Dataset Information

Pathological complete response after neoadjuvant treatment determines survival in esophageal squamous cell carcinoma patients (NEOCRTEC5010).

ABSTRACT:

Background

Few studies have exclusively investigated the value of pathological complete response (pCR), in esophageal squamous cell carcinoma (ESCC) patients, although it is a clinically significant parameter to evaluate the impact of neoadjuvant chemoradiotherapy (nCRT) on treatment outcome after surgery. The aim of our study was to explore the relationship between pCR after nCRT and survival among patients with local ESCC.

Methods

All patients receiving nCRT followed by surgery in NEOCRTEC5010-trial (NCT01216527) were included. Non-pCR patients were classified into three subgroups: ypTanyN0M0, ypT0NanyM0 and ypTanyNanyM0. The Kaplan-Meier method with log-rank test was employed to evaluate disease-free survival (DFS) and overall survival (OS). Multivariate regression analysis was performed using a Cox proportional hazards model to identify clinicopathological parameters associated with pCR.

Results

Among the 185 patients included, 80 (43.2%) achieved pCR after nCRT. The mean survival time of the pCR group was significantly longer than that of the non-pCR group (92.6 vs. 69.2 months; HR, 2.70; 95% CI: 1.48-4.92; P=0.001). The 5-year OS and DFS of the pCR group were 79.3% and 77% respectively, compared to 54.8% and 51.2%, respectively, in the non-pCR group. The results showed that the OS and DFS of the ypTanyN0M0 group were better than those of the ypT0NanyM0 group and the ypTanyNanyM0 group. We also found that the number of dissected lymph nodes and pCR were independent risk factors for DFS and OS rates.

Conclusions

pCR after nCRT is an important prognostic indicator of OS and DFS in patients with ESCC. In addition, lymph-node status could represent an important parameter in the prognostic evaluation of esophageal cancer patients.

SUBMITTER: Shen J

PROVIDER: S-EPMC8576689 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Pathological complete response after neoadjuvant treatment determines survival in esophageal squamous cell carcinoma patients (NEOCRTEC5010).

Shen Jianfei J Kong Min M Yang Hong H Jin Ke K Chen Yuping Y Fang Wentao W Yu Zhentao Z Mao Weimin W Xiang Jiaqing J Han Yongtao Y Chen Zhijian Z Yang Haihua H Wang Jiaming J Pang Qingsong Q Zheng Xiao X Yang Huanjun H Li Tao T Zhang Xu X Li Qun Q Wang Geng G Mao Teng T Guo Xufeng X Lin Ting T Liu Mengzhong M Ma Dehua D Ye Minhua M Wang Chunguo C Wang Zheng Z Brunelli Alessandro A Cerfolio Robert J RJ D'Journo Xavier Benoit XB Fernando Hiran C HC Lordick Florian F Fu Jianhua J Chen Baofu B Zhu Chengchu C

Annals of translational medicine 20211001 20

<h4>Background</h4>Few studies have exclusively investigated the value of pathological complete response (pCR), in esophageal squamous cell carcinoma (ESCC) patients, although it is a clinically significant parameter to evaluate the impact of neoadjuvant chemoradiotherapy (nCRT) on treatment outcome after surgery. The aim of our study was to explore the relationship between pCR after nCRT and survival among patients with local ESCC.<h4>Methods</h4>All patients receiving nCRT followed by surgery ...[more]

