The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
High-Throughput, Machine Learning-Based Quantification of Steatosis, Inflammation, Ballooning, and Fibrosis in Biopsies from Patients with Nonalcoholic Fatty Liver Disease
Open accessAbstract
BACKGROUND & AIMS: Liver biopsy is the reference standard for staging and grading nonalcoholic fatty liver disease (NAFLD), but histologic scoring systems are semiquantitative with marked interobserver and intraobserver variation. We used machine learning to develop fully automated software for quantification of steatosis, inflammation, ballooning, and fibrosis in biopsy specimens from patients with NAFLD and validated the technology in a separate group of patients. METHODS: We collected data from 246 consecutive patients with biopsy-proven NAFLD and followed up in London from January 2010 through December 2016. Biopsy specimens from the first 100 patients were used to derive the algorithm and biopsy specimens from the following 146 were used to validate it. Biopsy specimens were scored independently by pathologists using the Nonalcoholic Steatohepatitis Clinical Research Network criteria and digitalized. Areas of steatosis, inflammation, ballooning, and fibrosis were annotated on biopsy specimens by 2 hepatobiliary histopathologists to facilitate machine learning. Images of biopsies from the derivation and validation sets then were analyzed by the algorithm to compute percentages of fat, inflammation, ballooning, and fibrosis, as well as the collagen proportionate area, and compared with findings from pathologists' manual annotations and conventional scoring systems. RESULTS: In the derivation group, results from manual annotation and the software had an interclass correlation coefficient (ICC) of 0.97 for steatosis (95% CI, 0.95-0.99; P < .001); ICC of 0.96 for inflammation (95% CI, 0.9-0.98; P < .001); ICC of 0.94 for ballooning (95% CI, 0.87-0.98; P < .001); and ICC of 0.92 for fibrosis (95% CI, 0.88-0.96; P = .001). Percentages of fat, inflammation, ballooning, and the collagen proportionate area from the derivation group were confirmed in the validation cohort. The software identified histologic features of NAFLD with levels of interobserver and intraobserver agreement ranging from 0.95 to 0.99; this value was higher than that of semiquantitative scoring systems, which ranged from 0.58 to 0.88. In a subgroup of paired liver biopsy specimens, quantitative analysis was more sensitive in detecting differences compared with the nonalcoholic steatohepatitis Clinical Research Network scoring system. CONCLUSIONS: We used machine learning to develop software to rapidly and objectively analyze liver biopsy specimens for histologic features of NAFLD. The results from the software correlate with those from histopathologists, with high levels of interobserver and intraobserver agreement. Findings were validated in a separate group of patients. This tool might be used for objective assessment of response to therapy for NAFLD in practice and clinical trials.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Recently, several groups have developed deep learning algorithms to assess the degree of steatosis in liver biopsies (47–49).”
From Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation (2022) · cites this paper as [48]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Design and validation of a histological scoring system for nonalcoholic fatty liver disease†
Kleiner, D. E., Brunt, E. M., Van Natta, M., Behling, C., Contos, M. J., Cummings, O. W., Ferrell, L. D., Liu, Y., Torbenson, M. S., Unalp‐Arida, A., et al. (2005) · Hepatology · doi:10.1002/hep.20701 · Cited by 10,791
EASL–EASD–EASO Clinical Practice Guidelines for the management of non-alcoholic fatty liver disease
(2016) · Journal of Hepatology · doi:10.1016/j.jhep.2015.11.004 · Cited by 4,836
Nonalcoholic Steatohepatitis: A Proposal for Grading and Staging The Histological Lesions
Brunt, E. M., Janney, C. G., Di Bisceglie, A. M., Neuschwander-Tetri, B. A., & Bacon, B. R. (1999) · American Journal of Gastroenterology · doi:10.1111/j.1572-0241.1999.01377.x · Cited by 3,907
Fleiss, J. L., & Cohen, J. (1973) · Educational and Psychological Measurement · doi:10.1177/001316447303300309 · Cited by 3,354
