Validity of Using Pathological Response as a Surrogate for Overall Survival in Neoadjuvant Studies for Esophageal Cancer: A Systematic Review and Meta-analysis

Neoadjuvant chemoimmunotherapy was associated with better short-term survival of patients with locally advanced esophageal squamous cell carcinoma compared to neoadjuvant chemoradiotherapy

INTRODUCTION

The chemotherapy and immunotherapy combination is currently the primary strategy to treat metastatic esophageal squamous cell carcinoma (ESCC). Neoadjuvant chemoimmunotherapy (NCIT) is being intensively investigated for treating locally advanced ESCC.

OBJECTIVE

We compared the efficacy and safety of NCIT and neoadjuvant chemoradiotherapy (NCRT) to treat locally advanced ESCC.

METHODS

We included 214 locally advanced ESCC patients who were administered neoadjuvant therapy from May 2014 to April 2022. The patients were grouped according to two neoadjuvant protocols (NCIT and NCRT) routinely used at our institution. Perioperative findings, pathological results, and survival data were compared between the two groups by conducting unmatched and 1:1 propensity score matching (PSM) analyses.

RESULTS

Following 1:1 PSM analysis of the confounders, 66 patients were allocated to each of the two groups. Time span between neoadjuvant therapy completion and esophagectomy was significantly longer after NCRT than that after NCIT (47.1 ± 13.2 days vs. 34.7 ± 8.8 days; p < 0.001). The NCIT group exhibited significantly greater number of harvested lymph nodes than the NCRT group (33.6 ± 12.7 vs. 21.7 ± 10.2; p < 0.001). The pathological complete response and major pathological response rates were similar between the two groups [NCIT group: 25.8% (17/66) and 62.1% (41/66), respectively; NCRT group: 27.3% (18/66) and 56.1% (37/66), respectively (p > 0.05)]. The overall incidence of pneumonia, anastomotic leakage, or postoperative complications did not differ significantly between the two groups. The 2-year cumulative overall survival rates and the 2-year disease-free survival rates of the NCIT and NCRT groups were 80.2% and 62.2%, respectively (p = 0.029) and 70.0% and 50.8%, respectively (p = 0.023).

CONCLUSION

In locally advanced ESCC patients, short-term survival after NCIT is superior to that after NCRT, with similar perioperative and pathological outcomes.

Perioperative outcomes and survival after neoadjuvant immunochemotherapy for locally advanced esophageal squamous cell carcinoma

OBJECTIVE

This study aimed to compare the difference in perioperative outcomes and prognosis between neoadjuvant immunochemotherapy and neoadjuvant chemoradiotherapy for locally advanced esophageal squamous cell carcinoma.

METHODS

The patients with locally advanced esophageal squamous cell carcinoma receiving neoadjuvant immunochemotherapy or neoadjuvant chemoradiotherapy were identified from a prospectively maintained database at Zhongshan Hospital of Fudan University between January 2018 and March 2022. Propensity score matching was performed to balance the 2 groups.

RESULTS

A total of 124 patient pairs were enrolled in the final analysis. The complete pathological response rate (20.2% vs 29.0%, P = .140) was similar in the 2 groups, whereas the lower major pathological response rate (44.4% vs 61.3%, P = .011) was observed in the neoadjuvant immunochemotherapy group. Neoadjuvant immunochemotherapy was associated with a lower rate of adverse events (42.7% vs 55.6%, P = .047) without additional postoperative complications (38.7% vs 35.5%, P = .693). The neoadjuvant immunochemotherapy group had lower distant metastasis (6.5% vs 16.1%, P = .027) and overall recurrence (11.3% vs 23.4%, P = .019) in the postoperative 1 year. Also, neoadjuvant immunochemotherapy was associated with better progression-free survival (hazard ratio, 0.50; 95% CI, 0.32-0.77; P = .002). Cox proportional hazard analysis showed that neoadjuvant immunochemotherapy (univariable: hazard ratio, 0.55; 95% CI, 0.37-0.82; P = .003; multivariable: hazard ratio, 0.44; 95% CI, 0.29-0.65; P < .001) was one of the independent prognostic factors for progression-free survival. The 2 groups had similar overall survival (hazard ratio, 0.62; 95% CI, 0.36-1.09; P = .094) at the latest follow-up.

