Predicting Microsatellite Instability Status in Colorectal Cancer Based on Triphasic Enhanced Computed Tomography Radiomics Signatures: A Multicenter Study

Cao, Yuntai; Zhang, Guojin; Zhang, Jing; Yang, Yingjie; Ren, Jialiang; Yan, Xin; Zhan, Wei; Zhao, Zhiyong; Huang, Xiaoyu; Bao, Hui‐Fang

doi:10.3389/fonc.2021.687771

Cited by 22 publications

(33 citation statements)

References 47 publications

(67 reference statements)

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“…Radiogenomic models, which are developed from diagnostic imaging data, could identify radiomic signatures of antitumor activity after treatment with molecular target drugs or immunotherapy. [204][205][206][207] In this scenario, the future will be the development of a global functional and dynamic multiomic classification of mCRC. This will require major efforts for implementing standardized and reproducible technologies before it can be applied to clinical practice.…”

Section: Next Steps For Implementing Precision Medicine In the Clinic...mentioning

confidence: 99%

Clinical management of metastatic colorectal cancer in the era of precision medicine

Ciardiello

Martini

et al. 2022

CA A Cancer J Clinicians

250

161

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Colorectal cancer (CRC) represents approximately 10% of all cancers and is the second most common cause of cancer deaths. Initial clinical presentation as metastatic CRC (mCRC) occurs in approximately 20% of patients. Moreover, up to 50% of patients with localized disease eventually develop metastases. Appropriate clinical management of these patients is still a challenging medical issue. Major efforts have been made to unveil the molecular landscape of mCRC. This has resulted in the identification of several druggable tumor molecular targets with the aim of developing personalized treatments for each patient. This review summarizes the improvements in the clinical management of patients with mCRC in the emerging era of precision medicine. In fact, molecular stratification, on which the current treatment algorithm for mCRC is based, although it does not completely represent the complexity of this disease, has been the first significant step toward clinically informative genetic profiling for implementing more effective therapeutic approaches. This has resulted in a clinically relevant increase in mCRC disease control and patient survival. The next steps in the clinical management of mCRC will be to integrate the comprehensive knowledge of tumor gene alterations, of tumor and microenvironment gene and protein expression profiling, of host immune competence as well as the application of the resulting dynamic changes to a precision medicine-based continuum of care for each patient. This approach could result in the identification of individual prognostic and predictive parameters, which could help the clinician in choosing the most appropriate therapeutic program(s) throughout the entire disease journey for each patient with mCRC.

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Section: Next Steps For Implementing Precision Medicine In the Clinic...mentioning

confidence: 99%

Clinical management of metastatic colorectal cancer in the era of precision medicine

Ciardiello

Martini

et al. 2022

CA A Cancer J Clinicians

250

161

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“…Consistent with most relevant studies in recent years ( 41 – 44 ), we adopted the IHC method to determine MSI status. Although PCR is the gold standard for diagnosing MSI status, the operation process is complex and the cost is high.…”

Section: Discussionmentioning

confidence: 99%

Radiomics features based on internal and marginal areas of the tumor for the preoperative prediction of microsatellite instability status in colorectal cancer

Lin²,

Liu³

et al. 2022

Front. Oncol.

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ObjectivesTo explore whether the preoperative CT radiomics can predict the status of microsatellite instability (MSI) in colorectal cancer (CRC) patients and identify the region with the most stable and high-efficiency radiomics features.MethodsThis retrospective study involved 230 CRC patients with preoperative computed tomography scans and available MSI status between December 2019 and October 2021. Image segmentation and radiomic feature extraction were performed as follows. First, slices with the maximum tumor area (region of interest, ROI) were manually contoured. Subsequently, each ROI was shrunk inward by 1, 2, and 3 mm, respectively, where the remaining ROIs were considered as the internal region of the tumor (named as IROI1, IROI2, and IROI3), and the shrunk regions were considered as marginal regions of the tumor (named as MROI1, MROI2, and MROI3). Finally, radiomics features were extracted from each of the ROI. The intraclass correlation coefficient and least absolute shrinkage and selection operator method were used to choose the most reliable and relevant features of MSI status. Clinical, radiomics, and combined clinical radiomics models have been established. Calibration curve and decision curve analyses (DCA) were generated to explore the correction effect and assess the clinical applicability of the above models, respectively.ResultsIn the testing cohort, the radiomics model based on IROI3 yielded the highest average area under the curve (AUC) value of 0.908, compared with the remaining radiomics models. Additionally, hypertension and N stage were considered as clinically independent factors of MSI status. The combined clinical radiomics model achieved excellent diagnostic efficacy (AUC: 0.928; sensitivity: 0.840; specificity: 0.867) in the testing cohort, as well as favorable calibration and clinical utility by calibration curve and DCA analyses.ConclusionsThe IROI3 model, which is based on a 3-mm shrink in the largest areas of the tumor, could noninvasively reflect the heterogeneity and genetic instability within the tumor. This suggests that it is an important biomarker for the preoperative prediction of MSI status. The model can extract more robust and effective radiomics features, which lays a foundation for the radiomics study of hollow organs, such as in CRC.

