Table 3.
Description of studies using different radiomics featured to determine the histologic subtype classification of lung cancer.
| Year | References | Number of Cases | Imaging Modality | Group a | Validation * | Combined Model b | Diagnostic Performance |
|---|---|---|---|---|---|---|---|
| 2018 | Xinzhong Zhu [67] | 129 | CT | SPN | Yes (internal) | No | AUC = 0.905 (ADC vs. SCC) |
| 2021 | Yong Han [76] | 1419 | CT | SPN | Yes (internal) | No | AUC = 0.903 |
| 2021 | Huanhuan Li [73] | 200 | CT | SPN | Yes (internal) | No | AUC = 0.879 (ADC vs. SCC), 0.836 (ADC vs. SCLC), 0.783 (SCC vs. SCLC) |
| 2019 | Linning E [68] | 229 | CT | SPN | No | No | AUC = 0.801 (ADC vs. SCC), 0.857 (ADC vs. SCLC), 0.657 (SCC vs. SCLC) |
| 2021 | Yixian Guo [75] | 920 | CT | SPN | Yes (internal) | No | AUC = 0.84 (Accuracy = 71.6%) |
| 2021 | Fengchang Yang [77] | 324 | CT | SPN | Yes (internal) | No | AUC = 0.78 |
| 2021 | Zahra Khodabakhshi [74] | 354 | CT | SPN | No | No | AUC = 0.747 (Accuracy = 86.5%) |
| 2019 | Linning E [71] | 278 | CT | SPN | No | No | AUC = 0.741 (SCLC vs. NSCLC) |
| 2020 | Charlems Alvarez-Jimenez [65] | 171 | CT | SPN | Yes (external) | No | AUC = 0.72 (ADC vs. SCC) |
| 2019 | Jian Liu [70] | 349 | CT | SPN | Yes (internal) | No | Accuracy = 89% |
| 2021 | Yan lei Ji [72] | 253 | CT | SPN | Yes (internal) | Yes | AUC = 0.982 (ADC vs. SCC) |
| 2021 | Jianyuan ZHOU [78] | 182 | PET-CT | SPN | Yes (internal) | Yes | AUC = 0.862 (Sensitivity = 88%, Specificity = 72.73%, ADC vs. SCC) |
| 2019 | Xue Sha [69] | 100 | PET-CT | SPN | Yes (internal) | Yes | AUC = 0.781 (Sensitivity = 100%, Specificity = 70%, ADC vs. SCC) |
| 2016 | Weimiao Wu [66] | 350 | CT | SPN | Yes (internal) | Yes | AUC = 0.72 (ADC vs. SCC) |
ADC: adenocarcinoma; SCLC: small cell lung cancer; NSCLC: non-small cell lung cancer; SCC: squamous cell carcinoma. a Group: refers to the type of lung nodules analyzed in this study. b Combined model: refers to whether the model has clinical or semantic information added to it. * Internal stands for internal validation; external stands for external validation. Internal validation was defined as a prediction method drawn from a similar population as the original training cohort; external validation is the action of testing the developed prediction model in a set of the population independent of the original training cohort.