Feasibility of an ADC-based radiomics model for predicting pelvic lymph node metastases in patients with stage IB–IIA cervical squamous cell carcinoma

Abstract

Objectives: To investigate the prediction value of a radiomics model based on apparent diffusion coefficient (ADC) maps for pelvic lymph node metastasis (PLNM) in patients with stage IB–IIA cervical squamous cell carcinoma (CSCC). Methods: A total of 153 stage IB–IIA CSCC patients who underwent preoperative MRI including DWI from January 2015 to October 2017 were retrospectively studied and divided into a training cohort ( n = 102) and a validation cohort ( n = 51). Radiomics features were extracted from the ADC maps. The one-way ANOVA method, Mann-Whitney U test and Pearson’s correlation analysis were used for selecting radiomics features. Logistic regression analyses were used to develop the model. ROC analyses were used to evaluate the prediction performance of the model. Results: Clinical stage, tumor diameter, and MR-reported lymph node (LN) status were significantly associated with LN status ( p < 0.05 for both the training and validation cohorts). The radiomics model, which incorporated clinical stage, MR-reported LN status, and grey-level non-uniformity, showed good predictive performance in the training group (AUC 0.864; 95% CI, 0.782 – 0.924) and the validation group (AUC 0.870; 95% CI, 0.747 – 0.948). The performance of the radiomics model was significantly better than that of each predictive factor alone. Conclusion: The presented radiomics model, a non-invasive preoperative prediction tool, has the potential to have more predictive efficacy than clinical and radiological factors for differentiating between metastatic and non-metastatic lymph nodes. Advances in knowledge: A radiomics model derived from the ADC maps of primary lesions demonstrated good performance for predicting PLNM in stage IB-IIA CSCC patients and may help to improve clinical decision-making.

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

“…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.…”

“…The median survival time for DFS was 31 (IQR, 16-56) months in the primary cohort and 23 (IQR, [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33] months in the validation cohort. Figure 2C, D shows a significant difference between low-and high-risk patients from the hybrid model in the primary cohort (hazard ratio, 3.24; 95% CI, 1.64-6.44; P < .001) and validation cohort (hazard ratio, 4.59; 95% CI, 2.04-10.31; P < .001).…”

“…[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.…”

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

“…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.…”

“…This strategy has shown a great potential for improved diagnostic and prognostic in a wide range of cancer types ( 16 – 19 ). Few studies have suggested improvement in preoperative prediction of LNM by using different modalities-based radiomics analysis in cervical cancers ( 20 – 23 ). However, these studies might suffer from relatively small sample sizes, analysis of single sequence or single section rather than whole-tumor volume analysis, or lack of external validation.…”