Here we firstly developed and validated a new approach basedon CT radiomics for the evaluation of PFS before treatment in ESCC (stage I-III). The radiomics signature from CT images demonstrated better prognostic performance than traditional clinical informations alone. It could be competently differentiated between patients with high-risk and low-risk, who had significantly different 3-year PFS, and were defined according to the median Rad-score. The developed radiomics nomogram transcended both the traditional TNM staging system and clinical nomogram alone.
In clinical practice, CT, magnetic resonance imaging (MRI), positron emission tomography (PET), and endoscopic ultrasound (EUS) have their own advantages and disadvantages in the staging of esophageal cancer, or even cancer. But the use of these modalities is limited for their cost in both time and money. CT own the highest cost performance for its high availability and noninvasive process. However, the traditional prognosis was depended on the doctors’ observation, which is differ greatly according to the experience. Moreover, the evaluation from traditional clinical informations is even more inadequate. It is believed that there is still a lot of digital information that can be deeply excavated through the radiomics methodology, and used for judgement conversely. Therefore, we analyzed all acquired CT images and constructed a CT-based radiomics signature. And the results confirmed our expectations that the radiomics signatures have the potential for evaluating prognosis in ESCC.
To build the radiomics signature, we selected 16 potential predictors from 954 candidate features through both selecting highly correlated features with event outcomes and LASSO logistic regression. The radiomics features obtained are generally accurate. Because the regression coefficients of most features have shrunk towards zero during model fitting. It not only allowed the identification of features that had strongest association with PFS [35], but also avoided over fitting [36]. The radiomics signatures from CT images could revealed adequate discrimination in both the training cohort (C-index, 0.785) and the validation cohort (C-index, 0.692). Additionally, the selected features were used to improve radiomics signature and Rad-scores. We sorted the Rad-scores of all the patients with the labeled living status in Fig. 2a, suggested that the Rad-score could potentially differentiate the two types of patients. Other related statistical analysis also supported that the radiomics signature could be used as a biomarker in prognosis of ESCC. Compared to the traditional TNM staging system and clinical nomogram, we found the radiomics signature took a dominating factor position in our nomogram in both the training cohort and validation cohort. It means the radiomics signature has better discrimination and prognosis ability compared to that of classical radiologists, indicating the clinical importance of our findings due to the traditional clinical information and TNM staging are routinely used in clinical practice [37, 38].
Generally, doctors are using the traditional TNM staging system for risk pridiction and treatment planning making nowadays. However, there were obvious differences in PFS with the same clinical identified disease stage, indicating that tumor heterogeneity would affect the survival outcomes. The ESCC patients (stage I-III) with shorter PFS may benefit from the prognostic model, because they may give up aggressive treatments to avoid the suffering and overspending. Here, we developed the radiomics features possessing better prognostic ability than traditional TNM staging system for pretreatment PFS in validation cohort as well as training cohort. It might because that our study was focused on ESCC patients with stage I-III tumors (Table 1), and the patients with stage I accounted for a small proportion (11.8% in training cohort, 6% in validation cohort). In consequence, it might difficult to accurately stratify PFS since the similar information of clinical stage. Moreover, the traditional TNM stage mainly reflect the clinicopathologic features of cancer patients, such as tumor size, lymph node involvement and distant metastasis status, respectively. They do have prognostic value in tumor treatment, but neglected the intratumor heterogeneity, which was deemed as a crucial factor for tumor progression and prognosis [39]. As a result, it provided an inefficient nomogram performance in both the training cohort (C-index, 0.628) and the validation cohort (C-index, 0.515). While the radiomics approach did extract the features of entire tumor from medical images, by which produce a more comprehensive way to noninvasively involve the intratumor heterogeneity. This might be why the combination of radiomics signatures and traditional TNM staging could provide a better nomogram performance in both training cohort (C-index, 0.802) and validation cohort (C-index, 0.691). Hence, the radiomics signatures could asist prognosis for ESCC complementarily to the traditional TNM staging.
Previous studies reported that clinical infromations, including gender, pathological type, tumor differentiation, depth of invasion, and regional lymph node metastasis were associated with overall survival (OS) outcomes through univariate analysis. While multivariate analysis showed that pathologic type, depth of invasion, and regional lymph node metastasis were the independent predictors of OS [40]. Besides, the tumor volume of ESCC could be used as an important prognostic factor for radiotherapy and chemotherapy assessment [41–43]. Therefore, we exploited a clinical nomogram that combined available risk factors (age, gender, invasion degree, location, genetic history, metastasis) with overall stage, but it doesn't exibit well (C-index of training cohort, 0.683; C-index of validation cohort, 0.660). Then, we developed the nomogram by combining radiomics signature to it in both training cohort (C-index, 0.799) and validation cohort (C-index, 0.774). This process suggested that radiomics signatures have crucial prognostic value for ESCC patients.
Unlike the traditional methods, radiomics system is a noninvasive and low-spending approach, which could provide new insights into the associations between tumor intrinsic properties and biological behaviors. We analyzed the relationship between radiomics features and tumor-associated characteristics, and observed some radiomics features were related to the general information of patients (gender, drinking or smoking information, Fig. 5b). Additionally, our radiomics system showed some radiomics features were associated with invasion degree as well (Fig. 5b). As a result, the present study may provide some different insights into the mechanisms of lymphatic metastasis of ESCC, which require future investigation.
There were several limitations in our study. First, we used thick-slice CT images rather than thin-slice images for the extraction of radiomics signatures. Zhao et al. [44] found that thin-slice images could reflected texture features of tumor more complete than thick-slice images. For the measurement of tumor volumes, thin-slice images had less measurement variability. We will further study the effect of thin-slice CT images for the staging of ESCC and confirm whether the performance is comparable with thick-slice images. Second, all data involved in this study are derived from the same hospital, resulting the lack of multi-center validation. The further investigations on the applicability to the patients of other institutions is still required. Third,the analysis did not cover two-way or higher-order interactions of the radiomics features. If the interaction(s) strongly associated with the outcomes were applied, the prognostic performance of our nomogram may be significantly improved. However, to reveal the interactions of multiple factors is challenging. In brief, our study clearly showed that the radiomics approach is potential for the prognosis of ESCC patients.