Preoperative prediction of pelvic lymph nodes metastasis in early-stage cervical cancer using radiomics nomogram developed based on T2-weighted MRI and diffusion-weighted imaging

Wang, Tao; Gao, Tingting; Yang, Jingbo; Yan, Xuejiao; Wang, Yübo; Zhou, Xiaobo; Tian, Jie; Huang, Luqi; Zhang, Ming

doi:10.1016/j.ejrad.2019.01.003

Cited by 88 publications

(78 citation statements)

References 20 publications

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“…The clinical relevance of our study lies in providing an easy-to-use tool, the radiomics nomogram, for clinicians. Our study supported that the radiomics nomogram integrating the radiomics signature and MRI-reported LN status could achieve greater predictive efficacy than the subjective MRI evaluation alone, with a higher AUC and better calibration, consistent with previous studies (20,23). Nevertheless, some notes should be emphasized.…”

Section: Discussionsupporting

confidence: 90%

“…This strategy has shown a great potential for improved diagnostic and prognostic in a wide range of cancer types (16)(17)(18)(19). Few studies have suggested improvement in preoperative prediction of LNM by using different modalities-based radiomics analysis in cervical cancers (20)(21)(22)(23). However, these studies might suffer from relatively small sample sizes, analysis of single sequence or Abbreviations: LNM, lymph node metastasis; PLND, pelvic lymph node dissection; FIGO, International Federation of Gynecology and Obstetrics; MRI, magnetic resonance imaging; CSCC, cervical squamous cell cancer; DWI, diffusion-weighted imaging; FOV, field of view; ADC, apparent diffusion coefficient; ROI, region of interest; GLCM, gray-level co-occurrence matrix; GLRLM, gray-level run length matrix; GLSZM, gray-level size zone matrix; NGTDM, neighboring gray tone difference matrix; GLDM, gray-level dependence matrix; LoG, Laplacian of Gaussian; ICC, interclass correlation coefficient; MRMR, minimum redundancy maximum relevance; LASSO, least absolute shrinkage and selection operator; ROC, receiver operating characteristic; DCA, decision curve analysis.…”

Section: Introductionmentioning

confidence: 99%

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Radiomics Analysis of Multiparametric MRI for the Preoperative Prediction of Lymph Node Metastasis in Cervical Cancer

Hou

Zhou

Ren³

et al. 2020

Front. Oncol.

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Objective: To develop and validate a radiomics predictive model based on multiparameter MR imaging features and clinical features to predict lymph node metastasis (LNM) in patients with cervical cancer. Material and Methods: A total of 168 consecutive patients with cervical cancer from two centers were enrolled in our retrospective study. A total of 3,930 imaging features were extracted from T2-weighted (T2W), ADC, and contrast-enhanced T1-weighted (cT1W) images for each patient. Four-step procedures, mainly minimum redundancy maximum relevance (MRMR) and least absolute shrinkage and selection operator (LASSO) regression, were applied for feature selection and radiomics signature building in the training set from center I (n = 115). Combining clinical risk factors, a radiomics nomogram was then constructed. The models were then validated in the external validation set comprising 53 patients from center II. The predictive performance was determined by its calibration, discrimination, and clinical usefulness. Results: The radiomics signature derived from the combination of T2W, ADC, and cT1W images, composed of six LN-status-related features, was significantly associated with LNM and showed better predictive performance than signatures derived from either of them alone in both sets. Encouragingly, the radiomics signature also showed good discrimination in the MRI-reported LN-negative subgroup, with AUC of 0.825 (95% CI: 0.732-0.919). The radiomics nomogram that incorporated radiomics signature and MRI-reported LN status also showed good calibration and discrimination in both sets, with AUCs of 0.865 (95% CI: 0.794-0.936) and 0.861 (95% CI: 0.733-0.990), respectively. Decision curve analysis confirmed its clinical usefulness. Conclusion: The proposed MRI-based radiomics nomogram has good performance for predicting LN metastasis in cervical cancer and may be useful for improving clinical decision making.

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Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Radiomics Analysis of Multiparametric MRI for the Preoperative Prediction of Lymph Node Metastasis in Cervical Cancer

Hou

Zhou

Ren³

et al. 2020

Front. Oncol.

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“…MRI is able to portray the distribution of hydrogen nuclei in different tissues in the body by re-emission of the energy from radiofrequency pulses. (42)(43)(44)(45) MRI is able to discriminate between tissues based on their hydrogen content, facilitating the visualization of tissues with normal and pathologic morphology.…”

Section: Magnetic Resonance Imagingmentioning

confidence: 99%

“…weighted (T2W) images depends on the dephasing of the exited hydrogen nuclei. (42) More recently, advanced imaging techniques as diffusion weighted MRI (DWI) have been proposed. (46) DWI is a technique based on microscopic water diffusion properties.…”

Section: Magnetic Resonance Imagingmentioning

confidence: 99%

“…(41) Future studies should also take more advanced MRI based biomarkers in to account; radiomics could very well be the future. (42) However, we should not forget to explore the biological and molecular background of ADC. Preliminary studies in other cancer types showed ADC to be associated with angiogenic, hypoxic and immunologic markers.…”

Section: Reliable Measurements Of Adc and Its Role In Predicting Paramentioning

confidence: 99%

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Magnetic resonance imaging of cervical cancer

Mongula¹

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Development of a Deep Learning Model to Identify Lymph Node Metastasis on Magnetic Resonance Imaging in Patients With Cervical Cancer

Wang

Zhang

et al. 2020

JAMA Netw Open

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IMPORTANCE Accurate identification of lymph node metastasis preoperatively and noninvasively in patients with cervical cancer can avoid unnecessary surgical intervention and benefit treatment planning. OBJECTIVE To develop a deep learning model using preoperative magnetic resonance imaging for prediction of lymph node metastasis in cervical cancer. DESIGN, SETTING, AND PARTICIPANTS This diagnostic study developed an end-to-end deep learning model to identify lymph node metastasis in cervical cancer using magnetic resonance imaging (MRI). A total of 894 patients with stage IB to IIB cervical cancer who underwent radical hysterectomy and pelvic lymphadenectomy were reviewed. All patients underwent radical hysterectomy and pelvic lymphadenectomy, received pelvic MRI within 2 weeks before the operations, had no concurrent cancers, and received no preoperative treatment. To achieve the optimal model, the diagnostic value of 3 MRI sequences was compared, and the outcomes in the intratumoral and peritumoral regions were explored. To mine tumor information from both image and clinicopathologic levels, a hybrid model was built and its prognostic value was assessed by Kaplan-Meier analysis. The deep learning model and hybrid model were developed on a primary cohort consisting of 338 patients (218 patients from Sun Yat

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Preoperative prediction of pelvic lymph nodes metastasis in early-stage cervical cancer using radiomics nomogram developed based on T2-weighted MRI and diffusion-weighted imaging

Cited by 88 publications

References 20 publications

Radiomics Analysis of Multiparametric MRI for the Preoperative Prediction of Lymph Node Metastasis in Cervical Cancer

Radiomics Analysis of Multiparametric MRI for the Preoperative Prediction of Lymph Node Metastasis in Cervical Cancer

Magnetic resonance imaging of cervical cancer

Development of a Deep Learning Model to Identify Lymph Node Metastasis on Magnetic Resonance Imaging in Patients With Cervical Cancer

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