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

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. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-022-09174-8.


Introduction
Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, accounting for 85% of all cases [1,2]. The precise survival risk stratification of patients with NSCLC is a crucial step in treatment. Although the tumor, node, and metastasis (TNM) classification for lung cancer is the most objective and authoritative indicator of the prognosis, those in identical tumor stages still have heterogeneous prognoses [3][4][5]. To improve the management of NSCLC and make proper treatment decisions, numerous studies have reported other independent clinical prognostic factors, including age, sex, and performance status [6][7][8].
Radiomics based on medical imaging can assess the tumor and its environment in its entirety, which can provide additional information for predicting cancer outcomes [15][16][17]. Several studies have successfully applied intratumor radiomics features to predict the overall survival, the prognosis of cancer recurrence, and time to progression in patients with NSCLC [17][18][19]. Other studies have investigated the clinical use of quantifying peritumoral regions at CT to help predict tumor invasiveness, tumor spread through air spaces, and especially prognostic outcomes [20][21][22][23]. For example, Wang et al found that the combination of radiomics features extracted from intra-and peritumoral areas could enhance the accurate prognosis prediction of pure-solid NSCLC [23]. However, the added value of extratumoral radiomics and the quality of the studies have not been systematically assessed to further explore the potential association between peritumoral radiomics features and prognosis in NSCLC.
Therefore, the aim of this study was to systematically review and appraise the results from published studies that examined the prognostic value of CT-based peritumoral radiomics features in NSCLC patients, and the potential biological underpinnings were also summarized.

Materials and methods
This systematic review was reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines [24]. The review was registered on PROSPERO before initiation (registration no. CRD42022322916).

Search strategy
The PubMed, Embase, Web of Science, and Cochrane Library databases were comprehensively searched up to February 21, 2022, to identify studies that used CT-based peritumoral radiomics to evaluate the prognosis in patients with NSCLC.
The reference lists of the included articles and the relevant literature were also manually searched. The following basic search terms were used: NSCLC, pulmonary nodule, CT, radiomics, peritumoral, and prognosis. The detailed search criteria are described in the supplementary material. The retrieval was performed without language and date restrictions.

Study selection
Original research articles will be included in the study. Eligibility criteria included the following: (1) patients with NSCLC; (2) evaluating the prognosis of patients by a peritumoral radiomics approach on CT. Studies were excluded if they (1) were case studies, editorials, letters, review articles and conference abstracts; (2) were not in the field of interest; or (3) were overlaps in study populations.

Data extraction
Data to be extracted will include the following: (1) study details: first author, publication year, country, study design; (2) patient details: the source of data acquisition (single-center/ multicenter), type of cohort, sample size, TNM staging, histological subtype, type of treatment, prognostic outcome; (3) imaging details: CT tube voltage, reconstruction slice thickness (mm), plain or contrast CT; (4) radiomics details: segmentation software, segmentation method, peritumoral definition, and reference, feature extraction software, type of radiomics features, number of radiomics features, radiomics feature selection methods, type of models constructed, final classifier, number of radiomics features in the final model, type of radiomics features in the final model, and performance of the models. Two independent reviewers (L.W. and C.G.) completed the initial screening and extracted data from all enrolled studies.

Risk of bias assessment
The methodological quality of each study was evaluated by using the Radiomics Quality Score (RQS) [25] and the Prediction Model Risk of Bias Assessment Tool (PROBAST) [26]. The RQS provides a standardized and quantitative evaluation criterion for the methodology of radiomics researches. The RQS assessment contains sixteen key components from data selection, medical imaging, feature extraction, and exploratory analysis to modelling. Each item contributes to the final score and the total score ranges from -8 to 36 points [25]. Detail description of each item of RQS and the corresponding scores is provided in Table S1. PROBAST is a tool to assess the risk of bias (ROB) and the application of prediction models for diagnosis or prognosis. The risk of bias assessment of all enrolled studies was made by two reviewers (L.W. and C.G.) with a consensus agreement.

