Value of [18F]FDG PET radiomic features and VEGF expression in predicting pelvic lymphatic metastasis and their potential relationship in early-stage cervical squamous cell carcinoma

Li, Kexin; Sun, Hongzan; Lu, Zaiming; Xin, Jun; Zhang, Le; Guo, Yan; Guo, Qiyong

doi:10.1016/j.ejrad.2018.07.024

Cited by 44 publications

(39 citation statements)

References 30 publications

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“…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%

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: 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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“…Similarly, inverse difference moment and correlation were selected to calculate the Rad-score for predicting LVSI in our study. Recently, Li et al [17] study showed that the PET textures of primary tumor could predict lymphatic metastasis in early-stage cervical carcinoma (AUC = 0.757 in the validation data set; 95% CI, 0.545-0.904; p < 0.05). Other research have also shown that radiomics of primary tumor based on PET images could re ect tumor malignancy and were associated with nodal metastases and molecular subtypes of solid tumor [24,25].…”

Section: Discussionmentioning

confidence: 99%

“…Both had more than 10 years of experience and were blinded to patients' clinical data. The radiomics features were extracted from the PET images using AK software (Arti cial Intelligence Kit, GE Healthcare, Shanghai, China) [17].…”

Section: F-fdg Pet/ct Acquisition and Features Extractionmentioning

confidence: 99%

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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“…Similar to IPI score, these prognostic models also did not include tumor-specific biological factors, and can therefore not be used for targeted therapies (31,34). nm23, TOP2A, MUM-1 and VEGF are very common markers used to estimate the progression, infiltration and metastasis ability of many malignant tumors, particularly carcinomas (14,(40)(41)(42)(43)(44). Although their prognostic role in various carcinomas has been widely investigated over time, such as breast (45), pancreatic (12) and renal cell carcinoma (42), to the best of our knowledge, a limited number of have focused on their value in lymphomas.…”

Section: Discussionmentioning

confidence: 99%

nm23, TOP2A and VEGF expression: Potential prognostic biologic factors in peripheral T‑cell lymphoma, not otherwise specified

et al. 2019

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Value of [18F]FDG PET radiomic features and VEGF expression in predicting pelvic lymphatic metastasis and their potential relationship in early-stage cervical squamous cell carcinoma

Cited by 44 publications

References 30 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

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

nm23, TOP2A and VEGF expression: Potential prognostic biologic factors in peripheral T‑cell lymphoma, not otherwise specified

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