Predictive Value of Endoscopic Observations and Biopsy After Neoadjuvant Chemoradiotherapy in Assessing the Pathologic Complete Response of Patients With Esophageal Squamous Cell Carcinoma

Kermani, Ali Taghizadeh; Ghanbarzadeh, Raha; Mashhad, Mona Joudi; Javadinia, Seyed Alireza; Torghabeh, Ali Emadi

doi:10.3389/fonc.2022.859079

Cited by 3 publications

(3 citation statements)

References 13 publications

(10 reference statements)

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“…According to the criteria from 11th edition of the Esophageal Cancer Handling Regulations, endoscopic primary lesions were evaluated as follows: (1) all endoscopic findings suggestive of neoplastic lesions have disappeared, (2) there is pathologically no cancer detected by endoscopic biopsy of the primary lesion that was present before treatment, (3) the entire esophagus can be observed by endoscopy, (4) there are no endoscopic findings suggestive of active esophagitis (no swelling alteration, no white moss). A complete response was achieved when all of the above findings (1) to (4) were satisfied 16 . Patients who did not meet the response definition were categorized as non-responders.…”

Section: Methodsmentioning

confidence: 99%

A novel CT-based radiomics model for predicting response and prognosis of chemoradiotherapy in esophageal squamous cell carcinoma

Kasai,

Miyoshi,

Sato

et al. 2024

Sci Rep

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No clinically relevant biomarker has been identified for predicting the response of esophageal squamous cell carcinoma (ESCC) to chemoradiotherapy (CRT). Herein, we established a CT-based radiomics model with artificial intelligence (AI) to predict the response and prognosis of CRT in ESCC. A total of 44 ESCC patients (stage I-IV) were enrolled in this study; training (n = 27) and validation (n = 17) cohorts. First, we extracted a total of 476 radiomics features from three-dimensional CT images of cancer lesions in training cohort, selected 110 features associated with the CRT response by ROC analysis (AUC ≥ 0.7) and identified 12 independent features, excluding correlated features by Pearson’s correlation analysis (r ≥ 0.7). Based on the 12 features, we constructed 5 prediction models of different machine learning algorithms (Random Forest (RF), Ridge Regression, Naive Bayes, Support Vector Machine, and Artificial Neural Network models). Among those, the RF model showed the highest AUC in the training cohort (0.99 [95%CI 0.86–1.00]) as well as in the validation cohort (0.92 [95%CI 0.71–0.99]) to predict the CRT response. Additionally, Kaplan-Meyer analysis of the validation cohort and all the patient data showed significantly longer progression-free and overall survival in the high-prediction score group compared with the low-prediction score group in the RF model. Univariate and multivariate analyses revealed that the radiomics prediction score and lymph node metastasis were independent prognostic biomarkers for CRT of ESCC. In conclusion, we have developed a CT-based radiomics model using AI, which may have the potential to predict the CRT response as well as the prognosis for ESCC patients with non-invasiveness and cost-effectiveness.

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Section: Methodsmentioning

confidence: 99%

A novel CT-based radiomics model for predicting response and prognosis of chemoradiotherapy in esophageal squamous cell carcinoma

Kasai,

Miyoshi,

Sato

et al. 2024

Sci Rep

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“…The CROSS trial, which was a benchmark for nCRT, showed a pCR rate of 49% in the squamous cell cancer group [5]. Similarly, another study done in Iran showed that 40.6% of the patients with SCC achieved pCR [15].…”

Section: Discussionmentioning

confidence: 95%

The Use of Trimodality Treatment in Patients With Locally Advanced Oesophageal Squamous Cell Carcinoma: An Experience From a Large Cancer Centre in Pakistan

