each CNN branch were concatenated to the final fully connected layer. Our network was developed and evaluated on 455 HCC patients from two different centers. For data augmentation, more than 2000 patches for each scale were cropped from transverse section 2D region of interest on these patients. Besides, three-channel inputs including original CT image, tumor region and peritumoral component provided complementary knowledge. Experimental results demonstrated that the proposed method achieved encouraging prediction performance with AUC of 0.798 in testing dataset.Clinical Relevance-The proposed MS-DenseNet yielded an encouraging prediction performance for HCC histological grade and might assist the clinical diagnosis and decision making of HCC patients.

A critical role of AREG for bleomycin-induced skin fibrosis

We report our discovery of an important player in the development of skin fibrosis, a hallmark of scleroderma. Scleroderma is a fibrotic disease, affecting 70,000 to 150,000 Americans. Fibrosis is a pathological wound healing process that produces an excessive extracellular matrix to interfere with normal organ function. Fibrosis contributes to nearly half of human mortality. Scleroderma has heterogeneous phenotypes, unpredictable outcomes, no validated biomarkers, and no effective treatment. Thus, strategies to slow down scleroderma progression represent an urgent medical need. While a pathological wound healing process like fibrosis leaves scars and weakens organ function, oral mucosa wound healing is a scarless process. After re-analyses of gene expression datasets from oral mucosa wound healing and skin fibrosis, we discovered that several pathways constitutively activated in skin fibrosis are transiently induced during oral mucosa wound healing process, particularly the amphiregulin (Areg) gene. Areg expression is upregulated ~ 10 folds 24hrs after oral mucosa wound but reduced to the basal level 3 days later. During bleomycin-induced skin fibrosis, a commonly used mouse model for skin fibrosis, Areg is up-regulated throughout the fibrogenesis and is associated with elevated cell proliferation in the dermis. To demonstrate the role of Areg for skin fibrosis, we used mice with Areg knockout, and found that Areg deficiency essentially prevents bleomycin-induced skin fibrosis. We further determined that bleomycin-induced cell proliferation in the dermis was not observed in the Areg null mice. Furthermore, we found that inhibiting MEK, a downstream signaling effector of Areg, by selumetinib also effectively blocked bleomycin-based skin fibrosis model. Based on these results, we concluded that the Areg-EGFR-MEK signaling axis is critical for skin fibrosis development. Blocking this signaling axis may be effective in treating scleroderma.

Deep learning-based radiomics predicts response to chemotherapy in colorectal liver metastases

PURPOSE

The purpose of this study was to develop and validate a deep learning (DL)-based radiomics model to predict the response to chemotherapy in colorectal liver metastases (CRLM).

METHODS

In this retrospective study, we enrolled 192 patients diagnosed with CRLM who received first-line chemotherapy and were followed by response assessment. Tumor response was identified according to the Response Evaluation Criteria in Solid Tumors (RECIST). Contrast-enhanced multidetector computed tomography (MDCT) images were fed as inputs of the ResNet10-based DL radiomics model, and the possibility of response was predicted as the output. The final combined DL radiomics model was constructed by integrating the response-related clinical factors and the developed DL radiomics signature. A time-independent validation cohort (n = 48) was extracted from the 192 patients to evaluate the DL model with area under the receiver operating characteristic curve (AUC), specificity, and sensitivity. Meanwhile, a traditional radiomics model was constructed using least absolute shrinkage and selection operator (lasso) as comparisons with the DL-based model.

RESULTS

According to RECIST criteria, 131 patients were identified as responders with complete response, partial response, and stable disease, while 61 patients were nonresponders with progression disease. The selected predictive clinical factor turned out to be the carcinoembryonic antigen (CEA) level with AUC of 0.489 (95% confidence interval [CI], 0.380-0.599) and 0.558 (95% CI, 0.374-0.741) in the training and validation cohorts, respectively. The DL-based model provided better performance than the traditional classifier-based radiomics model with significantly higher AUC (training: 0.903 [95% CI, 0.851-0.955] vs 0.745 [95% CI, 0.659-0.831]; validation: 0.820 [95% CI, 0.681-0.959] vs 0.598 [95% CI, 0.422-0.774]). The combination of DL-based model with the CEA level provided slightly increased performance with AUC of 0.935 [95% CI, 0.897-0.973] in the training cohort and 0.830 [95% CI, 0.688-0.973] in the validation cohort.