Literature search and data extraction
The flow diagram of the literature search of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis is shown in Fig. 1. A total of 433 studies were identified, in which 432 studies were identified by the comprehensive literature search and one study was identified by a hand search of the relevant literature. After screening and evaluating, 13 studies with 2942 patients meeting the criteria were included in this systematic review [22,23,[27][28][29][30][31][32][33][34][35][36][37].

The peritumoral radiomics model and possible biological underpinnings
All the included studies segmented both the intra-and peritumoral regions; however, the definitions of peritumoral regions varied. Three different definitions for peritumoral regions were summarized in Fig. 2. Almost all the performances of erosion and dilation were based on the morphology of tumors and can be classified into three types. In type 1, the border mask was defined to be inward erosion 12.5/15 mm [27] or 3 mm [22,35,36] to outward dilation 7.5/10 mm [27] or 3 mm [22,35,36] along the tumor border. The outside mask was defined as an area expanding outside from the tumor to 17.5/22.5 mm [27] or 3/6 mm [35]. The exterior mask was defined as an area 3 to 9 mm away from the tumor [22]. In type 2, the border mask was defined to be the region that expands 3 mm away from the tumor boundary [32] while the criteria for the outside mask was 15 mm [23,28,29,33,34] or 20 mm [30] or 30 mm [31]. In type 3, the gross tumor volume equalled the original volume of the tumor lesion without any erosion or dilation performance. The clinical target volume contained gross tumor volume plus an area expanding outside from tumor boundary. The planning target volume was defined as the combination of tumor volume and the area dilated from the tumor border, which was necessary to manage internal motion and set-up reproducibility [37].
Several researchers have explored the biological underpinnings of peritumoral radiomics features in the prediction of the prognostic outcome of patients with NSCLC [27,[31][32][33].  Khorrami et al investigated associations between changes in radiomics features and the density of tumor-infiltrating lymphocytes on digitized hematoxylin-eosin images [31]. Pérez-Morales et al analyzed the associations between the final two radiomics features with gene probesets [32]. Vaidya et al investigated associations between prognostic radiomics features and tumorinfiltrating lymphocytes (radiopathomic analysis), as well as the radiomics features and mRNA sequencing data (radiogenomic analysis) [33]. Tunali et al explored potential biological underpinnings by analyzing the correlations of radiomics features with semantic radiological features [27]. Others also discussed the possible pathological basis of prognostic radiomics features from the peritumoral region, such as "real invasive front," hypoxic tumor environment, neovascularization and angiogenesis in the tumor microenvironment, lymphovascular tumor invasion and micrometastasis [22, 28-30, 34, 35].

Quality assessment
The total RQS and the percentages of the maximum score are summarized in Table 3. The median RQS of the studies was 13 (range [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19], and the corresponding percentage of the score was 36.11% (range 11.11-52.78%). Figure 3 shows the percentages of scores in the studies for the sixteen components of RQS. The results of the ROB and the applicability assessments of these studies were presented in Table 4. Figure 4 presents the percentage of the studies rated by level of concern, ROB, and applicability for each domain. All of studies were assessed as high ROB overall [22, 23 27-32, 33-37].
Using standardized radiomics analysis was advocated to eliminate unnecessary confounding variability [25,38]. With included studies having a wide range of section thicknesses (0.6-5 mm), the impact of section thickness on the performance of the model should be evaluated. Khorrami  ROB, risk of bias; PROBAST, prediction model risk of bias assessment tool; + indicates low ROB/low concern regarding applicability; − indicates high ROB; ? indicates unclear ROB/unclear concern regarding applicability Fig. 3 Quality assessment of included studies by the Radiomics Quality Score (RQS) and presenting the percentages of scores of the included studies evaluated the impact of section thickness on the performance of the classifier and found that the areas under the receiver operating characteristic curves for the radiomics model decreased slightly when the section thickness increased [28,29,31]. Bettinelli et al found that the agreement of seven radiomics software programs varied [39]. The test-retest and differences in the inter-CT and intra-CT protocols can affect the stability of radiomics features to different degrees [40]. Therefore, several studies selected stable and reproducible features on the test-retest RIDER lung CT dataset and retained features with an intraclass correlation coefficient of 0.75, 0.8, 0.85 or greater [22,[27][28][29][31][32][33][34]. ROIs can be segmented manually or (semi)automatically. However, manual segmentation remained the main method in the radiomics studies, and 69% of included studies segmented the ROI manually [22,23,28,29,31,[33][34][35]37]. The variability in manual delineations can be reduced by multiple segmentation, but it is time-consuming [25]. Hence, rapid and reliable automatic ROI segmentation is highly desired and is still challenging. Some efforts to automatically segment the lung nodules have been made, which is promising in the future [41][42][43]. Feature selection, modeling methodology, and validation were three major aspects of the radiomics model. Feature reduction for high-throughput radiomics features was performed to decrease the risk of overfitting by multiple methodologies, such as max-relevance and min-redundant, the least absolute shrinkage and selection operator method [22,28,29,33,35]. Validation is an indispensable component of radiomics analysis [25]. Most of the included studies conducted internal validation or even external validation from another center [22,23,[27][28][29][31][32][33][34][35].

