Radiomic signature as a predictive factor for lymph node metastasis in early‐stage cervical cancer

Kan, Yangyang; Dong, Di; Zhang, Yuchen; Jiang, Wenyan; Zhao, Nannan; Han, Lu; Fang, Mengjie; Zang, Yali; Hu, C. Y.; Tian, Jie; Li, Chunming; Luo, Yahong

doi:10.1002/jmri.26209

Cited by 83 publications

(74 citation statements)

References 27 publications

(46 reference statements)

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“…The radiomics signature developed in our study showed favorable discrimination for predicting LNM in the training and external validation sets, with AUCs of 0.859 and 0.833, respectively. The performance of our radiomics signature was comparable to previous studies (22,30). Kan et al (22) reported that the SVM-based radiomics signatures derived from T2W and cT1W images were associated with LNM, with an AUC of 0.753 in the primary cohort.…”

Section: Discussionsupporting

confidence: 82%

“…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: 82%

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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“…We defined the DL model-predicted LNM probability as the DL score. Owing to the inconsistency of previous research about the performance of MRI sequences, [13][14][15][16] we compared the DL model among 3 MRI sequences to find the optimal model for LNM prediction.…”

Section: Model Development and Visualizationmentioning

confidence: 99%

“…[10][11][12] Many attempts have been made to improve the performance of MRI in diagnosing LNM before surgery, for example, using radiomic features that extract the quantitative human-defined image features, such as shape, intensity, and texture features. [13][14][15][16] In previous research, the sensitivity of MR images to discriminate metastatic from nonmetastatic LN has shown improvement by using radiomic features. 13 However, radiomic features need time-consuming tumor delineation, and they might not be adaptive to specific clinical issues.…”

Section: Introductionmentioning

confidence: 99%

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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“…Cervical cancer is one of the most common malignant tumors diagnosed among females worldwide ( 1 ). Furthermore, cervical cancer has a high incidence rate and exhibits the second highest mortality rate associated with cancer in women ( 2 , 3 ). Thus, cervical cancer seriously affects the health of women ( 4 , 5 ).…”

Section: Introductionmentioning

confidence: 99%

Knockdown of BRCC3 exerts an anti‑tumor effect on cervical cancer in�vitro

Zhang

Zhou

2018

Mol Med Report

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Cervical cancer is a serious malignancy that affects the health of females. In the present study, the association between breast cancer type 1 susceptibility protein/breast cancer type 2 susceptibility protein-containing complex subunit 3 (BRCC3) and cervical cancer was investigated. Reverse transcription-quantitative polymerase chain reaction and western blotting were performed to determine BRCC3 mRNA and protein expression levels in cervical cancer tissues and cells, in addition to the expression levels of proteins associated with the epithelial-mesenchymal transition (EMT) process in HeLa and SiHa cells. Cell Counting Kit-8, Transwell and wound healing assays were performed to determine the cell viability, invasion and migration abilities of cervical cancer cells, respectively. The results of the present study revealed that BRCC3 expression was significantly increased in cervical cancer tissues, which was also revealed to be associated with the clinical stages and pathological grades of cervical cancer exhibited by patients, in addition to the survival time. Furthermore, BRCC3 expression levels were enhanced in HeLa, SiHa and C-33A cervical cancer cells. BRCC3 interference in HeLa and SiHa cells was revealed to suppress cell viability, invasion and migration abilities via upregulation of E-cadherin expression levels and downregulation of Vimentin, matrix metalloproteinase (MMP)-2, MMP-9, snail family transcriptional repressor (Snai)1 and Snai2 expression levels. In conclusion, the expression levels of BRCC3 were revealed to be increased in cervical cancer tissues, which were positively associated with clinical features of cervical cancer. Furthermore, BRCC3 interference inhibited the cell viability, invasion and migration abilities of HeLa and SiHa cells via regulation of EMT progression and expression levels of Snai family members. In addition, the results of the present study suggested that BRCC3 represents an oncogene associated with cervical cancer, and may also represent a novel therapeutic biomarker for the diagnosis, treatment and prognosis of patients with cervical cancer.

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Radiomic signature as a predictive factor for lymph node metastasis in early‐stage cervical cancer

Abstract: 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2018.

Cited by 83 publications

References 27 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

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

Knockdown of BRCC3 exerts an anti‑tumor effect on cervical cancer in�vitro

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