Nomograms Combining Clinical and Imaging Parameters to Predict Recurrence and Disease-free Survival After Concurrent Chemoradiotherapy in Patients With Locally Advanced Cervical Cancer

Liu, Long; Zhang, Kaiyue; Su, Rixin; Jia, Haodong; Qian, Liting; Dong, Junqiang

doi:10.1016/j.acra.2022.08.002

Cited by 21 publications

(7 citation statements)

References 34 publications

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“…The combined models showed the best performance in our study, with AUCs higher than those of radiomics and clinical models separately. This aligns with the favorable prognostic predictive ability of radiomics with clinical features in colorectal cancer [ 42 ], cervical cancer [ 43 ], esophageal cancer [ 22 ], and NSCLC [ 23 ]. These results showed that radiomic features combined with clinical factors were more available than single radiomic features and clinical factors for prognostic prediction.…”

Section: Discussionmentioning

confidence: 54%

Effectiveness of CT radiomic features combined with clinical factors in predicting prognosis in patients with limited-stage small cell lung cancer

Wu,

Zhou,

et al. 2024

BMC Cancer

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Background The prognosis of SCLC is poor and difficult to predict. The aim of this study was to explore whether a model based on radiomics and clinical features could predict the prognosis of patients with limited-stage small cell lung cancer (LS-SCLC). Methods Simulated positioning CT images and clinical features were retrospectively collected from 200 patients with histological diagnosis of LS-SCLC admitted between 2013 and 2021, which were randomly divided into the training (n = 140) and testing (n = 60) groups. Radiomics features were extracted from simulated positioning CT images, and the t-test and the least absolute shrinkage and selection operator (LASSO) were used to screen radiomics features. We then constructed radiomic score (RadScore) based on the filtered radiomics features. Clinical factors were analyzed using the Kaplan–Meier method. The Cox proportional hazards model was used for further analyses of possible prognostic features and clinical factors to build three models including a radiomic model, a clinical model, and a combined model including clinical factors and RadScore. When a model has prognostic predictive value (AUC > 0.7) in both train and test groups, a nomogram will be created. The performance of three models was evaluated using area under the receiver operating characteristic curve (AUC) and Kaplan–Meier analysis. Results A total of 1037 features were extracted from simulated positioning CT images which were contrast enhanced CT of the chest. The combined model showed the best prediction, with very poor AUC for the radiomic model and the clinical model. The combined model of OS included 4 clinical features and RadScore, with AUCs of 0.71 and 0.70 in the training and test groups. The combined model of PFS included 4 clinical features and RadScore, with AUCs of 0.72 and 0.71 in the training and test groups. T stages, ProGRP and smoke status were the independent variables for OS in the combined model, whereas T stages, ProGRP and prophylactic cranial irradiation (PCI) were the independent factors for PFS. There was a statistically significant difference between the low- and high-risk groups in the combined model of OS (training group, p < 0.0001; testing group, p = 0.0269) and PFS (training group, p < 0.0001; testing group, p < 0.0001). Conclusion Combined models involved RadScore and clinical factors can predict prognosis in LS-SCLC and show better performance than individual radiomics and clinical models.

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Section: Discussionmentioning

confidence: 54%

Effectiveness of CT radiomic features combined with clinical factors in predicting prognosis in patients with limited-stage small cell lung cancer

Wu,

Zhou,

et al. 2024

BMC Cancer

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“…At present, CCRT is mostly used for patients with advanced cervical cancer. Studies have shown that ( 22 ) CCRT can significantly improve overall survival and progression-free survival in patients with advanced cervical cancer, but there are also varying degrees of toxicity and complications ( 23 ). Therefore, it is important to review the prognosis of patients through timely and effective follow-up, identify the factors that can predict the risk of recurrence, and prevent cervical cancer recurrence through early clinical follow-up intervention and further optimization of treatment ( 24 ).…”

Section: Discussionmentioning

confidence: 99%

Contrast-enhanced ultrasound combined with elastic imaging for predicting the efficacy of concurrent chemoradiotherapy in cervical cancer: a feasibility study

Ma,

Zhao,

Chen

2024

Front. Oncol.

