Development and validation of a CT-based radiomic nomogram for preoperative prediction of early recurrence in advanced gastric cancer

Zhang, Wenjuan; Fang, Mengjie; Dong, Di; Wang, Xiaoxiao; Ke, Xiaoai; Zhang, Liwen; Hu, C. Y.; Guo, Ling‐Yun; Guan, Xiaoying; Zhang, Junlin; Xu, Shan; Tian, Jie

doi:10.1016/j.radonc.2019.11.023

Cited by 118 publications

(81 citation statements)

References 37 publications

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“…In a high‐throughput manner, many kinds of multi‐feature signatures have been developed based on radiomics to improve tumor detection, tumor classification, therapeutic response evaluation, and prognosis prediction in solid tumors 9‐12 . In predicting prognosis, the radiomics signatures have been reported to be a reliable prognostic model in breast and gastric cancer 13,14 . However, to our best knowledge, no previous studies have been conducted to develop a signature based on radiomics to predict prognosis for colon cancer.…”

Section: Figurementioning

confidence: 99%

Prognostic and predictive value of radiomics signatures in stage I‐III colon cancer

Dai

Han

et al. 2020

Clinical & Translational Med

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Accurate identification of patients with poor prognosis after radical surgery is essential for clinical management of colon cancer. Thus, we aimed to develop death and relapse specific radiomics signatures to individually estimate overall survival (OS) and relapse free survival (RFS) of colon cancer patients. In this study, 701 stage I‐III colon cancer patients were identified from Fudan University Shanghai Cancer Center. A total of 647 three‐dimensional features were extracted from computed tomography images. LASSO Cox was used to identify the significantly death‐ and relapse‐associated features and to build death and relapse specific radiomics signatures, respectively. A total of 13 death‐specific and 26 relapse‐specific features were identified from 647 screened radiomics features. The developed signatures can divide patients into two groups with significantly different death (Hazard Ratio (HR): 3.053; 95% CI, 1.78‐5.23; P < .001) or relapse risk (HR: 2.794; 95% CI, 1.87‐4.16; P < .001). Time‐dependent Relative operating characteristic curve showed that the signatures performed better than any other clinicopathological factors in predicting OS (AUC: 0.768; 95% CI, 0.745‐0.791) and RFS (AUC: 0.744; 95% CI, 0.687‐0.801). Further, survival decision curve analyses confirmed the good clinical utility of the two radiomics signatures. In conclusion, we successfully developed death‐ and relapse‐specific radiomics signatures that can accurately predict OS and RFS, which may facilitate personalized treatment.

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Section: Figurementioning

confidence: 99%

Prognostic and predictive value of radiomics signatures in stage I‐III colon cancer

Dai

Han

et al. 2020

Clinical & Translational Med

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“…Moreover, radiomics has recently been recognized as a newly emerging form of imaging technology in oncology using a series of statistical analysis tools or data-mining algorithms on high-throughput imaging features to obtain predictive or prognostic information (25). Its application has achieved successful prediction abilities in various tumors by building appropriate models with refined features and clinical data (26)(27)(28)(29)(30). For instance, radiomic features extracted from contrast-enhanced CT (CECT) have been proved to be useful in predicting microvascular invasion (MVI) and the long-term clinical outcomes in patients with HCC (31).…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Contrast-Enhanced CT-Based Radiomics Nomogram for Prediction of Therapeutic Efficacy of Anti-PD-1 Antibodies in Advanced HCC Patients

Yuan

Song

et al. 2021

Front. Immunol.

