Intratumoral and peritumoral radiomics for forecasting microsatellite status in gastric cancer: a multicenter study

This investigation attempted to examine the effectiveness of CT-derived peritumoral and intratumoral radiomics in forecasting microsatellite instability (MSI) status preoperatively among gastric cancer (GC) patients. A retrospective analysis was performed on GC patients from February 2019 to Decembe...

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Published inBMC cancer Vol. 25; no. 1; pp. 66 - 13
Main Authors Xiao, Yunzhou, Zhu, Jianping, Xie, Huanhuan, Wang, Zhongchu, Huang, Zhaohai, Su, Miaoguang
Format Journal Article
LanguageEnglish
Published England BioMed Central Ltd 11.01.2025
BioMed Central
BMC
Subjects
Adjuvants
Adult
Aged
Antigens
Biopsy
Cancer therapies
Complications and side effects
Computed tomography
Correlation analysis
Diagnostic imaging
Female
Gastric cancer
Genetic aspects
Health aspects
Hemoglobin
Hospitals
Humans
Machine Learning
Male
Medical imaging
Medical prognosis
Metastasis
Microsatellite Instability
Middle Aged
Patients
Peritumoral
Prognosis
Radiomics
Regression analysis
Retrospective Studies
ROC Curve
Scanners
Stomach cancer
Stomach Neoplasms - diagnostic imaging
Stomach Neoplasms - genetics
Stomach Neoplasms - pathology
Support Vector Machine
Surgery
Tomography, X-Ray Computed - methods
Tumors
China
Peritumoral
Microsatellite instability
Gastric cancer
Machine learning
Radiomics
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Abstract This investigation attempted to examine the effectiveness of CT-derived peritumoral and intratumoral radiomics in forecasting microsatellite instability (MSI) status preoperatively among gastric cancer (GC) patients. A retrospective analysis was performed on GC patients from February 2019 to December 2023 across three healthcare institutions. 364 patients (including 41 microsatellite instability-high (MSI-H) and 323 microsatellite instability-low/stable (MSI-L/S)) were stratified into a training set (n = 202), an internal validation set (n = 84), and an external validation set (n = 78). Radiomics features were obtained from both the intratumoral region (IR) and the intratumoral plus 3-mm peritumoral region (IPR) on preoperative contrast-enhanced CT images. After standardizing and reducing the dimensionality of these features, six radiomic models were constructed utilizing three machine learning techniques: Support Vector Machine (SVM), Linear Support Vector Classification (LinearSVC), and Logistic Regression (LR). The optimal model was determined by evaluating the Receiver Operating Characteristic (ROC) curve's Area Under the Curve (AUC), and the radiomics score (Radscore) was computed. A clinical model was developed using clinical characteristics and CT semantic features, with the Radscore integrated to create a combined model. Used ROC curves, calibration plots, and Decision Curve Analysis (DCA) to assess the performance of radiomics, clinical, and combined models. The LinearSVC model using the IPR achieved the highest AUC of 0.802 in the external validation set. The combined model yielded superior AUCs in internal and external validation sets (0.891 and 0.856) in comparison to clinical model [(0.724, P = 0.193) and (0.655, P = 0.072)] and radiomics model [(0.826, P = 0.160) and (0.802, P = 0.068)]. Furthermore, results from calibration and DCA underscored the model's suitability and clinical relevance. The combined model, which integrates IPR radiomics with clinical characteristics, accurately predicts MSI status and supports the development of personalized treatment strategies.
AbstractList This investigation attempted to examine the effectiveness of CT-derived peritumoral and intratumoral radiomics in forecasting microsatellite instability (MSI) status preoperatively among gastric cancer (GC) patients. A retrospective analysis was performed on GC patients from February 2019 to December 2023 across three healthcare institutions. 364 patients (including 41 microsatellite instability-high (MSI-H) and 323 microsatellite instability-low/stable (MSI-L/S)) were stratified into a training set (n = 202), an internal validation set (n = 84), and an external validation set (n = 78). Radiomics features were obtained from both the intratumoral region (IR) and the intratumoral plus 3-mm peritumoral region (IPR) on preoperative contrast-enhanced CT images. After standardizing and reducing the dimensionality of these features, six radiomic models were constructed utilizing three machine learning techniques: Support Vector Machine (SVM), Linear Support Vector Classification (LinearSVC), and Logistic Regression (LR). The optimal model was determined by evaluating the Receiver Operating Characteristic (ROC) curve's Area Under the Curve (AUC), and the radiomics score (Radscore) was computed. A clinical model was developed using clinical characteristics and CT semantic features, with the Radscore integrated to create a combined model. Used ROC curves, calibration plots, and Decision Curve Analysis (DCA) to assess the performance of radiomics, clinical, and combined models. The LinearSVC model using the IPR achieved the highest AUC of 0.802 in the external validation set. The combined model yielded superior AUCs in internal and external validation sets (0.891 and 0.856) in comparison to clinical model [(0.724, P = 0.193) and (0.655, P = 0.072)] and radiomics model [(0.826, P = 0.160) and (0.802, P = 0.068)]. Furthermore, results from calibration and DCA underscored the model's suitability and clinical relevance. The combined model, which integrates IPR radiomics with clinical characteristics, accurately predicts MSI status and supports the development of personalized treatment strategies.
