A multicenter study on the preoperative prediction of gastric cancer microsatellite instability status based on computed tomography radiomics

Liang, Xiuqun; Wu, Yinbo; Liu, Ying; Yu, Dapao; Huang, Chencui; Li, Zhi

doi:10.1007/s00261-022-03507-3

Cited by 8 publications

(10 citation statements)

References 31 publications

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“…In this study, we first attempted to apply deep learning algorithms to the calculation and reconstruction of GC-related MSI radiomic features. Compared with previous [ 45 ] research, our results were encouraging and obtained a higher AUC (0.883 and 0.802). This may be attributed in the optimization of radiomic features by deep learning algorithm, the increase in sample size, and the level of image segmentation in our study.…”

Section: Discussionsupporting

confidence: 51%

“…Li et al [ 44 ] used radiomics to construct a predictive model for detecting GC HER-2 expression, with an AUC of 0.799 [95% CI: 0.704 − 0.894]. Based on the information of 189 patients, Liang et al [ 45 ] first constructed a predictive model based on logistic regression analysis to explore the feasibility of predicting GC-related MSI status, and the AUC was 0.8228 [95% CI: 0.7355–0.9101]. However, traditional radiomic method brings challenges in image segmentation, standardization, acquisition, and reconstruction.…”

Section: Discussionmentioning

confidence: 99%

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A virtual biopsy study of microsatellite instability in gastric cancer based on deep learning radiomics

et al. 2023

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Objectives This study aims to develop and validate a virtual biopsy model to predict microsatellite instability (MSI) status in preoperative gastric cancer (GC) patients based on clinical information and the radiomics of deep learning algorithms. Methods A total of 223 GC patients with MSI status detected by postoperative immunohistochemical staining (IHC) were retrospectively recruited and randomly assigned to the training (n = 167) and testing (n = 56) sets in a 3:1 ratio. In the training set, 982 high-throughput radiomic features were extracted from preoperative abdominal dynamic contrast-enhanced CT (CECT) and screened. According to the deep learning multilayer perceptron (MLP), 15 optimal features were optimized to establish the radiomic feature score (Rad-score), and LASSO regression was used to screen out clinically independent predictors. Based on logistic regression, the Rad-score and clinically independent predictors were integrated to build the clinical radiomics model and visualized as a nomogram and independently verified in the testing set. The performance and clinical applicability of hybrid model in identifying MSI status were evaluated by the area under the receiver operating characteristic (AUC) curve, calibration curve, and decision curve (DCA). Results The AUCs of the clinical image model in training set and testing set were 0.883 [95% CI: 0.822–0.945] and 0.802 [95% CI: 0.666–0.937], respectively. This hybrid model showed good consistency in the calibration curve and clinical applicability in the DCA curve, respectively. Conclusions Using preoperative imaging and clinical information, we developed a deep-learning-based radiomics model for the non-invasive evaluation of MSI in GC patients. This model maybe can potentially support clinical treatment decision making for GC patients. Graphical abstract

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Section: Discussionsupporting

confidence: 51%

Section: Discussionmentioning

confidence: 99%

A virtual biopsy study of microsatellite instability in gastric cancer based on deep learning radiomics

et al. 2023

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“…For instance, Zhao et al constructed a clinical-radiomics combined model capable of predicting GC’s MSI status, yielding an AUC of 0.836 (95% CI: 0.780–0.893) in the training cohort and 0.834 (95% CI: 0.688–0.981) in the externally validated cohort [ 21 ]. Similarly, Liang et al presented a radiomics model with AUC values of 0.823 (95% CI: 0.736–0.910) and 0.760 (95% CI: 0.663–0.858) in the training and external validation cohorts, respectively [ 22 ]. However, they did not further confirm its clinical value in immunotherapy effectiveness and prognosis, nor did they deeply investigate its biological mechanisms.…”

Section: Discussionmentioning

confidence: 99%

“…Radiomics is a rapidly advancing field that utilizes advanced computational techniques to transform medical images, such as CT and MRI, into quantitative features, enabling the development of a signature for cancer diagnosis and treatment [ 18 , 19 ]. While several studies have demonstrated the potential and significance of radiomics in evaluating the MSI status of gastric cancer, these investigations have not thoroughly delved into the clinical value of their radiomics models within cohorts of patients undergoing immunotherapy [ 20 – 22 ]. Specifically, there is a lack of comprehensive validation regarding the efficacy of radiomics models in predicting immunotherapy outcomes.…”

Section: Introductionmentioning

confidence: 99%

A radiomics signature derived from CT imaging to predict MSI status and immunotherapy outcomes in gastric cancer: a multi-cohort study

