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

Neoadjuvant Chemoradiotherapy vs Chemoimmunotherapy for Esophageal Squamous Cell Carcinoma

Importance

The association of neoadjuvant chemoimmunotherapy (NCIT) vs chemoradiotherapy (NCRT) with tumor downstaging and survival in locally advanced esophageal squamous cell carcinoma (ESCC) remains unclear because of limited evidence.

Objective

To compare the associations of NCIT and NCRT with tumor regression and long-term survival in patients with locally advanced ESCC.

Design, Setting, and Participants

In this comparative effectiveness research study, from January 2016 to March 2023, patients with locally advanced ESCC who underwent esophagectomy following NCRT or NCIT were identified from a prospective database of 8 high-volume esophageal surgery centers in China. Follow-up began on the date of surgery and continued until the last recorded contact or March 2024, whichever occurred first. Data were analyzed between April and September 2024.

Main Outcomes and Measures

The primary end points were 2-year overall survival (OS) and disease-free survival (DFS). Secondary end points included major pathologic response (MPR) and pathologic complete response (pCR). Cox proportional hazard regression analysis was used to investigate the risk factors for OS and DFS.

Results

The study included 1428 patients (median [IQR] age, 63 [57-68] years; 1184 men [82.9%]), with 704 patients in the NCRT group and 724 patients in the NCIT group. After propensity score matching, there were 532 patients in each group. The 2-year OS (81.3% vs 71.3%; hazard ratio, 1.57; 95% CI, 1.26-1.96; P < .001) and DFS (73.9% vs 63.4%; hazard ratio, 1.37; 95% CI, 1.11-1.69; P < .001) rates were significantly higher in NCIT group than in the NCRT group. The NCRT group had a higher MPR rate than that of the NCIT group (71.8% vs 61.5%), whereas the pCR rates were similar (25.9% vs 22.9%). Multivariable Cox analysis demonstrated that NCIT and MPR were independently associated with both OS and DFS. The NCIT group exhibited a lower overall recurrence rate (126 patients [23.7%] vs 190 patients [35.7%]) and distant metastasis rate (72 patients [13.5%] vs 133 patients [25.0%]), although locoregional metastasis rates were similar (98 patients [18.4%] vs 111 patients [20.9%]). Better OS and DFS were obtained for the NCIT group than for the NCRT group, regardless of whether adjuvant immunotherapy was given.

Conclusions and Relevance

Compared with NCRT, patients with locally advanced ESCC receiving NCIT had better 2-year OS and DFS. The decrease in distant metastasis may be the main reason, but further prospective randomized clinical trials are needed to verify this finding.

A preoperative predictive model based on multi-modal features to predict pathological complete response after neoadjuvant chemoimmunotherapy in esophageal cancer patients

Background

This study aimed to develop a multi-modality model by incorporating pretreatment computed tomography (CT) radiomics and pathomics features along with clinical variables to predict pathologic complete response (pCR) to neoadjuvant chemoimmunotherapy in patients with locally advanced esophageal cancer (EC).

Method

A total of 223 EC patients who underwent neoadjuvant chemoimmunotherapy followed by surgical intervention between August 2021 and December 2023 were included in this study. Radiomics features were extracted from contrast-enhanced CT images using PyrRadiomics, while pathomics features were derived from whole-slide images (WSIs) of pathological specimens using a fine-tuned deep learning model (ResNet-50). After feature selection, three single-modality prediction models and a combined multi-modality model integrating two radiomics features, 11 pathomics features, and two clinicopathological features were constructed using the support vector machine (SVM) algorithm. The performance of the models were evaluated using receiver operating characteristic (ROC) analysis, calibration plots, and decision curve analysis (DCA). Shapley values were also utilized to explain the prediction model.

Results

The predictive capability of the multi-modality model in predicting pCR yielded an area under the curve (AUC) of 0.89 (95% confidence interval [CI], 0.75-1.00), outperforming the radiomics model (AUC 0.70 [95% CI 0.54-0.85]), pathomics model (AUC 0.77 [95% CI 0.53-1.00]), and clinical model (AUC 0.63 [95% CI 0.46-0.80]). Additionally, both the calibration plot and DCA curves support the clinical utility of the integrated multi-modality model.

Conclusions

The combined multi-modality model we propose can better predict the pCR status of esophageal cancer and help inform clinical treatment decisions.

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.

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.

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.