Risk factors and risk prediction models for colorectal cancer metastasis and recurrence: an umbrella review of systematic reviews and meta-analyses of observational studies

A systematic search was performed in PubMed, Cochrane Library (Wiley), Web of Science (Thomson Reuters), and EMBASE (Ovid) from inception to 7 October 2019, to identify systematic reviews of observational studies with or without a meta-analysis that evaluated the associations between risk factors and risk of metastasis and recurrence in CRC (Additional file 1: Table S1). We further hand-searched reference lists of the retrieved eligible publications to identify additional relevant studies. All identified publications went through a parallel review of the title, abstract, and full text (performed by WX and YM independently) based on pre-defined inclusion and exclusion criteria following “PICOS.” In particular, we included human participants from observational studies with no restriction to settings. Conversely, animal, in vitro, and in vivo experiments were excluded. For study outcomes, we included CRC metastasis (local, regional, or distant metastasis in tissues, lymph nodes, or organs at diagnosis) and CRC recurrence (local, regional, or distant metastatic recurrence in tissues, lymph nodes, or organs after a disease-free period). For study design, we included systematic reviews of observational studies with or without meta-analysis. Conversely, literature reviews, individual observational studies, systematic reviews, and meta-analyses that investigated the evidence on the efficacy of pharmaceutical drugs and therapeutic procedures were excluded. We included publications in peer-reviewed journals, and therefore, gray literature, comments, conference abstracts, and interviews were excluded.

First, when two or more meta-analyses examining associations between the same risk factor and the same outcome were identified, the most recent meta-analysis of prospective cohort studies with the largest event number was prioritized and retained for further analysis. We also compared whether the results reported in overlapping meta-analyses were concordant in terms of direction, statistical significance, and association magnitude.

Second, we estimated the following metrics for each unique meta-analysis: (1) The summary effect size along with 95% CI was estimated based on random-effects models (DerSimonian and Laird (DL)) when the number of component studies was five or more and the Hartung-Knapp-Sidik-Jonkman (HKSJ) when the number of component studies was less than five [12, 13]. (2) Heterogeneity was assessed by the I² statistic [14]. (3) The 95% prediction interval was estimated. (4) The small study effect was estimated by Egger’s regression asymmetry test [15]. (5) Excess significance was assessed by a chi-square test [16]. Based on these metrics and by applying a set of pre-defined criteria (Additional file 1: Table S8), we evaluated the credibility of the evidence for each risk factor and categorized the evidence as convincing, highly suggestive, suggestive, or weak [17, 18].

Lastly, for all meta-analyses that statistically represented at least 3-fold changes in the odds of the outcome, we evaluated the methodological quality and risk of bias based on the Assessment of Multiple Systematic Reviews 2.0 (AMSTAR 2.0) checklist [19]. We used an odds ratio of 3.0 as a threshold for what is a substantially large effect. There is no consensus on what an optimal threshold might be, but values between 2 and 5 are proposed typically [20].

We re-ran all meta-analyses by evaluating the outcome definitions of each individual component study reclassifying the outcomes to (i) CRC metastasis at presentation, (ii) CRC local recurrence after a disease-free period, and (iii) CRC distant recurrence after a disease-free period.

Twelve prognostic models have been developed for prediction of CRC metastasis [22‐33] (Table 3). The median number of included predictors was four (range 3–9), and 27 unique predictors were included in at least one model. Cancer stage (N = 9, 75%) was the most commonly used predictor variable in the 12 prognostic models. Other common predictors included histopathological risk factors such as positive lymph nodes (N = 3, 25%), tumor grade or differentiation (N = 2, 17%), and tumor histological type (N = 3, 25%); biomarker-carcinoembryonic antigen (CEA) (N = 3, 25%); age (N = 3, 25%); gender (N = 2, 17%); and clinical treatment such as surgery, chemotherapy, and radiotherapy (N = 3, 25%). Five models (42%) performed internal validation, and four models (33%) were validated in external datasets.

