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
Objective This 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.Design Clinicopathological data of 6517 primary oesophagea...
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BMJ Publishing Group
2024-07-01
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| Series: | BMJ Open Gastroenterology |
| Online Access: | https://bmjopengastro.bmj.com/content/11/1/e001253.full |
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| author | Jie Zhang Xiang Chen Dong Dong Lijie Tan Yajie Zhang Hecheng Li Yuqin Cao Binhao Huang Han Tang Tianzheng Shen Xijia Feng Jiahao Zhang Liqiang Shi Chengqiang Li Heng Jiao |
| author_facet | Jie Zhang Xiang Chen Dong Dong Lijie Tan Yajie Zhang Hecheng Li Yuqin Cao Binhao Huang Han Tang Tianzheng Shen Xijia Feng Jiahao Zhang Liqiang Shi Chengqiang Li Heng Jiao |
| author_sort | Jie Zhang |
| collection | DOAJ |
| description | Objective This 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.Design Clinicopathological 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.Results In 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.Conclusions The 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. |
| format | Article |
| id | doaj-art-932aae5cec3c426599651a8c55ad36aa |
| institution | Kabale University |
| issn | 2054-4774 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open Gastroenterology |
| spelling | doaj-art-932aae5cec3c426599651a8c55ad36aa2025-02-12T07:55:09ZengBMJ Publishing GroupBMJ Open Gastroenterology2054-47742024-07-0111110.1136/bmjgast-2023-001253Online 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 nomogramsJie Zhang0Xiang Chen1Dong Dong2Lijie Tan3Yajie Zhang4Hecheng Li5Yuqin Cao6Binhao Huang7Han Tang8Tianzheng Shen9Xijia Feng10Jiahao Zhang11Liqiang Shi12Chengqiang Li13Heng Jiao14Department of Anesthesiology, Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, ChinaDepartment of Dermatology, Xiangya Hospital, Central South University, Changsha, Hunan, ChinaDepartment of Thoracic Surgery, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, Shanghai, ChinaDepartment of Thoracic Surgery, Zhongshan Hospital Fudan University, Shanghai, Shanghai, ChinaDepartment of Thoracic Surgery, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, Shanghai, ChinaDepartment of Thoracic Surgery, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, Shanghai, ChinaDepartment of Thoracic Surgery, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, Shanghai, ChinaDepartment of Thoracic Surgery, Shanghai Chest Hospital, Shanghai, Shanghai, ChinaDepartment of Thoracic Surgery, Zhongshan Hospital Fudan University, Shanghai, Shanghai, ChinaDepartment of Thoracic Surgery, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, Shanghai, ChinaDepartment of Thoracic Surgery, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, Shanghai, ChinaDepartment of Thoracic Surgery, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, Shanghai, ChinaDepartment of Thoracic Surgery, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, Shanghai, ChinaDepartment of Thoracic Surgery, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, Shanghai, Shanghai, ChinaDepartment of Thoracic Surgery, Zhongshan Hospital Fudan University, Shanghai, Shanghai, ChinaObjective This 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.Design Clinicopathological 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.Results In 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.Conclusions The 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.https://bmjopengastro.bmj.com/content/11/1/e001253.full |
| spellingShingle | Jie Zhang Xiang Chen Dong Dong Lijie Tan Yajie Zhang Hecheng Li Yuqin Cao Binhao Huang Han Tang Tianzheng Shen Xijia Feng Jiahao Zhang Liqiang Shi Chengqiang Li Heng Jiao 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 BMJ Open Gastroenterology |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| url | https://bmjopengastro.bmj.com/content/11/1/e001253.full |
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