The role of 18F-FDG PET/CT in predicting the pathological response to neoadjuvant PD-1 blockade in combination with chemotherapy for resectable esophageal squamous cell carcinoma

Early Contrast-Enhanced MR for Diagnosing Complete Tumor Response of Locally Advanced Esophageal Squamous Cell Carcinoma After Neoadjuvant Therapy: A Retrospective Comparative Study

BACKGROUND

This study assessed the performance of early contrast-enhanced magnetic resonance (ECE-MR) in the detecting of complete tumor response (ypT0) in patients with esophageal squamous cell carcinoma following neoadjuvant therapy.

PATIENTS AND METHODS

Preoperative MR images of consecutive patients who underwent neoadjuvant therapy and surgical resection were reviewed retrospectively. The accuracy of ECE-MR and T2WI+DWI was evaluated by comparing the findings with pathological results. Receiver operating characteristic curve analysis was used to assess the diagnostic performance, and DeLong method was applied to compare the areas under the curves (AUC). Chi-squared analysis was conducted to explore the difference in pathological changes.

RESULTS

A total of 198 patients (mean age 62.6 ± 7.8 years, 166 men) with 201 lesions were included. The AUC of ECE-MR was 0.85 (95% CI 0.79-0.90) for diagnosing ypT1-4, which was significantly higher than that of T2WI+DWI (AUC 0.69, 95% CI 0.63-0.76, p < 0.001). The diagnostic performance of both T2WI+DWI and ECE-MR improved with increasing tumor stage. The AUCs of ECE-MRI were higher in ypT1 and ypT2 tumors than T2WI+DWI. Degree 2-3 tumor-infiltrating lymphocytes and neutrophils were commonly seen in ypT0 tumors misdiagnosed by ECE-MR.

CONCLUSIONS

Visual evaluation of ECE-MR is a promising diagnostic protocol for the detection of complete tumor response, especially for differentiation with early stage tumors. The accurate diagnosis of complete tumor response after neoadjuvant therapy using imaging modalities is of important significance for clinical decision-making for patients with esophageal squamous cell carcinoma. It is hoped that early contrast-enhanced MR will provide supportive advice for the development of individualized treatment options for patients.

Deep Learning For Automatic Gross Tumor Volumes Contouring in Esophageal Cancer Based on Contrast-Enhanced Computed Tomography Images: A Multi-Institutional Study

PURPOSE

To develop and externally validate an automatic artificial intelligence (AI) tool for delineating gross tumor volume (GTV) in patients with esophageal squamous cell carcinoma (ESCC), which can assist in neo-adjuvant or radical radiation therapy treatment planning.

METHODS AND MATERIALS

In this multi-institutional study, contrast-enhanced CT images from 580 eligible ESCC patients were retrospectively collected. The GTV contours delineated by 2 experts via consensus were used as ground truth. A 3-dimensional deep learning model was developed for GTV contouring in the training cohort and internally and externally validated in 3 validation cohorts. The AI tool was compared against 12 board-certified experts in 25 patients randomly selected from the external validation cohort to evaluate its assistance in improving contouring performance and reducing variation. Contouring performance was measured using dice similarity coefficient (DSC) and average surface distance. Additionally, our previously established radiomics model for predicting pathologic complete response was used to compare AI-generated and ground truth contours, to assess the potential of the AI contouring tool in radiomics analysis.

RESULTS

The AI tool demonstrated good GTV contouring performance in multicenter validation cohorts, with median DSC values of 0.865, 0.876, and 0.866 and median average surface distance values of 0.939, 0.789, and 0.875 mm, respectively. Furthermore, the AI tool significantly improved contouring performance for half of 12 board-certified experts (DSC values, 0.794-0.835 vs 0.856-0.881, P = .003-0.048), reduced the intra- and interobserver variations by 37.4% and 55.2%, respectively, and saved contouring time by 77.6%. In the radiomics analysis, 88.7% of radiomic features from ground truth and AI-generated contours demonstrated stable reproducibility, and similar pathologic complete response prediction performance for these contours (P = .430) was observed.

CONCLUSIONS

Our AI contouring tool can improve GTV contouring performance and facilitate radiomics analysis in ESCC patients, which indicates its potential for GTV contouring during radiation therapy treatment planning and radiomics studies.

Preoperative prediction of pancreatic neuroendocrine tumor grade based on 68Ga-DOTATATE PET/CT

OBJECTIVE

To establish a prediction model for preoperatively predicting grade 1 and grade 2/3 tumors in patients with pancreatic neuroendocrine tumors (PNETs) based on 68Ga-DOTATATE PET/CT.

METHODS

Clinical data of 41 patients with PNETs were included in this study. According to the pathological results, they were divided into grade 1 and grade 2/3. 68Ga-DOTATATE PET/CT images were collected within one month before surgery. The clinical risk factors and significant radiological features were filtered, and a clinical predictive model based on these clinical and radiological features was established. 3D slicer was used to extracted 107 radiomic features from the region of interest (ROI) of 68Ga-dotata PET/CT images. The Pearson correlation coefficient (PCC), recursive feature elimination (REF) based five-fold cross validation were adopted for the radiomic feature selection, and a radiomic score was computed subsequently. The comprehensive model combining the clinical risk factors and the rad-score was established as well as the nomogram. The performance of above clinical model and comprehensive model were evaluated and compared.

