Neoadjuvant Therapy Evaluation in Esophageal Carcinoma

Neoadjuvant therapy, encompassing chemotherapy and chemoradiotherapy delivered prior to surgical resection, has become a cornerstone in the management of locally advanced oesophageal carcinoma. The primary aim is to reduce tumour burden, eradicate micrometastatic disease and improve resectability, thereby enhancing long-term survival. Evaluation of therapeutic efficacy relies on a combination of clinical staging, imaging modalities such as 18F-FDG PET-CT, and detailed pathological examination of the resection specimen. Key pathological metrics include tumour regression grading, residual viable tumour fraction and lymph node status, all of which inform prognosis and guide postoperative treatment decisions. Emerging approaches integrate immunological profiling of the tumour microenvironment, notably the spatial distribution of tumour-infiltrating lymphocytes, alongside molecular biomarkers to predict response. Artificial intelligence and deep-learning models are now being developed to standardise assessment of histological response, offering rapid and reproducible quantification of residual tumour. Together, these advances are refining patient selection, personalising neoadjuvant regimens and laying the groundwork for adaptive treatment strategies that may spare responders from surgery or intensify therapy in poor responders.

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Neoadjuvant Therapy Evaluation in Esophageal Carcinoma publication trend

The graph below shows the total number of articles in neoadjuvant therapy evaluation in esophageal carcinoma across all publications each year (not limited to Nature Index journals).

Technical terms

Neoadjuvant therapy: Treatment given before surgical intervention, typically chemotherapy or chemoradiotherapy, to shrink tumours and address micrometastases.

Tumour Regression Grade (TRG): A histopathological score reflecting the extent of residual viable tumour cells relative to therapy-induced fibrosis.

ypN stage: Pathological assessment of regional lymph node involvement after neoadjuvant treatment, denoted by the prefix “yp”.

Pathological complete response (pCR): Absence of viable tumour cells in both primary site and regional nodes following neoadjuvant therapy.

Deep learning model: An artificial intelligence system trained on large datasets to recognise complex patterns and assist in diagnostic tasks such as quantifying residual tumour.

References

1. Pathological regression of primary tumour and metastatic lymph nodes following chemotherapy in resectable OG cancer: pooled analysis of two trials. British Journal of Cancer (2023).
2. Spatial distribution of tumor-infiltrating T cells indicated immune response status under chemoradiotherapy plus PD-1 blockade in esophageal cancer. Frontiers in Immunology (2023).
3. Prognostic value of Mandard score and nodal status for recurrence patterns and survival after multimodal treatment of oesophageal adenocarcinoma. British Journal of Surgery (2024).
4. Development of an Interpretable Deep Learning Model for Pathological Tumor Response Assessment After Neoadjuvant Therapy. Biological Procedures Online (2024).