Angulo, P., Kleiner, D. E., Dam-Larsen, S., Adams, L. A., Bjornsson, E. S., Charatcharoenwitthaya, P., Mills, P. R., Keach, J. C., Lafferty, H. D., Stahler, A., et al. (2015) · Gastroenterology · doi:10.1053/j.gastro.2015.04.043 · Cited by 2,987
Ekstedt, M., Hagström, H., Nasr, P., Fredrikson, M., Stål, P., Kechagias, S., & Hultcrantz, R. (2015) · Hepatology · doi:10.1002/hep.27368 · Cited by 2,287
Wong, R. J., Aguilar, M. E. F., Cheung, R. C., Perumpail, R. B., Harrison, S. A., Younossi, Z. M., & Ahmed, A. (2014) · Gastroenterology · doi:10.1053/j.gastro.2014.11.039 · Cited by 1,861
Brunt, E. M., Kleiner, D. E., Wilson, L. A., Belt, P., & Neuschwander-Tetri, B. A. (2011) · Hepatology · doi:10.1002/hep.24127 · Cited by 1,251
Hagström, H., Nasr, P., Ekstedt, M., Hammar, U., Stål, P., Hultcrantz, R., & Kechagias, S. (2017) · Journal of Hepatology · doi:10.1016/j.jhep.2017.07.027 · Cited by 1,067
Sayiner, M., Koenig, A., Henry, L., & Younossi, Z. M. (2016) · Clinics in Liver Disease · doi:10.1016/j.cld.2015.10.001 · Cited by 529
Portal chronic inflammation in nonalcoholic fatty liver disease (NAFLD)
Brunt, E. M., Kleiner, D. E., Wilson, L., Ünalp, A., Behling, C. A., Lavine, J. E., & Neuschwander‐Tetri, B. A. (2008) · Hepatology · doi:10.1002/hep.22724 · Cited by 387
One-Second Needle Biopsy of the Liver
Menghini, G. (1958) · Gastroenterology · doi:10.1016/s0016-5085(19)35563-5 · Cited by 383
Assessment of Hepatic Steatosis by Expert Pathologists
El‐Badry, A. M., Breitenstein, S., Jochum, W., Washington, K. M., Paradis, V., Rubbia‐Brandt, L., Puhan, M. A., Slankamenac, K., Graf, R., & Clavien, P. (2009) · Annals of Surgery · doi:10.1097/sla.0b013e3181bcd6dd · Cited by 312
Calvaruso, V., Burroughs, A. K., Standish, R. A., Manousou, P., Grillo, F., Leandro, G., Maimone, S., Pleguezuelo, M., Xirouchakis, I., Guerrini, G. P., et al. (2008) · Hepatology · doi:10.1002/hep.22745 · Cited by 288
A Systematic Review of the Quality of Liver Biopsy Specimens
Cholongitas, Ε., Senzolo, M., Standish, R. A., Marelli, L., Quaglia, A., Patch, D., Dhillon, A. P., & Burroughs, A. K. (2006) · American Journal of Clinical Pathology · doi:10.1309/w3xcnt4hkfbn2g0b · Cited by 263
Siddiqui, M. S., Harrison, S. A., Abdelmalek, M. F., Anstee, Q. M., Bédossa, P., Castéra, L., Dimick‐Santos, L., Friedman, S. L., Greene, K., Kleiner, D. E., et al. (2017) · Hepatology · doi:10.1002/hep.29607 · Cited by 170
Juluri, R., Vuppalanchi, R., Olson, J. D., Ünalp, A., Natta, M. L. V., Cummings, O. W., Tonascia, J. A., & Chalasani, N. (2010) · Journal of Clinical Gastroenterology · doi:10.1097/mcg.0b013e3181dd1348 · Cited by 150
Masseroli, M., Caballero, T., O’Valle, F., del Moral, R. M. G., Pérez-Milena, A., & del Moral, R. G. (2000) · Journal of Hepatology · doi:10.1016/s0168-8278(00)80397-9 · Cited by 136
Mann, J. P., Vito, R. D., Mosca, A., Alisi, A., Armstrong, M. J., Raponi, M., Baumann, U., & Nobili, V. (2015) · Hepatology · doi:10.1002/hep.28374 · Cited by 90
Buzzetti, E., Hall, A. R., Ekstedt, M., Manuguerra, R., Misas, M. G., Covelli, C., Leandro, G., Luong, T. V., Kechagias, S., Manesis, E. K., et al. (2019) · Alimentary Pharmacology & Therapeutics · doi:10.1111/apt.15219 · Cited by 74
Chang, J. P.-E., Goh, G. B., Leow, W. Q., Shen, L., Lim, K. H., & Tan, C. (2018) · PLoS ONE · doi:10.1371/journal.pone.0199166 · Cited by 57
Wright, M., Thursz, M. R., Pullen, R. A., Thomas, H. C., & Goldin, R. D. (2003) · Liver International · doi:10.1034/j.1600-0676.2003.01771.x · Cited by 55
Hepatic steatosis estimated microscopically versus digital image analysis
Hall, A. R., Dhillon, A. P., Green, A. C., Ferrell, L. D., Crawford, J. M., Alves, V. A. F., Balabaud, C. P., Bhathal, P. S., Bioulac‐Sage, P., Guido, M., et al. (2013) · Liver International · doi:10.1111/liv.12162 · Cited by 55
Vanderbeck, S., Bockhorst, J. P., Kleiner, D. E., KOMOROWSKI, R. A., Chalasani, N., & Gawrieh, S. (2015) · Human Pathology · doi:10.1016/j.humpath.2015.01.019 · Cited by 47
Liquori, G. E., Calamita, G., Cascella, D., Mastrodonato, M., Portincasa, P., & Ferri, D. (2009) · PubMed · doi:10.14670/hh-24.49 · Cited by 41
Comparing morphometric, biochemical, and visual measurements of macrovesicular steatosis of liver