PMID: 34790722

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Project description:ObjectivesThis study aimed to develop and validate a deep-learning radiomics model using CT, T2, and DWI images for predicting pathological complete response (pCR) in patients with esophageal squamous cell carcinoma (ESCC) undergoing neoadjuvant chemoradiotherapy (nCRT).Materials and methodsPatients with ESCC undergoing nCRT followed by surgery were retrospectively enrolled from three institutions and divided into training and testing cohorts. Both traditional and deep-learning radiomics features were extracted from pre-treatment CT, T2, and DWI. Multiple radiomics models were developed, both single modality and integrated, using machine learning algorithms. The models' performance was assessed using receiver operating characteristic curve analysis, with the area under the curve (AUC) as a primary metric, alongside sensitivity and specificity from the cut-off analysis.ResultsThe study involved 151 patients, among whom 63 achieved pCR. The training cohort consisted of 89 patients from Institution 1 (median age 62, 73 males) and the testing cohort included 52 patients from Institution 2 (median age 62, 41 males), and 10 in a clinical trial from Institution 3 (median age 69, 9 males). The integrated model, combining traditional and deep learning radiomics features from CT, T2, and DWI, demonstrated the best performance with an AUC of 0.868 (95% CI: 0.766-0.959), sensitivity of 88% (95% CI: 73.9-100), and specificity of 78.4% (95% CI: 63.6-90.2) in the testing cohort. This model outperformed single-modality models and the clinical model.ConclusionA multimodality deep learning radiomics model, utilizing CT, T2, and DWI images, was developed and validated for accurately predicting pCR of ESCC following nCRT.Critical relevance statementOur research demonstrates the satisfactory predictive value of multimodality deep learning radiomics for the response of nCRT in ESCC and provides a potentially helpful tool for personalized treatment including organ preservation strategy.Key pointsAfter neoadjuvant chemoradiotherapy, patients with ESCC have pCR rates of about 40%. The multimodality deep learning radiomics model, could predict pCR after nCRT with high accuracy. The multimodality radiomics can be helpful in personalized treatment of esophageal cancer.

Project description:ImportanceFor patients with locally advanced esophageal squamous cell carcinoma, neoadjuvant chemoradiation has been shown to improve long-term outcomes, but the treatment response varies among patients. Accurate pretreatment prediction of response remains an urgent need.ObjectiveTo determine whether peritumoral radiomics features derived from baseline computed tomography images could provide valuable information about neoadjuvant chemoradiation response and enhance the ability of intratumoral radiomics to estimate pathological complete response.Design, setting, and participantsA total of 231 patients with esophageal squamous cell carcinoma, who underwent baseline contrast-enhanced computed tomography and received neoadjuvant chemoradiation followed by surgery at 2 institutions in China, were consecutively included. This diagnostic study used single-institution data between April 2007 and December 2018 to extract radiomics features from intratumoral and peritumoral regions and established intratumoral, peritumoral, and combined radiomics models using different classifiers. External validation was conducted using independent data collected from another hospital during the same period. Radiogenomics analysis using gene expression profile was done in a subgroup of the training set for pathophysiological explanation. Data were analyzed from June to December 2019.ExposuresComputed tomography-based radiomics.Main outcomes and measuresThe discriminative performances of radiomics models were measured by area under the receiver operating characteristic curve.ResultsAmong the 231 patients included (192 men [83.1%]; mean [SD] age, 59.8 [8.7] years), the optimal intratumoral and peritumoral radiomics models yielded similar areas under the receiver operating characteristic curve of 0.730 (95% CI, 0.609-0.850) and 0.734 (0.613-0.854), respectively. The combined model was composed of 7 intratumoral and 6 peritumoral features and achieved better discriminative performance, with an area under the receiver operating characteristic curve of 0.852 (95% CI, 0.753-0.951), accuracy of 84.3%, sensitivity of 90.3%, and specificity of 79.5% in the test set. Gene sets associated with the combined model mainly involved lymphocyte-mediated immunity. The association of peritumoral area with response identification might be partially attributed to type I interferon-related biological process.Conclusions and relevanceA combination of peritumoral radiomics features appears to improve the predictive performance of intratumoral radiomics to estimate pathological complete response after neoadjuvant chemoradiation in patients with esophageal squamous cell carcinoma. This study underlines the significant application of peritumoral radiomics to assess treatment response in clinical practice.

