Adenomatous polyposis coli-mediated control of β-catenin is essential for both chondrogenic and osteogenic differentiation of skeletal precursors

Background: During skeletogenesis, protein levels of β-catenin in the canonical Wnt signaling pathway determine lineage commitment of skeletal precursor cells to osteoblasts and chondrocytes. Adenomatous polyposis coli (Apc) is a key controller of β-catenin turnover by down-regulating intracellular levels of β-catenin. Results: To investigate whether Apc is involved in lineage commitment of skeletal precursor cells, we generated conditional knockout mice lacking functional Apc in Col2a1-expressing cells. In contrast to other models in which an oncogenic variant of β-catenin was used, our approach resulted in the accumulation of wild type β-catenin protein due to functional loss of Apc. Conditional homozygous Apc mutant mice died perinatally showing greatly impaired skeletogenesis. All endochondral bones were misshaped and lacked structural integrity. Lack of functional Apc resulted in a pleiotropic skeletal cell phenotype. The majority of the precursor cells lacking Apc failed to differentiate into chondrocytes or osteoblasts. However, skeletal precursor cells in the proximal ribs were able to escape the noxious effect of functional loss of Apc resulting in formation of highly active osteoblasts. Inactivation of Apc in chondrocytes was associated with dedifferentiation of these cells. Conclusion: Our data indicate that a tight Apc-mediated control of β-catenin levels is essential for differentiation of skeletal precursors as well as for the maintenance of a chondrocytic phenotype in a spatio-temporal regulated manner

Automated Assessment of T2-Weighted MRI to Differentiate Malignant and Benign Primary Solid Liver Lesions in Noncirrhotic Livers Using Radiomics

Rationale and Objectives: Distinguishing malignant from benign liver lesions based on magnetic resonance imaging (MRI) is an important but often challenging task, especially in noncirrhotic livers. We developed and externally validated a radiomics model to quantitatively assess T2-weighted MRI to distinguish the most common malignant and benign primary solid liver lesions in noncirrhotic livers. Materials and Methods: Data sets were retrospectively collected from three tertiary referral centers (A, B, and C) between 2002 and 2018. Patients with malignant (hepatocellular carcinoma and intrahepatic cholangiocarcinoma) and benign (hepatocellular adenoma and focal nodular hyperplasia) lesions were included. A radiomics model based on T2-weighted MRI was developed in data set A using a combination of machine learning approaches. The model was internally evaluated on data set A through cross-validation, externally validated on data sets B and C, and compared to visual scoring of two experienced abdominal radiologists on data set C. Results: The overall data set included 486 patients (A: 187, B: 98, and C: 201). The radiomics model had a mean area under the curve (AUC) of 0.78 upon internal validation on data set A and a similar AUC in external validation (B: 0.74 and C: 0.76). In data set C, the two radiologists showed moderate agreement (Cohen's κ: 0.61) and achieved AUCs of 0.86 and 0.82. Conclusion: Our T2-weighted MRI radiomics model shows potential for distinguishing malignant from benign primary solid liver lesions. External validation indicated that the model is generalizable despite substantial MRI acquisition protocol differences. Pending further optimization and generalization, this model may aid radiologists in improving the diagnostic workup of patients with liver lesions.</p

Reproducible radiomics through automated machine learning validated on twelve clinical applications

Radiomics uses quantitative medical imaging features to predict clinical outcomes. Currently, in a new clinical application, findingthe optimal radiomics method out of the wide range of available options has to be done manually through a heuristic trial-anderror process. In this study we propose a framework for automatically optimizing the construction of radiomics workflows perapplication. To this end, we formulate radiomics as a modular workflow and include a large collection of common algorithms foreach component. To optimize the workflow per application, we employ automated machine learning using a random search andensembling. We evaluate our method in twelve different clinical applications, resulting in the following area under the curves: 1)liposarcoma (0.83); 2) desmoid-type fibromatosis (0.82); 3) primary liver tumors (0.80); 4) gastrointestinal stromal tumors (0.77);5) colorectal liver metastases (0.61); 6) melanoma metastases (0.45); 7) hepatocellular carcinoma (0.75); 8) mesenteric fibrosis(0.80); 9) prostate cancer (0.72); 10) glioma (0.71); 11) Alzheimer’s disease (0.87); and 12) head and neck cancer (0.84). Weshow that our framework has a competitive performance compared human experts, outperforms a radiomics baseline, and performssimilar or superior to Bayesian optimization and more advanced ensemble approaches. Concluding, our method fully automaticallyoptimizes the construction of radiomics workflows, thereby streamlining the search for radiomics biomarkers in new applications.To facilitate reproducibility and future research, we publicly release six datasets, the software implementation of our framework,and the code to reproduce this study

