MesoGraph: automatic profiling of mesothelioma subtypes from histological images

Mesothelioma is classified into three histological subtypes, epithelioid, sarcomatoid, and biphasic, according to the relative proportions of epithelioid and sarcomatoid tumor cells present. Current guidelines recommend that the sarcomatoid component of each mesothelioma is quantified, as a higher percentage of sarcomatoid pattern in biphasic mesothelioma shows poorer prognosis. In this work, we develop a dual-task graph neural network (GNN) architecture with ranking loss to learn a model capable of scoring regions of tissue down to cellular resolution. This allows quantitative profiling of a tumor sample according to the aggregate sarcomatoid association score. Tissue is represented by a cell graph with both cell-level morphological and regional features. We use an external multicentric test set from Mesobank, on which we demonstrate the predictive performance of our model. We additionally validate our model predictions through an analysis of the typical morphological features of cells according to their predicted score

Preparation of large biological samples for high-resolution, hierarchical, synchrotron phase-contrast tomography with multimodal imaging compatibility

Imaging across different scales is essential for understanding healthy organ morphology and pathophysiological changes. The macro- and microscale three-dimensional morphology of large samples, including intact human organs, is possible with X-ray microtomography (using laboratory or synchrotron sources). Preparation of large samples for high-resolution imaging, however, is challenging due to limitations such as sample shrinkage, insufficient contrast, movement of the sample and bubble formation during mounting or scanning. Here, we describe the preparation, stabilization, dehydration and mounting of large soft-tissue samples for X-ray microtomography. We detail the protocol applied to whole human organs and hierarchical phase-contrast tomography at the European Synchrotron Radiation Facility, yet it is applicable to a range of biological samples, including complete organisms. The protocol enhances the contrast when using X-ray imaging, while preventing sample motion during the scan, even with different sample orientations. Bubbles trapped during mounting and those formed during scanning (in the case of synchrotron X-ray imaging) are mitigated by multiple degassing steps. The sample preparation is also compatible with magnetic resonance imaging, computed tomography and histological observation. The sample preparation and mounting require 24-36 d for a large organ such as a whole human brain or heart. The preparation time varies depending on the composition, size and fragility of the tissue. Use of the protocol enables scanning of intact organs with a diameter of 150 mm with a local voxel size of 1 μm. The protocol requires users with expertise in handling human or animal organs, laboratory operation and X-ray imaging

Malignant Mesothelioma subtyping via sampling driven multiple instance prediction on tissue image and cell morphology data

Malignant Mesothelioma is a difficult to diagnose and highly lethal cancer usually associated with asbestos exposure. It can be broadly classified into three subtypes: Epithelioid, Sarcomatoid, and a hybrid Biphasic subtype in which significant components of both of the previous subtypes are present. Early diagnosis and identification of the subtype informs treatment and can help improve patient outcome. However, the subtyping of malignant mesothelioma, and specifically the recognition of transitional features from routine histology slides has a high level of inter-observer variability. In this work, we propose an end-to-end multiple instance learning (MIL) approach for malignant mesothelioma subtyping. This uses an adaptive instance-based sampling scheme for training deep convolutional neural networks on bags of image patches that allows learning on a wider range of relevant instances compared to max or top-N based MIL approaches. We also investigate augmenting the instance representation to include aggregate cellular morphology features from cell segmentation. The proposed MIL approach enables identification of malignant mesothelial subtypes of specific tissue regions. From this a continuous characterisation of a sample according to predominance of sarcomatoid vs epithelioid regions is possible, thus avoiding the arbitrary and highly subjective categorisation by currently used subtypes. Instance scoring also enables studying tumor heterogeneity and identifying patterns associated with different subtypes. We have evaluated the proposed method on a dataset of 234 tissue micro-array cores with an AUROC of 0.89±0.05 for this task. The dataset and developed methodology is available for the community at: https://github.com/measty/PINS

MesoGraph: Automatic profiling of mesothelioma subtypes from histological images.

Mesothelioma is classified into three histological subtypes, epithelioid, sarcomatoid, and biphasic, according to the relative proportions of epithelioid and sarcomatoid tumor cells present. Current guidelines recommend that the sarcomatoid component of each mesothelioma is quantified, as a higher percentage of sarcomatoid pattern in biphasic mesothelioma shows poorer prognosis. In this work, we develop a dual-task graph neural network (GNN) architecture with ranking loss to learn a model capable of scoring regions of tissue down to cellular resolution. This allows quantitative profiling of a tumor sample according to the aggregate sarcomatoid association score. Tissue is represented by a cell graph with both cell-level morphological and regional features. We use an external multicentric test set from Mesobank, on which we demonstrate the predictive performance of our model. We additionally validate our model predictions through an analysis of the typical morphological features of cells according to their predicted score

Malignant Mesothelioma subtyping via sampling driven multiple instance prediction on tissue image and cell morphology data