CONCLUSIONS

The developed DL-based radiomics model could improve the efficiency to predict the response to chemotherapy in CRLM, which may assist in subsequent personalized treatment decision-making in CRLM management.

Radiomics in liver diseases: Current progress and future opportunities

Liver diseases, a wide spectrum of pathologies from inflammation to neoplasm, have become an increasingly significant health problem worldwide. Noninvasive imaging plays a critical role in the clinical workflow of liver diseases, but conventional imaging assessment may provide limited information. Accurate detection, characterization and monitoring remain challenging. With progress in quantitative imaging analysis techniques, radiomics emerged as an efficient tool that shows promise to aid in personalized diagnosis and treatment decision-making. Radiomics could reflect the heterogeneity of liver lesions via extracting high-throughput and high-dimensional features from multi-modality imaging. Machine learning algorithms are then used to construct clinical target-oriented imaging biomarkers to assist disease management. Here, we review the methodological process in liver disease radiomics studies in a stepwise fashion from data acquisition and curation, region of interest segmentation, liver-specific feature extraction, to task-oriented modelling. Furthermore, the applications of radiomics in liver diseases are outlined in aspects of diagnosis and staging, evaluation of liver tumour biological behaviours, and prognosis according to different disease type. Finally, we discuss the current limitations of radiomics in liver disease studies and explore its future opportunities.

Identification and characterization of a large source of primary mesenchymal stem cells tightly adhered to bone surfaces of human vertebral body marrow cavities

Background:

Therapeutic allogeneic mesenchymal stromal cells (MSCs) are currently in clinical trials to evaluate their effectiveness in treating many different disease indications. Eventual commercialization for broad distribution will require further improvements in manufacturing processes to economically manufacture MSCs at scales sufficient to satisfy projected demands. A key contributor to the present high cost of goods sold for MSC manufacturing is the need to create master cell banks from multiple donors, which leads to variability in large-scale manufacturing runs. Therefore, the availability of large single donor depots of primary MSCs would greatly benefit the cell therapy market by reducing costs associated with manufacturing.

Methods:

We have discovered that an abundant population of cells possessing all the hallmarks of MSCs is tightly associated with the vertebral body (VB) bone matrix and only liberated by proteolytic digestion. Here we demonstrate that these vertebral bone-adherent (vBA) MSCs possess all the International Society of Cell and Gene Therapy-defined characteristics (e.g., plastic adherence, surface marker expression and trilineage differentiation) of MSCs, and we have therefore termed them vBA-MSCs to distinguish this population from loosely associated MSCs recovered through aspiration or rinsing of the bone marrow compartment.

Results:

Pilot banking and expansion were performed with vBA-MSCs obtained from 3 deceased donors, and it was demonstrated that bank sizes averaging 2.9 × 10⁸ ± 1.35 × 10⁸ vBA-MSCs at passage 1 were obtainable from only 5 g of digested VB bone fragments. Each bank of cells demonstrated robust proliferation through a total of 9 passages, without significant reduction in population doubling times. The theoretical total cell yield from the entire amount of bone fragments (approximately 300 g) from each donor with limited expansion through 4 passages is 100 trillion (1 × 10¹⁴) vBA-MSCs, equating to over 10⁵ doses at 10 × 10⁶ cells/kg for an average 70-kg recipient.

Discussion:

Thus, we have established a novel and plentiful source of MSCs that will benefit the cell therapy market by overcoming manufacturing and regulatory inefficiencies due to donor-to-donor variability.

