Failure pattern of elective nodal irradiation for esophageal squamous cell cancer treated with neoadjuvant chemoradiotherapy

Proton Beam Therapy for Esophageal Cancer

Simple Summary

Early-stage esophageal cancer is managed surgically, with the addition of radiotherapy for locally advanced disease. Current photon-based radiotherapy results in a high treatment-related complications, due to proximal organ involvement. The anatomic location of the esophagus raises challenges due to the anatomical changes associated with diaphragmatic motion, weight loss, tumor changes, and set-up variability. These propelled the interest in proton beam therapy (PBT), which theoretically offers a reduction in the radiation exposure to healthy neighboring tissues with improvements in the therapeutic ratio. In this review, we present the role of PBT for esophageal cancer, including treatment planning, early clinical comparisons with photon-based techniques, ongoing trials, current challenges, toxicities, and issues of equity and health services.

Abstract

Early-stage esophageal cancer is often primarily managed surgically, with the addition of radiotherapy for locally advanced disease. However, current photon-based radiotherapy regimens and surgery results in a high incidence of treatment-related cardiac and pulmonary complications due to the involvement of proximal organs at risk. In addition, the anatomic location of the esophagus raises challenges for radiotherapy due to the anatomical changes associated with diaphragmatic motion, weight loss, tumor changes, and set-up variability. These challenges propelled the interest in proton beam therapy (PBT), which theoretically offers a reduction in the radiation exposure to healthy neighboring tissues with improvements in the therapeutic ratio. Several dosimetric studies support the potential advantages of PBT for esophageal cancer treatment however, translation of these results to improved clinical outcomes remains unclear with limited clinical data, especially in large populations. Studies on the effect on quality of life are likewise lacking. Here, we review the existing and emerging role of PBT for esophageal cancer, including treatment planning, early clinical comparisons of PBT with photon-based techniques, recently concluded and ongoing clinical trials, challenges and toxicities, effects on quality of life, and global inequities in the treatment of esophageal cancer.

Is Performance of Fluorine-18-fluorodeoxyglucose Positron Emission Tomography/Computed tomography (CT) or Contrast-enhanced CT Efficient Enough to Guide the Hilar Lymph Node Staging for Patients with Esophageal Squamous Cell Carcinoma?

Introduction

We evaluated the diagnostic performance of fluorine-18-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) and contrast-enhanced CT in the detection of hilar lymph node metastasis (LNM) in esophageal squamous cell carcinoma (ESCC) to determine their value in guiding hilar lymph node staging and delineating radiation target volume.

Methods

Consecutive patients with ESCC who underwent both PET/CT and contrast-enhanced CT before radical lymphadenectomy and esophagectomy at our institution from September 2009 to November 2018 were enrolled. The sensitivity (SE), specificity (SP), positive predictive value (PPV), and negative predictive value (NPV) of FDG-PET/CT and contrast-enhanced CT for diagnosing hilar LNM were calculated.

Results

Of the 174 patients included, contrast-enhanced CT predicted nine positive cases, while PET/CT predicted one, and eight (4.6%) were identified as pathologically positive for their resected hilar lymph nodes. The SE, SP, PPV, and NPV of PET/CT and contrast-enhanced CT were 0.000, 0.994, 0.000, and 0.954; and 0.125, 0.952, 0.111, and 0.958, respectively. The specificity showed a significant difference (P=0.037). PET/CT is slightly more specific than contrast-enhanced CT.

Conclusions

PET/CT and contrast-enhanced CT may be useful tools for predicting the negativity of hilar LN status, but they are not recommended for guiding the hilar lymph node staging and the delineating of hilar LNM in radiotherapy planning of ESCC patients based on their low PPV.

Development and validation of a radiomics-based model to predict local progression-free survival after chemo-radiotherapy in patients with esophageal squamous cell cancer

PURPOSE

To develop a nomogram model for predicting local progress-free survival (LPFS) in esophageal squamous cell carcinoma (ESCC) patients treated with concurrent chemo-radiotherapy (CCRT).

METHODS

We collected the clinical data of ESCC patients treated with CCRT in our hospital. Eligible patients were randomly divided into training cohort and validation cohort. The least absolute shrinkage and selection operator (LASSO) with COX regression was performed to select optimal radiomic features to calculate Rad-score for predicting LPFS in the training cohort. The univariate and multivariate analyses were performed to identify the predictive clinical factors for developing a nomogram model. The C-index was used to assess the performance of the predictive model and calibration curve was used to evaluate the accuracy.

RESULTS

A total of 221 ESCC patients were included in our study, with 155 patients in training cohort and 66 patients in validation cohort. Seventeen radiomic features were selected by LASSO COX regression analysis to calculate Rad-score for predicting LPFS. The patients with a Rad-score ≥ 0.1411 had high risk of local recurrence, and those with a Rad-score < 0.1411 had low risk of local recurrence. Multivariate analysis showed that N stage, CR status and Rad-score were independent predictive factors for LPFS. A nomogram model was built based on the result of multivariate analysis. The C-index of the nomogram was 0.745 (95% CI 0.7700-0.790) in training cohort and 0.723(95% CI 0.654-0.791) in validation cohort. The 3-year LPFS rate predicted by the nomogram model was highly consistent with the actual 3-year LPFS rate both in the training cohort and the validation cohort.

CONCLUSION

We developed and validated a prediction model based on radiomic features and clinical factors, which can be used to predict LPFS of patients after CCRT. This model is conducive to identifying the patients with ESCC benefited more from CCRT.