Can quantitative peritumoral CT radiomics features predict the prognosis of patients with non-small cell lung cancer? A systematic review

Abstract

Objectives: To provide an overarching evaluation of the value of peritumoral CT radiomics features for predicting the prognosis of non-small cell lung cancer and to assess the quality of the available studies.

Methods: The PubMed, Embase, Web of Science, and Cochrane Library databases were searched for studies predicting the prognosis in patients with non-small cell lung cancer (NSCLC) using CT-based peritumoral radiomics features. Information about the patient, CT-scanner, and radiomics analyses were all extracted for the included studies. Study quality was assessed using the Radiomics Quality Score (RQS) and the Prediction Model Risk of Bias Assessment Tool (PROBAST).

Results: Thirteen studies were included with 2942 patients from 2017 to 2022. Only one study was prospective, and the others were all retrospectively designed. Manual segmentation and multicenter studies were performed by 69% and 46% of the included studies, respectively. 3D-Slicer and MATLAB software were most commonly used for the segmentation of lesions and extraction of features. The peritumoral region was most frequently defined as dilated from the tumor boundary of 15 mm, 20 mm, or 30 mm. The median RQS of the studies was 13 (range 4-19), while all of included studies were assessed as having a high risk of bias (ROB) overall.

Conclusions: Peritumoral radiomics features based on CT images showed promise in predicting the prognosis of NSCLC, although well-designed studies and further biological validation are still needed.

Key points: • Peritumoral radiomics features based on CT images are promising and encouraging for predicting the prognosis of non-small cell lung cancer. • The peritumoral region was often dilated from the tumor boundary of 15 mm or 20 mm because these were considered safe margins. • The median Radiomics Quality Score of the included studies was 13 (range 4-19), and all of studies were considered to have a high risk of bias overall.

Keywords: Carcinoma, non-small-cell lung; Machine learning; Prognosis; Solitary pulmonary nodule; Tomography, X-ray computed.

Fig. 1

Flowchart of the study screening and selection process of this systematic review

Fig. 2

The three different types of definitions for peritumoral regions were as follows: Type 1: Border Mask: (−12.5 or −15 to + 7.5 or +10) mm [27], (−3 to + 3) mm [22, 35, 36]; Outside Mask: (0 to +17.5 or + 22.5) mm [27], (0 to + 3 or + 6) mm [35]; Exterior Mask: (+3 to +9) mm [22]. Type 2: Border Mask: (0 to +3) mm [32]; Outside Mask: (0 to +15) mm [23, 28, 29, 33, 34], (0 to +20) mm [30], (0 to +30) mm [31]. Type 3: Tumor Mask: gross tumor volume; Border Mask: clinical target volume minus Tumor Mask; Outside Mask: planning target volume minus Tumor Mask [37]. −: inward erosion; +: outward dilation; 0: tumor boundary

Fig. 3

Quality assessment of included studies by the Radiomics Quality Score (RQS) and presenting the percentages of scores of the included studies

Fig. 4

The percentage of the included studies rated by the risk of bias and applicability using the Prediction Model Risk of Bias Assessment Tool (PROBAST)