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Ambrosetti MC, Ambrosetti A, Bariani M, Malleo G, Mansueto G, Zamboni GA. Quantitative Edge Analysis Can Differentiate Pancreatic Carcinoma from Normal Pancreatic Parenchyma. Diagnostics (Basel) 2024; 14:1681. [PMID: 39125557 PMCID: PMC11312275 DOI: 10.3390/diagnostics14151681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Revised: 07/29/2024] [Accepted: 07/30/2024] [Indexed: 08/12/2024] Open
Abstract
This study aimed to introduce specific image feature analysis, focusing on pancreatic margins, and to provide a quantitative measure of edge irregularity, evidencing correlations with the presence/absence of pancreatic adenocarcinoma. We selected 50 patients (36 men, 14 women; mean age 63.7 years) who underwent Multi-detector computed tomography (MDCT) for the staging of pancreatic adenocarcinoma of the tail of the pancreas. Computer-assisted quantitative edge analysis was performed on the border fragments in MDCT images of neoplastic and healthy glandular parenchyma, from which we obtained the root mean square deviation SD of the actual border from the average boundary line. The SD values relative to healthy and neoplastic borders were compared using a paired t-test. A significant SD difference was observed between healthy and neoplastic borders. A threshold SD value was also found, enabling the differentiation of adenocarcinoma with 96% specificity and sensitivity. We introduced a quantitative measure of boundary irregularity, which correlates with the presence/absence of pancreatic adenocarcinoma. Quantitative edge analysis can be promptly performed on select border fragments in MDCT images, providing a useful supporting tool for diagnostics and a possible starting point for machine learning recognition based on lower-dimensional feature space.
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Affiliation(s)
- Maria Chiara Ambrosetti
- Radiology Unit, Department of Pathology and Diagnostics, Azienda Ospedaliera Universitaria Integrata Verona, 37134 Verona, Italy;
| | - Alberto Ambrosetti
- Department of Physics and Astronomy “Galileo Galilei”, University of Padova, 35121 Padova, Italy;
| | - Matilde Bariani
- Radiology Unit, Department of Pathology and Diagnostics, Azienda Ospedaliera Universitaria Integrata Verona, 37134 Verona, Italy;
| | - Giuseppe Malleo
- Department of General and Pancreatic Surgery, The Pancreas Institute, University of Verona Hospital Trust, 37134 Verona, Italy;
| | - Giancarlo Mansueto
- Institute of Radiology, Department of Diagnostics and Public Health, Policlinico GB Rossi, University of Verona, 37134 Verona, Italy; (G.M.); (G.A.Z.)
| | - Giulia A. Zamboni
- Institute of Radiology, Department of Diagnostics and Public Health, Policlinico GB Rossi, University of Verona, 37134 Verona, Italy; (G.M.); (G.A.Z.)
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Elkassem AMA, Mresh R, Farag A, Rothenberg S, Lirette ST, Smith AD, Kulkarni T. Pulmonary Surface Irregularity Score as a New Quantitative CT Marker for Idiopathic Pulmonary Fibrosis-a Pilot Study. J Digit Imaging 2023; 36:2382-2391. [PMID: 37670182 PMCID: PMC10584743 DOI: 10.1007/s10278-023-00896-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 08/06/2023] [Accepted: 08/07/2023] [Indexed: 09/07/2023] Open
Abstract
The purpose of this study is to evaluate the accuracy and inter-observer agreement of a quantitative pulmonary surface irregularity (PSI) score on high-resolution chest CT (HRCT) for predicting transplant-free survival in patients with IPF. For this IRB-approved HIPAA-compliant retrospective single-center study, adult patients with IPF and HRCT imaging (N = 50) and an age- and gender-matched negative control group with normal HRCT imaging (N = 50) were identified. Four independent readers measured the PSI score in the midlungs on HRCT images using dedicated software while blinded to clinical data. A t-test was used to compare the PSI scores between negative control and IPF cohorts. In the IPF cohort, multivariate cox regression analysis was used to associate PSI score and clinical parameters with transplant-free survival. Inter-observer agreement for the PSI score was assessed by intraclass correlation coefficient (ICC). The technical failure rate of the midlung PSI score was 0% (0/100). The mean PSI score of 5.38 in the IPF cohort was significantly higher than 3.14 in the negative control cohort (p < .001). In the IPF cohort, patients with a high PSI score (≥ median) were 8 times more likely to die than patients with a low PSI score (HR: 8.36; 95%CI: 2.91-24.03; p < .001). In a multivariate model including age, gender, FVC, DLCO, and PSI score, only the PSI score was associated with transplant-free survival (HR:2.11 per unit increase; 95%CI: 0.26-3.51; p = .004). Inter-observer agreement for the PSI score among 4 readers was good (ICC: 0.88; 95%CI: 0.84-0.91). The PSI score had high accuracy and good inter-observer agreement on HRCT for predicting transplant-free survival in patients with IPF.
