MR‐Based Radiomics Nomogram of Cervical Cancer in Prediction of the Lymph‐Vascular Space Invasion preoperatively

Li, Zhicong; Li, Hailin; Wang, Shiyu; Dong, Di; Yin, Fang‐Fang; Chen, An; Wang, Siwen; Zhao, Guangming; Fang, Mengjie; Tian, Jie; Wu, Sufang; Wang, Han

doi:10.1002/jmri.26531

Cited by 85 publications

(63 citation statements)

References 38 publications

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“…First, the optimal radiomics features were put into a logistic regression model to obtain the regression coefficients . Then the Rad_Score of each patient was calculated using a linear combination of these selected features with their corresponding regression coefficients.…”

Section: Methodsmentioning

confidence: 99%

“…First, the optimal radiomics features were put into a logistic regression model to obtain the regression coefficients. 32 Then the Rad_Score of each patient was calculated using a linear combination of these selected features with their corresponding regression coefficients. Finally, the Rad_Score and the clinical potential factors in the training cohort were analyzed using the univariate and multivariate regression to select the independent predictors.…”

Section: Performance Evaluation Using Both Radiomics Features and CLImentioning

confidence: 99%

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A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors

Wang

et al. 2019

Magnetic Resonance Imaging

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Background Preoperative prediction of bladder cancer (BCa) recurrence risk is critical for individualized clinical management of BCa patients. Purpose To develop and validate a nomogram based on radiomics and clinical predictors for personalized prediction of the first 2 years (TFTY) recurrence risk. Study Type Retrospective. Population Preoperative MRI datasets of 71 BCa patients (34 recurrent) were collected, and divided into training (n = 50) and validation cohorts (n = 21). Field Strength/Sequence 3.0T MRI/T2‐weighted (T2W), multi‐b‐value diffusion‐weighted (DW), and dynamic contrast‐enhanced (DCE) sequences. Assessment Radiomics features were extracted from the T2W, DW, apparent diffusion coefficient, and DCE images. A Rad_Score model was constructed using the support vector machine‐based recursive feature elimination approach and a logistic regression model. Combined with the important clinical factors, including age, gender, grade, and muscle‐invasive status (MIS) of the archived lesion, tumor size and number, surgery, and image signs like stalk and submucosal linear enhancement, a radiomics‐clinical nomogram was developed, and its performance was evaluated in the training and the validation cohorts. The potential clinical usefulness was analyzed by the decision curve. Statistical Tests Univariate and multivariate analyses were performed to explore the independent predictors for BCa recurrence prediction. Results Of the 1872 features, the 32 with the highest area under the curve (AUC) of receiver operating characteristic were selected for the Rad_Score calculation. The nomogram developed by two independent predictors, MIS and Rad_Score, showed good performance in the training (accuracy 88%, AUC 0.915, P << 0.01) and validation cohorts (accuracy 80.95%, AUC 0.838, P = 0.009). The decision curve exhibited when the risk threshold was larger than 0.3, more benefit was observed by using the radiomics‐clinical nomogram than using the radiomics or clinical model alone. Data Conclusion The proposed radiomics‐clinical nomogram has potential in the preoperative prediction of TFTY BCa recurrence. Level of Evidence: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;50:1893–1904.

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Section: Methodsmentioning

confidence: 99%

Section: Performance Evaluation Using Both Radiomics Features and CLImentioning

confidence: 99%

A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors

Wang

et al. 2019

Magnetic Resonance Imaging

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“…According to Hua et al [33], the model based on multiparametric MRI showed the best prediction results, with an AUC of 0.842 (95% CI, 0.772-0.913; sensitivity = 0.773; speci city = 0.776) in the training cohort and 0.775 (95% CI, 0.637-0.912; sensitivity = 0.739; speci city = 0.667) in the validation cohort. Similarly, Li et al [34] also found that the radiomics nomogram derived from MRI showed favorable discrimination between LVSI and non-LVSI groups, with an AUC of 0.754 (95% CI, 0.6326-0.8745) in the training cohort and 0.727 (95% CI, 0.5449-0.9097) in the validation cohort. We initially used the combination of PET radiomics with protein molecule to predict LVSI, which showed that the radiomics and combined models based on 18 F-FDG PET imaging showed better results than those of previous studies.…”

Section: Discussionmentioning

confidence: 76%

Prediction of lymphovascular space invasion using a combination of tenascin-C, Cox-2, and PET/CT radiomics in patients with early-stage cervical squamous cell carcinoma

