Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review
Abstract
:1. Introduction
2. Results
2.1. Eligible Studies and Characteristics
2.2. Applications of Deep Learning in Colorectal Cancer Pathology Image Analysis
2.2.1. Gland Segmentation
2.2.2. Tumor Classification
2.2.3. Tumor Microenvironment Analysis
2.2.4. Prognosis Prediction
3. Discussion
3.1. Challenges in Pathological Diagnosis of CRC
3.1.1. Small Tissue Artifacts
3.1.2. Regenerative Atypia
3.1.3. Inter-Observer Variation in Adenoma Grading and Subtype Classification
3.1.4. Importance of Tumor Microenvironment in CRCs
3.1.5. Prognostic Prediction in CRCs
3.2. Application of Deep Learning Models in CRC Pathological Diagnosis
3.2.1. Gland Segmentation
3.2.2. Tumor Classification
3.2.3. Tumor Microenvironment Analysis
3.2.4. Prognosis Prediction
3.3. Limitation of This Study
4. Materials and Methods
4.1. Literature Search
4.2. Study Selection, Reviewing, and Data Retrieval
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Model No. | Author/Team | Dataset | Base Model | Performance Metrics | Year | Country | Ref. | |||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
F1-Score | Object Dice | Object Hausdorff | ||||||||||
Part A | Part B | Part A | Part B | Part A | Part B | |||||||
1 * | CUMed Vision2 | WQD | DCAN | 0.912 | 0.716 | 0.897 | 0.781 | 45.4 | 160.3 | 2015 | Hong Kong | [21] |
2 * | ExB1 | WQD | Two path CNN | 0.891 | 0.703 | 0.882 | 0.786 | 57.4 | 145.6 | 2015 | Germany | [22] |
3 * | ExB3 | WQD | Two path CNN | 0.896 | 0.719 | 0.886 | 0.765 | 57.4 | 159.9 | 2015 | Germany | [22] |
4 * | Freiberg2 | WQD | U-Net | 0.87 | 0.695 | 0.876 | 0.786 | 57.1 | 148.5 | 2015 | Germany | [22] |
5 * | CUMed Vision1 | WQD | FCN | 0.868 | 0.769 | 0.867 | 0.8 | 74.6 | 153.6 | 2015 | Hong Kong | [21] |
6 * | ExB2 | WQD | Two path CNN | 0.892 | 0.686 | 0.884 | 0.754 | 54.8 | 187.4 | 2015 | Germany | [22] |
7 * | Freiburg1 | WQD | U-Net | 0.834 | 0.605 | 0.875 | 0.783 | 57.2 | 146.6 | 2015 | Germany | [22] |
8 * | CVML | WQD | CNN | 0.652 | 0.541 | 0.644 | 0.654 | 155.4 | 176.2 | 2015 | UK | [22] |
9 * | LIB | WQD | K-means/naïve Bayesian | 0.777 | 0.306 | 0.781 | 0.617 | 112.7 | 190.4 | 2015 | France | [22] |
10 * | Vision4GlaS | WQD | Object-Net/Separator-Net | 0.635 | 0.527 | 0.737 | 0.61 | 107.5 | 210.1 | 2015 | Austria | [22] |
11 ǂ | BenTaieb | WQD | FCN+Smoothness+Topology | NA | NA | 0.80 ± 0.12 | NA | NA | 2016 | Canada | [23] | |
12 ǂ | Li | 85 WQD | CNN+HC-SVM | NA | NA | 0.87 ± 0.08 | NA | NA | 2016 | UK | [24] | |