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BackgroundThere is no study accessible now assessing the prognostic aspect of radiomics for anti-PD-1 therapy for patients with HCC.AimThe aim of this study was to develop and validate a radiomics nomogram by incorporating the pretreatment contrast-enhanced Computed tomography (CT) images and clinical risk factors to estimate the anti-PD-1 treatment efficacy in Hepatocellular Carcinoma (HCC) patients.MethodsA total of 58 patients with advanced HCC who were refractory to the standard first-line of therapy, and received PD-1 inhibitor treatment with Toripalimab, Camrelizumab, or Sintilimab from 1st January 2019 to 31 July 2020 were enrolled and divided into two sets randomly: training set (n = 40) and validation set (n = 18). Radiomics features were extracted from non-enhanced and contrast-enhanced CT scans and selected by using the least absolute shrinkage and selection operator (LASSO) method. Finally, a radiomics nomogram was developed based on by univariate and multivariate logistic regression analysis. The performance of the nomogram was evaluated by discrimination, calibration, and clinical utility.ResultsEight radiomics features from the whole tumor and peritumoral regions were selected and comprised of the Fusion Radiomics score. Together with two clinical factors (tumor embolus and ALBI grade), a radiomics nomogram was developed with an area under the curve (AUC) of 0.894 (95% CI, 0.797–0.991) and 0.883 (95% CI, 0.716–0.998) in the training and validation cohort, respectively. The calibration curve and decision curve analysis (DCA) confirmed that nomogram had good consistency and clinical usefulness.ConclusionsThis study has developed and validated a radiomics nomogram by incorporating the pretreatment CECT images and clinical factors to predict the anti-PD-1 treatment efficacy in patients with advanced HCC.

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“…Radiomics utilizes automated quantitative characterization algorithms to transform a large number of excavatable spatial ROI-based image data into representative and effective radiomic features [ 6 ]. Recent advancements in radiomics have provided new ideas for individualized management of GC, including lymphatic metastasis prediction [ 7 , 8 ], distant metastasis prediction [ 9 ], therapeutic response evaluation [ 10 ], and prognostic evaluation [ 11 , 12 ]. These studies highlighted the value of radiomics, suggesting that radiomics could be a potential tool for the Lauren classification in GC.…”

Section: Introductionmentioning

confidence: 99%

Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer

et al. 2020

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Background Preoperative prediction of the Lauren classification in gastric cancer (GC) is very important to the choice of therapy, the evaluation of prognosis, and the improvement of quality of life. However, there is not yet radiomics analysis concerning the prediction of Lauren classification straightly. In this study, a radiomic nomogram was developed to preoperatively differentiate Lauren diffuse type from intestinal type in GC. Methods A total of 539 GC patients were enrolled in this study and later randomly allocated to two cohorts at a 7:3 ratio for training and validation. Two sets of radiomic features were derived from tumor regions and peritumor regions on venous phase computed tomography (CT) images, respectively. With the least absolute shrinkage and selection operator logistic regression, a combined radiomic signature was constructed. Also, a tumor-based model and a peripheral ring-based model were built for comparison. Afterwards, a radiomic nomogram integrating the combined radiomic signature and clinical characteristics was developed. All the models were evaluated regarding classification ability and clinical usefulness. Results The combined radiomic signature achieved an area under receiver operating characteristic curve (AUC) of 0.715 (95% confidence interval [CI], 0.663–0.767) in the training cohort and 0.714 (95% CI, 0.636–0.792) in the validation cohort. The radiomic nomogram incorporating the combined radiomic signature, age, CT T stage, and CT N stage outperformed the other models with a training AUC of 0.745 (95% CI, 0.696–0.795) and a validation AUC of 0.758 (95% CI, 0.685–0.831). The significantly improved sensitivity of radiomic nomogram (0.765 and 0.793) indicated better identification of diffuse type GC patients. Further, calibration curves and decision curves demonstrated its great model fitness and clinical usefulness. Conclusions The radiomic nomogram involving the combined radiomic signature and clinical characteristics holds potential in differentiating Lauren diffuse type from intestinal type for reasonable clinical treatment strategy.

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Development and validation of a CT-based radiomic nomogram for preoperative prediction of early recurrence in advanced gastric cancer

Cited by 118 publications

References 37 publications

Prognostic and predictive value of radiomics signatures in stage I‐III colon cancer

Prognostic and predictive value of radiomics signatures in stage I‐III colon cancer

Development and Validation of a Contrast-Enhanced CT-Based Radiomics Nomogram for Prediction of Therapeutic Efficacy of Anti-PD-1 Antibodies in Advanced HCC Patients

Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer

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