ObjectiveThis investigation attempted to examine the effectiveness of CT-derived peritumoral and intratumoral radiomics in forecasting microsatellite instability (MSI) status preoperatively among gastric cancer (GC) patients.MethodsA retrospective analysis was performed on GC patients from February 2019 to December 2023 across three healthcare institutions. 364 patients (including 41 microsatellite instability-high (MSI-H) and 323 microsatellite instability-low/stable (MSI-L/S)) were stratified into a training set (n = 202), an internal validation set (n = 84), and an external validation set (n = 78). Radiomics features were obtained from both the intratumoral region (IR) and the intratumoral plus 3-mm peritumoral region (IPR) on preoperative contrast-enhanced CT images. After standardizing and reducing the dimensionality of these features, six radiomic models were constructed utilizing three machine learning techniques: Support Vector Machine (SVM), Linear Support Vector Classification (LinearSVC), and Logistic Regression (LR). The optimal model was determined by evaluating the Receiver Operating Characteristic (ROC) curve's Area Under the Curve (AUC), and the radiomics score (Radscore) was computed. A clinical model was developed using clinical characteristics and CT semantic features, with the Radscore integrated to create a combined model. Used ROC curves, calibration plots, and Decision Curve Analysis (DCA) to assess the performance of radiomics, clinical, and combined models.ResultsThe LinearSVC model using the IPR achieved the highest AUC of 0.802 in the external validation set. The combined model yielded superior AUCs in internal and external validation sets (0.891 and 0.856) in comparison to clinical model [(0.724, P = 0.193) and (0.655, P = 0.072)] and radiomics model [(0.826, P = 0.160) and (0.802, P = 0.068)]. Furthermore, results from calibration and DCA underscored the model's suitability and clinical relevance.ConclusionThe combined model, which integrates IPR radiomics with clinical characteristics, accurately predicts MSI status and supports the development of personalized treatment strategies.
Objective This investigation attempted to examine the effectiveness of CT-derived peritumoral and intratumoral radiomics in forecasting microsatellite instability (MSI) status preoperatively among gastric cancer (GC) patients. Methods A retrospective analysis was performed on GC patients from February 2019 to December 2023 across three healthcare institutions. 364 patients (including 41 microsatellite instability-high (MSI-H) and 323 microsatellite instability-low/stable (MSI-L/S)) were stratified into a training set (n = 202), an internal validation set (n = 84), and an external validation set (n = 78). Radiomics features were obtained from both the intratumoral region (IR) and the intratumoral plus 3-mm peritumoral region (IPR) on preoperative contrast-enhanced CT images. After standardizing and reducing the dimensionality of these features, six radiomic models were constructed utilizing three machine learning techniques: Support Vector Machine (SVM), Linear Support Vector Classification (LinearSVC), and Logistic Regression (LR). The optimal model was determined by evaluating the Receiver Operating Characteristic (ROC) curve's Area Under the Curve (AUC), and the radiomics score (Radscore) was computed. A clinical model was developed using clinical characteristics and CT semantic features, with the Radscore integrated to create a combined model. Used ROC curves, calibration plots, and Decision Curve Analysis (DCA) to assess the performance of radiomics, clinical, and combined models. Results The LinearSVC model using the IPR achieved the highest AUC of 0.802 in the external validation set. The combined model yielded superior AUCs in internal and external validation sets (0.891 and 0.856) in comparison to clinical model [(0.724, P = 0.193) and (0.655, P = 0.072)] and radiomics model [(0.826, P = 0.160) and (0.802, P = 0.068)]. Furthermore, results from calibration and DCA underscored the model's suitability and clinical relevance. Conclusion The combined model, which integrates IPR radiomics with clinical characteristics, accurately predicts MSI status and supports the development of personalized treatment strategies. Keywords: Radiomics, Gastric cancer, Microsatellite instability, Peritumoral, Machine learning
