Copyright
©The Author(s) 2022.
Artif Intell Gastroenterol. Dec 28, 2022; 3(5): 142-162
Published online Dec 28, 2022. doi: 10.35712/aig.v3.i5.142
Published online Dec 28, 2022. doi: 10.35712/aig.v3.i5.142
Table 2 AI-based applications in pathology for the determination of tumor behavior in colorectal carcinomas
| Ref. | Task | Data sets | Algorithm/Model | Performance | Comments |
| Xu et al[55] | NL/ADC/MC/SC/PC/CCTA | 717 patches | AlexNet | Accuracy: 97% | The model provides the classifications of tumor subtypes |
| Korbar et al[56] | NL/HP/SSP/TSA/TA/TVA-VA | Training set: 458 WSIs; Test set: 239 WSıs | ResNET | F1 Score: 88.8%; Accuracy: 93%; Precision: 89.7%; Recall: 88.3% | The model may reduce the workload of pathologists in the assessment of colorectal polyps |
| Haj-Hassan et al[57] | NL/AD/ADC | 30 patients, Multispectral image patches | CNN | Accuracy: 99.2% | CNN allows the classification of CRC tissue types using pre-segmented regions of interest |
| Ponzio et al[58] | NL/AD/ADC | 27 WSIs | VGG16 | Accuracy: 96% | TL considerably outperforms the CNN fully trained on CRC samples on the same test dataset |
| Sena et al[59] | NL/HP/AD/ADC | 393 images | CNN | Accuracy: 80% | DL may provide a valuable tool to assist pathologists in the histological classification of CR tumors |
| Iizuka et al[60] | NL/AD/ADC | 4036 WSIs + 500WSIs | CNN/RNN | AUCs: 0.96-0.99 | Integrating DL models in pathology workflow would be of high benefit for easing the workload of pathologists |
| Wei et al[61] | NL//TA/TVA/VA/HP | 1182 WSIs | ResNet | Accuracy: 93.5% (Internal test set); Accuracy: 87% (External test set) | This model may assist pathologists by improving the accuracy of CRC screening |
| Awan et al[62] | NL/Low GR/High GR | 139 images | CNN | Accuracy: 97% (two-class), 91% (three-class) | The model provides the classifications of tumor subtypes based on the shape of glands |
| Sirinukunwattana et al[97] | Prediction of MSTs | 510 WSIs (FOCUS), 431 WSIs (TCGA), 265 WSIs (GRAMPIAN cohort) | Inception V3 | AUCs: 0.9 (FOCUS); 0.94 (TCGA), 0.85 (GRAMPIAN cohort) | RNA expression classifiers can predict from H-E stained images, opening the door to cheap and reliable biological stratification within routine workflows |
| Echle et al[98] | MSI vs MSS | 6406 WSIs (Training); 771 WSIs (External validation) | ShuffleNet | AUC: 0.92 (Training); AUC: 0.96 (External validation) | The model provides a low-cost evaluation of MSI without molecular testing |
| Kather et al[80] | MSI vs MSS | 60894 patches (TCGA-CRC-KR); 93408 patches (TCGA-CRC-DX) | ResNet18 | AUC: 0.84 (TCGA-CRC-KR); AUC: 0.77 (TCGA-CRC-DX) | This method may lead to improvements in molecular subtype screening workload in pathology |
| Kather et al[77] | Prediction of molecular Als | 426 patients (TCGA-CRC); 379 patients (DACHS) | ShuffleNet | AUROC: 0.76 | The algorithm predicts a wide range of molecular alterations from routine, H-E stained slides |
| Kruger et al[99] | Prediction of MSTs | 919 WSIs | ResNet 34 | AUCs: Mean: 0.87; CMS1: 0.85; CMS2: 0.92, CMS3: 0.85; CMS4: 0.86 | The MIL framework can identify morphological features indicative of different molecular subtypes |
| Popovici et al[100] | Prediction of MSTs | 300 WSIs | VGG-F | Accuracy: 0.84; Recall: 0.85; Precision: 0.84 | The image-based classifier shows a significant prognostic value similar to the molecular counterparts |
| Cao et al[101] | MSI vs MSS | 429 patients (TCGA-COAD); 785 patients (Asian-CRC) | EPLA | AUC: 0.88 (TCGA-COAD); AUC: 0.85 (Asian-CRC) | This pathomics-based model provides MSI estimation directly from images without molecular testing |