13 ǂ | Yang | WQD | FCN | 0.921 | 0.855 | 0.904 | 0.858 | 44.7 | 97.0 | 2017 | USA | [25] |
14 ǂ | Kainz | WQD | Object-Net | 0.670 | 0.570 | 0.70 | 0.620 | 137.4 | 216.4 | 2017 | Austria | [26] |
Separator-Net | 0.680 | 0.610 | 0.750 | 0.650 | 103.5 | 187.8 | ||||||
15 ǂ | Zhang | WQD | DAN | 0.916 | 0.855 | 0.903 | 0.838 | 45.3 | 105.0 | 2017 | USA | [27] |
16 ǂ | Xu | WQD | FCN/DCAN/RPN/CNN | 0.893 | 0.843 | 0.908 | 0.833 | 44.1 | 116.8 | 2017 | China | [28] |
17 ǂ | Graham | WQD /16 CRAG WSIs | MILD-Net | 0.914 | 0.844 | 0.913 | 0.836 | 41.5 | 105.9 | 2018 | UK | [29] |
18 ǂ | Yan | WQD | Holistically-nested networks | 0.924 | 0.844 | 0.902 | 0.840 | 49.9 | 106.1 | 2018 | Hong Kong | [30] |
19 ǂ | Manivannan | WQD | FCN | 0.892 | 0.801 | 0.887 | 0.853 | 51.2 | 87.0 | 2018 | UK | [31] |
20 ǂ | Tang | WQD | Segnet | NA | NA | 0.882 | 0.836 | 106.6 | 102.6 | 2018 | China | [32] |
21 ǂ | Ding | WQD/213 CRAG images | TCC-MSFCN | 0.914 | 0.85 | 0.913 | 0.858 | 39.8 | 93.2 | 2019 | China | [33] |
22 ǂ | Raza | WQD | MIMO-Net | 0.913 | 0.724 | 0.906 | 0.785 | 49.2 | 134.0 | 2019 | UK | [34] |
23 ǂ | Liu | WQD | Wavelet Scattering Network | 0.874 | 0.71 | 0.875 | 0.791 | 56.6 | 146.6 | 2019 | China | [35] |
24 ǂ | Binder | WQD | U-Net | NA | NA | 0.920 | 11.0 | 2019 | France | [36] | ||
25 ǂ | Khvostikov | WQD/20 PATH- DT-MSU images | U-Net | NA | NA | 0:880 | NA | NA | 2019 | Russia | [37] |
Model No. | Feature | Task | Dataset | External Cross-Validation | Base Model | Performance | Author/Team | Year | Country | Ref. |
---|---|---|---|---|---|---|---|---|---|---|
1 | Tumor Classification | 6 classes (cancer subtypes): NL/ADC/MC/SC/PC/CCTA | 717 patches | Not done | AlexNet | Accuracy—97.5% | Xu | 2017 | China | [38] |
2 | 5 classes (polyp subtypes): | 2074 patches 936 WSI | Not done | ResNet | Accuracy—93.0% | Korbar | 2017 | USA | [39] | |
NL/HP/SSP/TSA/TA/TVA-VA, | ||||||||||
3 | 3 classes: NL/AD/ADC | 30 multispectral image patches | Not done | CNN | Accuracy—99.2% | Haj-Hassan | 2017 | France | [40] | |
4 | 2 classes: NL/Tumor | 57 WSI (10,280 patches) | Not done | VGG | Accuracy—93.5%, | Yoon | 2018 | South Korea | [41] | |
Sensitivity—95.1% | ||||||||||
Specificity—92.8% | ||||||||||
5 | 3 classes: NL/AD/ADC | 27 WSI (13,500 patches) | Not done | VGG16 | Accuracy—96 % | Ponzio | 2018 | Italy | [42] | |
6 | 4 classes: NL/HP/AD/ADC | 393 WSI | Not done | CNN | Accuracy—80% | Sena | 2019 | Italy | [43] | |
(12,565 patches) | ||||||||||
