DW-MRI and DCE-MRI are of complementary value in predicting pathologic response to neoadjuvant chemoradiotherapy for esophageal cancer

Abstract Objectives To assess the complementary value of human epidermal growth factor receptor 2 (HER2)-related biological tumor markers to clinico-radiomic models in predicting complete response to neoadjuvant chemoradiotherapy (NCRT) in esophageal cancer patients. Methods Expression of HER2 was assessed by immunohistochemistry in pre-treatment tumor biopsies of 96 patients with locally advanced esophageal cancer. Five other potentially active HER2-related biological tumor markers in esophageal cancer were examined in a sub-analysis on 43 patients. Patients received at least four of the five cycles of chemotherapy and full radiotherapy regimen followed by esophagectomy. Three reference clinico-radiomic models based on 18F-FDG PET were constructed to predict pathologic response, which was categorized into complete versus incomplete (Mandard tumor regression grade 1 vs. 2–5). The complementary value of the biological tumor markers was evaluated by internal validation through bootstrapping. Results Pathologic examination revealed 21 (22%) complete and 75 (78%) incomplete responders. HER2 and cluster of differentiation 44 (CD44), analyzed in the sub-analysis, were univariably associated with pathologic response. Incorporation of HER2 and CD44 into the reference models improved the overall performance (R2s of 0.221, 0.270, and 0.225) and discrimination AUCs of 0.759, 0.857, and 0.816. All models exhibited moderate to good calibration. The remaining studied biological tumor markers did not yield model improvement. Conclusions Incorporation of HER2 and CD44 into clinico-radiomic prediction models improved NCRT response prediction in esophageal cancer. These biological tumor markers are promising in initial response evaluation. Key Points • A multimodality approach, integrating independent genomic and radiomic information, is promising to improve prediction of γpCR in patients with esophageal cancer. • HER2 and CD44 are potential biological tumor markers in the initial work-up of patients with esophageal cancer. • Prediction models combining 18F-FDG PET radiomic features with HER2 and CD44 may be useful in the decision to omit surgery after neoadjuvant chemoradiotherapy in patients with esophageal cancer.

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Complete Pathologic Response After Neoadjuvant Chemoradiotherapy for Esophageal Cancer Is Associated With Enhanced Survival

Yearbook of Surgery ◽

10.1016/s0090-3671(09)79353-x ◽

2009 ◽

Vol 2009 ◽

pp. 363-364

Author(s):

D.R. Jones

Keyword(s):

Esophageal Cancer ◽

Neoadjuvant Chemoradiotherapy ◽

Pathologic Response ◽

Complete Pathologic Response

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Preoperative Prediction of Pathologic Response to Neoadjuvant Chemoradiotherapy in Patients With Esophageal Cancer Using 18F-FDG PET/CT and DW-MRI: A Prospective Multicenter Study

International Journal of Radiation Oncology*Biology*Physics ◽

10.1016/j.ijrobp.2019.12.038 ◽

2020 ◽

Vol 106 (5) ◽

pp. 998-1009 ◽

Cited By ~ 4

Author(s):

Alicia S. Borggreve ◽

Lucas Goense ◽

Peter S.N. van Rossum ◽

Sophie E. Heethuis ◽

Richard van Hillegersberg ◽

...

Keyword(s):

Esophageal Cancer ◽

Multicenter Study ◽

Fdg Pet ◽

Neoadjuvant Chemoradiotherapy ◽

Pathologic Response ◽

Prospective Multicenter Study ◽

Preoperative Prediction ◽

Pet Ct ◽

18F Fdg ◽

Fdg Pet Ct

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FDG-PET in the prediction of pathologic response after neoadjuvant chemoradiotherapy in locally advanced, resectable esophageal cancer

International Journal of Radiation Oncology*Biology*Physics ◽

10.1016/j.ijrobp.2005.03.033 ◽

2005 ◽

Vol 63 (4) ◽

pp. 1053-1059 ◽

Cited By ~ 92

Author(s):

Si Yeol Song ◽

Jong Hoon Kim ◽

Jin Sook Ryu ◽

Gin Hyug Lee ◽

Sung Bae Kim ◽

...

Keyword(s):

Esophageal Cancer ◽

Fdg Pet ◽

Locally Advanced ◽

Neoadjuvant Chemoradiotherapy ◽

Pathologic Response ◽

Resectable Esophageal ◽

Resectable Esophageal Cancer

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Interim FDG-PET imaging during neoadjuvant chemoradiotherapy for esophageal cancer: Correlation with pathologic response.

Journal of Clinical Oncology ◽

10.1200/jco.2017.35.4_suppl.175 ◽

2017 ◽

Vol 35 (4_suppl) ◽

pp. 175-175

Author(s):

Daniel Tandberg ◽

Julian C. Hong ◽

Yunfeng Cui ◽

Brad Ackerson ◽

Brian G. Czito ◽

...