Small molecule inhibitors of WNT/β-catenin signaling block IL-1β- and TNFα-induced cartilage degradation

Introduction: In this study, we tested the ability of small molecule inhibitors of WNT/beta-catenin signaling to block IL1beta/TNFalpha induced cartilage degradation. Pro-inflammatory cytokines like IL1beta and TNFalpha are potent inducers of cartilage degradation by up-regulating MMP expression and activity. Since WNT/beta-catenin signaling was found to be involved in IL1beta/TNFalpha induced upregulation of MMP activity, we hypothesized that inhibition of WNT/beta-catenin signaling might block IL1beta/TNFalpha induced cartilage degradation. We tested the effect of small molecules that block the interaction between beta-catenin and TCF/LEF transcription factors on IL1beta/TNFalpha induced cartilage degradation in mouse fetal metatarsals. Methods: We used mouse fetal metatarsals treated with IL1beta and TNFalpha as an ex vivo model for cytokine induced cartilage degradation. Metatarsals were treated with IL1beta and TNFalpha in combination with small molecules PKF115-584, PKF118-310 and CGP049090 at different concentrations and harvested for histology and gene expression analysis. Results: We found that IL1beta/TNFalpha induced cartilage degradation in mouse fetal metatarsals was blocked by inhibiting WNT/beta-catenin signaling using small molecules PKF115-584 and partially using CGP049090, dose-dependently. In addition, we found that PKF115-584 blocked IL1beta and TNFalpha induced MMP mRNA expression, but did not reverse the inhibitory effect of IL1beta on the expression of cartilage anabolic genes. Conclusion: In this study, we showed that inhibition of WNT/beta-catenin signaling by small molecules can effectively prevent IL1beta/TNFalpha induced cartilage degradation, by blocking MMP expression and activity. Furthermore, we elucidate the involvement of WNT/beta-catenin signaling in IL1beta/TNFalpha induced cartilage degradation

Anaplastic Large Cell T Cell Lymphoma in a Patient With Severe Therapy-refractory Crohn's Disease on Long-standing Immunosuppressive Medication During Ustekinumab Treatment:A Case Report and Review of the Literature

Use of ustekinumab in Crohn's disease was approved in 2016, and consequently data regarding its real-world safety are still limited. We here present a 29-year-old woman with severe therapy-refractory Crohn's disease, who developed an anaplastic large cell T cell lymphoma during treatment with ustekinumab

Generation of synthetic ground glass nodules using generative adversarial networks (GANs)

Abstract Background Data shortage is a common challenge in developing computer-aided diagnosis systems. We developed a generative adversarial network (GAN) model to generate synthetic lung lesions mimicking ground glass nodules (GGNs). Methods We used 216 computed tomography images with 340 GGNs from the Lung Image Database Consortium and Image Database Resource Initiative database. A GAN model retrieving information from the whole image and the GGN region was built. The generated samples were evaluated with visual Turing test performed by four experienced radiologists or pulmonologists. Radiomic features were compared between real and synthetic nodules. Performances were evaluated by area under the curve (AUC) at receiver operating characteristic analysis. In addition, we trained a classification model (ResNet) to investigate whether the synthetic GGNs can improve the performances algorithm and how performances changed as a function of labelled data used in training. Results Of 51 synthetic GGNs, 19 (37%) were classified as real by clinicians. Of 93 radiomic features, 58 (62.4%) showed no significant difference between synthetic and real GGNs (p ≥ 0.052). The discrimination performances of physicians (AUC 0.68) and radiomics (AUC 0.66) were similar, with no-significantly different (p = 0.23), but clinicians achieved a better accuracy (AUC 0.74) than radiomics (AUC 0.62) (p < 0.001). The classification model trained on datasets with synthetic data performed better than models without the addition of synthetic data. Conclusions GAN has promising potential for generating GGNs. Through similar AUC, clinicians achieved better ability to diagnose whether the data is synthetic than radiomics