Project description:BackgroundNeoadjuvant chemoradiotherapy (neo-CRT) plus surgery has greatly improved the prognosis of locally advanced esophageal cancer (EC) patients. But which factors may influence the pathological tumor response and long-term survival remains unclear. The purpose of this study was to identify the prognostic biomarkers of locally advanced EC patients receiving neo-CRT.MethodsWe reviewed the data of 72 patients with cT2-4N0-3M0 EC who underwent neo-CRT at our hospital. The patients received intensity-modulated radiation therapy with a total radiation dose of 41.4-60.0 Gy. Most patients received platinum + paclitaxel-based combination regimens every three weeks for 2-4 cycles. The recorded data included age, sex, smoking history, alcohol use, histology, tumor location, clinical TNM stage, tumor length, gross tumor volume (GTV), GTV of primary tumor (GTVp), GTV of lymph nodes (GTVn), radiation dose, and number of chemotherapy cycles. Overall survival (OS), progression-free survival (PFS), and pathological complete response (pCR) were analyzed.ResultsThe 3-year OS and PFS rates of these patients who underwent neo-CRT were 51.14% and 43.28%, respectively. In the univariate analyses, smoking history, clinical stage, GTV, GTVp, and GTVn were significantly associated with OS, whereas alcohol use, GTV, GTVp, and GTVn were significantly associated with PFS. Furthermore, in the multivariate analysis, GTV was an independent prognostic predictor of OS (hazard ratio (HR): 14.14, 95% confidence interval (CI): 3.747-53.33, P < 0.0001) and PFS (HR: 6.090, 95% CI: 2.398-15.47, P < 0.0001). In addition, GTV < 60.50 cm3 compared to > 60.50 cm3 was significantly associated with higher pCR rate (59.3% and 27.8%, respectively, P = 0.038). High dose (> 50 Gy) and increased number of chemotherapy cycles (≥ 3) didn't improve the OS or PFS in patients with GTV > 60.50 cm3.ConclusionGTV was an independent prognostic factor of long-term survival in EC patients, which may be because GTV is associated with histological response to neo-CRT. Additionally, patients with GTV > 60.50 cm3 didn't benefit from increased radiation dose or increased number of chemotherapy cycles.

Project description:IntroductionPredicting the efficacy of neoadjuvant immunochemotherapy (NICT) for esophageal squamous cell carcinoma (ESSC) prior to surgery can minimize unnecessary surgical interventions and facilitate personalized treatment strategies. Our goal is to develop and validate an image-based radiomic model using preoperative computed tomography (CT) scans and clinical data to predict pathological complete response (pCR) in resectable ESSC following neoadjuvant immunotherapy.MethodsWe retrospectively collected data from patients diagnosed with ESCC at the First Affiliated Hospital of Soochow University between January 2018 and May 2023, who received preoperative neoadjuvant immunochemotherapy. Eligible patients were randomly divided into training and validation sets. Radiomic features extracted from preprocessed CT images were used to develop a radiomic model, incorporating Radiomic score (Rad-score) and clinical factors through multivariate logistic regression analysis. The model's performance was assessed for calibration, discrimination, and clinical utility in an independent validation cohort.ResultsWe enrolled a total of 105 eligible participants who were randomly divided into two groups: a training set (N=74) and a validation set (N=31). After data dimension reduction and feature selection, we identified 11 radiomic features, which collectively formed the Rad-score. Rad-score had an area under the curve (AUC) of 0.83 (95% CI 0.72-0.93) in the training set and 0.78 (95% CI 0.60-0.95) in the validation set. Multivariate analysis revealed that radiological response and Neutrophil-Lymphocyte Ratio (NLR) were independent predictors of pCR, with p-values of 0.0026 and 0.0414, respectively. We developed and validated a nomogram combining Rad-score and clinical features, achieving AUCs of 0.90 (95% CI 0.82-0.98) in the training set and 0.85 (95% CI 0.70-0.99) in the validation set. The Delong test confirmed the nomogram's superiority over pure radiomic and clinical models. Decision curve analysis (DCA) and integrated discrimination improvement (IDI) assessment supported the clinical value and superiority of the combined model.ConclusionThe nomogram, which integrates Rad-score and clinical features, offers a precise and reliable method for predicting pCR status in ESCC patients who have undergone neoadjuvant immunochemotherapy. This tool aids in tailoring treatment strategies to individual patients.

Dataset Information

Pathological complete response after neoadjuvant treatment determines survival in esophageal squamous cell carcinoma patients (NEOCRTEC5010).

Background

Methods

Results

Conclusions

Publications

Pathological complete response after neoadjuvant treatment determines survival in esophageal squamous cell carcinoma patients (NEOCRTEC5010).

Similar Datasets

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