Identification of Predominant Histopathological Growth Patterns of Colorectal Liver Metastasis by Multi-Habitat and Multi-Sequence Based Radiomics Analysis

Purpose: Developing an MRI-based radiomics model to effectively and accurately predict the predominant histopathologic growth patterns (HGPs) of colorectal liver metastases (CRLMs). Materials and Methods: In this study, 182 resected and histopathological proven CRLMs of chemotherapy-naive patients from two institutions, including 123 replacement CRLMs and 59 desmoplastic CRLMs, were retrospectively analyzed. Radiomics analysis was performed on two regions of interest (ROI), the tumor zone and the tumor-liver interface (TLI) zone. Decision tree (DT) algorithm was used for radiomics modeling on each MR sequence, and fused radiomics model was constructed by combining the radiomics signature of each sequence. The clinical and combination models were developed through multivariate logistic regression method. The performance of the developed models was assessed by receiver operating characteristic (ROC) curves with indicators of area under curve (AUC), accuracy, sensitivity, and specificity. A nomogram was constructed to evaluate the discrimination, calibration, and usefulness. Results: The fused radiomics^tumor and radiomics^TLI models showed better performance than any single sequence and clinical model. In addition, the radiomics^TLI model exhibited better performance than radiomics^tumor model (AUC of 0.912 vs. 0.879) in internal validation cohort. The combination model showed good discrimination, and the AUC of nomogram was 0.971, 0.909, and 0.905 in the training, internal validation, and external validation cohorts, respectively. Conclusion: MRI-based radiomics method has high potential in predicting the predominant HGPs of CRLM. Preoperative non-invasive identification of predominant HGPs could further explore the ability of HGPs as a potential biomarker for clinical treatment strategy, reflecting different biological pathways.

MRI-Based Radiomics Signature: A Potential Biomarker for Identifying Glypican 3-Positive Hepatocellular Carcinoma

BACKGROUND

Glypican 3 (GPC3) expression has proved to be a critical risk factor related to prognosis in hepatocellular carcinoma (HCC) patients.

PURPOSE

To investigate the performance of MRI-based radiomics signature in identifying GPC3-positive HCC.

STUDY TYPE

Retrospective.

POPULATION

An initial cohort of 293 patients with pathologically confirmed HCC was involved in this study, and patients were randomly divided into training (195) and validation (98) cohorts.

FIELD STRENGTH/SEQUENCES

Contrast-enhanced T₁ -weight MRI was performed with a 1.5T scanner.

ASSESSMENT

A total of 853 radiomic features were extracted from the volume imaging. Univariate analysis and Fisher scoring were utilized for feature reduction. Subsequently, forward stepwise feature selection and radiomics signature building were performed based on a support vector machine (SVM). Incorporating independent risk factors, a combined nomogram was developed by multivariable logistic regression modeling.

STATISTICAL TESTS

The predictive performance of the nomogram was calculated using the area under the receive operating characteristic curve (AUC). Decision curve analysis (DCA) was applied to estimate the clinical usefulness.

RESULTS

The radiomics signature consisting of 10 selected features achieved good prediction efficacy (training cohort: AUC = 0.879, validation cohort: AUC = 0.871). Additionally, the combined nomogram integrating independent clinical risk factor α-fetoprotein (AFP) and radiomics signature showed improved calibration and prominent predictive performance with AUCs of 0.926 and 0.914 in the training and validation cohorts, respectively.

DATA CONCLUSION

The proposed MR-based radiomics signature is strongly related to GPC3-positive. The combined nomogram incorporating AFP and radiomics signature may provide an effective tool for noninvasive and individualized prediction of GPC3-positive in patients with HCC. J. MAGN. RESON. IMAGING 2020;52:1679-1687.

Accurate Preoperative Distinction of Intracranial Hemangiopericytoma From Meningioma Using a Multihabitat and Multisequence-Based Radiomics Diagnostic Technique

Background: Intracranial hemangiopericytoma (IHPC) and meningioma are both meningeal neoplasms, but they have extremely different malignancy and outcomes. Because of their similar radiological characteristics, they are difficult to distinguish prior to surgery, leading to a high rate of misdiagnosis.

