Can the computed tomography texture analysis of colorectal liver metastases predict the response to first-line cytotoxic chemotherapy?

BACKGROUND Artificial intelligence in radiology has the potential to assist with the diagnosis,prognostication and therapeutic response prediction of various cancers.A few studies have reported that texture analysis can be helpful in predicting the response to chemotherapy for colorectal liver metastases,however,the results have varied.Necrotic metastases were not clearly excluded in these studies and in most studies the full range of texture analysis features were not evaluated.This study was designed to determine if the computed tomography(CT)texture analysis results of non-necrotic colorectal liver metastases differ from previous reports.A larger range of texture features were also evaluated to identify potential new biomarkers.AIM To identify potential new imaging biomarkers with CT texture analysis which can predict the response to first-line cytotoxic chemotherapy in non-necrotic colorectal liver metastases(CRLMs).METHODS Patients who presented with CRLMs from 2012 to 2020 were retrospectively selected on the institutional radiology information system of our private radiology practice.The inclusion criteria were non-necrotic CRLMs with a minimum size of 10 mm(diagnosed on archived 1.25 mm portal venous phase CT(FOLFOX,FOLFIRI,FOLFOXIRI,CAPE-OX,CAPE-IRI or capecitabine).The final study cohort consisted of 29 patients.The treatment response of the CRLMs was classified according to the RECIST 1.1 criteria.By means of CT texture analysis,various first and second order texture features were extracted from a single nonnecrotic target CRLM in each responding and non-responding patient.Associations between features and response to chemotherapy were assessed by logistic regression models.The prognostic accuracy of selected features was evaluated by using the area under the curve.RESULTS There were 15 responders(partial response)and 14 non-responders(7 stable and 7 with progressive disease).The responders presented with a higher number of CRLMs(P=0.05).In univariable analysis,eight texture features of the responding CRLMs were associated with treatment response,but due to strong correlations among some of the features,only two features,namely minimum histogram gradient intensity and long run low grey level emphasis,were included in the multiple analysis.The area under the receiver operating characteristic curve of the multiple model was 0.80(95%CI:0.64 to 0.96),with a sensitivity of 0.73(95%CI:0.48 to 0.89)and a specificity of 0.79(95%CI:0.52 to 0.92).CONCLUSION Eight first and second order texture features,but particularly minimum histogram gradient intensity and long run low grey level emphasis are significantly correlated with treatment response in non-necrotic CRLMs.

Differentiation of pyogenic hepatic abscesses from malignant mimickers using multislice-based texture acquired from contrast-enhanced computed tomography

BACKGROUND： Pyogenic hepatic abscess may mimic primary or secondary carcinoma of the liver on contrast-enhanced computed tomography（CECT）. The present study was to explore the usefulness of the analysis of multislice-based texture acquired from CECT in the differentiation between pyogenic hepatic abscesses and malignant mimickers.METHODS： This retrospective study included 25 abscesses in 20 patients and 33 tumors in 26 subjects who underwent CECT. To make comparison, we also enrolled 19 patients with hepatic single simple cyst. The images from CECT were analyzed using a Laplacian of Gaussian band-pass filter（5 filter levels with sigma weighting ranging from 1.0 to 2.5）. We also quantified the uniformity, entropy, kurtosis and skewness of the multislice-based texture at different sigma weightings. Statistical significance for these parameters was tested with oneway ANOVA followed by Tukey honestly significant difference（HSD） test. Diagnostic performance was evaluated using the receiver operating characteristic（ROC） curve analysis.RESULTS： There were significant differences in entropy and uniformity at all sigma weightings（P〈0.001） among hepatic abscesses, malignant mimickers and simple cysts. The significant difference in kurtosis and skewness was shown at sigma 1.8 and 2.0 weightings（P=0.002-0.006）. Tukey HSD testshowed that the abscesses had a significantly higher entropy and lower uniformity compared with malignant mimickers（P=0.000-0.004）. Entropy（at a sigma 2.0 weighting） had the largest area under the ROC curve（0.888） in differentiating abscesses from malignant mimickers, with a sensitivity of 81.8% and a specificity of 88.0% when the cutoff value was set to 3.64.CONCLUSION： Multislice-based texture analysis may be useful for differentiating pyogenic hepatic abscesses from malignant mimickers.

