Dynamic contrast-enhanced MRI histogram parameters predict progression-free survival in patients with advanced esophageal squamous carcinoma receiving concurrent chemoradiotherapy

The gap before real clinical application of imaging-based machine-learning and radiomic models for chemoradiation outcome prediction in esophageal cancer: a systematic review and meta-analysis

BACKGROUND

Due to tumoral heterogeneity and the lack of robust biomarkers, the prediction of chemoradiotherapy response and prognosis in patients with esophageal cancer (EC) is challenging. The goal of this study was to assess the study quality and clinical value of machine learning and radiomic-based quantitative imaging studies for predicting the outcomes of EC patients after chemoradiotherapy.

MATERIALS AND METHODS

PubMed, Embase, and Cochrane were searched for eligible articles. The methodological quality and risk of bias were evaluated using the Radiomics Quality Score (RQS), Image Biomarkers Standardization Initiative (IBSI) Guideline, and Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement, as well as the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. A meta-analysis of the evidence focusing on predicting chemoradiotherapy response and outcome in EC patients was implemented.

RESULTS

Forty-six studies were eligible for qualitative synthesis. The mean RQS score was 9.07, with an adherence rate of 42.52%. The adherence rates of the TRIPOD and IBSI were 61.70 and 43.17%, respectively. Ultimately, 24 studies were included in the meta-analysis, of which 16 studies had a pooled sensitivity, specificity, and area under the curve (AUC) of 0.83 (0.76-0.89), 0.83 (0.79-0.86), and 0.84 (0.81-0.87) in neoadjuvant chemoradiotherapy datasets, as well as 0.84 (0.75-0.93), 0.89 (0.83-0.93), and 0.93 (0.90-0.95) in definitive chemoradiotherapy datasets, respectively. Moreover, radiomics could distinguish patients from the low-risk and high-risk groups with different disease-free survival (DFS) (pooled hazard ratio: 3.43, 95% CI 2.39-4.92) and overall survival (pooled hazard ratio: 2.49, 95% CI 1.91-3.25). The results of subgroup and regression analyses showed that some of the heterogeneity was explained by the combination with clinical factors, sample size, and usage of the deep learning (DL) signature.

CONCLUSIONS

Noninvasive radiomics offers promising potential for optimizing treatment decision-making in EC patients. However, it is necessary to make scientific advancements in EC radiomics regarding reproducibility, clinical usefulness analysis, and open science categories. Improved model reporting of study objectives, blind assessment, and image processing steps are required to help promote real clinical applications of radiomics in EC research.

Prognostic value of fibroblast activation protein expressing tumor volume calculated from [68 Ga]Ga-FAPI PET/CT in patients with esophageal squamous cell carcinoma

BACKGROUND

This study aimed to investigate the prognostic value of semiquantitative parameters derived from [68 Ga]Ga-fibroblast activation protein inhibitor (FAPI) PET/CT for patients with esophageal squamous cell carcinoma (ESCC) treated with definitive chemoradiotherapy.

METHODS

We conducted a retrospective analysis on patients from a prospective parent study (NCT04416165). A total of 45 patients with locally advanced ESCC who underwent [68 Ga]Ga-FAPI from December 2019 to March 2021 were included. The maximum standard uptake value (SUVmax), gross tumor volume (GTV), and total lesion-FAPI (TL-FAPI) of the primary tumor were calculated from the corresponding PET/CT image. Unpaired parameters were compared using Student's t test or the Mann-Whitney U test. Paired parameters were compared using the paired t test or the Wilcoxon matched-pairs signed-rank test. Kaplan-Meier curves were generated to calculate progression-free survival (PFS) and overall survival (OS) rates, and Cox regression analysis was performed to determine which PET/CT parameters were prognostic factors for PFS and/or OS.

RESULTS

Thirty-four of the 45 patients met the criteria, and the median follow-up time was 24 months (16-29 months). SUVmax-FAPI, GTVFAPI, and TL-FAPI in patients with stage T4 tumors were significantly higher than those in patients with stage T2/T3 tumors (all P < 0.01). In the univariate Cox regression analysis, T stage, N stage, GTVFAPI, and TL-FAPI were associated with PFS, and T stage, GTVFAPI, and TL-FAPI were associated with OS. Upon multivariable analysis, GTVFAPI was an independent prognostic factor for both PFS (hazard ratio (HR), 5.76; 95% confidence interval (CI), 2.13-15.57, P = 0.001) and OS (HR, 4.96; 95% CI, 2.55-18.79, P = 0.001).

CONCLUSION

This pilot study revealed that [68 Ga]Ga-FAPI PET/CT may have prognostic value for patients with ESCC treated with definitive chemoradiotherapy. It may aid in personalized patient management by steering treatment modifications before therapy. Prospective studies with larger samples and longer observation periods are needed.