We conducted a cross-assessment between these predictors and 34 risk factors that were evaluated in our umbrella review. Six of 27 unique predictors (tumor budding, tumor differentiation, tumor size, vascular invasion, submucosal invasion, and sex) were evaluated in the umbrella review (Table 5). The associated ORs for these six risk factors varied from 2.23 to 6.76, and four of them (67%) corresponded to ≥ 3-fold change in the odds of the outcome. Of the remaining 28 risk factors that were not employed in prediction models, ORs varied from 0.45 to 6.78, and 13 (46%) represented ≥ 3-fold change in the odds of the outcome.

In addition, we compared the overlapping outcomes to investigate whether prediction models had included influential risk factors (those presented convincing evidence or with 3-fold change in the odds of the outcome) when they predicted the same outcomes as those evaluated in the umbrella review (Table 6). In total, four overlapping outcomes were found in this cross-assessment (LNM in pT1 CRC, LNM in CRC, hepatic metastasis in CRC, and distant metastasis in CRC). For only one outcome (LNM in pT1 CRC), two prognostic models [22, 28] included four risk predictors that were also evaluated in the umbrella review, two of which corresponded to ≥ 3-fold change in the odds of the outcome (tumor budding, tumor differentiation).

Twelve prognostic models [31, 32, 34‐43] were developed for prediction of CRC recurrence (Table 4). The median number of risk predictors applied in 12 prognostic models was five (range 2–8), and 25 unique predictors were included in at least one model. AJCC (TNM) cancer stage was the predictor variable (N = 7, 58%) used in the majority of CRC recurrence risk prediction models. Other common predictor variables included histopathological risk factors such as positive lymph nodes (N = 5, 42%), tumor grade or differentiation (N = 4, 33%), and tumor size (N = 4, 33%); biomarker-CEA (N = 4, 33%); cancer location (N = 4, 33%); and clinical treatment such as surgery, chemotherapy, radiotherapy, and transfusion (N = 5, 42%). Two models (17%) performed internal validation, and 4 models (33%) were validated in external datasets.

In our cross-evaluation, five of 25 unique predictors (intramural vascular invasion, extramural vascular invasion, being underweight, being overweight, and being obese) were evaluated in the umbrella review (Table 5). The associated ORs for these five factors varied from 1.00 to 3.91, and only one (20%) (extramural vascular invasion) corresponded to ≥ 3-fold change in the odds of the outcome. Of the remaining 12 factors evaluated in the umbrella review, ORs varied from 0.07 to 5.50, and three (25%) represented ≥ 3-fold change in the odds of the outcome.

In relation to overlapping outcomes, only one outcome (overall recurrence in CRC) was identified (Table 6). However, the prognostic model [36] included risk predictors that were not evaluated in the umbrella review (cancer stage, tumor differentiation, and gene signature). Meanwhile, within the evaluated nine risk factors for overall recurrence in CRC that were not employed as predictors in this model, only two influential risk factors (tumor budding, absence of peritoneal free tumor cells in post-resection) had ≥ 3-fold change in the odds of the outcome.

According to our pre-defined criteria for assessing the credibility of the evidence, only one risk factor was classified as convincing (vascular invasion for LNM in pT1 CRC), reflecting strong statistical significance and no hints of bias. Many studies have demonstrated that the invasion of blood vessels leading to tumor cell dissemination and metastasis is a strong risk factor for disease prognosis, which is in line with our umbrella review [44, 45]. Based on our findings, a large proportion of studies (17/25, 68%) investigated lymphatic and vascular invasion as separate risk factors, while 32% of studies categorized them jointly as lymphovascular invasion. It has been shown though that the predictive ability of lymphovascular invasion is lower than that of vascular invasion [46].