Keyword(s):

Esophageal Cancer ◽

Fdg Pet ◽

Tumor Response ◽

Neoadjuvant Chemoradiotherapy ◽

Pathologic Response ◽

Relative Reduction ◽

Interim Pet ◽

Significant Difference ◽

Manual Contour ◽

Pre Treatment

175 Background: In this prospective study we evaluated whether changes in metabolic tumor parameters on interim flurodeoxyglucose positron emission tomography (FDG-PET) performed during neoadjuvant chemoradiotherapy (CRT) for esophageal cancer correlates with histopathologic tumor response. Methods: From February 2012 to February 2016, 60 patients with esophageal cancer underwent PET scans before therapy and after 30-36 Gy. Patients who underwent surgery after carboplatin/paclitaxel CRT were eligible for the current analysis. PET metrics of the primary site including maximum standardized uptake value (SUVmax), SUV mean, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) were extracted from the pre-treatment and interim PET based on a manual contour and SUV 2.5 threshold. Patients were called histopathologic responders if they had a complete or near complete tumor response based on the modified Ryan scheme. Relative changes in PET metrics between pre-treatment and interim PET were compared between histopathologic responders and non-responders using the Mann-Whitney test and binary logistic regression. Results: Twenty-six patients were included in the analysis. Adenocarcinoma was the most common histology (n = 23). Eleven patients (42%) had a complete or near complete pathologic response to CRT (histopathologic responders). Changes in PET metrics from pre-treatment to interim PET based on the manual contour were not significantly different between responding and nonresponding tumors. The relative reduction of SUVmax (Mean ± SD) was 38.2% ± 28.4% for histopathologic responders and 27.9% ± 31.4% for non-responders. The relative reduction in MTV, SUV mean and TLG was 36.1% ± 26.2%, 23.5% ± 21.3%, and 49.3% ± 28.3% for histopathologic responders and 28.6% ± 32.0%, 11.8% ± 19.1%, and 33.1% ± 38.5% for histopathologic non-responders, respectively. When analyzed based on the SUV 2.5 threshold there continued to be no significant difference in PET metrics. Conclusions: In this pilot study we observed changes in metabolic tumor parameters on PET performed during CRT for esophageal cancer. However, these changes did not predict for histopathologic responders.

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177 CT RADIOMICS BASED ON MACHINE LEARNING PREDICTING PATHOLOGIC COMPLETE RESPONSE AFTER NEOADJUVANT CHEMORADIOTHERAPY FOR ESOPHAGEAL CANCER

Diseases of the Esophagus ◽

10.1093/dote/doab052.177 ◽

2021 ◽

Vol 34 (Supplement_1) ◽

Author(s):

Xue-Feng Leng ◽

Qi-Feng Wang ◽

Jin-Yi Lang ◽

Tao Li ◽

Yong-Tao Han ◽

...

Keyword(s):

Machine Learning ◽

Esophageal Cancer ◽

Squamous Cell ◽

Pathologic Complete Response ◽

Neoadjuvant Chemoradiotherapy ◽

Complete Response ◽

Pathologic Response ◽

Training Set ◽

Term Survival ◽

Training Cohort

Abstract Neoadjuvant chemoradiotherapy (nCRT) has become the standard treatment for patients with locally advanced squamous cell esophageal cancer (LA-ESCC). Patients with the complete pathologic response (pCR) have significantly improved long-term survival. All efforts should be to improve the accuracy of predicting pCR. In this study, we investigate the use of radiomics based on machine learning to identify the pathologic complete response of patients with esophageal squamous cell carcinoma (ESCC) based on Computed Tomography (CT). Methods The study included 155 patients with pathologically confirmed LA-ESCC. All 155 patients underwent simulation CT before nCRT, Quantitative radiomics features were extracted from CT images of each patient. To explore the relationship between radiomics features and the pCR, we used five-fold cross validation to classify the training and the testing cohorts. The Least Absolute Shrinkage and Selectionator operator (Lasso) were used to select features useful for the grading of pCR in the training cohort. Different models were measured in the training cohort using accuracy, sensitivity, specificity, and area under the curve of the receiver operating characteristic curve (AUC). Results There were 155 patients. The pretreatment clinical stage was II in 16 patients (10.3%), III in 132 (85.2%), and IV in 7 (4.5%). Pathologic response was complete in 69 patients (44.5%), near-partial complete in 86 (55.5%). A total of 2193 radiomics features were extracted in the training set. After the use of statistical dimensionality reduction, five radiomics features were selected by Lasso to build radiomics signature. Prediction models for pCR were developed, and the model was able to predict pCR well in the training set(AUC = 0.902). In the testing cohorts, the model had a good performance in predicting pCR (AUC = 0.78). Conclusion This study showed that CT-based radiomics features could be used as biomarkers to predict the complete pathological response of esophageal cancer underwent Neoadjuvant chemoradiotherapy.

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