Radiomics Signature for Prediction of Long-term Survival and Recurrence Patterns in Gastric Cancer: A Multicenter Study
EAES Academy. Weng K. 07/05/22; 363199; P263
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Abstract
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Purpose:
To develop and validate a radiomics score to predict the long-term survival of gastric cancer (GC) after distal gastrectomy and patterns of recurrence.
Methods:
A total of 513 patients who underwent distal gastrectomy for GC after curative resection between 2008 and 2016 at two institutions were analyzed. A radiomics score was generated using the least absolute shrinkage and selection operator (LASSO) Cox regression model oin 327 patients and was validated in 186 patients. A nomogram consisting of the radiomics score and clinicopathological factors was further created and compared with the tumor-lymph node-metastasis (TNM) staging system. The Modelmodel performance was assessed usingvia calibration, discrimination, and clinical usefulness.
Results:
The radiomics score was established based on five5 selected features. A higher score was significantly associated with poorer recurrence-free survival (RFS) and overall survival (OS) rates both in the training and validation cohorts (P all <0.05). Multivariate analysis demonstrated that the radiomics score was an independent prognostic factor for both RFS and OS (P all <0.05). A nomogram incorporating the radiomics score had a significantly better prognostic value thancompared with the traditional staging TNM system alone. Moreover, a high score was significantly associated with an increased risk of distant recurrence, a medium score was significantly associated with an increased risk of peritoneal recurrence, and a low score was significantly associated with an increased risk of locoregional recurrence in the entire cohort (P all <0.05).
Conclusions:
The newly proposed radiomics score may be a powerful predictor of long-term outcomes and recurrence patterns of GC. Further studies are warranted to confirm these findings.
Keywords: Gastric Cancer; Radiomics Score; Nomogram; Prognosis; Recurrence Patterns.
To develop and validate a radiomics score to predict the long-term survival of gastric cancer (GC) after distal gastrectomy and patterns of recurrence.
Methods:
A total of 513 patients who underwent distal gastrectomy for GC after curative resection between 2008 and 2016 at two institutions were analyzed. A radiomics score was generated using the least absolute shrinkage and selection operator (LASSO) Cox regression model oin 327 patients and was validated in 186 patients. A nomogram consisting of the radiomics score and clinicopathological factors was further created and compared with the tumor-lymph node-metastasis (TNM) staging system. The Modelmodel performance was assessed usingvia calibration, discrimination, and clinical usefulness.
Results:
The radiomics score was established based on five5 selected features. A higher score was significantly associated with poorer recurrence-free survival (RFS) and overall survival (OS) rates both in the training and validation cohorts (P all <0.05). Multivariate analysis demonstrated that the radiomics score was an independent prognostic factor for both RFS and OS (P all <0.05). A nomogram incorporating the radiomics score had a significantly better prognostic value thancompared with the traditional staging TNM system alone. Moreover, a high score was significantly associated with an increased risk of distant recurrence, a medium score was significantly associated with an increased risk of peritoneal recurrence, and a low score was significantly associated with an increased risk of locoregional recurrence in the entire cohort (P all <0.05).
Conclusions:
The newly proposed radiomics score may be a powerful predictor of long-term outcomes and recurrence patterns of GC. Further studies are warranted to confirm these findings.
Keywords: Gastric Cancer; Radiomics Score; Nomogram; Prognosis; Recurrence Patterns.
Purpose:
To develop and validate a radiomics score to predict the long-term survival of gastric cancer (GC) after distal gastrectomy and patterns of recurrence.
Methods:
A total of 513 patients who underwent distal gastrectomy for GC after curative resection between 2008 and 2016 at two institutions were analyzed. A radiomics score was generated using the least absolute shrinkage and selection operator (LASSO) Cox regression model oin 327 patients and was validated in 186 patients. A nomogram consisting of the radiomics score and clinicopathological factors was further created and compared with the tumor-lymph node-metastasis (TNM) staging system. The Modelmodel performance was assessed usingvia calibration, discrimination, and clinical usefulness.
Results:
The radiomics score was established based on five5 selected features. A higher score was significantly associated with poorer recurrence-free survival (RFS) and overall survival (OS) rates both in the training and validation cohorts (P all <0.05). Multivariate analysis demonstrated that the radiomics score was an independent prognostic factor for both RFS and OS (P all <0.05). A nomogram incorporating the radiomics score had a significantly better prognostic value thancompared with the traditional staging TNM system alone. Moreover, a high score was significantly associated with an increased risk of distant recurrence, a medium score was significantly associated with an increased risk of peritoneal recurrence, and a low score was significantly associated with an increased risk of locoregional recurrence in the entire cohort (P all <0.05).
Conclusions:
The newly proposed radiomics score may be a powerful predictor of long-term outcomes and recurrence patterns of GC. Further studies are warranted to confirm these findings.
Keywords: Gastric Cancer; Radiomics Score; Nomogram; Prognosis; Recurrence Patterns.
To develop and validate a radiomics score to predict the long-term survival of gastric cancer (GC) after distal gastrectomy and patterns of recurrence.
Methods:
A total of 513 patients who underwent distal gastrectomy for GC after curative resection between 2008 and 2016 at two institutions were analyzed. A radiomics score was generated using the least absolute shrinkage and selection operator (LASSO) Cox regression model oin 327 patients and was validated in 186 patients. A nomogram consisting of the radiomics score and clinicopathological factors was further created and compared with the tumor-lymph node-metastasis (TNM) staging system. The Modelmodel performance was assessed usingvia calibration, discrimination, and clinical usefulness.
Results:
The radiomics score was established based on five5 selected features. A higher score was significantly associated with poorer recurrence-free survival (RFS) and overall survival (OS) rates both in the training and validation cohorts (P all <0.05). Multivariate analysis demonstrated that the radiomics score was an independent prognostic factor for both RFS and OS (P all <0.05). A nomogram incorporating the radiomics score had a significantly better prognostic value thancompared with the traditional staging TNM system alone. Moreover, a high score was significantly associated with an increased risk of distant recurrence, a medium score was significantly associated with an increased risk of peritoneal recurrence, and a low score was significantly associated with an increased risk of locoregional recurrence in the entire cohort (P all <0.05).
Conclusions:
The newly proposed radiomics score may be a powerful predictor of long-term outcomes and recurrence patterns of GC. Further studies are warranted to confirm these findings.
Keywords: Gastric Cancer; Radiomics Score; Nomogram; Prognosis; Recurrence Patterns.
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