Qamar,

Hanif,

Haider

et al. 2023

Cureus

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IntroductionEsophageal cancer is one of the most common cancers worldwide. Neoadjuvant chemoradiotherapy followed by surgery is the standard treatment for locally advanced squamous cell carcinoma (SCC). Pathological complete response (pCR) after surgery is associated with better outcomes in terms of overall survival and disease-free survival. We aim to determine the effectiveness of neoadjuvant chemoradiotherapy in patients with locally advanced SCC at our institute, the largest purpose-built cancer center in Pakistan. We also aim to identify various factors influencing pCR, such as chemotherapy regimen, total radiation dose, clinical stage at presentation, and gender. Materials and methodsThis is a retrospective review of all patients with esophageal SCC presented between January 2019 and 2021 to the institute for treatment. Patients received neoadjuvant chemoradiotherapy (nCRT) as per the CROSS trial protocol, followed by surgery. We assessed the pCR rate. Statistical analyses were performed using IBM SPSS Statistics for Windows, Version 22 (Released 2013; IBM Corp., Armonk, New York). pCR was studied alongside associated factors such as age, gender, stage of disease, chemotherapy regimen, and total dose of radiotherapy. A p-value of <0.05 was considered statistically significant. The chi-square test was used to compare categorical variables. Univariate and multivariate logistic regression was employed to evaluate factors affecting pCR. ResultsA total of 218 patients were included in the study. pCR was achieved in 64.2% of the patients. The female gender was associated with better outcomes, as 70.4% (n=81) of female patients achieved a complete pathological response, compared to 57.3% (n=59) of males, with a p-value of 0.03. On univariate analysis, the complete pathological response was 69.6% (n=94) in the age group of 45 years and below, whereas it was 55.4% (n=46) in the age group above 45 years, with a p-value of 0.024. Though statistically insignificant, outcomes were slightly better for those with node-negative disease, as 67.2% (n=41) achieved complete pathological response compared to those with node-positive disease at 63.1% (n=99). Univariate logistic regression analysis identified gender (p=0.044, OR=1.77, 95% CI: 1.016-3.108) and age group (p=0.034, OR=1.844, 95% CI: 1.046-3.252) as significantly associated with pCR. Female patients were 77% more likely to achieve pCR compared to male patients (OR=1.77, 95% CI: 1.016-3.108). Younger patients (≤45 years) were 84.4% more likely to achieve pCR compared to the older age group (OR=1.844, 95% CI: 1.046-3.252). However, these did not maintain significance in multivariate logistic regression analysis.

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“…According to the criteria from 11th edition of the Esophageal Cancer Handling Regulations, endoscopic primary lesions were evaluated as follows: 1) all endoscopic ndings suggestive of neoplastic lesions have disappeared, 2) there is pathologically no cancer detected by endoscopic biopsy of the primary lesion that was present before treatment, 3) the entire esophagus can be observed by endoscopy, 4) there are no endoscopic ndings suggestive of active esophagitis (no swelling alteration, no white moss). A complete response was achieved when all of the above ndings 1) to 4) were satis ed 19 . Patients who did not meet the response de nition were categorized as non-responders.…”

Section: Treatment Evaluationmentioning

confidence: 99%

A Novel CT-Based Radiomics Model for Predicting Response and Prognosis of Chemoradiotherapy in Esophageal Squamous Cell Carcinoma

Kasai

Miyoshi

Sato

et al. 2023

Preprint

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No clinically relevant biomarker has been identified for predicting the response of esophageal squamous cell carcinoma (ESCC) to chemoradiotherapy (CRT). Herein, we established a CT-based radiomics model with artificial intelligence (AI) to predict the response and prognosis of CRT in ESCC. A total of 44 ESCC patients (stage I-IV) were enrolled in this study; training (n = 27) and validation (n = 17) cohorts. First, we extracted a total of 476 radiomics features from three-dimensional CT images of cancer lesions in training cohort, selected 110 features associated with the CRT response by ROC analysis (AUC ≥ 0.7) and identified 12 independent features, excluding correlated features by Pearson’s correlation analysis (r ≥ 0.7). Based on the 12 features, we constructed 5 prediction models of different machine learning algorithms (Random Forest (RF), Ridge Regression, Naive Bayes, Support Vector Machine, and Artificial Neural Network models). Among those, the RF model showed the highest AUC in the training cohort (0.99, p < 0.001) as well as in the validation cohort (0.92, p < 0.001) to predict the CRT response. Additionally, Kaplan-Meyer analysis of the validation cohort and all the patient data revealed that the PFS and OS in the high-prediction score group were significantly longer than those in the low-prediction score group. Univariate and multivariate analyses revealed that the radiomics prediction score could be an independent prognostic biomarker, and moreover significantly superior to serum SCC-Ag, the conventional tumor marker of ESCC. In conclusion, we have developed a novel and robust CT-based radiomics model using AI, which successfully predicts the CRT response as well as the prognosis for ESCC patients with high accuracy, non-invasiveness, and cost-effectiveness.

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Predictive Value of Endoscopic Observations and Biopsy After Neoadjuvant Chemoradiotherapy in Assessing the Pathologic Complete Response of Patients With Esophageal Squamous Cell Carcinoma

Cited by 3 publications

References 13 publications

A novel CT-based radiomics model for predicting response and prognosis of chemoradiotherapy in esophageal squamous cell carcinoma

A novel CT-based radiomics model for predicting response and prognosis of chemoradiotherapy in esophageal squamous cell carcinoma

The Use of Trimodality Treatment in Patients With Locally Advanced Oesophageal Squamous Cell Carcinoma: An Experience From a Large Cancer Centre in Pakistan

A Novel CT-Based Radiomics Model for Predicting Response and Prognosis of Chemoradiotherapy in Esophageal Squamous Cell Carcinoma

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