Methods: We enrolled 292 patients (IHPC, 155; meningiomas, 137) with complete clinic-radiological and histopathological data, from a 10-year database established at Tiantan hospital. Radiomics analysis of tumor and peritumoral edema was performed on multisequence magnetic resonance images, and a fusion radiomics signature was generated using a machine-learning strategy. By combining clinic-radiological data with the fusion radiomics signature, we developed an integrated diagnostic approach that we named the IHPC and Meningioma Diagnostic Tool (HMDT).

Results: The HMDT displayed remarkable diagnostic ability, with areas under the curve (AUCs) of 0.985 and 0.917 in the training and validation cohorts, respectively. The calibration curve showed excellent agreement between the diagnosis predicted by HMDT and the histological outcome, with p-values of 0.801 and 0.622 for the training and the validation cohorts, respectively. Cross-validation showed no statistical difference across three divisions of the cohort, with average AUCs of 0.980 and 0.941 for the training and validation cohorts, respectively. Stratification analysis showed consistent performance of the HMDT in distinguishing IHPC from highly misdiagnosed subgroups of grade I meningioma and angiomatous meningioma (AM) with AUCs of 0.913 and 0.914 in the validation cohorts for the two subgroups.

Conclusions: By integrating clinic-radiological information with radiomics signature, the proposed HMDT could assist in preoperative diagnosis to distinguish IHPC from meningioma, providing the basis for strategic decisions regarding surgery.

Development and External Validation of Radiomics Approach for Nuclear Grading in Clear Cell Renal Cell Carcinoma

BACKGROUND AND PURPOSE

Nuclear grades of clear cell renal cell carcinoma (ccRCC) are usually confirmed by invasive methods. Radiomics is a quantitative tool that uses non-invasive medical imaging for tumor diagnosis and prognosis. In this study, a radiomics approach was proposed to analyze the association between preoperative computed tomography (CT) images and nuclear grades of ccRCC.

METHODS

Our dataset included 320 ccRCC patients from two centers and was divided into a training set (n = 124), an internal test set (n = 123), and an external test set (n = 73). A radiomic feature set was extracted from unenhanced, corticomedullary phase, and nephrographic phase CT images. The maximizing independent classification information criteria function and recursive feature elimination with cross-validation were used to select effective features. Random forests were used to build a final model for predicting nuclear grades, and area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of radiomic features and models.

RESULTS

The radiomic features from the three CT phases could effectively distinguished the four nuclear grades. A combined model, merging radiomic features and clinical characteristics, obtained good predictive performances in the internal test set (AUC 0.77, 0.75, 0.79, and 0.85 for the four grades, respectively), and performance was further confirmed in the external test set, with AUCs of 0.75, 0.68, and 0.73 (no fourth-level data).

CONCLUSION

The combination of CT radiomic features and clinical characteristics could discriminate the nuclear grades in ccRCC, which may help in assisting treatment decision making.

A radiomics-based biomarker for cytokeratin 19 status of hepatocellular carcinoma with gadoxetic acid-enhanced MRI

OBJECTIVES

We aimed to develop a radiomics-based model derived from gadoxetic acid-enhanced MR images to preoperatively identify cytokeratin (CK) 19 status of hepatocellular carcinoma (HCC).

METHODS

A cohort of 227 patients with single HCC was classified into a training set (n = 159) and a time-independent validated set (n = 68). A total of 647 radiomic features were extracted from multi-sequence MR images. The least absolute shrinkage and selection operator regression and decision tree methods were utilized for feature selection and radiomics signature construction. A multivariable logistic regression model incorporating clinico-radiological features and the fusion radiomics signature was built for prediction of CK19 status by evaluating area under curve (AUC).

RESULTS

In the whole cohort, 57 patients were CK19 positive and 170 patients were CK19 negative. By combining 11 and 6 radiomic features extracted in arterial phase and hepatobiliary phase images, respectively, a fusion radiomics signature achieved AUCs of 0.951 and 0.822 in training and validation datasets. The final combined model integrated a-fetoprotein levels, arterial rim enhancement pattern, irregular tumor margin, and the fusion radiomics signature, with a sensitivity of 0.818 and specificity of 0.974 in the training cohort and that of 0.769 and 0.818 in the validated cohort. The nomogram based on the combined model showed satisfactory prediction performance in training (C-index 0.959) and validation (C-index 0.846) dataset.

