FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2899366142> ?p ?o ?g. }

• W2899366142 endingPage "1007" @default.
• W2899366142 startingPage "1001" @default.
• W2899366142 abstract "Rationale and Objectives We present a novel method to quantify the degree of liver fibrosis using fibrosis area fraction based on statistical chi-square analysis of heterogeneity of echo texture within liver on routine ultrasound images. We demonstrate, in a clinical study, that fibrosis area fraction derived this way has the potential to become a noninvasive, quantitative radiometric discriminator of normal or low-grade liver fibrosis (Ishak fibrosis score range = F0–3) and advanced liver fibrosis or cirrhosis (Ishak fibrosis score range = F4–6) on routine ultrasound images. Materials and Methods This retrospective patient study was institutional review board approved. Ultrasound images of 100 patients (61 males, 39 females; 18–81 years) who underwent nontargeted ultrasound-guided biopsy were randomly divided into two groups: a training group consisted of 31 cases, and a validation group that contained the rest cases. An investigator manually selected a primary region of interest (ROI; approximately 4–6 cm2) in the liver tissue while avoiding nonhepatic parenchyma. The primary ROI contained a large number of secondary ROIs (25 × 25 pixels) to maintain the precision of statistical analysis. Sample variance σ2 of image gradient (a texture feature related to the amount of edge structures) was calculated in secondary ROIs in a roster scan fashion. A theoretical derivation was presented to estimate population variance σ 0 2 ~ of image gradient across the primary ROI from mean gradient µ of secondary ROIs. The χ2 (χ2 = σ2/ σ 0 2 ~ ) was computed at each secondary ROI, forming a χ2 map of liver tissue heterogeneity. A cut-off value was optimized from datasets in the training group by comparing to the fibrosis grades determined by biopsy. This cut-off value was then applied to the datasets in the validation group to convert the χ2 maps into binary images, from which fibrosis area fractions (fraction of fibrosis area to the total area of the primary ROI) were calculated and entered in a statistical analysis. Results In the training group, the optimal setting was found to be T χ 2 = 6.0, which resulted a maximum discrimination of F0–3 vs F4–6: p < 0.0001, area under curve = 0.985, sensitivity = 93.7%, specificity = 93.3%. When this setting was applied to the datasets in the validation group, a distinct separation was seen between the two classes (p < 0.0001). F0–3 class had an average fibrosis area fraction of 4.7% (1.7%−11.4%), whereas the F4–6 class had an average fibrosis area fraction of 17.3% (9.8%−29.6%). A strong correlation was demonstrated between the fibrosis area fraction and histological fibrosis grade determined by biopsy (area under curve = 0.89, sensitivity = 87.9%, specificity = 90.3%). Conclusion The presented method is a promising noninvasive tool for distinguishing normal or low-grade liver fibrosis (F0–3) and advanced liver fibrosis or cirrhosis (F4–6) from routine ultrasound images. These findings support the further development of texture heterogeneity analysis, particularly fibrosis area fraction, as a quantitative biomarker for distinguishing various liver fibrosis grades. We present a novel method to quantify the degree of liver fibrosis using fibrosis area fraction based on statistical chi-square analysis of heterogeneity of echo texture within liver on routine ultrasound images. We demonstrate, in a clinical study, that fibrosis area fraction derived this way has the potential to become a noninvasive, quantitative radiometric discriminator of normal or low-grade liver fibrosis (Ishak fibrosis score range = F0–3) and advanced liver fibrosis or cirrhosis (Ishak fibrosis score range = F4–6) on routine ultrasound images. This retrospective patient study was institutional review board approved. Ultrasound images of 100 patients (61 males, 39 females; 18–81 years) who underwent nontargeted ultrasound-guided biopsy were randomly divided into two groups: a training group consisted of 31 cases, and a validation group that contained the rest cases. An investigator manually selected a primary region of interest (ROI; approximately 4–6 cm2) in the liver tissue while avoiding nonhepatic parenchyma. The primary ROI contained a large number of secondary ROIs (25 × 25 pixels) to maintain the precision of statistical analysis. Sample variance σ2 of image gradient (a texture feature related to the amount of edge structures) was calculated in secondary ROIs in a roster scan fashion. A theoretical derivation was presented to estimate population variance σ 0 2 ~ of image gradient across the primary ROI from mean gradient µ of secondary ROIs. The χ2 (χ2 = σ2/ σ 0 2 ~ ) was computed at each secondary ROI, forming a χ2 map of liver tissue heterogeneity. A cut-off value was optimized from datasets in the training group by comparing to the fibrosis grades determined by biopsy. This cut-off value was then applied to the datasets in the validation group to convert the χ2 maps into binary images, from which fibrosis area fractions (fraction of fibrosis area to the total area of the primary ROI) were calculated and entered in a statistical analysis. In the training group, the optimal setting was found to be T χ 2 = 6.0, which resulted a maximum discrimination of F0–3 vs F4–6: p < 0.0001, area under curve = 0.985, sensitivity = 93.7%, specificity = 93.3%. When this setting was applied to the datasets in the validation group, a distinct separation was seen between the two classes (p < 0.0001). F0–3 class had an average fibrosis area fraction of 4.7% (1.7%−11.4%), whereas the F4–6 class had an average fibrosis area fraction of 17.3% (9.8%−29.6%). A strong correlation was demonstrated between the fibrosis area fraction and histological fibrosis grade determined by biopsy (area under curve = 0.89, sensitivity = 87.9%, specificity = 90.3%). The presented method is a promising noninvasive tool for distinguishing normal or low-grade liver fibrosis (F0–3) and advanced liver fibrosis or cirrhosis (F4–6) from routine ultrasound images. These findings support the further development of texture heterogeneity analysis, particularly fibrosis area fraction, as a quantitative biomarker for distinguishing various liver fibrosis grades." @default.