CONCLUSIONS

This retrospective study showed that neoadjuvant immunochemotherapy was safe and effective for patients with locally advanced esophageal squamous cell carcinoma. Further verification is needed in randomized controlled trials.

Online tools to predict individualised survival for primary oesophageal cancer patients with and without pathological complete response after neoadjuvant therapy followed by oesophagectomy: development and external validation of two independent nomograms

OBJECTIVE

This study aimed to develop and validate robust predictive models for patients with oesophageal cancer who achieved a pathological complete response (pCR) and those who did not (non-pCR) after neoadjuvant therapy and oesophagectomy.

DESIGN

Clinicopathological data of 6517 primary oesophageal cancer patients who underwent neoadjuvant therapy and oesophagectomy were obtained from the National Cancer Database for the training cohort. An independent cohort of 444 Chinese patients served as the validation set. Two distinct multivariable Cox models of overall survival (OS) were constructed for pCR and non-pCR patients, respectively, and were presented using web-based dynamic nomograms (graphical representation of predicted OS based on the clinical characteristics that a patient could input into the website). The calibration plot, concordance index and decision curve analysis were employed to assess calibration, discrimination and clinical usefulness of the predictive models.

RESULTS

In total, 13 and 15 variables were used to predict OS for pCR and non-pCR patients undergoing neoadjuvant therapy followed by oesophagectomy, respectively. Key predictors included demographic characteristics, pretreatment clinical stage, surgical approach, pathological information and postoperative treatments. The predictive models for pCR and non-pCR patients demonstrated good calibration and clinical utility, with acceptable discrimination that surpassed that of the current tumour, node, metastases staging system.

CONCLUSIONS

The web-based dynamic nomograms for pCR (https://predict-survival.shinyapps.io/pCR-eso/) and non-pCR patients (https://predict-survival.shinyapps.io/non-pCR-eso/) developed in this study can facilitate the calculation of OS probability for individual patients undergoing neoadjuvant therapy and radical oesophagectomy, aiding clinicians and patients in making personalised treatment decisions.

A machine learning approach using 18F-FDG PET and enhanced CT scan-based radiomics combined with clinical model to predict pathological complete response in ESCC patients after neoadjuvant chemoradiotherapy and anti-PD-1 inhibitors

Background

We aim to evaluate the value of an integrated multimodal radiomics with machine learning model to predict the pathological complete response (pCR) of primary tumor in a prospective cohort of esophageal squamous cell carcinoma (ESCC) treated with neoadjuvant chemoradiotherapy (nCRT) and anti-PD-1 inhibitors.

Materials and methods

Clinical information of 126 ESCC patients were included for analysis. Radiomics features were extracted from 18F-FDG PET and enhanced plan CT images. Four machine learning algorithms, including SVM (Support Vector Machine), Random Forest (RF), and eXtreme Gradient Boosting (XGB) and logistic regression (LR), were applied using k-fold cross-validation to predict pCR after nCRT. The predictive ability of the models was assessed using receiver operating characteristics (ROC) curve analysis.

Results

A total of 842 features were extracted. Among the four machine learning algorithms, SVM achieved the most promising performance on the test set for PET(AUC:0.775), CT (AUC:0.710) and clinical model (AUC:0.722). For all combinations of various modalities-based models, the combination model of 18 F-FDG PET, CT and clinical features with SVM machine learning had the highest AUC of 0.852 in the test set when compared to single-modality models in various algorithms. The other combined models had AUC ranged 0.716 to 0.775.

Conclusion

Machine learning models utilizing radiomics features from 18F-FDG PET and enhanced plan CT exhibit promising performance in predicting pCR in ESCC after nCRT and anti-PD-1 inhibitors. The fusion of features from multiple modalities radiomics and clinical features enhances the better predictive performance compared to using a single modality alone.