CONCLUSIONS

The combined model based on a fusion radiomics signature derived from arterial and hepatobiliary phase images of gadoxetic acid-enhanced MRI can be a reliable biomarker for CK19 status of HCC.

KEY POINTS

• Arterial rim enhancement pattern and irregular tumor margin on hepatobiliary phase on gadoxetic acid-enhanced MRI can be useful for evaluating CK19 status of HCC. • A radiomics-based model performed better than the clinico-radiological model both in training and validation datasets for predicting CK19 status of HCC. • The nomogram based on the fusion radiomics signature can be easily used for CK19 stratification of HCC.

Associations between Serum Sex Hormone Concentrations and Telomere Length among U.S. Adults, 1999-2002

BACKGROUND AND OBJECTIVES

Sex hormone concentrations and telomere length are age related responses of human body, while whether there is a direct relation between sex hormone and telomere length is uncertain. Therefore, we used the data of National Health and Nutrition Examination Survey (NHANES) to quantify their direct association.

RESEARCH DESIGN AND METHODS

A total of 710 women aged 35-60 years and 539 men aged 20-85 years were included from two cycles of the NHANES (1999-2002). Telomere length relative to standard reference DNA (T/S ratio) was measured using quantitative polymerase chain reaction method. Seven hormones in serum (5 in men and 2 in women) were assayed. Logistic regressions were used to calculate the odds ratios to evaluate the telomere length-sex hormones association.

RESULTS

Men with vigorous physical activity (71.1%) and without history of cardiovascular diseases, diabetes, and lipid-lowering drugs using tended to have a longer telomere length (all P-values < 0.05); while women with longer sedentary time, smaller pregnant or live birth, and with older ages of firth/last birth were likely with longer telomere length (all P-values < 0.05). After adjusted for potential confounders, only anti-Mullerian hormone was positively and stably associated with short leukocytes telomere length in men (OR: 1.098; 95% CI: 1.034, 1.165). We did not detect any significant association of short telomere length with sex hormones in men and women. Discussion and Implications: Serum anti-Mullerian hormone in men was positively and stably associated with telomere length. More large-scaled and well-designed prospective studies are warranted to reconfirm our conclusions.

Prediction of Histopathologic Growth Patterns of Colorectal Liver Metastases with a Noninvasive Imaging Method

OBJECTIVES

To predict histopathologic growth patterns (HGPs) in colorectal liver metastases (CRLMs) with a noninvasive radiomics model.

METHODS

Patients with chemotherapy-naive CRLMs who underwent abdominal contrast-enhanced multidetector CT (MDCT) followed by partial hepatectomy between January 2007 and January 2019 from two institutions were included in this retrospective study. Hematoxylin- and eosin-stained histopathologic sections of CRLMs were reviewed, with HGPs defined according to international consensus. Lesions were divided into training and validation datasets based on patients' sources. Radiomic features were extracted from pre- and post-contrast (arterial and portal venous) phase MDCT images, with review focusing on the segmented tumor-liver interface zones of CRLMs. Minimum redundancy maximum relevance and decision tree methods were used for radiomics modeling. Multivariable logistic regression analyses and ROC curves were used to assess the predictive performance of these models in predicting HGP types.

RESULTS

A total of 126 CRLMs with histopathologic-demonstrated desmoplastic (n = 68) or replacement (n = 58) HGPs were assessed. The radiomics signature consisted of 20 features of each phase selected. The 3 phases fused radiomics signature demonstrated the best predictive performance in distinguishing between replacement and desmoplastic HGPs (AUCs of 0.926 and 0.939 in the training and external validation cohorts, respectively). The clinical-radiomics combined model showed good discrimination (C-indices of 0.941 and 0.833 in the training and external validation cohorts, respectively).

CONCLUSIONS

A radiomics model derived from MDCT images may effectively predict the HGP of CRLMs, thus providing a basis for prognostic stratification and therapeutic decision-making.