On the road towards the global analysis of human synapses

Synapses are essential units for the flow of information in the brain.Over the last 70 years,synapses have been widely studied in multiple animal models including worms,fruit flies,and rodents.In comparison,the study of human synapses has evolved significantly slower,mainly because of technical limitations.However,three novel methods allowing the analysis of molecular,morphological,and functional properties of human synapses may expand our knowledge of the human brain.Here,we briefly describe these methods,and evaluate how the information provided by each unique approach may contribute to the functional and anatomical analysis of the synaptic component of human brain circuitries.In particular,using tissue from cryopreserved human brains,synaptic plasticity can be studied in isolated synaptosomes by fluorescence analysis of single-synapse long-term potentiation（FASS-LTP）,and subpopulations of synapses can be thoroughly assessed in the ribbons of brain tissue by array tomography（AT）.Currently,it is also possible to quantify synaptic density in the living human brain by positron emission tomography（PET）,using a novel synaptic radio-ligand.Overall,data provided by FASS-LTP,AT,and PET may significantly contribute to the global understanding of synaptic structure and function in both healthy and diseased human brains,thus directly impacting translational research.

Unveiling the influence of dendrite characteristics on the slip/twinning activity and the strain hardening capacity of Mg-Sn-Li-Zn cast alloys

This work explores the correlation between the characteristics of the cast structure(dendrite growth pattern,dendrite morphology and macro-texture)and strain hardening capacity during high temperature deformation of Mg-5Sn-0.3Li-0 and 3Zn multi-component alloys.The three dimensional(3D)morphology of the dendrite structure demonstrates the transition of the growth directions from<1123>,<1120>and<1122>to<1123>and<1120>due to the addition of Zn.The simultaneous effects of growing tendency and the decrement of dendrite coarsening rate at the solidification interval lead to dendrite morphology transition from the globular-like to the hyper-branch structure.This morphology transition results in the variation of the solidification macro-texture,which has effectively influenced the dominant deformation mechanisms(slip/twin activity).The higher activity of the slip systems increases the tendency of the dendrite arms for bending along the deformation direction and fragmentation.Apart from this,the dendrite holding hyper-branch structure with an average thickness below 20μm are more favorable for fragmentation.The dendrite fragmentation leads to considerable softening fractions,and as an effective strain compensation mechanism increases the workability of dendritic structure.

Grayscale image statistics of COVID-19 patient CT scans characterize lung condition with machine and deep learning

Background:Grayscale image attributes of computed tomography(CT)of pulmonary scans contain valuable information relating to patients with respiratory ailments.These attributes are used to evaluate the severity of lung conditions of patients confirmed to be with and without COVID-19.Method:Five hundred thirteen CT images relating to 57 patients(49 with COVID-19;8 free of COVID-19)were collected at Namazi Medical Centre(Shiraz,Iran)in 2020 and 2021.Five visual scores(VS:0,1,2,3,or 4)are clinically assigned to these images with the score increasing with the severity of COVID-19-related lung conditions.Eleven deep learning and machine learning techniques(DL/ML)are used to distinguish the VS class based on 12 grayscale image attributes.Results:The convolutional neural network achieves 96.49%VS accuracy(18 errors from 513 images)successfully distinguishing VS Classes 0 and 1,outperforming clinicians’visual inspections.An algorithmic score(AS),involving just five grayscale image attributes,is developed independently of clinicians’assessments(99.81%AS accuracy;1 error from 513 images).Conclusion:Grayscale CT image attributes can be successfully used to distinguish the severity of COVID-19 lung damage.The AS technique developed provides a suitable basis for an automated system using ML/DL methods and 12 image attributes.