ArticleBMJ open gastroenterology2024
Online tools to predict individualised survival for primary oesophageal cancer patients with and without pathological complete response after neoadjuvant therapy followed by oesophagectomy: development and external validation of two independent nomograms.
Article in BMJ open gastroenterology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Authors and funding
15 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to develop and validate robust predictive models for patients with oesophageal cancer who achieved a pathological complete response (pCR) and those who did not (non-pCR) after neoadjuvant therapy and oesophagectomy.
designClinicopathological data of 6517 primary oesophageal cancer patients who underwent neoadjuvant therapy and oesophagectomy were obtained from the National Cancer Database for the training cohort. An independent cohort of 444 Chinese patients served as the validation set. Two distinct multivariable Cox models of overall survival (OS) were constructed for pCR and non-pCR patients, respectively, and were presented using web-based dynamic nomograms (graphical representation of predicted OS based on the clinical characteristics that a patient could input into the website). The calibration plot, concordance index and decision curve analysis were employed to assess calibration, discrimination and clinical usefulness of the predictive models.
resultsIn total, 13 and 15 variables were used to predict OS for pCR and non-pCR patients undergoing neoadjuvant therapy followed by oesophagectomy, respectively. Key predictors included demographic characteristics, pretreatment clinical stage, surgical approach, pathological information and postoperative treatments. The predictive models for pCR and non-pCR patients demonstrated good calibration and clinical utility, with acceptable discrimination that surpassed that of the current tumour, node, metastases staging system.
conclusionsThe web-based dynamic nomograms for pCR (https://predict-survival.shinyapps.io/pCR-eso/) and non-pCR patients (https://predict-survival.shinyapps.io/non-pCR-eso/) developed in this study can facilitate the calculation of OS probability for individual patients undergoing neoadjuvant therapy and radical oesophagectomy, aiding clinicians and patients in making personalised treatment decisions.
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