MRI radiomics analysis for predicting preoperative synchronous distant metastasis in patients with rectal cancer

Liu, Huanhuan; Zhang, Caiyuan; Wang, Lijun; Luo, Ran; Li, Jinning; Zheng, Hui; Yin, Qiufeng; Zhang, Zhongyang; Duan, Shaofeng; Li, Xin; Wang, Dengbin

doi:10.1007/s00330-018-5802-7

Cited by 74 publications

(61 citation statements)

References 35 publications

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“…Radiomics can perform quantitative analysis of lesions through a large number of radiological characteristics, which effectively solved the problem that tumor heterogeneity is difficult to be quantitatively evaluated. This has an important clinical application value and has been widely used to predict the prognosis of various cancer patients and improve the treatment strategies (19,28,29). Besides, previous studies have proposed that gene expression and pathway status could be evaluated by radiomics features in lung cancer and glioblastoma (30,31).…”

Section: Discussionmentioning

confidence: 99%

A Novel Model Based on CXCL8-Derived Radiomics for Prognosis Prediction in Colorectal Cancer

Chu

Zeng

et al. 2020

Front. Oncol.

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Introduction: Prognosis prediction is essential to improve therapeutic strategies and to achieve better clinical outcomes in colorectal cancer (CRC) patients. Radiomics based on high-throughput mining of quantitative medical imaging is an emerging field in recent years. However, the relationship among prognosis, radiomics features, and gene expression remains unknown. Methods: We retrospectively analyzed 141 patients (from study 1) diagnosed with CRC from February 2018 to October 2019 and randomly divided them into training (N = 99) and testing (N = 42) cohorts. Radiomics features in venous phase image were extracted from preoperative computed tomography (CT) images. Gene expression was detected by RNA-sequencing on tumor tissues. The least absolute shrinkage and selection operator (LASSO) regression model was used for selecting imaging features and building the radiomics model. A total of 45 CRC patients (study 2) with immunohistochemical (IHC) staining of CXCL8 diagnosed with CRC from January 2014 to October 2018 were included in the independent testing cohort. A clinical model was validated for prognosis prediction in prognostic testing cohort (163 CRC patients from 2014 to 2018, study 3). We performed a combined radiomics model that was composed of radiomics score, tumor stage, and CXCL8-derived radiomics model to make comparison with the clinical model. Results: In our study, we identified the CXCL8 as a hub gene in affecting prognosis, which is mainly through regulating cytokine-cytokine receptor interaction and neutrophil migration pathway. The radiomics model incorporated 12 radiomics features screened by LASSO according to CXCL8 expression in the training cohort and showed good performance in testing and IHC testing cohorts. Finally, the CXCL8-derived radiomics model combined with tumor stage performed high ability in predicting the prognosis of CRC patients in the prognostic testing cohort, with an area under the curve (AUC) of 0.774 [95% confidence interval (CI): 0.674-0.874]. Kaplan-Meier analysis of the overall Chu et al. CXCL8-Derived Radiomics for Prognosis Prediction survival probability in CRC patients stratified by combined model revealed that high-risk patients have a poor prognosis compared with low-risk patients (Log-rank P < 0.0001). Conclusion: We demonstrated that the radiomics model reflected by CXCL8 combined with tumor stage information is a reliable approach to predict the prognosis in CRC patients and has a potential ability in assisting clinical decision-making.

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Section: Discussionmentioning

confidence: 99%

A Novel Model Based on CXCL8-Derived Radiomics for Prognosis Prediction in Colorectal Cancer

Chu

Zeng

et al. 2020

Front. Oncol.

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“…Meanwhile, radiomics is a new method of medical image analysis that further characterizes the phenotype of tumor by transforming conventional medical images into quantitative, high-dimensional, and exploitable radiology data (17)(18)(19)(20). Currently, many studies have applied radiomics characteristics to predict lymph node metastasis and perineural invasion (PNI) in colorectal cancer (21,22), evaluate the response to neoadjuvant therapy (23), determine preoperative synchronous distant metastasis (24), and predict staging of rectal cancer (25). However, its use in LVI prediction is still rare.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multimodal Radiomics Model for Preoperative Prediction of Lymphovascular Invasion in Rectal Cancer

Zhang

Guo

et al. 2020

Front. Oncol.