Chen

et al. 2020

Preprint

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Background Lymphovascular space invasion is an independent prognostic factor in early-stage cervical cancer. However, there is a lack of non-invasive methods to detect lymphovascular space invasion. Some researchers found that Tenascin-C and Cyclooxygenase-2 was correlated with lymphovascular space invasion. Radiomics has been studied as an emerging tool for distinguishing tumor pathology stage, evaluating treatment response, and predicting prognosis. Materials and methods Eighty-six patients with early-stage cervical squamous cell carcinoma who underwent PET/CT examination were retrospectively analyzed. 401 radiomics features based on PET/CT images were extracted and integrated into radiomics score (Rad-score). Immunohistochemical analysis was performed to evaluate TNC and COX-2 expression. Mann-Whitney U test was used to distinguish differences in the Rad-score, TNC, and COX-2 between LVSI and non-LVSI groups. The correlations of characteristics were tested by Spearman analysis. Machine learning models including radiomics model, protein model and combined model were established by logistic regression algorithm and evaluated by ROC curve. Pairwise comparisons of ROC curves were tested by DeLong test. Results The Rad-score of patients with LVSI was significantly higher than those without. A significant correlation was shown between LVSI and Rad-score (r = 0.631, p < 0.001). TNC was correlated to both the Rad-score (r = 0.244, p = 0.024) and COX-2 (r = 0.227, p = 0.036). The radiomics model had the best predictive performance among all models (AUC = 0.914, p < 0.001) in training dataset. However, in testing dataset, the combined model had better efficiency for predicting LVSI than other models (AUCs: 0.801 vs. 0.756 and 0.801 vs. 0.631, respectively). Conclusion The machine learning model of the combination of PET radiomics with COX-2 and TNC provides a new tool for detecting LVSI in patients with early-stage cervical cancer.

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“…LNM is an intricate biological process in AGC, in which the primary tumor lesions undoubtedly play an important role (14–16). Jiang's study (4) established a radiomic nomogram based on CT images and clinicopathological findings to estimate the LNM in patients with gastric cancer.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Lymph Node Metastasis in Advanced Gastric Cancer Using Magnetic Resonance Imaging-Based Radiomics

et al. 2019

Self Cite

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Objective: To develop and evaluate a diffusion-weighted imaging (DWI)-based radiomic nomogram for lymph node metastasis (LNM) prediction in advanced gastric cancer (AGC) patients.Overall Study: This retrospective study was conducted with 146 consecutively included pathologically confirmed AGC patients from two centers. All patients underwent preoperative 3.0 T magnetic resonance imaging (MRI) examination. The dataset was allocated to a training cohort (n = 71) and an internal validation cohort (n = 47) from one center along with an external validation cohort (n = 28) from another. A summary of 1,305 radiomic features were extracted per patient. The least absolute shrinkage and selection operator (LASSO) logistic regression and learning vector quantization (LVQ) methods with cross-validations were adopted to select significant features in a radiomic signature. Combining the radiomic signature and independent clinical factors, a radiomic nomogram was established. The MRI-reported N staging and the MRI-derived model were built for comparison. Model performance was evaluated considering receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA).Results: A two-feature radiomic signature was found significantly associated with LNM (p < 0.01, training and internal validation cohorts). A radiomic nomogram was established by incorporating the clinical minimum apparent diffusion coefficient (ADC) and MRI-reported N staging. The radiomic nomogram showed a favorable classification ability with an area under ROC curve of 0.850 [95% confidence interval (CI), 0.758–0.942] in the training cohort, which was then confirmed with an AUC of 0.857 (95% CI, 0.714–1.000) in internal validation cohort and 0.878 (95% CI, 0.696–1.000) in external validation cohort. Meanwhile, the specificity, sensitivity, and accuracy were 0.846, 0.853, and 0.851 in internal validation cohort, and 0.714, 0.952, and 0.893 in external validation cohort, compensating for the MRI-reported N staging and MRI-derived model. DCA demonstrated good clinical use of radiomic nomogram.Conclusions: This study put forward a DWI-based radiomic nomogram incorporating the radiomic signature, minimum ADC, and MRI-reported N staging for individualized preoperative detection of LNM in patients with AGC.

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MR‐Based Radiomics Nomogram of Cervical Cancer in Prediction of the Lymph‐Vascular Space Invasion preoperatively

Cited by 85 publications

References 38 publications

A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors

A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors

Prediction of lymphovascular space invasion using a combination of tenascin-C, Cox-2, and PET/CT radiomics in patients with early-stage cervical squamous cell carcinoma

Evaluation of Lymph Node Metastasis in Advanced Gastric Cancer Using Magnetic Resonance Imaging-Based Radiomics

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