Li, M., Song, J., Mirkov, S., Xiao, S., Hart, J. A. L., & Liu, W. (2010) · Human Pathology · doi:10.1016/j.humpath.2010.07.013 · Cited by 38
Calvaruso, V., Marco, V. D., Bavetta, M. G., Cabibi, D., Conte, E., Bronte, F., Simone, F., Burroughs, A. K., & Craxı̀, A. (2015) · Alimentary Pharmacology & Therapeutics · doi:10.1111/apt.13051 · Cited by 24
Tsipouras, M. G., Γιαννακέας, Ν., Tzallas, A. T., Tsianou, Z. E., Manousou, P., Hall, A. R., Tsoulos, I. G., & Tsianos, E. V. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.11.012 · Cited by 19
Surrogate endpoints for clinical trials in non-alcoholic steatohepatitis
Tsochatzis, E. A., Buzzetti, E., & Pinzani, M. (2017) · The Lancet. Gastroenterology & hepatology · doi:10.1016/s2468-1253(17)30184-x · Cited by 2
Rexhepaj, E., Nathalie, D., Benoit, N., Alison, H., Bernhardt, C., Walczak, R., Megnien, S., Hum, D., Staels, B., & Brozek, J. (2018) · Journal of Hepatology · doi:10.1016/s0168-8278(18)30458-6 · Cited by 2
Flagged first, then by citations
Research works printed but not linked: 2
- Calvaruso, 2009, Computer-assisted image analysis of liver collagen: relationship to Ishak scoring and hepatic venous pressure gradient, Hepatology, 49, 1236, 10.1002/hep.22745 · DOI
- Hall, 2013, Hepatic steatosis estimated microscopically versus digital image analysis, Liver Int, 33, 926, 10.1111/liv.12162 · DOI
Cited by
Sorted by citations
Suboptimal reliability of liver biopsy evaluation has implications for randomized clinical trials
Davison, B. A., Harrison, S. A., Cotter, G., Alkhouri, N., Sanyal, A., Edwards, C., Colca, J. R., Iwashita, J., Koch, G. G., & Dittrich, H. C. (2020) · Journal of Hepatology · doi:10.1016/j.jhep.2020.06.025 · Cited by 448
Nam, D., Chapiro, J., Paradis, V., Seraphin, T. P., & Kather, J. N. (2022) · JHEP Reports · doi:10.1016/j.jhepr.2022.100443 · Cited by 244
Application of Artificial Intelligence for the Diagnosis and Treatment of Liver Diseases
Ahn, J. C., Connell, A., Simonetto, D. A., Hughes, C., & Shah, V. H. (2020) · Hepatology · doi:10.1002/hep.31603 · Cited by 201
Taylor-Weiner, A. N., Pokkalla, H., Han, L., Jia, C., Huss, R. S., Chung, C., Elliott, H. L., Glass, B., Pethia, K., Carrasco‐Zevallos, O. M., et al. (2021) · Hepatology · doi:10.1002/hep.31750 · Cited by 194
Current therapies and new developments in NASH
Dufour, J., Anstee, Q. M., Bugianesi, E., Harrison, S. A., Loomba, R. S., Paradis, V., Tilg, H., Wong, V. W., & Zelber‐Sagi, S. (2022) · Gut · doi:10.1136/gutjnl-2021-326874 · Cited by 192
Metadata
- Authors
- Forlano, R., Mullish, B. H., Giannakeas, N., Maurice, J. B., Angkathunyakul, N., Lloyd, J., Tzallas, A. T., Tsipouras, M., Yee, M., Thursz, M. R., Goldin, R. D., & Manousou, P.
- Affiliations
- University of Ioannina (GR)
- University of Western Macedonia (GR)
- Imperial College Healthcare NHS Trust (GB)
- Corresponding author
- Manousou, Pinelopi
- ORCID
- Venue
- Clinical Gastroenterology and Hepatology
- Publisher
- Elsevier BV
- Published
- 2020-08
- Volume
- 18
- Issue
- 9
- Pages
- 2081-2090.e9
- ISSN
- 1542-3565 (ISSN-L)
- PMC
- PMC7397508
- OpenAlex
- W2997774926
- Cited by
- 142
- References
- 31
- Citations by year
- 2026: 15 · 2025: 17 · 2024: 24 · 2023: 27 · 2022: 30 · 2021: 24 · 2020: 5
- FWCI
- 11.23
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Clinical Gastroenterology and Hepatology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Spiral (Imperial College London) (submittedVersion, cc-by)
- Funders
- Medical Research Council
- European Association for the Study of the Liver
- National Institute for Health Research
- Topics
- Health Sciences › Medicine › Hepatology › Liver Disease Diagnosis and Treatment
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- non-alcoholic fatty liver disease, liver biopsy, machine learning, steatosis, liver inflammation, ballooning, liver fibrosis, collagen proportionate area, histologic scoring system, interobserver agreement, histopathology image analysis
- MeSH terms
- Machine Learning, Biopsy, Fibrosis, Humans, Inflammation, Liver, Liver Cirrhosis, Severity of Illness Index, Non-alcoholic Fatty Liver Disease
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