A Radiomics Nomogram for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma

BACKGROUND

Radiomics has emerged as a new approach that can help identify imaging information associated with tumor pathophysiology. We developed and validated a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

METHODS

Two hundred and eight patients with pathologically confirmed HCC (training cohort: n = 146; validation cohort: n = 62) who underwent preoperative gadoxetic acid-enhanced magnetic resonance (MR) imaging were included. Least absolute shrinkage and selection operator logistic regression was applied to select features and construct signatures derived from MR images. Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and radiomics signatures associated with MVI, which were then incorporated into the predictive nomogram. The performance of the radiomics nomogram was evaluated by its calibration, discrimination, and clinical utility.

RESULTS

Higher α-fetoprotein level (p = 0.046), nonsmooth tumor margin (p = 0.003), arterial peritumoral enhancement (p < 0.001), and the radiomics signatures of hepatobiliary phase (HBP) T1-weighted images (p < 0.001) and HBP T1 maps (p < 0.001) were independent risk factors of MVI. The predictive model that incorporated the clinicoradiological factors and the radiomic features derived from HBP images outperformed the combination of clinicoradiological factors in the training cohort (area under the curves [AUCs] 0.943 vs. 0.850; p = 0.002), though the validation did not have a statistical significance (AUCs 0.861 vs. 0.759; p = 0.111). The nomogram based on the model exhibited C-index of 0.936 (95% CI 0.895-0.976) and 0.864 (95% CI 0.761-0.967) in the training and validation cohort, fitting well in calibration curves (p > 0.05). Decision curve analysis further confirmed the clinical usefulness of the nomogram.

CONCLUSIONS

The nomogram incorporating clinicoradiological risk factors and radiomic features derived from HBP images achieved satisfactory preoperative prediction of the individualized risk of MVI in patients with HCC.

Radiomic Nomogram: Pretreatment Evaluation of Local Recurrence in Nasopharyngeal Carcinoma based on MR Imaging

Background: To develop and validate a radiomic nomogram incorporating radiomic features with clinical variables for individual local recurrence risk assessment in nasopharyngeal carcinoma (NPC) patients before initial treatment. Methods: One hundred and forty patients were randomly divided into a training cohort (n = 80) and a validation cohort (n = 60). A total of 970 radiomic features were extracted from pretreatment magnetic resonance (MR) images of NPC patients from May 2007 to December 2013. Univariate and multivariate analyses were used for selecting radiomic features associated with local recurrence, and multivariate analyses was used for building radiomic nomogram. Results: Eight contrast-enhanced T1-weighted (CET1-w) image features and seven T2-weighted (T2-w) image features were selected to build a Cox proportional hazard model in the training cohort, respectively. The radiomic nomogram, which combined radiomic features and multiple clinical variables, had a good evaluation ability (C-index: 0.74 [95% CI: 0.58, 0.85]) in the validation cohort. The radiomic nomogram successfully categorized those patients into low- and high-risk groups with significant differences in the rate of local recurrence-free survival (P <0.05). Conclusions: This study demonstrates that MR imaging-based radiomics can be used as an aid tool for the evaluation of local recurrence, in order to develop tailored treatment targeting specific characteristics of individual patients.

CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study

OBJECTIVE

To develop and validate a radiomics-based nomogram for preoperatively predicting grade 1 and grade 2/3 tumors in patients with pancreatic neuroendocrine tumors (PNETs).

METHODS

One hundred thirty-eight patients derived from two institutions with pathologically confirmed PNETs (104 in the training cohort and 34 in the validation cohort) were included in this retrospective study. A total of 853 radiomic features were extracted from arterial and portal venous phase CT images respectively. Minimum redundancy maximum relevance and random forest methods were adopted for the significant radiomic feature selection and radiomic signature construction. A fusion radiomic signature was generated by combining both the single-phase signatures. The nomogram based on a comprehensive model incorporating the clinical risk factors and the fusion radiomic signature was established, and decision curve analysis was applied for clinical use.

