Feasibility of Predicting Pelvic Lymph Node Metastasis Based on IVIM-DWI and Texture Parameters of the Primary Lesion and Lymph Nodes in Patients with Cervical Cancer

Zhang, Kai-yue; Jia, Haodong; Fang, Xin; Lin, Tingting; Wei, Chao; Qian, Liting; Dong, Jianying

doi:10.1016/j.acra.2021.08.026

Cited by 13 publications

(8 citation statements)

References 31 publications

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“…Metastasis is closely related to the heterogeneity of malignant tumors, and radiomics features can quantify the microscopic heterogeneity at the histopathological level [ 23 , 24 ]. MRI-based radiomics has been considered a reliable tool for accurate preoperative assessment of LNM in several cancers, such as cervical cancer [ 25 ], rectal cancer [ 15 ], and oral tongue squamous carcinoma [ 26 ]. From this, we developed a Rads-score based on 3D radiomics features from axial T 2 WI, axial DWI ( b = 1000 s/mm 2 ), and axial CE-T1WI delay period to improve diagnostic efficiency.…”

Section: Discussionmentioning

confidence: 99%

Preoperative Prediction Value of Pelvic Lymph Node Metastasis of Endometrial Cancer: Combining of ADC Value and Radiomics Features of the Primary Lesion and Clinical Parameters

Jia

Zhang

et al. 2022

Journal of Oncology

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Objective. To investigate the value of apparent diffusion coefficient (ADC) value of endometrial cancer (EC) primary lesion and magnetic resonance imaging (MRI) three-dimensional (3D) radiomics features combined with clinical parameters for preoperative prediction of pelvic lymph node metastasis (PLNM). Methods. A total of 136 patients with EC confirmed by postoperative pathology were retrospectively reviewed and analyzed. Patients were randomly divided into training set (n = 95) and test set (n = 41) at a ratio of 7 : 3. Radiomics features based on T2WI, DWI, and contrast-enhanced T1WI (CE-T1WI) sequence were extracted and screened, and then radiomics score (Rads-score) was calculated. Clinical parameters and ADC value of EC primary lesion were measured and collected, and their correlation with PLNM was analyzed. Receiver operating characteristic (ROC) curve was plotted to assess the diagnostic efficacy of the model. A nomogram for PLNM was created based on the multivariate logistic regression model. Results. The ADC value of the EC primary lesion showed inverse correlation with PLNM, while CA125 and Rads-score were positively associated with PLNM. A predictive model was proposed based on ADC value, Rads-score, CA125, and MR-reported pelvic lymph node status (PLNS) for PLNM in EC. The area under the curve (AUC) of the model is 0.940; the sensitivity and specificity (87.1% and 90.6%) of the model were significantly higher than that of the MRI morphological signs. Conclusion. A combination of ADC value, MRI 3D radiomics features of the EC primary lesion, and clinical parameters generated a prediction model for PLNM in EC and had a good diagnostic performance; it was a useful supplement to MR-reported PLNS based on MRI morphological signs.

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Section: Discussionmentioning

confidence: 99%

Preoperative Prediction Value of Pelvic Lymph Node Metastasis of Endometrial Cancer: Combining of ADC Value and Radiomics Features of the Primary Lesion and Clinical Parameters

Jia

Zhang

et al. 2022

Journal of Oncology

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“…In this study, we found that the D* value, which represents the diffusion effect of microcirculation perfusion in voxels and reflects the average blood flow velocity of capillaries, [20][21][22] of the primary lesion in the LN+ group was significantly lower than that in the LNÀ group. The reason may be the fact that the primary lesion of the rectal adenocarcinoma in the LN+ group was more aggressive and had more tumor angiogenesis 21 ; the blood vessels were potentially immature, and the formation of vascular tumor thrombus in the blood vessels can easily lead to the decrease of the average blood flow velocity within capillaries in the tumor.…”

Section: Discussionmentioning

confidence: 73%

“…A prior study by Bakke et al found LNM was associated with tumor vascularity and tumor microenvironment in the rectal cancer, 17 this suggested that the pathological and microscopic changes of the primary lesion may predict the occurrence of LNM. Zhang et al found that IVIM‐DWI and texture parameters performed well in diagnosing pelvic LNM of cervical cancer by reflecting the pathological changes of the primary lesion such as cell density, microcirculation status, and heterogeneity 20 . Therefore, we tried to combine IVIM‐DWI with radiomics features to evaluate N‐LNM in order to improve individualized treatment of rectal adenocarcinoma.…”

Section: Discussionmentioning

confidence: 99%

“…Zhang et al found that IVIM-DWI and texture parameters performed well in diagnosing pelvic LNM of cervical cancer by reflecting the pathological changes of the primary lesion such as cell density, microcirculation status, and heterogeneity. 20 Therefore, we tried to combine IVIM-DWI with radiomics features to evaluate N-LNM in order to improve individualized treatment of rectal adenocarcinoma.…”

Section: Discussionmentioning

confidence: 99%

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A Nomogram of Combining IVIM‐DWI and MRI Radiomics From the Primary Lesion of Rectal Adenocarcinoma to Assess Nonenlarged Lymph Node Metastasis Preoperatively