| Bilal et al[102] | Prediction of molecular Als | 502 slides (TCGA-CRC-DX); 47 slides (PAIP) | ResNet18, ResNet34, HoVerNet | AUROCS: HM (0.81 vs 0.71); MSI (0.86 vs 0.74); CIN (0.83 vs 0.73), BRAFmut (0.79 vs 0.66), TP53mut (0·vs 0.64), KRASmut (0.60), CIMP (0.79) | This algorithm is based on non-annotated images and uses only slide-level labels to predict the status of CRC pathways and mutations |
| Kwak et al[110] | LNM prediction | 164 patients | CNN, U-Net | AUROC: 67% | PTS score is a potential prognostic parameter for LNM in CRC |
| Pai et al[111] | LNM prediction | 230 patients (training), (136 testing) | CNN | AUROC: 79% | The model allows to identify and quantify a broad spectrum of histological features, including LNM in CRC |
| Kiehl et al[112] | LNM prediction | 3013 patients | ResNET18 | AUROC: 74.1% | DL-based analysis may help predict the LNM of patients with CRC using routine HE-stained slides |
| Weis et al[120] | Tumor Budding (Pan-CK) | 381 patients | CNN | Spatial clusters of tumor buds correlates to N status (P: 0.003) | The model is a feasible and valid assessment tool for tumor budding on WSIs and can predict prognosis |
| Kather et al[121] | ADI, DEB, LYM, MUC, SM | 86 slides (Training), 25 slides (Testing); 862 slide (TCGA-COAD) | VGG19 | AUC: 98.7% HR: 2.29 (OS); 1.92 (RFS); Deep stroma score HR: 1.99 (P: 0.002), Shorter OS | This model can assess the human TME and predict prognosis directly from histopathological images |
| Shapcott et al[122] | TME (EC/IC/FC/MC) | 853 patches, 142 images (TCGA-COAD) | CNN | Accuracy: 76% (detection), 65% (classification) | The model provides the assessment of TME in CRC slides |
| Sirinukunwattana et al[123] | a-4 tissues classes; b- prediction of DM | 102 cases | Spatially Constrained CNN | a-AUROC: 90.4-99.9%; b-AUROC: 58.6-63.8% | The algorithm provides a digital marker for estimating the risk of DM |
| Swiderska-Chadaj et al[124] | TME Detection of ICs | 28 WSIs | FCN/LSM/U-Net | F1-score of 0.80; Sensitivity: 74%; Precision: 86% | DL approaches are reliable for automatically detecting lymphocytes in IHC-stained CRC tissue sections |
| Geessink et al[115] | TSR | 129 slides | CNN | HR: 2.48 (DSS); 2.05 (DFS) | CNN defined TSR as an independent prognosticator |
| Zhao et al[125] | TSR | 499 patients (Discovery cohort); 315 patients (Validation cohort:) | CNN | TSR, independent prognostic parameter. HRs: 2.48 (Discovery cohort); 2.08 (Validation cohort) | CNN allows objective evaluation of TSR |
| Zhao et al[126] | Mucus tumor ratio low vs mucus tumor ratio high | 814 patients | CNN | HRs: 1.88 (Discovery cohort); 2.09 (Validation cohort) | The DL quantified mucus tumor ratio is an independent prognostic factor in CRC |
| Bychkov et al[132] | Prognosis LR vs HR | 420 TMA | VGG-16 | HR: 2.3 | The model extracts more prognostic information from the tissue morphology than the experienced human observer |
| Skrede et al[133] | Prognosis (CSS) | 1122 patients (Validation cohort) | DoMorev1 | HRs: 1.89 (uncertain vs good); 3.84 (poor vs good) | The digital marker has the potential to identify patients at LR and HR and provides the selection of treatment |
| Jiang et al[134] | a-HRR vs LRR b-Poor vs good prognosis | 101 patients (Traning); 67 patients (Validation); 47 (TCGA-COAD) | InceptionResNetV2 | a-HRs: 8.98 (training); 10.69 (other 2 test groups); b-HRs: 10.687 (training); 5.03 (other 2 test groups) | The selected model offers an independent prognostic predictor which allows stratification of stage III CRC into risk groups |
- Citation: Yavuz A, Alpsoy A, Gedik EO, Celik MY, Bassorgun CI, Unal B, Elpek GO. Artificial intelligence applications in predicting the behavior of gastrointestinal cancers in pathology. Artif Intell Gastroenterol 2022; 3(5): 142-162
- URL: https://www.wjgnet.com/2644-3236/full/v3/i5/142.htm
- DOI: https://dx.doi.org/10.35712/aig.v3.i5.142