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Affiliation(s)
- Asser M Abou Elkassem
- Department of Radiology, University of Alabama at Birmingham, 619 19th Street South, Birmingham, AL, 35249, USA.
| | - Rafah Mresh
- Department of Radiology, University of Alabama at Birmingham, 619 19th Street South, Birmingham, AL, 35249, USA
| | - Ahmed Farag
- Department of Radiology, University of Alabama at Birmingham, 619 19th Street South, Birmingham, AL, 35249, USA
| | - Steven Rothenberg
- Department of Radiology, University of Alabama at Birmingham, 619 19th Street South, Birmingham, AL, 35249, USA
| | - Seth T Lirette
- Department of Data Science, University of Mississippi Medical Center, Translational Research Center, 2500 N. State St., Jackson, MS, 39216, USA
| | - Andrew D Smith
- Department of Radiology, University of Alabama at Birmingham, 619 19th Street South, Birmingham, AL, 35249, USA
| | - Tejaswini Kulkarni
- Department of Medicine, University of Alabama at Birmingham, 1900 University Blvd, Birmingham, AL, 35294, USA
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Zheng S, He K, Zhang L, Li M, Zhang H, Gao P. Conventional and artificial intelligence-based computed tomography and magnetic resonance imaging quantitative techniques for non-invasive liver fibrosis staging. Eur J Radiol 2023; 165:110912. [PMID: 37290363 DOI: 10.1016/j.ejrad.2023.110912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 05/25/2023] [Accepted: 05/30/2023] [Indexed: 06/10/2023]
Abstract
Chronic liver disease (CLD) ultimately develops into liver fibrosis and cirrhosis and is a major public health problem globally. The assessment of liver fibrosis is important for patients with CLD for prognostication, treatment decisions, and surveillance. Liver biopsies are traditionally performed to determine the stage of liver fibrosis. However, the risks of complications and technical limitations restrict their application to screening and sequential monitoring in clinical practice. CT and MRI are essential for evaluating cirrhosis-associated complications in patients with CLD, and several non-invasive methods based on them have been proposed. Artificial intelligence (AI) techniques have also been applied to stage liver fibrosis. This review aimed to explore the values of conventional and AI-based CT and MRI quantitative techniques for non-invasive liver fibrosis staging and summarized their diagnostic performance, advantages, and limitations.
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Affiliation(s)
- Shuang Zheng
- Department of Radiology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China.
| | - Kan He
- Department of Radiology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China.
| | - Lei Zhang
- Department of Radiology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China.
| | - Mingyang Li
- Department of Radiology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China.
| | - Huimao Zhang
- Department of Radiology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China.
| | - Pujun Gao
- Department of Hepatology, the First Hospital of Jilin University, No. 71 Xinmin Street, Changchun, Jilin, China.
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Rubino JG, Nasirzadeh AR, van der Pol CB, Dhindsa K, Chung AD. Quantitative and qualitative liver CT: imaging feature association with histopathologically confirmed hepatic cirrhosis. Abdom Radiol (NY) 2022; 47:2314-2324. [PMID: 35583820 DOI: 10.1007/s00261-022-03550-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 04/27/2022] [Accepted: 04/28/2022] [Indexed: 11/25/2022]
Abstract
PURPOSE To assess the diagnostic performance of quantitative and qualitative imaging features of hepatic cirrhosis on CT. METHODS A single-center retrospective cohort study was performed on all patients who had undergone non-targeted liver biopsy < 3 months following abdominal CT imaging between 2007 and 2020. Histopathology was required as a reference standard for hepatic cirrhosis diagnosis. Two readers independently assessed all CT quantitative and qualitative features, blinded to the clinical history and the reference standard. The diagnostic performance of each imaging feature was assessed using multivariate regression and logistic regression in a recursive feature elimination framework. RESULTS 98 consecutive patients met inclusion criteria including 26 with histopathologically confirmed hepatic cirrhosis, and 72 without cirrhosis. Liver surface nodularity (p < 0.0001), lobar redistribution (p < 0.0001), and expanded gallbladder fossa (p < 0.0016) were qualitative CT features associated with liver cirrhosis consistent between both reviewers. Liver surface nodularity demonstrated highest sensitivity (73-77%) and specificity (79-82%). Falciform space width was the only quantitative feature associated with cirrhosis, for a single reviewer (p < 0.04). Using a recursive feature elimination framework, liver surface nodularity and falciform space width were the strongest performing features for identifying cirrhosis. No feature combinations strengthened diagnostic performance. CONCLUSION Many quantitative and qualitative CT imaging signs of hepatic cirrhosis have either poor accuracy or poor inter-observer agreement. Qualitative imaging features of hepatic cirrhosis on CT performed better than quantitative metrics, with liver surface nodularity the most optimal feature for diagnosing hepatic cirrhosis.
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Affiliation(s)
| | - Amir Reza Nasirzadeh
- Department of Radiology, Kingston Health Sciences Centre, Queen's University, Kingston, ON, Canada
| | - Christian B van der Pol
- Department of Radiology, Juravinski Hospital and Cancer Centre, McMaster University, Hamilton, ON, Canada
| | - Kiret Dhindsa
- Berlin Institute of Health and Department of Neurology and Experimental Neurology, Brain Simulation Section, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Andrew D Chung
- Department of Radiology, Kingston Health Sciences Centre, Queen's University, Kingston, ON, Canada.
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Editor's Notebook: May 2022. AJR Am J Roentgenol 2022; 218:765-766. [PMID: 35451870 DOI: 10.2214/ajr.22.27486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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