Artificial Intelligence-Based Opportunities in Liver Pathology—A Systematic Review
Abstract
:1. Introduction
1.1. Introduction to AI
1.2. Principles of Deep Neural Networks Algorithms
1.3. Evaluation of Algorithms by Performance Metrics
1.4. Aim of the Present Review
2. Materials and Methods
2.1. Inclusion and Exclusion Criteria
2.2. Online Registration Information
2.3. Data Sources and Literature Search Strategy
2.4. Studies Selection and Data Extraction
2.5. Assessment of the Risk of Bias and Applicability
3. Results
3.1. Search Results
3.2. Tumoral
Authors (Year) [Reference] | Country | Algorithm Goal | Development Dataset | External Validation Dataset | Performance Metrics |
---|---|---|---|---|---|
Feng Y. et al. (2021) [11] | France | segmentation (HCC) | 60 WSI | 40 WSI | Jaccard score 0.90 F1 score 0.47 |
Cancian P. et al. (2021) [12] | Italy | segmentation (LM) | 303 WSI | no | Jaccard score 0.89 |
Roy M. et al. (2021) [13] | USA | segmentation (HCC) | 60 WSI | 40 WSI | F1 score 0.94 |
Wang X. et al. (2021) [14] | China | segmentation (HCC) | 60 WSI | 40 WSI | Jaccard score 0.797 |
Feng S. et al. (2021) [15] | China | segmentation (HCC) | 592 WSI | 157 WSI (TCGA) | Accuracy 0.88 |
Yang TL. et al. (2022) [16] | Taiwan | segmentation (HCC) | 46 WSI | no | Jaccard score 0.89 |
Li S. et al. (2017) [17] | China | segmentation (HCC) | 127 WSI | not provided | Accuracy 0.97 F1 score 0.94 |
Kiani A. et al. (2020) [18] | USA | classification (HCC and CCK) | 70 WSI | 80 WSI | Accuracy 0.84 |
Schau GF. et al. (2020) [19] | USA | classification (LM) | 257 WSI | no | F1 score 0.77 |
Ercan C. et al. (2022) [20] | Switzerland | classification (HCC) | 98 WSI | no | Accuracy 0.84 F1 score 0.91 |
Diao S. et al. (2022) [21] | China | classification (HCC) | 100 WSI (TCGA) | no | AUC 0.92 |
Cheng N. et al. (2022) [22] | China | classification (HCC) | 649 WSI | 234 WSI | AUC 0.94 |
Chen M. et al. (2020) [23] | China | prediction (HCC) | 387 WSI | 101 WSI | AUC 0.71 |
Liao H. et al. (2020) [24] | China | prediction (HCC) | not provided | not provided | AUC 0.89 |
Saillard C. et al. (2020) [25] | France | prediction (HCC) | 390 WSI | 342 WSI (TCGA) | c-index 0.70 |
Yamashita et al. (2021) [26] | USA | prediction (HCC) | 299 WSI (TCGA) | 198 WSI | c-index 0.67 |
Saito A. et al. (2021) [27] | Japan | prediction (HCC) | 158 WSI | no | Accuracy 0.90 |
Xiao C. et al. (2022) [28] | China | prediction (LM) | 611 WSI | no | AUC 0.85 |
Chen Q. et al. (2022) [29] | China | prediction (HCC) | 2917 WSI | 504 WSI | AUC 0.87 |
Zeng Q. et al. (2022) [30] | France | prediction (HCC) | 349 WSI (TCGA) | 139 WSI | AUC 0.92 |
Qu WF. et al. (2022) [31] | China | prediction (HCC) | 576 WSI | 147 WSI (TCGA) | c-index 0.71 |
Xie J. et al. (2022) [32] | China | prediction (CCK) | 766 WSI | no | AUC 0.68 |
3.3. Non-Tumoral
Authors (Year) [Reference] | Country | Algorithm Goal | Development Dataset | External Validation Dataset | Performance Metrics |
---|---|---|---|---|---|
Guo X. et al. (2018) [34] | USA | segmentation (steatosis) | 451 WSI | no | Jaccard score 0.77 F1 score 0.66 |
Jirik M. et al. (2020) [35] | Czech Republic | segmentation (intra vs. extralobular) | 33 WSI | no | Accuracy 0.91 |
Roy M. et al. (2020) [36] | USA | segmentation (steatosis) | 36 WSI | no | F1 score 0.94 |
Yu H. et al. (2022) [37] | USA | segmentation (portal tracts) | 53 WSI | no | Jaccard score 0.80 F1 score 0.89 |
Vanderbeck S. et al. (2014) [38] | USA | classification (steatosis, bile ducts, vascular structures) | 47 WSI | no | AUC 0.83 |
Vanderbeck S. et al. (2015) [39] | USA | classification (NASH) | 59 WSI | no | AUC 0.98 |
Wang TH et al. (2015) [40] | Taiwan | classification (fibrosis) | 175 WSI | no | AUC 0.82 |
Munsterman I. et al. (2019) [41] | Netherlands | classification (NASH) | 79 WSI | no | AUC 0.97 |
Klimov S. et al. (2019) [42] | USA | classification (fibrosis) | 115 WSI | no | AUC 0.70 |
Puri M. (2020) [43] | USA | classification (DILI) | 1277 WSI | no | Accuracy 0.99 |
Forlano et al. (2020) [44] | UK | classification (NASH) | 246 WSI | no | AUC 0.80 |
Teramoto T. et al. (2020) [45] | Japan | classification (NASH) | 79 WSI | no | AUC 0.85 |
Salvi M. et al. (2020) [46] | Italy | classification (steatosis) | 385 WSI | no | Accuracy 0.97 |
Gawrieh S. et al. (2020) [47] | USA | classification (NASH) | 18 WSI | no | AUC 0.79 |
Perez-Sans F. et al. (2021) [48] | Spain | classification (steatosis) | 20 WSI | no | AUC 0.98 |