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ObjectiveContrast-enhanced ultrasound (CEUS) and elastography are of great value in the diagnosis of cervical cancer (CC). However, there is limited research on the role of contrast-enhanced ultrasound combined with elastography in predicting concurrent chemoradiotherapy and disease progression for cervical cancer. The purpose of this study was to evaluate the feasibility of contrast-enhanced ultrasound combined with elastography and tumor prognosis.MethodsMRI was performed on 98 patients with cervical cancer before and after treatment. Before, during, and 1 week after the treatment, contrast-enhanced ultrasound and elastography were conducted, and the alterations of ultrasound-related parameters at each time point of the treatment were compared. The correlation between contrast-enhanced ultrasound combined with elastic imaging and oncological outcome was assessed.ResultsThere was no notable difference in overall clinical data between the complete remission (CR) group and the partial remission (PR) group (P>0.05). Before treatment, there were no statistically significant differences in elasticity score, time to peak (TTP), and peak intensity (PI) between the CR group and the PR group. However, there were no statistical differences in elastic strain ratio (SR) and area under the curve (AUC) before and after treatment between the CR group and the PR group, and there were also no statistical differences in the elastic strain ratio (SR) and area under the curve (AUC) of contrast-enhanced ultrasound parameters between the CR group and the PR group before and during treatment. There was a statistically significant difference after treatment (P<0.05).At present, the follow-up of patients is about 1 year, 7 patients were excluded due to loss to follow-up, and 91 patients were included in the follow-up study. Through the review of the cases and combined with MRI (version RECIST1.1) and serology and other related examinations, if the patient has a new lesion or the lesion is larger than before, the tumor marker Squamous cell carcinoma antigen (SCC-Ag) is significantly increased twice in a row, and the patient is divided into progressive disease (PD). Those who did not see significant changes were divided into stable disease (SD) group. The relationship between clinical characteristics, ultrasound parameters and disease progression in 91 patients was compared. There was no significant difference in age and clinical stage between the two groups (P>0.05), but there was a significant difference in the elevation of tumor marker squamous cell carcinoma antigen (SCC-Ag) between the two groups (P<0.05).With the growth of tumors, TTP decreased, elasticity score and PI increased, and the difference was statistically significant (P<0.05). The AUC of SCC-Ag was 0.655, the sensitivity was 85.3%, and the specificity was 45.6%.The AUC, sensitivity and specificity of ultrasound parameters combined with SCC-Ag predicted disease progression was 0.959, 91.2% and 94.8%.ConclusionsUsing contrast-enhanced ultrasound and elastography to predict the efficacy and disease progression of concurrent chemoradiotherapy is feasible. In addition, the combination of SCC-Ag with contrast-enhanced ultrasound and elastography can further enhance the efficiency of predicting disease progression.

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“…Jia [ 29 ] et al explored the value of a nomogram model that combined IVIM-DWI and radiomics features of rectal adenocarcinoma primary lesions in the preoperative assessment of nonenlarged lymph node metastasis (N-LNM), and the results showed that the nomogram model that combined IVIM-DWI parameters ( D * and f ) and radiomics features had better evaluation performance (AUC = 0.864) than any other model in the training cohort. In addition, Zhang [ 30 ] showed that a nomogram based on clinical, IVIM-DWI and radiological parameters had high clinical value in predicting recurrence and disease-free survival (DFS) in patients with locally advanced cervical cancer (LACC) after concurrent chemoradiotherapy (CCRT), providing a reference for the prognostic assessment and individualized treatment of patients with cervical cancer. The present study used a radiomics model based on contrast-enhanced MRI sequences to noninvasively predict Ang-2 expression in HCC tissues, and a model established by combining radiomics features and CR factors obtained a higher predictive value and clinical net benefit.…”

Section: Discussionmentioning

confidence: 99%

DCE-MRI-based radiomics in predicting angiopoietin-2 expression in hepatocellular carcinoma

Zheng

Yang

et al. 2023

Abdom Radiol

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Background Hepatocellular carcinoma (HCC) is the sixth most common cancer, and the third leading cause of cancer death worldwide. Studies have shown that increased angiopoietin-2 (Ang-2) expression relative to Ang-1 expression in tumors is associated with a poor prognosis.The purpose of this study was to investigate the efficacy of predicting Ang-2 expression in HCC by preoperative dynamic contrast‐enhanced magnetic resonance imaging (DCE-MRI)-based radiomics. Methods The data of 52 patients with HCC who underwent surgical resection in our hospital were retrospectively analyzed. Ang-2 expression in HCC was analyzed by immunohistochemistry. All patients underwent preoperative upper abdominal DCE-MRI and intravoxel incoherent motion diffusion-weighted imaging scans. Radiomics features were extracted from the early and late arterial and portal phases of axial DCE-MRI. Univariate analysis and least absolute shrinkage and selection operator (LASSO) was performed to select the optimal radiomics features for analysis. A logistic regression analysis was performed to establish a DCE-MRI radiomics model, clinic-radiologic (CR) model and combined model integrating the radiomics score with CR factors. The stability of each model was verified by 10-fold cross-validation. Receiver operating characteristic (ROC) curve analysis, calibration curve analysis and decision curve analysis (DCA) were employed to evaluate these models. Results Among the 52 HCC patients, high Ang-2 expression was found in 30, and low Ang-2 expression was found in 22. The areas under the ROC curve (AUCs) for the radiomics model, CR model and combined model for predicting Ang-2 expression were 0.800, 0.874, and 0.933, respectively. The DeLong test showed that there was no significant difference in the AUC between the radiomics model and the CR model (p > 0.05) but that the AUC for the combined model was significantly greater than those for the other 2 models (p < 0.05). The DCA results showed that the combined model outperformed the other 2 models and had the highest net benefit. Conclusion The DCE-MRI-based radiomics model has the potential to predict Ang-2 expression in HCC patients; the combined model integrating the radiomics score with CR factors can further improve the prediction performance. Graphical abstract

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Nomograms Combining Clinical and Imaging Parameters to Predict Recurrence and Disease-free Survival After Concurrent Chemoradiotherapy in Patients With Locally Advanced Cervical Cancer

Cited by 21 publications

References 34 publications

Effectiveness of CT radiomic features combined with clinical factors in predicting prognosis in patients with limited-stage small cell lung cancer

Effectiveness of CT radiomic features combined with clinical factors in predicting prognosis in patients with limited-stage small cell lung cancer

Contrast-enhanced ultrasound combined with elastic imaging for predicting the efficacy of concurrent chemoradiotherapy in cervical cancer: a feasibility study

DCE-MRI-based radiomics in predicting angiopoietin-2 expression in hepatocellular carcinoma

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