7 | 3 classes: NL/AD/ADC | 4036 WSI | Not done | CNN/RNN | AUCs—0.96 (ADC) | Iizuka | 2020 | Japan | [44] | |
0.99 (AD) | ||||||||||
8. | 2 classes: NL/Tumor | 94 WSI, | Done using 378 DACHS data | ResNet18 | AUC > 0.99 | Kather | 2019 | Germany | [45] | |
370 TCGA-KR, | ||||||||||
(60,894 patches) | ||||||||||
378 TCGA-DX, | ||||||||||
(93,408 patches) | ||||||||||
9 | Tumor Microenvironment Analysis | Classification, Segmentation and Detection: EC/IC/FC/MC | 21,135 patches | Not done | DCRN/R2U-Net | Classification | Alom | 2018 | USA | [46] |
F1-score—0.81 | ||||||||||
AUC—0.96 | ||||||||||
Accuracy—91.1% | ||||||||||
Segmentation | ||||||||||
Accuracy—92.1% | ||||||||||
Detection | ||||||||||
F1 score—0.831 | ||||||||||
10 | Detection of immune cell CD3+, CD8+ | 28 WSI IHC | Not done | FCN/LSM/U-Net | FI score—0.80 Sensitivity—74.0% Precision—86 | Swiderska-Chadaj | 2019 | Netherland | [47] | |
11 | Detection and classification EC/IC/FC/MC | 853 patches & 142 TCGA images | Not done | CNN | Detection Accuracy—65% Classification Accuracy—76 % | Shapcott | 2019 | UK | [48] | |
12 | Classification of 9 cell types ADI, BAC, DEB, LYM, MUC, SM, NL, SC and EC | 86 WSI (100,000) NCT&UMM | Not done | VGG19 | Accuracy—94–99% | Kather | 2019 | Germany | [20] | |
13 | Prognosis Prediction | 5-year disease-specific survival | 420 TMA | Not done | LSTM | AUC—0.69 | Bychkov | 2018 | Finland | [49] |
14 | Survival predictions | 25 DACHS WSI | Not done | VGG19 | Accuracy—94–99% | Kather | 2019 | Germany | [20] | |
862 TCGA WSI | ||||||||||
409 DACHS WSI | ||||||||||
15 | MSI predictions | 360 TCGA- DX (93,408 patches) 378 TCGA- KR (60,894 patches) | Done using 378 DACHS data | ResNet18 | AUC TCGA-DX—0.77 TCGA-KR—0.84 | Kather | 2019 | Germany | [45] |
Model No. | Author/Team | Performance Metrics | Rank-Sum | Rank-Sum Rank | Year | Ref. | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
F1-Score | Object Dice | Object Hausdorff | |||||||||||||||
Part A | Rank | Part B | Rank | Part A | Rank | Part B | Rank | Part B | Rank | Part B | Rank | ||||||
19 ǂ | Ding | 0.914 | 4 | 0.850 | 3 | 0.913 | 1 | 0.858 | 1 | 39.8 | 1 | 93.2 | 2 | 12 | 1 | 2019 | [33] |
11 ǂ | Yang | 0.921 | 2 | 0.855 | 1 | 0.904 | 5 | 0.858 | 1 | 44.7 | 4 | 97.0 | 3 | 16 | 2 | 2017 | [25] |
15 ǂ | Graham | 0.914 | 4 | 0.844 | 4 | 0.913 | 1 | 0.836 | 6 | 41.5 | 2 | 105.9 | 5 | 22 | 3 | 2018 | [29] |
13 ǂ | Zhang | 0.916 | 3 | 0.855 | 1 | 0.903 | 6 | 0.838 | 5 | 45.3 | 5 | 105.0 | 4 | 24 | 4 | 2017 | [27] |
16 ǂ | Yan | 0.924 | 1 | 0.844 | 4 | 0.902 | 7 | 0.840 | 4 | 49.9 | 8 | 106.1 | 6 | 30 | 5 | 2018 | [30] |
