Hepatitis C Related Chronic Liver Cirrhosis: Feasibility of Texture Analysis of MR Images for Classification of Fibrosis Stage and Necroinflammatory Activity Grade

<div><p>Purpose</p><p>To assess the feasibility of texture analysis for classifying fibrosis stage and necroinflammatory activity grade in patients with chronic hepatitis C on T2-weighted (T2W), T1-weighted (T1W) and Gd-EOB-DTPA-enhanced hepatocyte-phase (EOB-HP) imaging.</p><p>Materials and methods</p><p>From April 2008 to June 2012, MR images from 123 patients with pathologically proven chronic hepatitis C were retrospectively analyzed. Texture parameters derived from histogram, gradient, run-length matrix, co-occurrence matrix, autoregressive model and wavelet transform methods were estimated with imaging software. Fisher, probability of classification error and average correlation, and mutual information coefficients were used to extract subsets of optimized texture features. Linear discriminant analysis in combination with 1-nearest neighbor classifier (LDA/1-NN) was used for lesion classification. In compliance with the software requirement, classification was performed based on datasets from all patients, the patient group with necroinflammatory activity grade 1, and that with fibrosis stage 4, respectively.</p><p>Results</p><p>Based on all patient dataset, LDA/1-NN produced misclassification rates of 28.46%, 35.77% and 20.33% for fibrosis staging and 34.15%, 25.20% and 28.46% for necroinflammatory activity grading in T2W, T1W and EOB-HP images. In the patient group with necroinflammatory activity grade 1, LDA/1-NN yielded misclassification rates of 5.00%, 0% and 12.50% for fibrosis staging in T2W, T1W and EOB-HP images respectively. In the patient group with fibrosis stage 4, LDA/1-NN yielded misclassification rates of 5.88%, 12.94% and 11.76% for necroinflammatory activity grading in T2W, T1W and EOB-HP images respectively.</p><p>Conclusion</p><p>Texture quantitative parameters of MR images facilitate classification of the fibrosis stage as well as necroinflammatory activity grade in chronic hepatitis C, especially after categorizing the input dataset according to the activity or fibrosis degree in order to remove the interference between the fibrosis stage and necroinflammatory activity grade on texture features.</p></div

Discrimination of necroinflammatory activity grades based on all patient dataset.

<p>Misclassification rates were 34.15%, 25.20% and 28.46% in T2W <b>(A)</b>, T1W <b>(B)</b> and EOB-HP <b>(C)</b> images, respectively. The three-dimensional distribution of data vectors is based on the top three of the 30 texture features that were extracted using Fisher+POE+ACC+MI method, following by LDA/1-NN classification: necroinflammatory activity grade 1 (1), grade 2 (2) and grade 3 (3). MDF1 and MDF 2 are the most discriminating features axes used in LDA to represent the classification graphically.</p

Discrimination of fibrosis stages based on patient group with necroinflammatory activity grade 1.

<p>Misclassification rates were 5.00%, 0% and 12.50% in T2W <b>(A)</b>, T1W <b>(B)</b> and EOB-HP <b>(C)</b> images, respectively. The three-dimensional distribution of data vectors is based on the top three of the 30 texture features that were extracted using Fisher+POE+ACC+MI method, followed by LDA/1-NN classification: fibrosis stage 1 (1), stage 2 (2), stage 3 (3), and stage 4 (4). MDF1, MDF 2 and MDF 3 are the most discriminating features axes used in LDA to represent the classification graphically.</p

Texture feature list in MaZda software.

<p>Texture feature list in MaZda software.</p

Demographic variables classified by fibrosis stage and necroinflammatory activity grade.

<p>Data are the means± standard deviation. BMI body mass index (the individual’s body weight [in kilograms] divided by the square of his or her height [in meters]).</p><p>*Numbers in parentheses are the number of patients with missing data.</p><p>Demographic variables classified by fibrosis stage and necroinflammatory activity grade.</p

Discrimination of necroinflammatory activity grades based on patient group with fibrosis stage 4.

<p>Misclassification rates were 5.88%, 12.94% and 11.76% in T2W <b>(A)</b>, T1W <b>(B)</b> and EOB-HP <b>(C)</b> images, respectively. The three-dimensional distribution of data vectors is based on the top three of the 30 texture features that were extracted using Fisher +POE+ACC +MI method, following by LDA/1-NN classification: necroinflammatory activity grade 1 (1), grade 2 (2) and grade 3 (3). MDF1 and MDF 2 are the most discriminating features axes used in LDA to represent the classification graphically.</p

Distribution of Various Stages of Fibrosis and Grades of Necroinflammatory Activity.

<p>Data indicate number of patients.</p><p>Distribution of Various Stages of Fibrosis and Grades of Necroinflammatory Activity.</p

Results of texture-based classification of liver fibrosis stages or necroinflammatory activity grades of chronic liver hepatitis C calculated for T2W, T1W and EOB-HP imaging sequences, according to linear discrimination analysis /1-nearest neighbor (LDA/1-NN) classification method.

<p>Misclassification percentage (mis %) was given in columns.</p><p>Results of texture-based classification of liver fibrosis stages or necroinflammatory activity grades of chronic liver hepatitis C calculated for T2W, T1W and EOB-HP imaging sequences, according to linear discrimination analysis /1-nearest neighbor (LDA/1-NN) classification method.</p

Discrimination of fibrosis stages based on all patient dataset.

<p>Misclassification rates were 28.46%, 35.77% and 20.33% in T2W <b>(A)</b>, T1W <b>(B)</b> and EOB-HP <b>(C)</b> images, respectively. The three-dimensional distribution of data vectors is based on the top three of the 30 texture features that were extracted using Fisher coefficients + classification error probability combined with average correlation coefficients + mutual information coefficients (Fisher+POE+ACC+MI) methods, followed by linear discrimination analysis /1-nearest neighbor (LDA/1-NN) classification: fibrosis stage 1 (1), stage 2 (2), stage 3 (3), and stage 4 (4). Most discriminating factor1 (MDF1), MDF 2 and MDF 3 are the most discriminating feature axes used in LDA to represent the classification graphically.</p

Kansainvälinen opiskelija – kuka olet, minne menet?

Kansainvälisten opiskelijoiden määrä Suomessa on lisääntynyt nopeasti lyhyessä ajassa. Globaalien työelämän ja liikkuvuuden muutosten sekä opiskelijoiden määrällisen kasvun myötä joukko moninaistuu entisestään. Suomeen tullaan monenlaisin kieli-, kulttuuri- ja koulutustaustoin, eikä opiskelijan polku korkeakouluopintojen jälkeen ole kiveen hakattu: jäädä vai lähteä? Tässä artikkelissa tarkastellaan kansainvälisen opiskelijan käsitettä ja muutaman kansainvälisen opiskelijan ajatuksia opiskelusta ja tulevaisuudesta Suomessa. Artikkeli pohjautuu keväällä 2016 julkaistuun selvitykseen (Saarinen, Vaarala, Haapakangas & Kyckling 2016).nonPeerReviewe