RESULTS

Adjacent organ invasion, N staging, and M staging were the risk factors for PNET grading (p < 0.05). 12 optimal radiomic features (3 PET radiomic features, 9 CT radiomic features) were screen out. The clinical predictive model achieved an area under the curve (AUC) of 0.785. The comprehensive model has better predictive performance (AUC = 0.953).

CONCLUSION

We proposed a comprehensive nomogram model based on 68Ga-DOTATATE PET/CT to predict grade 1 and grade 2/3 of PNETs and assist personalized clinical diagnosis and treatment plans for patients with PNETs.

Value of 68Ga-FAPI-04 and 18F-FDG PET/CT in Early Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer

This prospective study investigated whether PET parameters from 18F-FDG and 68Ga-fibroblast activation protein inhibitor (FAPI)-04 PET/CT can predict a pathologic response to neoadjuvant chemotherapy (NAC) early in patients with locally advanced gastric cancer (LAGC). Methods: The study included 28 patients with LAGC who underwent 18F-FDG PET/CT and 68Ga-FAPI-04 PET/CT at baseline and after 1 cycle of NAC. PET parameters including SUV and tumor-to-background ratio (TBR), as well as the change rate of SUV and TBR, were recorded. Patients were classified as major or minor pathologic responders according to postoperative pathology findings. We compared the PET parameters between the 2 pathologic response groups and different treatment regimens and analyzed their predictive performance for tumor pathologic response. Results: Major pathologic responders had significantly lower 68Ga-FAPI change rates (percentage SUVmax [%SUVmax], percentage SUVpeak [%SUVpeak], and percentage TBR [%TBR]) than minor pathologic responders. Among the PET parameters, 68Ga-FAPI %SUVmax (area under the curve, 0.856; P = 0.009), %SUVpeak (area under the curve, 0.811; P = 0.022), and %TBR (area under the curve, 0.864; P = 0.007) were significant parameters for early prediction of pathologic response to NAC in LAGC; they had the same predictive accuracy of 89.29%, with the thresholds of decrease to at least 52.43%, 60.46%, and 52.96%, respectively. In addition, 68Ga-FAPI %SUVmax and %TBR showed significant differences between the different treatment regimens. Conclusion: In this preliminary study, 68Ga-FAPI-04 PET change rate parameters were preferable to 18F-FDG in predicting pathologic response to NAC at an early stage in LAGC. 68Ga-FAPI %SUVmax and %TBR may be better predictors of therapeutic response between different treatment regimens. These findings may help optimize the treatment for patients with LAGC.

Enhancing Prediction for Tumor Pathologic Response to Neoadjuvant Immunochemotherapy in Locally Advanced Esophageal Cancer by Dynamic Parameters from Clinical Assessments

To develop accurate and accessible prediction methods for assessing pathologic response following NICT prior to surgery, we conducted a retrospective study including 137 patients with esophageal squamous cell carcinoma (ESCC) who underwent surgery after two cycles of NICT between January 2019 and March 2022 at our center. We collected clinical parameters to evaluate the dynamic changes in the primary tumor. Univariate and multivariate analyses were performed to determine the correlations between these parameters and the pathologic response of the primary tumor. Subsequently, we constructed prediction models for pCR and MPR using multivariate logistic regression. The MPR prediction Model 2 was internally validated using bootstrapping and externally validated using an independent cohort from our center. The univariate logistic analysis revealed significant differences in clinical parameters reflecting tumor regression among patients with varying pathologic responses. The clinical models based on these assessments demonstrated excellent predictive performance, with the training cohort achieving a C-index of 0.879 for pCR and 0.912 for MPR, while the testing cohort also achieved a C-index of 0.912 for MPR. Notably, the MPR prediction Model 2, with a threshold cut-off of 0.74, exhibited 92.7% specificity and greater than 70% sensitivity, indicating a low rate of underestimating residual tumors. In conclusion, our study demonstrated the high accuracy of clinical assessment-based models in pathologic response prediction, aiding in decision-making regarding organ preservation and radiotherapy adjustments after induction immunochemotherapy.

18 F-FDG PET/CT predicts the role of neoadjuvant immunochemotherapy in the pathological response of esophageal squamous cell carcinoma

BACKGROUND

This study aimed to investigate the predictive value of 18 F-FDG PET/CT for pathological response after neoadjuvant immunochemotherapy (NICT) in patients with esophageal squamous cell carcinoma (ESCC).

METHODS

The clinical data of 54 patients with ESCC who underwent two cycles of NICT followed by surgery were retrospectively analyzed. NICT consisted of PD-1 blockade therapy combined with chemotherapy. 18 F-FDG PET/CT scans were performed before and after NICT. The pathological results after surgery were used to assess the degree of pathological response. The scan parameters of 18 F-FDG PET/CT and their changes before and after NICT were compared with the pathological response.