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Objective: To explore a new predictive model of lymphatic vascular infiltration (LVI) in rectal cancer based on magnetic resonance (MR) and computed tomography (CT). Methods: A retrospective study was conducted on 94 patients with histologically confirmed rectal cancer, they were randomly divided into training cohort (n = 65) and validation cohort (n = 29). All patients underwent MR and CT examination within 2 weeks before treatment. On each slice of the tumor, we delineated the volume of interest on T2-weighted imaging, diffusion weighted imaging, and enhanced CT images, respectively. A total of 1,188 radiological features were extracted from each patient. Then, we used the student t-test or Mann-Whitney U-test, Spearman's rank correlation and least absolute shrinkage and selection operator (LASSO) algorithm to select the strongest features to establish a single and multimodal logic model for predicting LVI. Receiver operating characteristic (ROC) curves and calibration curves were plotted to determine how well they explored LVI prediction performance in the training and validation cohorts. Results: An optimal multi-mode radiology nomogram for LVI estimation was established, which had significant predictive power in training (AUC, 0.884; 95% CI, 0.803-0.964) and validation (AUC, 0.876; 95% CI, 0.721-1.000). Calibration curve and decision curve analysis showed that the multimodal radiomics model provides greater clinical benefits. Conclusion: Multimodal (MR/CT) radiomics models can serve as an effective visual prognostic tool for predicting LVI in rectal cancer. It demonstrated great potential of preoperative prediction to improve treatment decisions.

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“…Another machine learning approach radiomics rapidly developed in recent years can be widely available through open-source software and the radiomics signature is easily utilized. The potential for diagnosing and predicting outcomes of different lesions has been proven in the prior reproducible investigations [14,15], as well as our previous studies in predicting preoperative synchronous distant metastasis in patients with rectal cancer [28,29]. In this study, 8 radiomics features, mainly focus on the textural features, were selected to build the radiomics signature and the proposed combined radiomics model performed well not only in the training cohort but also in the validation and testing cohorts with AUCs of 1.00, 0.98, and 0.93, respectively.…”

Section: Discussionmentioning

confidence: 91%

CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS

Liu

Ren

et al. 2020

Preprint

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Background Limited data was available for rapid and accurate detection of COVID-19 using CT-based machine learning model. This study aimed to investigate the value of chest CT radiomics for diagnosing COVID-19 pneumonia compared with clinical model and COVID-19 reporting and data system (CO-RADS), and develop an open-source diagnostic tool with the constructed radiomics model. Methods This study enrolled 115 laboratory-confirmed COVID-19 and 435 non-COVID-19 pneunomia patients (training dataset, n = 379; validation dataset, n = 131; testing dataset, n = 40). Key radiomics features extracted from chest CT images were selected to build a radiomics signature using least absolute shrinkage and selection operator (LASSO) regression. Clinical and clinico-radiomics combined models were constructed. The combined model was further validated in the viral pneumonia cohort, and compared with performance of two radiologists with CO-RADS. The diagnostic performance was assessed by receiver operating characteristics curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). Results Eight radiomics features and 5 clinical variables were selected to construct the combined radiomics model, which outperformed the clinical model in diagnosing COVID-19 pneumonia with an area under the ROC (AUC) of 0.98 and good calibration in the validation cohort. The combined model also performed better in distinguishing COVID-19 from other viral pneumonia with an AUC of 0.93 compared with 0.75 (P = 0.03) for clinical model, and 0.69 (P = 0.008) or 0.82 (P = 0.15) for two trained radiologists using CO-RADS. The sensitivity and specificity of the combined model can be achieved to 0.85 and 0.90. The DCA confirmed the clinical utility of the combined model. An easy-to-use open-source diagnostic tool was developed using the combined model. Conclusions The combined radiomics model outperformed clinical model and CO-RADS for diagnosing COVID-19 pneumonia, which can facilitate more rapid and accurate detection.

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MRI radiomics analysis for predicting preoperative synchronous distant metastasis in patients with rectal cancer

Cited by 74 publications

References 35 publications

A Novel Model Based on CXCL8-Derived Radiomics for Prognosis Prediction in Colorectal Cancer

A Novel Model Based on CXCL8-Derived Radiomics for Prognosis Prediction in Colorectal Cancer

A Novel Multimodal Radiomics Model for Preoperative Prediction of Lymphovascular Invasion in Rectal Cancer

CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS

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