14 ǂ | Xu | 0.893 | 9 | 0.843 | 6 | 0.908 | 3 | 0.833 | 7 | 44.1 | 3 | 116.8 | 7 | 35 | 6 | 2017 | [28] |
17 ǂ | Manivannan | 0.892 | 10 | 0.801 | 7 | 0.887 | 9 | 0.853 | 3 | 51.2 | 9 | 87.0 | 1 | 39 | 7 | 2018 | [31] |
20 ǂ | Raza | 0.913 | 6 | 0.724 | 9 | 0.906 | 4 | 0.785 | 12 | 49.2 | 7 | 134.0 | 8 | 46 | 8 | 2019 | [34] |
1 * | CUMedVision2 | 0.912 | 7 | 0.716 | 11 | 0.897 | 8 | 0.781 | 14 | 45.4 | 6 | 160.3 | 15 | 61 | 9 | 2015 | [21] |
21 ǂ | Liu | 0.874 | 13 | 0.710 | 12 | 0.875 | 14 | 0.791 | 9 | 56.6 | 11 | 146.6 | 10 | 69 | 10 | 2019 | [35] |
2 * | ExB1 | 0.891 | 12 | 0.703 | 13 | 0.882 | 12 | 0.786 | 10 | 57.4 | 15 | 145.6 | 9 | 71 | 11 | 2015 | [22] |
3 * | ExB3 | 0.896 | 8 | 0.719 | 10 | 0.886 | 10 | 0.765 | 15 | 57.4 | 14 | 159.9 | 14 | 71 | 11 | 2015 | [22] |
4 * | Freiberg2 | 0.870 | 14 | 0.695 | 14 | 0.876 | 13 | 0.786 | 10 | 57.1 | 12 | 148.5 | 12 | 75 | 13 | 2015 | [22] |
5 * | CUMedVision1 | 0.868 | 15 | 0.769 | 8 | 0.867 | 16 | 0.800 | 8 | 74.6 | 16 | 153.6 | 13 | 76 | 14 | 2015 | [21] |
6 * | ExB2 | 0.892 | 10 | 0.686 | 15 | 0.884 | 11 | 0.754 | 16 | 54.8 | 10 | 187.4 | 17 | 79 | 15 | 2015 | [22] |
7 * | Freiburg1 | 0.834 | 16 | 0.605 | 17 | 0.875 | 14 | 0.783 | 13 | 57.2 | 13 | 146.6 | 10 | 83 | 16 | 2015 | [22] |
12 ǂ | Kainz | 0.680 | 18 | 0.610 | 16 | 0.750 | 18 | 0.650 | 18 | 103.5 | 17 | 187.8 | 18 | 105 | 17 | 2017 | [26] |
8 * | CVML | 0.652 | 19 | 0.541 | 18 | 0.644 | 20 | 0.654 | 17 | 155.4 | 20 | 176.2 | 16 | 110 | 18 | 2015 | [22] |
9 * | LIB | 0.777 | 17 | 0.306 | 20 | 0.781 | 17 | 0.617 | 19 | 112.7 | 19 | 190.4 | 19 | 111 | 19 | 2015 | [22] |
10 * | Vision4GlaS | 0.635 | 20 | 0.527 | 19 | 0.737 | 19 | 0.610 | 20 | 107.5 | 18 | 210.1 | 20 | 116 | 20 | 2015 | [22] |
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Share and Cite
Thakur, N.; Yoon, H.; Chong, Y. Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review. Cancers 2020, 12, 1884. https://doi.org/10.3390/cancers12071884
Thakur N, Yoon H, Chong Y. Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review. Cancers. 2020; 12(7):1884. https://doi.org/10.3390/cancers12071884
Chicago/Turabian StyleThakur, Nishant, Hongjun Yoon, and Yosep Chong. 2020. "Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review" Cancers 12, no. 7: 1884. https://doi.org/10.3390/cancers12071884
APA StyleThakur, N., Yoon, H., & Chong, Y. (2020). Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review. Cancers, 12(7), 1884. https://doi.org/10.3390/cancers12071884