Malignant Mesothelioma is a difficult to diagnose and highly lethal cancer usually associated with asbestos exposure. It can be broadly classified into three subtypes: Epithelioid, Sarcomatoid, and a hybrid Biphasic subtype in which significant components of both of the previous subtypes are present. Early diagnosis and identification of the subtype informs treatment and can help improve patient outcome. However, the subtyping of malignant mesothelioma, and specifically the recognition of transitional features from routine histology slides has a high level of inter-observer variability. In this work, we propose an end-to-end multiple instance learning (MIL) approach for malignant mesothelioma subtyping. This uses an adaptive instance-based sampling scheme for training deep convolutional neural networks on bags of image patches that allows learning on a wider range of relevant instances compared to max or top-N based MIL approaches. We also investigate augmenting the instance representation to include aggregate cellular morphology features from cell segmentation. The proposed MIL approach enables identification of malignant mesothelial subtypes of specific tissue regions. From this a continuous characterisation of a sample according to predominance of sarcomatoid vs epithelioid regions is possible, thus avoiding the arbitrary and highly subjective categorisation by currently used subtypes. Instance scoring also enables studying tumor heterogeneity and identifying patterns associated with different subtypes. We have evaluated the proposed method on a dataset of 234 tissue micro-array cores with an AUROC of 0.89 ± 0.05 for this task. The dataset and developed methodology is available for the community at: https://github.com/measty/PINS

Quantitative analysis of airway obstruction in lymphangio-leio-myomatosis

Lymphangioleiomyomatosis (LAM) is a rare, cystic lung disease with progressive pulmonary function loss caused by progressively proliferating LAM cells. The degree of airway obstruction has not been well investigated within the pathogenesis of LAM. Using a combination of ex vivo computed tomography (CT), microCT and histology, the site and nature of airway obstruction in LAM explant lungs was compared with matched control lungs (n=5 each). The total number of airways per generation, total airway counts, terminal bronchioles number and surface density were compared in LAM versus control. Ex vivo CT analysis demonstrated a reduced number of airways from generation 7 on (p<0.0001) in LAM compared with control, whereas whole-lung microCT analysis confirmed the three- to four-fold reduction in the number of airways. Specimen microCT analysis further demonstrated a four-fold decrease in the number of terminal bronchioles (p=0.0079) and a decreased surface density (p=0.0079). Serial microCT and histology images directly showed the loss of functional airways by collapse of airways on the cysts and filling of the airway by exudate. LAM lungs show a three- to four-fold decrease in the number of (small) airways, caused by cystic destruction which is the likely culprit for the progressive loss of pulmonary function

Hyperferritinemia and hypergammaglobulinemia predict the treatment response to standard therapy in autoimmune hepatitis.

Autoimmune hepatitis (AIH) is a chronic hepatitis with an increasing incidence. The majority of patients require life-long immunosuppression and incomplete treatment response is associated with a disease progression. An abnormal iron homeostasis or hyperferritinemia is associated with worse outcome in other chronic liver diseases and after liver transplantation. We assessed the capacity of baseline parameters including the iron status to predict the treatment response upon standard therapy in 109 patients with untreated AIH type 1 (AIH-1) in a retrospective single center study. Thereby, a hyperferritinemia (> 2.09 times upper limit of normal; Odds ratio (OR) = 8.82; 95% confidence interval (CI): 2.25-34.52) and lower immunoglobulins (<1.89 times upper limit of normal; OR = 6.78; CI: 1.87-24.59) at baseline were independently associated with the achievement of complete biochemical remission upon standard therapy. The predictive value increased when both variables were combined to a single treatment response score, when the cohort was randomly split into a training (area under the curve (AUC) = 0.749; CI 0.635-0.863) and internal validation cohort (AUC = 0.741; CI 0.558-0.924). Patients with a low treatment response score (<1) had significantly higher cumulative remission rates in the training (p<0.001) and the validation cohort (p = 0.024). The baseline hyperferritinemia was accompanied by a high serum iron, elevated transferrin saturations and mild hepatic iron depositions in the majority of patients. However, the abnormal iron status was quickly reversible under therapy. Mechanistically, the iron parameters were not stringently related to a hepatocellular damage. Ferritin rather seems deregulated from the master regulator hepcidin, which was down regulated, potentially mediated by the elevated hepatocyte growth factor. In conclusion, baseline levels of serum ferritin and immunoglobulins, which are part of the diagnostic work-up of AIH, can be used to predict the treatment response upon standard therapy in AIH-1, although confirmation from larger multicenter studies is pending

Improved diagnostics targeting c-MET in non-small cell lung cancer: expression, amplification and activation?

Background: Several c-MET targeting inhibitory molecules have already shown promising results in the treatment of patients with Non-small Cell Lung Cancer (NSCLC). Combination of EGFR-and c-MET-specific molecules may overcome EGFR tyrosine kinase inhibitor (TKI) resistance. The aim of this study was to allow for the identification of patients who might benefit from TKI treatments targeting MET and to narrow in on the diagnostic assessment of MET. Methods: 222 tumor tissues of patients with NSCLC were analyzed concerning c-MET expression and activation in terms of phosphorylation (Y1234/1235 and Y1349) using a microarray format employing immunohistochemistry (IHC). Furthermore, protein expression and MET activation was correlated with the amplification status by Fluorescence in Situ Hybridization (FISH). Results: Correlation was observed between phosphorylation of c-MET at Y1234/1235 and Y1349 (spearman correlation coefficient r(s) = 0.41;p 0.05). c-MET gene amplification was detected in eight of 214 patients (3.7 %). No significant association was observed between c-MET amplification, c-MET protein expression and phosphorylation. Conclusion: Our data indicate, that neither expression of c-MET nor the gene amplification status might be the best way to select patients for MET targeting therapies, since no correlation with the activation status of MET was observed. We propose to take into account analyzing the phosphorylation status of MET by IHC to select patients for MET targeting therapies. Signaling of the receptor and the activation of downstream molecules might be more crucial for the benefit of therapeutics targeting MET receptor tyrosine kinases than expression levels alone