Primary chondrocytes enhance cartilage tissue formation upon co-culture with a range of cell types

Co-culture models have been increasingly used in tissue engineering applications to understand cell–cell interactions and consequently improve regenerative medicine strategies. Aiming at further elucidating cartilage tissue formation, we co-cultured bovine primary chondrocytes (BPCs) with human expanded chondrocytes (HECs), human dermal fibroblasts (HDFs), mouse embryonic stem cells (MESCs), or mouse-3T3 feeder cells (M3T3s) in micromasses. BPCs were either co-cultured (1:5 ratio) with all cell types allowing direct cell–cell contacts or as separate micromasses in the same well with HECs. In co-culture groups with direct cell–cell contacts cartilaginous tissue was formed in all experimental groups. In situ hybridization showed that only 16–27% of the cells expressed type II collagen mRNA. Corresponding with the fact that micromasses consisted for approximately 20% only of BPCs, the amount of GAG was similar between 100% BPC micromass and the co-culture groups with HECs and HDFs. Therefore, co-culture micromasses support cartilage tissue formation predominantly originating from primary chondrocytes in direct contact with a variety of cell types. These findings potentially could be applied to optimize cell-therapy treatments for cartilage regeneration

Classification of malignant and benign liver tumors using a radiomics approach

Correct diagnosis of the liver tumor phenotype is crucial for treatment planning, especially the distinction between malignant and benign lesions. Clinical practice includes manual scoring of the tumors on Magnetic Resonance (MR) images by a radiologist. As this is challenging and subjective, it is often followed by a biopsy. In this study, we propose a radiomics approach as an objective and non-invasive alternative for distinguishing between malignant and benign phenotypes. T2-weighted (T2w) MR sequences of 119 patients from multiple centers were collected. We developed an efficient semi-automatic segmentation method, which was used by a radiologist to delineate the tumors. Within these regions, features quantifying tumor shape, intensity, texture, heterogeneity and orientation were extracted. Patient characteristics and semantic features were added for a total of 424 features. Classification was performed using Support Vector Machines (SVMs). The performance was evaluated using internal random-split cross-validation. On the training set within each iteration, feature selection and hyperparameter optimization were performed. To this end, another cross validation was performed by splitting the training sets in training and validation parts. The optimal settings were evaluated on the independent test sets. Manual scoring by a radiologist was also performed. The radiomics approach resulted in 95% confidence intervals of the AUC of [0.75, 0.92], specificity [0.76, 0.96] and sensitivity [0.52, 0.82]. These approach the performance of the radiologist, which were an AUC of 0.93, specificity 0.70 and sensitivity 0.93. Hence, radiomics has the potential to predict the liver tumor benignity in an objective and non-invasive manner.ImPhys/Quantitative Imagin

Reproducibility of CT-Based Hepatocellular Carcinoma Radiomic Features across Different Contrast Imaging Phases: A Proof of Concept on SORAMIC Trial Data.

Handcrafted radiomic features (HRFs) are quantitative imaging features extracted from regions of interest on medical images which can be correlated with clinical outcomes and biologic characteristics. While HRFs have been used to train predictive and prognostic models, their reproducibility has been reported to be affected by variations in scan acquisition and reconstruction parameters, even within the same imaging vendor. In this work, we evaluated the reproducibility of HRFs across the arterial and portal venous phases of contrast-enhanced computed tomography images depicting hepatocellular carcinomas, as well as the potential of ComBat harmonization to correct for this difference. ComBat harmonization is a method based on Bayesian estimates that was developed for gene expression arrays, and has been investigated as a potential method for harmonizing HRFs. Our results show that the majority of HRFs are not reproducible between the arterial and portal venous imaging phases, yet a number of HRFs could be used interchangeably between those phases. Furthermore, ComBat harmonization increased the number of reproducible HRFs across both phases by 1%. Our results guide the pooling of arterial and venous phases from different patients in an effort to increase cohort size, as well as joint analysis of the phases