et al
CT images may contain information that reflects the underlying pathophysiology of the tumor and that results in the conversion of images into structured data to assist in clinical decision support [38]. Peritumoral mask segmentation is usually based on morphologic operations (dilation) from the lesion boundary. Features are often extracted from three-dimension volume of interest and/or a section-by-section basis [22,23,[27][28][29][30][31], while a few studies extracted from the three slices have the maximum area of the tumor [33,34]. With an underlying biological rationale, such as "real invasive front" and micrometastasis around the tumor, the peritumoral regions of the included studies were dilated from the tumor boundary between 3 and 30 mm [22,23,[27][28][29][30][31][32][33][34][35][36]. The biological underpinning of radiomics is significantly important to its wider use and further validation. Efforts to explain the biological meaning of radiomics are emerging, including relationships with semantic features, gene expression, microscopic histopathologic findings, and macroscopic histopathologic marker expression [44]. Encouragingly, several researchers have investigated the correlation between prognostic radiomics features and the density of tumor-infiltrating lymphocytes and gene and mRNA sequencing data [31][32][33]. This exploration will reinforce our understanding of the biological meaning of peri-tumoral radiomics in the predicting prognosis of NSCLC patients.
The RQS was used to assess the methodology, analysis, and reporting of a radiomics study. The median RQS of the studies was 13 (range [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19], which indicates that most of the included studies did not reach a median level of radiomics quality. All the included studies conducted feature reduction, and biological correlates discussions. None of the included studies conducted a cost-effectiveness analysis, and most of the studies lacked open science. According to the PROBAST, all of the studies were considered to have a high ROB overall. The reasons for model development and validation studies with high ROB may be as follows: (1) Most of the included studies (12/13, 92%) were retrospective studies. (2) The calibration was not evaluated in most studies. (3) Whether predictors were assessed without knowledge of outcome information was also not mentioned.
This systematic review has several limitations that should be noted. First, the number of eligible studies was relatively small. Second, because high heterogeneity was found in 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) radiomics analysis, such as the type of treatment, outcome of prognosis, and radiomics modeling, a meta-analysis of pooled outcomes was not conducted. Third, most of the studies were evaluated as having low RQS and high ROB, so the results should be interpreted with caution.
In conclusion, growing evidence has shown that peritumoral CT-based radiomics features in predicting the prognosis of NSCLC are promising, although they need standardization in radiomics analysis. Because most of the studies were performed retrospectively, studies based on prospective, multiple centers as well as biological correlations should be further conducted to promote their clinical use.

Declarations
Guarantor The scientific guarantor of this publication is Chen Gao.

Conflict of interest
The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry
No complex statistical methods were necessary for this paper.
Informed consent Written informed consent was not required for this study because this was a systematic review.
Ethical approval Institutional Review Board approval was not required because this was a systematic review.

• retrospective • Systematic review
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