RESULTS

Among the 54 patients, 10 (18.5%) achieved complete pathological response (pCR) and 21 (38.9%) achieved major pathological response (MPR). The post-NICT scan parameters and their changes were significantly associated with the pathological response. In addition, the values of the changes in the scanned parameters before and after treatment can further predict the pathological response of the patient.

CONCLUSION

18 F-FDG PET/CT is a useful tool to evaluate the efficacy of NICT and predict pathological response in patients with ESCC. The post-NICT scan parameters and their changes can help identify patients who are likely to achieve pCR or MPR.

Evaluation of neoadjuvant immunotherapy and traditional neoadjuvant therapy for resectable esophageal cancer: a systematic review and single-arm and network meta-analysis

Objective

This systematic review and meta-analysis aimed to investigate the role of neoadjuvant immunochemotherapy with or without radiotherapy [NIC(R)T] compared to traditional neoadjuvant therapies, without immunotherapy [NC(R)T].

Summary background data

NCRT followed by surgical resection is recommended for patients with early-stage esophageal cancer. However, it is uncertain whether adding immunotherapy to preoperative neoadjuvant therapy would improve patient outcomes when radical surgery is performed following neoadjuvant therapy.

Methods

We searched PubMed, Web of Science, Embase, and Cochrane Central databases, as well as international conference abstracts. Outcomes included R0, pathological complete response (pCR), major pathological response (mPR), overall survival (OS) and disease-free survival (DFS) rates.

Results

We included data from 5,034 patients from 86 studies published between 2019 and 2022. We found no significant differences between NICRT and NCRT in pCR or mPR rates. Both were better than NICT, with NCT showing the lowest response rate. Neoadjuvant immunotherapy has a significant advantage over traditional neoadjuvant therapy in terms of 1-year OS and DFS, with NICT having better outcomes than any of the other three treatments. There were no significant differences among the four neoadjuvant treatments in terms of R0 rates.

Conclusions

Among the four neoadjuvant treatment modalities, NICRT and NCRT had the highest pCR and mPR rates. There were no significant differences in the R0 rates among the four treatments. Adding immunotherapy to neoadjuvant therapy improved 1-year OS and DFS, with NICT having the highest rates compared to the other three modalities.

Systematic Review Registration

https://inplasy.com/inplasy-2022-12-0060/, identifier INPLASY2022120060.

Residual tumor model in esophageal squamous cell carcinoma after neoadjuvant immunochemotherapy: Frequently involves the mucosa and/or submucosa

Objectives

The efficacy and safety of neoadjuvant immunochemotherapy (nICT) are widely explored in locally advanced esophageal squamous cell carcinoma (ESCC). Whether the "wait-and-see" strategy is applicable in ESCC after nICT is still lacking a theoretical basis. This study aimed to preliminarily explore the distribution of residual tumors and the regression pattern of ESCC after nICT.

Methods

Patients undergoing radical esophagectomy after nICT in Fujian Medical University Union Hospital between January 2020 and March 2022 were identified. The resection specimens were re-evaluated by one experienced pathologist. The pathological response was assessed by tumor regression grade (TRG) (modified Ryan scheme). The TRG grade was divided into grades 0 (pathological complete response), 1, 2, and 3. The pathological stage was evaluated in the Eighth Edition AJCC. In the non-pCR group, the residual model was divided into four types: Type I, regression towards the lumen; type II, regression towards the invasive front; type III, concentric regression; and type IV, scattered regression.

Results

A total of 95 consecutive patients were included for analysis. Seventy-six (80.0%) of 95 patients were in non-pCR (pathological complete response), and nine patients (9/76, 11.84%) had isolated residual tumors in lymph nodes. There was no significant difference in baseline characteristics between the pCR group and the non-pCR group (p > 0.05). The overall distribution of TRG for all esophageal wall layers was TRG 0 = 28 (28/95, 29.5%), TRG 1 = 17 (17/95, 17.9%), TRG 2 = 18 (18.9%, 18/95), and TRG 3 = 32 (32/95, 33.7%). In 67 patients with residual tumors in the esophageal wall (TRG ≧1), 63 (63/67, 94.0%) had residual tumor cells in the mucosa and/or submucosa, and four had isolated residual tumors in the muscle layer (4/67, 6.0%). Further analysis showed eight (8/67, 11.9%) patients with submucosal involvement but without mucosal involvement. The distribution of regression patterns was type I (n = 35, 52.2%), type II (n = 3, 4.5%), type III (n = 8, 11.9%), and type IV (n = 21, 31.3%).

Conclusions

The mucosa and/or submucosa are frequently involved in residual malignancy, and the frequent regression models are regression toward the lumen and random regression. There is an opportunity to carefully test the residual tumors in a subgroup of the population with ESCC following nICT. However, some patients had residual tumors only in the muscle layer or lymph nodes. The clinical application of the wait-and-see strategy in ESCC after nICT should be explored using an appropriate evaluation protocol.