Abstract Objective This investigation attempted to examine the effectiveness of CT-derived peritumoral and intratumoral radiomics in forecasting microsatellite instability (MSI) status preoperatively among gastric cancer (GC) patients. Methods A retrospective analysis was performed on GC patients from February 2019 to December 2023 across three healthcare institutions. 364 patients (including 41 microsatellite instability-high (MSI-H) and 323 microsatellite instability-low/stable (MSI-L/S)) were stratified into a training set (n = 202), an internal validation set (n = 84), and an external validation set (n = 78). Radiomics features were obtained from both the intratumoral region (IR) and the intratumoral plus 3-mm peritumoral region (IPR) on preoperative contrast-enhanced CT images. After standardizing and reducing the dimensionality of these features, six radiomic models were constructed utilizing three machine learning techniques: Support Vector Machine (SVM), Linear Support Vector Classification (LinearSVC), and Logistic Regression (LR). The optimal model was determined by evaluating the Receiver Operating Characteristic (ROC) curve's Area Under the Curve (AUC), and the radiomics score (Radscore) was computed. A clinical model was developed using clinical characteristics and CT semantic features, with the Radscore integrated to create a combined model. Used ROC curves, calibration plots, and Decision Curve Analysis (DCA) to assess the performance of radiomics, clinical, and combined models. Results The LinearSVC model using the IPR achieved the highest AUC of 0.802 in the external validation set. The combined model yielded superior AUCs in internal and external validation sets (0.891 and 0.856) in comparison to clinical model [(0.724, P = 0.193) and (0.655, P = 0.072)] and radiomics model [(0.826, P = 0.160) and (0.802, P = 0.068)]. Furthermore, results from calibration and DCA underscored the model's suitability and clinical relevance. Conclusion The combined model, which integrates IPR radiomics with clinical characteristics, accurately predicts MSI status and supports the development of personalized treatment strategies.
This investigation attempted to examine the effectiveness of CT-derived peritumoral and intratumoral radiomics in forecasting microsatellite instability (MSI) status preoperatively among gastric cancer (GC) patients. A retrospective analysis was performed on GC patients from February 2019 to December 2023 across three healthcare institutions. 364 patients (including 41 microsatellite instability-high (MSI-H) and 323 microsatellite instability-low/stable (MSI-L/S)) were stratified into a training set (n = 202), an internal validation set (n = 84), and an external validation set (n = 78). Radiomics features were obtained from both the intratumoral region (IR) and the intratumoral plus 3-mm peritumoral region (IPR) on preoperative contrast-enhanced CT images. After standardizing and reducing the dimensionality of these features, six radiomic models were constructed utilizing three machine learning techniques: Support Vector Machine (SVM), Linear Support Vector Classification (LinearSVC), and Logistic Regression (LR). The optimal model was determined by evaluating the Receiver Operating Characteristic (ROC) curve's Area Under the Curve (AUC), and the radiomics score (Radscore) was computed. A clinical model was developed using clinical characteristics and CT semantic features, with the Radscore integrated to create a combined model. Used ROC curves, calibration plots, and Decision Curve Analysis (DCA) to assess the performance of radiomics, clinical, and combined models. The LinearSVC model using the IPR achieved the highest AUC of 0.802 in the external validation set. The combined model yielded superior AUCs in internal and external validation sets (0.891 and 0.856) in comparison to clinical model [(0.724, P = 0.193) and (0.655, P = 0.072)] and radiomics model [(0.826, P = 0.160) and (0.802, P = 0.068)]. Furthermore, results from calibration and DCA underscored the model's suitability and clinical relevance. The combined model, which integrates IPR radiomics with clinical characteristics, accurately predicts MSI status and supports the development of personalized treatment strategies.