RESULTS

The fusion radiomic signature has significant association with histologic grade (p < 0.001). The nomogram integrating independent clinical risk factor tumor margin and fusion radiomic signature showed strong discrimination with an area under the curve (AUC) of 0.974 (95% CI 0.950-0.998) in the training cohort and 0.902 (95% CI 0.798-1.000) in the validation cohort with good calibration. Decision curve analysis verified the clinical usefulness of the predictive nomogram.

CONCLUSION

We proposed a comprehensive nomogram consisting of tumor margin and fusion radiomic signature as a powerful tool to predict grade 1 and grade 2/3 PNET preoperatively and assist the clinical decision-making for PNET patients.

KEY POINTS

• Radiomic signature has strong discriminatory ability for the histologic grade of PNETs. • Arterial and portal venous phase CT imaging are complementary for the prediction of PNET grading. • The comprehensive nomogram outperformed clinical factors in assisting therapy strategy in PNET patients.

Application of two alternative shutdown severe accident management guideline (SSAMG) entry conditions for CPR1000

Recently, with the development and application of full-scope level 2 probabilistic safety assessment (PSA) method around the world, severe accident phenomena during shutdown and low power conditions have aroused extensive attention in nuclear industry. And the shutdown severe accident management guideline (SSAMG) is claimed to be developed, and the verification of the traditional and alternative entry conditions is the first consideration in this procedure. Thus in this paper, the feasibility of the hot leg pipe temperature and the modified jakob number are analyzed based on a SBO sequence firstly. Subsequently, verification work is conducted under a SBO sequence with pressurizer manhole open and a SBO sequence along with SBLOCA. The results proved the excellent effectiveness of the two parameters to be used as alternative SSAMG entry conditions. Also, a relational figure is constructed based on the results of diverse sequences with various primary system pressure to provide visualized guidance for operators. What's more, the value of modified jakob number which indicates the SSAMG entry is thought to be in the range of 0.5–1.

Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast-enhanced CT

OBJECTIVES

To develop and validate a radiomics nomogram for preoperative prediction of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC).

METHODS

The study included 157 patients with histologically confirmed HCC with or without MVI, and 110 patients were allocated to the training dataset and 47 to the validation dataset. Baseline clinical factor (CF) data were collected from our medical records, and radiomics features were extracted from the artery phase (AP), portal venous phase (PVP) and delay phase (DP) of preoperatively acquired CT in all patients. Radiomics analysis included tumour segmentation, feature extraction, model construction and model evaluation. A final nomogram for predicting MVI of HCC was established. Nomogram performance was assessed via both calibration and discrimination statistics.

RESULTS

Five AP features, seven PVP features and nine DP features were effective for MVI prediction in HCC radiomics signatures. PVP radiomics signatures exhibited better performance than AP and DP radiomics signatures in the validation datasets, with the AUC 0.793. In the clinical model, age, maximum tumour diameter, alpha-fetoprotein and hepatitis B antigen were effective predictors. The final nomogram integrated the PVP radiomics signature and four CFs. Good calibration was achieved for the nomogram in both the training and validated datasets, with respective C-indexes of 0.827 and 0.820. Decision curve analysis suggested that the proposed nomogram was clinically useful, with a corresponding net benefit of 0.357.

CONCLUSIONS

The above-described radiomics nomogram can preoperatively predict MVI in patients with HCC and may constitute a usefully clinical tool to guide subsequent personalised treatment.

KEY POINTS

• No previously reported study has utilised radiomics nomograms to preoperatively predict the MVI of HCC using 3D contrast-enhanced CT imaging. • The combined radiomics clinical factor (CF) nomogram for predicting MVI achieved superior performance than either the radiomics signature or the CF nomogram alone. • Nomograms combing PVP radiomics and CF may be useful as an imaging marker for predicting MVI of HCC preoperatively and could guide personalised treatment.

Pretreatment prediction of immunoscore in hepatocellular cancer: a radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging

OBJECTIVES

Immunoscore evaluates the density of CD3+ and CD8+ T cells in both the tumor core and invasive margin. Pretreatment prediction of immunoscore in hepatocellular cancer (HCC) is important for precision immunotherapy. We aimed to develop a radiomics model based on gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced MRI for pretreatment prediction of immunoscore (0-2 vs. 3-4) in HCC.