Jia

Jiang

Zhang

et al. 2022

Magnetic Resonance Imaging

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Background: Lymph node (LN) staging plays an important role in treatment decision-making. Current problem is that preoperative detection of LN involvement is always highly challenging for radiologists. Purpose: To explore the value of the nomogram model combining intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and radiomics features from the primary lesion of rectal adenocarcinoma in assessing the non-enlarged lymph node metastasis (N-LNM) preoperatively. Study Type: Retrospective. Population: A total of 126 patients (43% female) comprising a training group (n = 87) and a validation group (n = 39) with pathologically confirmed rectal adenocarcinoma. Field Strength/Sequence: A 3.0 Tesla (T); T 2 Àweighted imaging (T 2 WI) with fast spin-echo (FSE) sequence; IVIM-DWI spin-echo echo-planar imaging sequence. Assessment: Based on pathological analysis of the surgical specimen, patients were classified into negative LN (LNÀ) and positive LN (LN+) groups. Apparent diffusion coefficient (ADC), diffusion coefficient (D), pseudo diffusion coefficient (D*) and microvascular volume fraction (f) values of primary lesion of rectal adenocarcinoma were measured. Three-dimensional (3D) radiomics features were measured on T 2 WI and IVIM-DWI. A nomogram model including IVIM-DWI and radiomics features was developed. Statistical Tests: General_univariate_analysis and multivariate logistic regression were used for radiomics features selection. The performance of the nomogram was assessed by the receiver operating characteristic (ROC) curve, calibration, and decision curve analysis (DCA). Results: The LN+ group had a significantly lower D* value ([13.20 AE 13.66 vs. 23.25 AE 18.71] Â 10 À3 mm 2 /sec) and a higher f value (0.43 AE 0.12 vs. 0.34 AE 0.10) than the LNÀ group in the training cohort. The nomogram model combined D*, f, and radiomics features had a better evaluated performance (AUC = 0.864) than any other model in the training cohort. Date Conclusion:The nomogram model including IVIM-DWI and MRI radiomics features in the primary lesion of rectal adenocarcinoma was associated with the N-LNM.

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“…The last component of our radiomics model is the SAE, which is a measure of the distribution of small size zones, with a higher value indicating a larger number of small size zones and finer textures. Zhang et al (39) reported that radiomics features, including SAE, could help diagnose lymph node metastasis in cervical cancer. The radiomics model built on these features allows non-invasive and effective screening of patients who require anti-IL1B adjuvant therapy.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a CECT-based radiomics model for predicting IL1B expression and prognosis of head and neck squamous cell carcinoma

et al. 2023

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IntroductionIt is necessary to explore a noninvasive method to stratify head and neck squamous cell carcinoma (HNSCC)’s prognosis and to seek new indicators for individualized precision treatment. As a vital inflammatory cytokine, IL1B might drive a new tumor subtype that could be reflected in overall survival (OS) and predicted using the radiomics method.MethodsA total of 139 patients with RNA-Seq data from The Cancer Genome Atlas (TCGA) and matched CECT data from The Cancer Image Archive (TCIA) were included in the analysis. The prognostic value of IL1B expression in patients with HNSCC was analyzed using Kaplan-Meier analysis, Cox regression analysis and subgroup analysis. Furthermore, the molecular function of IL1B on HNSCC was explored using function enrichment and immunocytes infiltration analyses. Radiomic features were extracted with PyRadiomics and processed using max-relevance minredundancy, recursive feature elimination, and gradient boosting machine algorithm to construct aradiomics model for predicting IL1B expression. The area under the receiver operating characteristic curve (AUC), calibration curve, precision recall (PR) curve, and decision curve analysis (DCA) curve were used to examine the performance of the model.ResultsIncreased IL1B expression in patients with HNSCC indicated a poor prognosis (hazard ratio [HR] = 1.56, P = 0.003) and was harmful in patients who underwent radiotherapy (HR = 1.87, P = 0.007) or chemotherapy (HR = 2.514, P < 0.001). Shape_Sphericity, glszm_SmallAreaEmphasis, and firstorder_Kurtosis were included in the radiomics model (AUC: training cohort, 0.861; validation cohort, 0.703). The calibration curves, PR curves and DCA showed good diagnostic effect of the model. The rad-score was close related to IL1B (P = 4.490*10-9), and shared the same corelated trend to EMT-related genes with IL1B. A higher rad-score was associated with worse overall survival (P = 0.041).DiscussionThe CECT-based radiomics model provides preoperative IL1B expression predictionand offers non-invasive instructions for the prognosis and individualized treatment of patients withHNSCC.

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Feasibility of Predicting Pelvic Lymph Node Metastasis Based on IVIM-DWI and Texture Parameters of the Primary Lesion and Lymph Nodes in Patients with Cervical Cancer

Cited by 13 publications

References 31 publications

Preoperative Prediction Value of Pelvic Lymph Node Metastasis of Endometrial Cancer: Combining of ADC Value and Radiomics Features of the Primary Lesion and Clinical Parameters

Preoperative Prediction Value of Pelvic Lymph Node Metastasis of Endometrial Cancer: Combining of ADC Value and Radiomics Features of the Primary Lesion and Clinical Parameters

A Nomogram of Combining IVIM‐DWI and MRI Radiomics From the Primary Lesion of Rectal Adenocarcinoma to Assess Nonenlarged Lymph Node Metastasis Preoperatively

Development and validation of a CECT-based radiomics model for predicting IL1B expression and prognosis of head and neck squamous cell carcinoma

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