This investigation attempted to examine the effectiveness of CT-derived peritumoral and intratumoral radiomics in forecasting microsatellite instability (MSI) status preoperatively among gastric cancer (GC) patients.OBJECTIVEThis investigation attempted to examine the effectiveness of CT-derived peritumoral and intratumoral radiomics in forecasting microsatellite instability (MSI) status preoperatively among gastric cancer (GC) patients.A retrospective analysis was performed on GC patients from February 2019 to December 2023 across three healthcare institutions. 364 patients (including 41 microsatellite instability-high (MSI-H) and 323 microsatellite instability-low/stable (MSI-L/S)) were stratified into a training set (n = 202), an internal validation set (n = 84), and an external validation set (n = 78). Radiomics features were obtained from both the intratumoral region (IR) and the intratumoral plus 3-mm peritumoral region (IPR) on preoperative contrast-enhanced CT images. After standardizing and reducing the dimensionality of these features, six radiomic models were constructed utilizing three machine learning techniques: Support Vector Machine (SVM), Linear Support Vector Classification (LinearSVC), and Logistic Regression (LR). The optimal model was determined by evaluating the Receiver Operating Characteristic (ROC) curve's Area Under the Curve (AUC), and the radiomics score (Radscore) was computed. A clinical model was developed using clinical characteristics and CT semantic features, with the Radscore integrated to create a combined model. Used ROC curves, calibration plots, and Decision Curve Analysis (DCA) to assess the performance of radiomics, clinical, and combined models.METHODSA retrospective analysis was performed on GC patients from February 2019 to December 2023 across three healthcare institutions. 364 patients (including 41 microsatellite instability-high (MSI-H) and 323 microsatellite instability-low/stable (MSI-L/S)) were stratified into a training set (n = 202), an internal validation set (n = 84), and an external validation set (n = 78). Radiomics features were obtained from both the intratumoral region (IR) and the intratumoral plus 3-mm peritumoral region (IPR) on preoperative contrast-enhanced CT images. After standardizing and reducing the dimensionality of these features, six radiomic models were constructed utilizing three machine learning techniques: Support Vector Machine (SVM), Linear Support Vector Classification (LinearSVC), and Logistic Regression (LR). The optimal model was determined by evaluating the Receiver Operating Characteristic (ROC) curve's Area Under the Curve (AUC), and the radiomics score (Radscore) was computed. A clinical model was developed using clinical characteristics and CT semantic features, with the Radscore integrated to create a combined model. Used ROC curves, calibration plots, and Decision Curve Analysis (DCA) to assess the performance of radiomics, clinical, and combined models.The LinearSVC model using the IPR achieved the highest AUC of 0.802 in the external validation set. The combined model yielded superior AUCs in internal and external validation sets (0.891 and 0.856) in comparison to clinical model [(0.724, P = 0.193) and (0.655, P = 0.072)] and radiomics model [(0.826, P = 0.160) and (0.802, P = 0.068)]. Furthermore, results from calibration and DCA underscored the model's suitability and clinical relevance.RESULTSThe LinearSVC model using the IPR achieved the highest AUC of 0.802 in the external validation set. The combined model yielded superior AUCs in internal and external validation sets (0.891 and 0.856) in comparison to clinical model [(0.724, P = 0.193) and (0.655, P = 0.072)] and radiomics model [(0.826, P = 0.160) and (0.802, P = 0.068)]. Furthermore, results from calibration and DCA underscored the model's suitability and clinical relevance.The combined model, which integrates IPR radiomics with clinical characteristics, accurately predicts MSI status and supports the development of personalized treatment strategies.CONCLUSIONThe combined model, which integrates IPR radiomics with clinical characteristics, accurately predicts MSI status and supports the development of personalized treatment strategies.
ArticleNumber 66
Audience Academic
Author Wang, Zhongchu
Huang, Zhaohai
Xiao, Yunzhou
Zhu, Jianping
Xie, Huanhuan
Su, Miaoguang
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Keywords Peritumoral
Microsatellite instability
Gastric cancer
Machine learning
Radiomics
Language English
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Snippet This investigation attempted to examine the effectiveness of CT-derived peritumoral and intratumoral radiomics in forecasting microsatellite instability (MSI)...
Objective This investigation attempted to examine the effectiveness of CT-derived peritumoral and intratumoral radiomics in forecasting microsatellite...
ObjectiveThis investigation attempted to examine the effectiveness of CT-derived peritumoral and intratumoral radiomics in forecasting microsatellite...
Abstract Objective This investigation attempted to examine the effectiveness of CT-derived peritumoral and intratumoral radiomics in forecasting microsatellite...
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SubjectTerms Adjuvants
Adult
Aged
Antigens
Biopsy
Cancer therapies
Complications and side effects
Computed tomography
Correlation analysis
Diagnostic imaging
Female
Gastric cancer
Genetic aspects
Health aspects
Hemoglobin
Hospitals
Humans
Machine Learning
Male
Medical imaging
Medical prognosis
Metastasis
Microsatellite Instability
Middle Aged
Patients
Peritumoral
Prognosis
Radiomics
Regression analysis
Retrospective Studies
ROC Curve
Scanners
Stomach cancer
Stomach Neoplasms - diagnostic imaging
Stomach Neoplasms - genetics
Stomach Neoplasms - pathology
Support Vector Machine
Surgery
Tomography, X-Ray Computed - methods
Tumors
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Title Intratumoral and peritumoral radiomics for forecasting microsatellite status in gastric cancer: a multicenter study
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