Table 1.
Comprehensive list of artificial intelligence-based investigations in pancreatic ductal adenocarcinoma
|
Ref.
|
Modality
|
Type of algorithm
|
Sensitivity (%)
|
Specificity (%)
|
ROC-AUC (or accuracy %)
|
| PDAC risk prediction | |||||
| Boursi et al[25], 2021 | 7 clinical variables | Logistic regression | 66.53 | 54.91 | 0.71 |
| Appelbaum et al[29], 2021 | 18 risk factors | Logistic regression | NA | NA | 0.71 |
| Muhammad et al[30], 2018 | Personal health data (18 features) | ANN | 80.7 | 80.7 | 0.85 |
| Hsieh et al[28], 2018 | ICD-9 code | Logistic regression | NA | NA | 0.727 |
| Boursi et al[26], 2017 | 10 clinical variables | Logistic regression | 44.7 | 94 | 0.82 |
| Cai et al[27], 2011 | 5 clinical variables | Logistic regression | NA | NA | 0.72 |
| Early diagnosis of PDAC | |||||
| Zhang et al[34], 2020 | Nine-gene signature | Support vector machine | 98.65 | 100 | 93.3 |
| Zhang et al[83], 2020 | CT | DCNN | 83.76 | 91.79 | 0.9455 |
| Si et al[42], 2021 | CT | Fully end-to-end deep learning | 86.8 | 69.5 | 0.871 |
| Liu et al[54], 2020 | CT | CNN | 79 (United States) | 97.6 (United States) | 0.920 (United States) |
| Ma et al[84], 2020 | CT | CNN | 98.2 | 91.6 | 95 |
| Chu et al[85], 2019 | CT | Deep learning (details are NA) | 94.1 | 98.5 | NA |
| Liu et al[53], 2019 | CT | CNN | NA | NA | 0.9632 |
| Tonozuka et al[86], 2021 | EUS | CNN | 90.2 | 74.9 | 0.924 |
| Ozkan et al[87], 2016 | EUS | ANN | 83.3 | 93.3 | 87.5 |
| Săftoiu et al[88], 2015 | EUS | ANN | 94.64 | 94.44 | NA |
| Zhu et al[63], 2013 | EUS | Support vector machine | 92.52 | 93.03 | NA |
| Zhang et al[62], 2010 | EUS | Support vector machine | 94.32 | 99.45 | NA |
| Das et al[61], 2008 | EUS | ANN | 93 | 92 | 0.93 |
| Săftoiu et al[89] 2008 | EUS elastography | NN | 91.4 | 87.9 | 89.7 |
| Norton et al[60], 2001 | EUS | NN | 73 | NA | 83 |
| Alizadeh Savareh et al[40], 2020 | Circulating microRNA signatures | PSO + ANN + NCA | 93 | 92 | 93 |
| Urman et al[90], 2020 | Bile juice | NN | 88 | 100 | 0.98 |
| Pancreatic fistula after pancreaticoduodenectomy | |||||
| Kambakamba et al[71], 2020 | CT | k-NN, random forest classifier, etc | 96 | 98 | 0.95 |
| Mu et al[72], 2020 | CT | CNN | 86.7 | 87.3 | 0.89 |
| Pathological tumor response to neoadjuvant chemotherapy | |||||
| Watson et al[80], 2020 | CT and CA19-9 | CNN | NA | NA | 0.785 |
| Survival model | |||||
| Zhang et al[77], 2020 | CT | CNN | NA | NA | 11.81% in IPA |
| Alizadeh Savareh et al[40], 2020 | Circulating microRNA signatures | PSO + ANN + NCA | NA | NA | NA |
| Kaissis et al[66], 2019 | MRI | Random forest | 87 | 80 | 0.90 |
| Walczak et al[79], 2017 | 14 clinical variables | ANN | 91 | 38 | 0.6576 |
| Molecular subtype | |||||
| Kaissis et al[68], 2020 | CT | Random forest | 84 | 92 | 0.93 |
| Tumor subtype (QM vs non-QM) | |||||
| Kaissis et al[67], 2019 | MRI | Gradient boosting decision tree | 90 | 92 | 0.93 |
| Molecular subtype (KRT81 positive vs negative) | |||||
| Microsatellite instability status | |||||
| Li et al[19], 2020 | PreMSIm (15-gene signature) | k-NN | 85 | 97 | 95 |
AI: Artificial intelligence; PDAC: Pancreatic ductal adenocarcinoma; NA: Not available; ROC-AUC: Area under the receiver operating characteristic curve; ICD-9: International Classification of Diseases 9th Revision; ANN: Artificial neural network; CT: Computed tomography; DCNN: Deep convolutional neural network; EUS: Endoscopic ultrasound; NN: Neural network; CA19-9: Carbohydrate antigen 19-9; IPA: Index of prediction accuracy; MRI: Magnetic resonance imaging; QM: Quasi-mesenchymal; PSO: Particle swarm optimization; NCA: Neighborhood components analysis; k-NN: k-Nearest neighbor.