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“…To elucidate which factors contribute to a more favorable prediction of MSI-H tumors, clinical and pathological characteristics as well as the radiomic features were analyzed in 788 patients with 97 MSI-H and 691 MSS. Since radiological images were closely connected with its pathological characteristics [20] , the quantitative radiomic features showed the potential to predict the MSI-H Therefore, an integrative nomogram including clinical characteristics, pathological characteristics, and radiomics became the most important modality to predict the MSI-H status of RCs, noninvasively. Previous studies almost exclusively focused on the evaluation of the MSI-H phenotype of CRCs, ignoring a specialized analysis of RCs.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies almost exclusively focused on the evaluation of the MSI-H phenotype of CRCs, ignoring a specialized analysis of RCs. Data from the study of YT Cao et al [20] suggested that the radiomics signature of triphasic enhanced CT was a reliable method to predict MSI in CRCs, and the clinical-radimoics nomogram including age, location, CEA, and radiomics has shown promising prediction. Our integrative clinical-pathological-radiomic nomogram including CEA, hypertension, LNR, and radiomic score was the most meaningful model in predicting MSI-H phenotype of RCs with-the highest AUC of 0.757 (95%CI, 0.726-0.787) than that of simple LM-Clin, LM-Patho, and LM-Radio.…”

Section: Discussionmentioning

confidence: 99%

An Integrating of Clinical, Pathological, and Radiomic Approach to Predict the Microsatellite Instability in Rectal Carcinoma

Yuan

et al. 2021

Preprint

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Objective: To develop an integrative model with clinical, pathological, and radiomic characteristics to predict the status of microsatellite instability (MSI) in rectal carcinoma (RC). Methods: A cohort of 788 RCs with 97 high MSI status (MSI-H) and 691 microsatellite stable status (MSS) were enrolled. The clinical and pathological characteristics were recorded. The radiomic features were calculated after segmentation of volume of interests and then patients were divided into the training set and validation set with a random proportion of 7:3. The logistic models of simple clinical characteristics (LM-Clin), pathological characteristics (LM-Patho), and radiomic features (LM-Radio) were constructed to distinguish MSI-H from MSS. The relevant radiomic score was calculated. Finally, a integrative nomogram (LM-Nomo) including significant clinical, pathological characteristics, and radiomics was developed. The area under receiver operator curve (AUC) was calculated to evaluate the efficacy of prediction. Results: The AUC of simple LM-Clin including variables of CEA and hypertension and LM-Patho including characteristics of gross type and lymph node metastasis ratio (LNR) was 0.584 (95%CI, 0.549-0.619) and 0.585 (95%CI, 0.550-0.619), which was lower than that of LM-Radio including 12 radiomic features with AUC of 0.737 (95%CI, 0.675-0.799). The LM-Nomo contained CEA, hypertension, LNR, and radiomic score, and the AUC was 0.757 (95%CI, 0.726-0.787). Conclusion: The AUCs of LM-Clin and LM-Patho were disappointing and lower than that of LM-Radio. The LM-Nomo demonstrated the best performance in predicting MSI-H status.

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Predicting Microsatellite Instability Status in Colorectal Cancer Based on Triphasic Enhanced Computed Tomography Radiomics Signatures: A Multicenter Study

Cited by 22 publications

References 47 publications

Clinical management of metastatic colorectal cancer in the era of precision medicine

Clinical management of metastatic colorectal cancer in the era of precision medicine

Radiomics features based on internal and marginal areas of the tumor for the preoperative prediction of microsatellite instability status in colorectal cancer

An Integrating of Clinical, Pathological, and Radiomic Approach to Predict the Microsatellite Instability in Rectal Carcinoma

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