MATERIALS AND METHODS

The study included 207 (training cohort: n = 150; validation cohort: n = 57) HCC patients with hepatectomy who underwent preoperative Gd-EOB-DTPA-enhanced MRI. The volumes of interest enclosing hepatic lesions including intratumoral and peritumoral regions were manually delineated in the hepatobiliary phase of MRI images, from which 1044 quantitative features were extracted and analyzed. Extremely randomized tree method was used to select radiomics features for building radiomics model. Predicting performance in immunoscore was compared among three models: (1) using only intratumoral radiomics features (intratumoral radiomics model); (2) using combined intratumoral and peritumoral radiomics features (combined radiomics model); (3) using clinical data and selected combined radiomics features (combined radiomics-based clinical model).

RESULTS

The combined radiomics model showed a better predicting performance in immunoscore than intratumoral radiomics model (AUC, 0.904 (95% CI 0.855-0.953) vs. 0.823 (95% CI 0.747-0.899)). The combined radiomics-based clinical model showed an improvement over the combined radiomics model in predicting immunoscore (AUC, 0·926 (95% CI 0·884-0·967) vs. 0·904 (95% CI 0·855-0·953)), although differences were not statistically significant. Results were confirmed in validation cohort and calibration curves showed good agreement.

CONCLUSION

The MRI-based combined radiomics nomogram is effective in predicting immunoscore in HCC and may help making treatment decisions.

KEY POINTS

• Radiomics obtained from Gd-EOB-DTPA-enhanced MRI help predicting immunoscore in hepatocellular carcinoma. • Combined intratumoral and peritumoral radiomics are superior to intratumoral radiomics only in predicting immunoscore. • We developed a combined clinical and radiomicsnomogram to predict immunoscore in hepatocellular carcinoma.

Simultaneous Inhibition of MEK and Hh Signaling Reduces Pancreatic Cancer Metastasis

Pancreatic cancer, mostly pancreatic ductal adenocarcinoma (PDAC), is one of the most lethal cancer types, with an estimated 44,330 death in 2018 in the US alone. While targeted therapies and immune checkpoint inhibitors have significantly improved treatment options for patients with lung cancer and renal cell carcinomas, little progress has been made in pancreatic cancer, with a dismal 5-year survival rate currently at ~8%. Upon diagnosis, the majority of pancreatic cancer cases (~80%) are already metastatic. Thus, identifying ways to reduce pancreatic cancer metastasis is an unmet medical need. Furthermore, pancreatic cancer is notorious resistant to chemotherapy. While Kirsten RAt Sarcoma virus oncogene (K-RAS) mutation is the major driver for pancreatic cancer, specific inhibition of RAS signaling has been very challenging, and combination therapy is thought to be promising. In this study, we report that combination of hedgehog (Hh) and Mitogen-activated Protein/Extracellular Signal-regulated Kinase Kinase (MEK) signaling inhibitors reduces pancreatic cancer metastasis in mouse models. In mouse models of pancreatic cancer metastasis using human pancreatic cancer cells, we found that Hh target gene Gli1 is up-regulated during pancreatic cancer metastasis. Specific inhibition of smoothened signaling significantly altered the gene expression profile of the tumor microenvironment but had no significant effects on cancer metastasis. By combining Hh signaling inhibitor BMS833923 with RAS downstream MEK signaling inhibitor AZD6244, we observed reduced number of metastatic nodules in several mouse models for pancreatic cancer metastasis. These two inhibitors also decreased cell proliferation significantly and reduced CD45⁺ cells (particularly Ly6G⁺CD11b⁺ cells). We demonstrated that depleting Ly6G⁺ CD11b⁺ cells is sufficient to reduce cancer cell proliferation and the number of metastatic nodules. In vitro, Ly6G⁺ CD11b⁺ cells can stimulate cancer cell proliferation, and this effect is sensitive to MEK and Hh inhibition. Our studies may help design novel therapeutic strategies to mitigate pancreatic cancer metastasis.