Table 2. Results from the ROB assessment of nine studies using PROBAST.
| Study | ROB | Applicability | Overall | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Participants | Predictors | Outcome | Analysis | Participants | Predictors | Outcome | ROB | Applicability | |||
| The development and external validation of an overall survival nomogram in medically inoperable centrally located early-stage non-small cell lung carcinoma | – | – | – | + | + | ||||||
| A nomogram based on CT deep learning signature: a potential tool for the prediction of overall survival in resected non-small cell lung cancer patients | – | – | – | + | + | ||||||
| Development and validation of a nomogram for preoperative prediction of lymph node metastasis in lung adenocarcinoma based on radiomics signature and deep learning signature | – | – | – | + | + | ||||||
| A seven-gene signature with close immune correlation was identified for survival prediction of lung adenocarcinoma | ? | – | – | + | + | ||||||
| Identification and validation of a tumor microenvironment-related gene signature for prognostic prediction in advanced-stage non-small-cell lung cancer | – | – | – | + | + | ||||||
| Development of an immune-related gene pairs signature for predicting clinical outcome in lung adenocarcinoma | ? | – | – | + | + | ||||||
| Identification of a 5-gene metabolic signature for predicting prognosis based on an integrated analysis of tumor microenvironment in lung adenocarcinoma | ? | – | – | + | + | ||||||
| A model of twenty-three metabolic-related genes predicting overall survival for lung adenocarcinoma | – | – | – | + | + | ||||||
| A prognostic nomogram combining immune-related gene signature and clinical factors predicts survival in patients with lung adenocarcinoma | ? | – | – | + | + | ||||||
+, low ROB/low concern regarding applicability; –, high ROB/high concern regarding applicability; ?, unclear ROB/unclear concern regarding applicability. ROB, risk of bias.