• W2899366142 created "2018-11-09" @default.
• W2899366142 creator A5004423703 @default.
• W2899366142 creator A5011192002 @default.
• W2899366142 creator A5036975470 @default.
• W2899366142 creator A5040696427 @default.
• W2899366142 creator A5062797260 @default.
• W2899366142 creator A5083899187 @default.
• W2899366142 date "2019-08-01" @default.
• W2899366142 modified "2023-09-25" @default.
• W2899366142 title "Quantification of Degree of Liver Fibrosis Using Fibrosis Area Fraction Based on Statistical Chi-Square Analysis of Heterogeneity of Liver Tissue Texture on Routine Ultrasound Images" @default.
• W2899366142 cites W1951830964 @default.
• W2899366142 cites W1965170884 @default.
• W2899366142 cites W1981772845 @default.
• W2899366142 cites W1981900811 @default.
• W2899366142 cites W1983195234 @default.
• W2899366142 cites W2013641403 @default.
• W2899366142 cites W2015165062 @default.
• W2899366142 cites W2021756065 @default.
• W2899366142 cites W2025811877 @default.
• W2899366142 cites W2057320475 @default.
• W2899366142 cites W2072997218 @default.
• W2899366142 cites W2081948448 @default.
• W2899366142 cites W2091071330 @default.
• W2899366142 cites W2112748532 @default.
• W2899366142 cites W2128366104 @default.
• W2899366142 cites W2147982258 @default.
• W2899366142 cites W2155250252 @default.
• W2899366142 cites W2209944545 @default.
• W2899366142 cites W2316367947 @default.
• W2899366142 cites W2428038294 @default.
• W2899366142 cites W2550380376 @default.
• W2899366142 cites W2580009538 @default.
• W2899366142 cites W2603308445 @default.
• W2899366142 cites W2606167931 @default.
• W2899366142 cites W2762950827 @default.
• W2899366142 cites W4238801417 @default.
• W2899366142 doi "https://doi.org/10.1016/j.acra.2018.10.004" @default.
• W2899366142 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30393055" @default.
• W2899366142 hasPublicationYear "2019" @default.
• W2899366142 type Work @default.
• W2899366142 sameAs 2899366142 @default.
• W2899366142 citedByCount "6" @default.
• W2899366142 countsByYear W28993661422019 @default.
• W2899366142 countsByYear W28993661422020 @default.
• W2899366142 countsByYear W28993661422022 @default.
• W2899366142 countsByYear W28993661422023 @default.
• W2899366142 crossrefType "journal-article" @default.
• W2899366142 hasAuthorship W2899366142A5004423703 @default.
• W2899366142 hasAuthorship W2899366142A5011192002 @default.
• W2899366142 hasAuthorship W2899366142A5036975470 @default.
• W2899366142 hasAuthorship W2899366142A5040696427 @default.
• W2899366142 hasAuthorship W2899366142A5062797260 @default.
• W2899366142 hasAuthorship W2899366142A5083899187 @default.
• W2899366142 hasConcept C126322002 @default.
• W2899366142 hasConcept C126838900 @default.
• W2899366142 hasConcept C142724271 @default.
• W2899366142 hasConcept C143753070 @default.
• W2899366142 hasConcept C19609008 @default.
• W2899366142 hasConcept C2775934546 @default.
• W2899366142 hasConcept C2777214474 @default.
• W2899366142 hasConcept C2780559512 @default.
• W2899366142 hasConcept C2908647359 @default.
• W2899366142 hasConcept C2989005 @default.
• W2899366142 hasConcept C71924100 @default.
• W2899366142 hasConcept C99454951 @default.
• W2899366142 hasConceptScore W2899366142C126322002 @default.
• W2899366142 hasConceptScore W2899366142C126838900 @default.
• W2899366142 hasConceptScore W2899366142C142724271 @default.
• W2899366142 hasConceptScore W2899366142C143753070 @default.
• W2899366142 hasConceptScore W2899366142C19609008 @default.
• W2899366142 hasConceptScore W2899366142C2775934546 @default.
• W2899366142 hasConceptScore W2899366142C2777214474 @default.
• W2899366142 hasConceptScore W2899366142C2780559512 @default.
• W2899366142 hasConceptScore W2899366142C2908647359 @default.
• W2899366142 hasConceptScore W2899366142C2989005 @default.
• W2899366142 hasConceptScore W2899366142C71924100 @default.
• W2899366142 hasConceptScore W2899366142C99454951 @default.
• W2899366142 hasIssue "8" @default.
• W2899366142 hasLocation W28993661421 @default.
• W2899366142 hasLocation W28993661422 @default.
• W2899366142 hasOpenAccess W2899366142 @default.
• W2899366142 hasPrimaryLocation W28993661421 @default.
• W2899366142 hasRelatedWork W2009510553 @default.
• W2899366142 hasRelatedWork W2013691450 @default.
• W2899366142 hasRelatedWork W2113909686 @default.
• W2899366142 hasRelatedWork W2219774581 @default.
• W2899366142 hasRelatedWork W2356087349 @default.
• W2899366142 hasRelatedWork W2363784292 @default.
• W2899366142 hasRelatedWork W2365810144 @default.
• W2899366142 hasRelatedWork W2390577146 @default.
• W2899366142 hasRelatedWork W2410762110 @default.
• W2899366142 hasRelatedWork W59148547 @default.
• W2899366142 hasVolume "26" @default.
• W2899366142 isParatext "false" @default.
• W2899366142 isRetracted "false" @default.
• W2899366142 magId "2899366142" @default.
• W2899366142 workType "article" @default.