In our study, we investigated the prognostic ability of combined [18F]-FDG PET/CT radiomics features complemented with clinical parameter and sarcopenic status among patients with advanced, metastatic esophageal and gastroesophageal cancer with regards to OS and PFS. The main finding of our study demonstrates a stepwise improvement of the survival prognostication when adding sarcopenic status, independent CT and PET features to the solely clinical model, indicating superior prognostic ability of the overall combined model for both OS and PFS.
[18F]-FDG PET/CT is an important imaging modality for staging, assessing treatment response and the detection of recurrence after treatment in patients with gastroesophageal cancer [27, 28]. There is conflicting literature about the prognostic ability of quantitative metabolic measurements in terms of prognostication. While several studies suggesting standard metabolic parameters, such as SUVmean and SUVmax, can be helpful prognostic tools among patients with esophageal and gastroesophageal cancer [29, 30], the results of several other studies do not support this finding, showing no improvement in outcome prediction taking into account these parameters [31, 32]. More advanced volumetric parameters, including metabolic tumor volume (MTV) or total lesion glycolysis (TLG), which integrate metabolically active tumor volume with tumor FDG uptake have also been proposed as effective prognostic tools [29]. However, the FDG uptake of a primary tumor may be heterogeneously distributed, partly due to underlying pathophysiological conditions, like metabolism, hypoxia, necrosis and cellular proliferation [15, 33, 34]. In addition, the intra-tumoral heterogeneity can be related to tumor aggressiveness, therapy response and prognosis, and established [18F]-FDG PET/CT parameters may not fully reveal these characteristics and is not reflective of the spatial tumoral heterogeneity [33, 35]. Thus, different and more advanced quantitative measures are needed to capture those underlying aspects of the tumor. In recent years, the field of radiomics, enabling the extraction of high-dimensional data from various sources of medical images, including functional imaging like PET, has shown promising results with regard to response and outcome prediction among a broad range of malignancies, including gastroesophageal cancer [36-38]. However, to the best of our knowledge, no study so far investigated independent CT and PET features in combination with clinical variables and sarcopenic measurements in a more holistic model for outcome prediction, among patients with advanced, metastatic esophageal and gastroesophageal cancer. Most studies so far correlated textural features with tumor stage or evaluated the ability of predicting tumor response to neoadjuvant chemoradiotherapy [38-40] and only very limited studies correlated textural features with survival prognostication. The reason for this may be the significantly reduced life expectancy in this patient population since those patients are treated mostly palliatively. However, with the introduction of multi-line therapy options, including immunotherapeutic agents, the prognosis in these patients may be improved over time [41]. Within the current literature, Dong et al. [36] investigated 116 patients with esophageal squamous cell carcinoma who underwent surgical resection. The authors applied an area under the cumulative SUV volume histogram (AUC-CSH) method, which might be used as a simplified, quantitative parameter of metabolic heterogeneity. The results of their study indicate that higher intra-tumoral metabolic heterogeneity may predict postoperative recurrence and survival in patients with resected primary. Similar results were found by Yip et al. [38], who evaluated a smaller cohort of 54 patients with esophageal squamous cell carcinoma and adenocarcinoma, who underwent mainly surgery after the neo-adjuvant chemoradiotherapy, showing that all textural features from [18F]-FDG PET/CT were better correlated to pathologic response and overall survival than standard metabolic parameters like SUVmax and SUVmean. For example, entropy and run-length matrix (RLM) texture features significantly discriminated patients with good and poor overall survival. This confirms the results of our study, demonstrating enhanced survival prognostication when applying radiomics features in an even larger and more homogenous patient cohort. A further difference to our study is the application of texture analysis, whereas radiomics analysis was used in our study. Foley et al. [42] showed that TLG, histogram energy and histogram kurtosis were independent predictors for worse OS in a large retrospective cohort of 403 patients with either esophageal squamous cell carcinoma or adenocarcinoma, deemed to have a potentially curable disease, following contrast-enhanced CT (CECT), however approximately 50% were considered palliative following [18F]-FDG PET/CT. When comparing to our results, certain differences and similarities can be pointed out. Our results demonstrated that coarseness and contrast from CT feature analysis and kurtosis from PET feature analysis were associated with worse OS and PFS. Similar to prior studies, including the study by Foley et al. [42], this may indicate that features which measure local intensity variations and the shape of the intensity distribution of data seem to have potential predictive value. Also, we evaluated both esophageal squamous cell carcinoma and adenocarcinoma, however all patients in our cohort had advanced metastatic disease and were treated with a standard palliative therapy regimen, indicating a more homogenous study cohort. Notably, we also included both PET and CT radiomics features in our final model, whereas Foley et al. [42] applied textural analysis of PET images only.
Nakajo et al. [43] performed textural analysis on 52 patients with esophageal squamous cell carcinoma, to evaluate whether [18F]-FDG PET/CT-derived features predict response and prognosis in patients treated with neoadjuvant chemoradiotherapy prior to surgery. TLG, MTV, intensity variability and size-zone variability were independent predictors for treatment response but not for OS and PFS. Discrepancies to the results of our study may be explained by the inclusion of PET-derived radiomics features only, the smaller population and different study cohort characteristics, where we included only patients with advanced disease with distant metastases and palliative treatment intent.
Xiong et al. [44] developed a prognostic model, incorporating clinical variables in combination with textural features from pre – and mid-treatment [18F]-FDG PET/CT, demonstrating high accuracy (accuracy 93.3%, specificity 95.7, sensitivity 85.7%) for the prediction of PFS in a cohort of 30 patients with esophageal squamous cell carcinoma, treated with definite chemoradiotherapy. Our study demonstrates similar results, however we additionally/exclusively incorporated sarcopenia measurements to the final model in addition to clinical variables, independent CT and PET features, reaching stepwise improvement of the ability to predict OS and PFS, except for late stage (24 -36 months) disease where the combination of only clinical variables with sarcopenic status showed the best performance with regards to PFS (AUC 0.86 (clinical + SMI) vs. 0.81 (overall combined final model)) at 30 months of follow-up. This can likely be explained by the fact that usually, patients would change to another line of therapy after progression and thus, the predictive value decreases.
The following study limitations must be acknowledged. First, there are inherent drawbacks, due to the retrospective nature of the study and the relatively small sample size. Second, we did not perform radiomics analysis and sarcopenia measurements on post-treatment imaging, since [18F]-FDG PET/CT is only funded for staging purposes in our current environment.
In conclusion, our study indicates that combined standard of care [18F]-FDG PET/CT-derived radiomics features (both CT and PET) in addition to sarcopenic status and clinical parameters has incremental value in survival prognostication among patients with metastasized esophageal and gastroesophageal cancer.