Systematic Review, Meta-Analysis and Radiomics Quality Score Assessment of CT Radiomics-Based Models Predicting Tumor EGFR Mutation Status in Patients with Non-Small-Cell Lung Cancer.

Assessment of the quality and current performance of computed tomography (CT) radiomics-based models in predicting epidermal growth factor receptor (EGFR) mutation status in patients with non-small-cell lung carcinoma (NSCLC). Two medical literature databases were systematically searched, and articles presenting original studies on CT radiomics-based models for predicting EGFR mutation status were retrieved. Forest plots and related statistical tests were performed to summarize the model performance and inter-study heterogeneity. The methodological quality of the selected studies was assessed via the Radiomics Quality Score (RQS). The performance of the models was evaluated using the area under the curve (ROC AUC). The range of the Risk RQS across the selected articles varied from 11 to 24, indicating a notable heterogeneity in the quality and methodology of the included studies. The average score was 15.25, which accounted for 42.34% of the maximum possible score. The pooled Area Under the Curve (AUC) value was 0.801, indicating the accuracy of CT radiomics-based models in predicting the EGFR mutation status. CT radiomics-based models show promising results as non-invasive alternatives for predicting EGFR mutation status in NSCLC patients. However, the quality of the studies using CT radiomics-based models varies widely, and further harmonization and prospective validation are needed before the generalization of these models.

Radiology workflow for RECIST assessment in clinical trials: Can we reconcile time-efficiency and quality?

PURPOSE: In oncology clinical trials, nonconformity issues are frequently reported. Radiological workload is increasing, thus reducing radiologists' availability and affecting diagnostic quality. We compared performances of a standard radiological workflow (SW) and a novel "hybrid workflow" (HW). METHOD: We prospectively studied imaging data of 40 patients included in RECIST 1.1 clinical trials. Ninety-six time-points were reviewed by 7 radiologists and one trained technologist. Nonconformities using the SW were retrieved from hospital archives. For the HW, radiologists performed all baseline evaluations; the technologist made subsequent measurements. Finally, the radiologists checked the technologist's findings before confirming the evaluations. The HW enabled implementation of an electronic reporting system. An independent body compared SW and HW reading times and nonconformity occurrences. RESULTS: Using SW, 19 types of nonconformity were found: blank report (13%); unsigned report (11%); undocumented change of tumor burden (10%); undocumented new lesions (9%); missing/wrong patients' appointment dates (7%); undocumented tumor location (5%); error in tumor burden change (5%). SW and HW nonconformities affected 55% (179/323) and 5% (2/40) of reports, respectively (p < 0.001). HW nonconformities were: one inaccurate login name was used on the platform, and one erroneous time-point number. On average, SW required 11'30″ [10'06″; 13'20″] per time-point. HW required 1'35″ [40″; 5'08″] for radiologists, and 12'18″ [11'12″; 14'18″] for the technologist. CONCLUSIONS: HW significantly reduced the number of trial nonconformities and saved 87% of radiologists' time while enabling them to apply their expertise to final decisions. HW could offer an effective opportunity for cost reduction associated with improved imaging trial quality.