Authors : Faith Ottilia Chimpeni

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/2s4c72v6

Scribd : https://tinyurl.com/5av3em3f

DOI : https://doi.org/10.38124/ijisrt/26apr740

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Dysphagia is the most debilitating symptom in oesophageal cancer, yet no consensus exists on patient-specific selection among endoscopic dilatation, self-expanding metal stents, or neoadjuvant therapy pathways. This study aimed to identify clinical predictors of treatment failure and develop a validated predictive model to guide individualized intervention selection. We conducted a retrospective cohort study of 487 consecutive patients with oesophageal cancer and dysphagia treated between 2018 and 2023. Patients received dilatation, stents, or neoadjuvant therapy as the primary dysphagia relief strategy. The primary outcome was time to re-intervention for recurrent dysphagia. Random Survival Forest analysis identified tumour length, ECOG performance status, baseline dysphagia grade, and treatment modality as dominant predictors. A significant treatment-tumour length interaction emerged: for tumours greater than seven centimetres, stents reduced re-intervention hazard by 68 percent versus dilatation and 53 percent versus neoadjuvant therapy, whereas for tumours of five centimetres or less, no significant difference existed between stents and neoadjuvant therapy. The final model demonstrated excellent discrimination with a concordance index of 0.76. A clinical decision tool stratifies patients into four phenotypes with specific recommendations. Tumour length and performance status are paramount in selecting optimal dysphagia intervention. Stents are superior for long tumours or frail patients, while neoadjuvant therapy or dilatation suffices for fit patients with localized disease. This validated nomogram enables evidence-based, personalized decisionmaking.

Keywords : Oesophageal Cancer; Dysphagia; Stents; Dilatation; Neoadjuvant Therapy; Predictive Modelling; Machine Learning.

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