Zhan,

Yang,

Liu

et al. 2024

BMC Cancer

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Background Accurate microsatellite instability (MSI) testing is essential for identifying gastric cancer (GC) patients eligible for immunotherapy. We aimed to develop and validate a CT-based radiomics signature to predict MSI and immunotherapy outcomes in GC. Methods This retrospective multicohort study included a total of 457 GC patients from two independent medical centers in China and The Cancer Imaging Archive (TCIA) databases. The primary cohort (n = 201, center 1, 2017–2022), was used for signature development via Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression analysis. Two independent immunotherapy cohorts, one from center 1 (n = 184, 2018–2021) and another from center 2 (n = 43, 2020–2021), were utilized to assess the signature’s association with immunotherapy response and survival. Diagnostic efficiency was evaluated using the area under the receiver operating characteristic curve (AUC), and survival outcomes were analyzed via the Kaplan-Meier method. The TCIA cohort (n = 29) was included to evaluate the immune infiltration landscape of the radiomics signature subgroups using both CT images and mRNA sequencing data. Results Nine radiomics features were identified for signature development, exhibiting excellent discriminative performance in both the training (AUC: 0.851, 95%CI: 0.782, 0.919) and validation cohorts (AUC: 0.816, 95%CI: 0.706, 0.926). The radscore, calculated using the signature, demonstrated strong predictive abilities for objective response in immunotherapy cohorts (AUC: 0.734, 95%CI: 0.662, 0.806; AUC: 0.724, 95%CI: 0.572, 0.877). Additionally, the radscore showed a significant association with PFS and OS, with GC patients with a low radscore experiencing a significant survival benefit from immunotherapy. Immune infiltration analysis revealed significantly higher levels of CD8 + T cells, activated CD4 + B cells, and TNFRSF18 expression in the low radscore group, while the high radscore group exhibited higher levels of T cells regulatory and HHLA2 expression. Conclusion This study developed a robust radiomics signature with the potential to serve as a non-invasive biomarker for GC’s MSI status and immunotherapy response, demonstrating notable links to post-immunotherapy PFS and OS. Additionally, distinct immune profiles were observed between low and high radscore groups, highlighting their potential clinical implications.

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“…The majority of CT radiomics studies currently are conducted by single institution, resulting in limited validation of their conclusions. Furthermore, variations in research locations, suppliers, or protocols may impact the voxel intensity spectrum, thereby compromising the universality of the model [ 34 ]. To address this, our study adopted a multicenter approach, leveraging data from two prominent institutions for the training and internal validation datasets.…”

Section: Discussionmentioning

confidence: 99%

Contrast-enhanced CT radiomics for preoperative prediction of stage in epithelial ovarian cancer: a multicenter study

Leng,

Kan,

Wang

et al. 2024

BMC Cancer

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Background Preoperative prediction of International Federation of Gynecology and Obstetrics (FIGO) stage in patients with epithelial ovarian cancer (EOC) is crucial for determining appropriate treatment strategy. This study aimed to explore the value of contrast-enhanced CT (CECT) radiomics in predicting preoperative FIGO staging of EOC, and to validate the stability of the model through an independent external dataset. Methods A total of 201 EOC patients from three centers, divided into a training cohort (n = 106), internal (n = 46) and external (n = 49) validation cohorts. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used for screening radiomics features. Five machine learning algorithms, namely logistic regression, support vector machine, random forest, light gradient boosting machine (LightGBM), and decision tree, were utilized in developing the radiomics model. The optimal performing algorithm was selected to establish the radiomics model, clinical model, and the combined model. The diagnostic performances of the models were evaluated through receiver operating characteristic analysis, and the comparison of the area under curves (AUCs) were conducted using the Delong test or F-test. Results Seven optimal radiomics features were retained by the LASSO algorithm. The five radiomics models demonstrate that the LightGBM model exhibits notable prediction efficiency and robustness, as evidenced by AUCs of 0.83 in the training cohort, 0.80 in the internal validation cohort, and 0.68 in the external validation cohort. The multivariate logistic regression analysis indicated that carcinoma antigen 125 and tumor location were identified as independent predictors for the FIGO staging of EOC. The combined model exhibited best diagnostic efficiency, with AUCs of 0.95 in the training cohort, 0.83 in the internal validation cohort, and 0.79 in the external validation cohort. The F-test indicated that the combined model exhibited a significantly superior AUC value compared to the radiomics model in the training cohort (P < 0.001). Conclusions The combined model integrating clinical characteristics and radiomics features shows potential as a non-invasive adjunctive diagnostic modality for preoperative evaluation of the FIGO staging status of EOC, thereby facilitating clinical decision-making and enhancing patient outcomes.

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A multicenter study on the preoperative prediction of gastric cancer microsatellite instability status based on computed tomography radiomics

Cited by 8 publications

References 31 publications

A virtual biopsy study of microsatellite instability in gastric cancer based on deep learning radiomics

A virtual biopsy study of microsatellite instability in gastric cancer based on deep learning radiomics

A radiomics signature derived from CT imaging to predict MSI status and immunotherapy outcomes in gastric cancer: a multi-cohort study

Contrast-enhanced CT radiomics for preoperative prediction of stage in epithelial ovarian cancer: a multicenter study

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