Marti-Aguado D. et al. (2021) [49] | Spain | Classification (chronic hepatitis) | 156 WSI | no | AUC 0.75 (NASH) AUC 0.99 (Chronic Hepatitis model) |
Sjöblom N. et al. (2021) [50] | Finland | classification (chronic cholestatis) | 210 WSI | no | Accuracy 0.93 |
Ramkissoon R. et al. (2022) [51] | USA | classification (NASH) | 97 WSI | no | AUC 0.96 |
Heinemann F. et al. (2022) [52] | USA | classification (NASH) | 467 WSI | no | F1 score 0.37 to 0.85 |
Constantinescu C. et al. (2022) [53] | Romania | prediction (liver surgery complications) | 500 WSI | no | AUC 0.97 |
3.4. Assessment of the Risk of Bias and Applicability through the QUADAS-2 Tool
4. Discussion
4.1. AI in Tumoral Liver Pathology: What to Remember
4.2. AI in Non-Tumoral Liver Pathology: What to Remember
4.3. Persistent Limitations to the Implementation of AI in Daily Practice
4.4. Present Review’s Limitations
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Performance Metric | Rationnal | Interpretation | Key Concepts | Main Applications |
---|---|---|---|---|
Jaccard Index | Also known as the ratio of Intersection over Union (IoU) | 0 ≤ x ≤ 1 the closer to 1 the best | Very simple to use, the Jaccard index is a way of conceptualizing accuracy for object detection. It quantifies the similarity of the algorithm vision with those of the annotated ground truth | object/area segmentation (may also be employed for binary classification) |
Accuracy | Number of correct predictions (true positives and true negatives) divided by the total number of predictions | 0 ≤ x ≤ 1 the closer to 1 the best | Very simple to use, accuracy quantifies the percentage of correct predictions by the algorithm. However, it is not adapted to imbalanced problems (where positive and negative proportions are greatly different) nor to problems where the “cost” of false positives/negatives must be taken into account (such as screening situations). Therefore, it may not be suited for evaluating algorithms in certain medical situations | classification/prediction |
F1-score | Combines Precision (ratio of true positives/total positives predicted) and Recall (ratio of true positives/total positives in ground truth) | 0 ≤ x ≤ 1 the closer to 1 the best enhancing Precision OR Recall leads to a better score | Useful for evaluating performance in situations where Accuracy would be misleading (see above). It is adapted to unbalanced problems. It is, however, difficult to interpret when low: is it because of low Precision (too much false positives) or low Recall (not enough true positives)? | classification/prediction |
AUROC (or AUC) | Combines Recall (ratio of true positives/total positives in ground truth) and Fallout (ratio of false positives/total negatives in ground truth) | 0 ≤ x≤1 the closer to 1 the best enhancing Precision OR diminishing Fallout leads to a better score | Useful for evaluating the diagnostic ability of a (binary) classifier, because it takes both true positives and true negatives into account. Therefore (and contrary to F1-score), diminishing the number of false negatives is taken into account (which is of importance in screening situations) | classification/prediction |
c-index (concordance index) | Generalization of AUROC for assessing the correct ranking of events | 0 ≤ x ≤ 1 the closer to 1 the best | Adapted to datasets with censored data (survival studies, prediction of adverse events, etc.) |
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Allaume, P.; Rabilloud, N.; Turlin, B.; Bardou-Jacquet, E.; Loréal, O.; Calderaro, J.; Khene, Z.-E.; Acosta, O.; De Crevoisier, R.; Rioux-Leclercq, N.; et al. Artificial Intelligence-Based Opportunities in Liver Pathology—A Systematic Review. Diagnostics 2023, 13, 1799. https://doi.org/10.3390/diagnostics13101799
Allaume P, Rabilloud N, Turlin B, Bardou-Jacquet E, Loréal O, Calderaro J, Khene Z-E, Acosta O, De Crevoisier R, Rioux-Leclercq N, et al. Artificial Intelligence-Based Opportunities in Liver Pathology—A Systematic Review. Diagnostics. 2023; 13(10):1799. https://doi.org/10.3390/diagnostics13101799
Chicago/Turabian StyleAllaume, Pierre, Noémie Rabilloud, Bruno Turlin, Edouard Bardou-Jacquet, Olivier Loréal, Julien Calderaro, Zine-Eddine Khene, Oscar Acosta, Renaud De Crevoisier, Nathalie Rioux-Leclercq, and et al. 2023. "Artificial Intelligence-Based Opportunities in Liver Pathology—A Systematic Review" Diagnostics 13, no. 10: 1799. https://doi.org/10.3390/diagnostics13101799