Twelve (35%) of 34 probed risk factors for metastasis had an effect size suggesting ≥ 3-fold change in the odds of the outcome with p < 0.05. Four of these risk factors (lymphatic invasion for LNM in pT1 CRC; tumor budding for LNM in pT1 CRC; tumor budding for LNM in all stage CRC; tumor size > 1 cm for LNM in rectal cancer) were classified as highly suggestive. As discussed above, lymphatic invasion could be an indicator of tumor cells metastasizing to lymph nodes. This finding agrees with three recently published studies manifesting that lymphatic invasion is causally associated with the risk of LNM in CRC [47‐49]. Tumor budding is recognized as a negative prognostic risk factor for LNM in CRC, and our findings are concordant with previous studies [50‐52]. Individual component studies vary in their definitions of tumor budding (e.g., how many cancer cells comprise a tumor bud, and how many buds signify tumor budding) and vary in the pathologic staining methods to detect tumor budding (e.g., hematoxylin and eosin [H&E], immunohistochemistry [IHC]). Furthermore, a systematic review summarized pathologic methods to detect tumor budding and revealed that all studies even when utilizing different methods showed that tumor budding increases the risk of CRC metastasis [53]. Notably, substantial between-study heterogeneity (I² > 50%) was found in the meta-analysis investigating tumor budding for LNM in all CRC stages, indicating that this association needs to be interpreted with caution. The observed heterogeneity may be influenced by the inclusion of different tumor stages. Finally, tumor size > 1 cm is associated with an increased risk of LNM in rectal cancer. This largely agrees with the European Society for Medical Oncology (ESMO) clinical practice guideline manifesting that a rectal lesion less than 1 cm has a lower risk of metastasis, and therefore, local excision (TEM) is suggested [54].

In regard to 17 probed risk factors for CRC recurrence, four (24%) had an effect size suggesting ≥ 3-fold change in the odds of the outcome with p < 0.05. None of them presented convincing evidence. Three (tumor budding for overall recurrence in CRC; perineural invasion [PNI] for local recurrence in rectal cancer; MRI-detected extramural vascular invasion [mrEMVI] for distant metastatic recurrence in rectal cancer) were classified as highly suggestive. Our findings suggest that tumor budding is a common highly suggestive risk factor for both CRC LNM and overall recurrence. However, there is a need for standardization of the histopathological definition of tumor budding [46]. Another histopathological risk factor, PNI, which is a common pathological feature in rectal cancer, strongly signifies local recurrence. Compared to colon cancer, PNI occurs more frequently in rectal cancer, since there is a cluster of intensive neural plexuses surrounding the pelvis in the rectum [55]. The National Comprehensive Cancer Network (NCCN) guidelines also suggest that patients with PNI positive are at higher risk of local recurrence [56]. However, there is no consensus in the definition of PNI positive, with two of the most frequently used definitions being SS-PNI (when tumor cells surround at least 33% of the nerve) and TS-PNI (when tumor cells surround any of the three layers of the nerve) [57‐60]. Finally, we found that mrEMVI increases the risk of distant metastatic recurrence. EMVI is the venous invasion beyond the muscularis propria, which has long been recognized as a risk factor for distant recurrence [61‐63]. The 5-point MRI-detected EMVI scoring system is precise for detecting this invasion, and it is recommended as a post-operation follow-up strategy in clinical settings [64]. In addition, a recently published meta-analysis is also in line with our findings, reporting that around 90% of patients with liver metastases are mrEMVI positive [65].

In our effort for a consistent definition of metastasis and recurrence, we re-categorized all the component studies to three distinct disease outcomes: metastasis at presentation, local recurrence, and distant recurrence. This could generate insight into metastasis and recurrence patterns and provide investigators and clinicians with a more comprehensive summary of risk factors for these CRC prognostic outcomes with clinical significance [66]. Our sensitivity analyses reported a dearth of convincing evidence. However, a total of 244 (62%) individual component studies were excluded from our sensitivity analyses due to missing information in relation to outcome definition.