Cervical Carcinoma: Evaluation Using Diffusion MRI With a Fractional Order Calculus Model and its Correlation With Histopathologic Findings

Shao, Xian; Liu, An; Liu, Hui; Feng, Hui; Dai, Yongming; Yu, Bin; Zhang, Jin

doi:10.3389/fonc.2022.851677

Cited by 2 publications

(1 citation statement)

References 36 publications

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“…Apparent diffusion coe cient (ADC) derived from the Gaussian diffusion model is a quantitative parameter of DWI, which has been shown as a possible imaging biomarker for predicting prognosis in many malignancies [11,12]. Intravoxel incoherent motion (IVIM) diffusion weighted imaging can accurately re ect the cell density and microcirculation state of tissues by effectively distinguishing water molecule movement from microcirculation perfusion, and e ciently evaluate microscopic information, such as the pathophysiological information, pathological type and grade of CC [9,13,14]. It was indicated that IVIM parameters as independent potential biomarkers could evaluate the pathological characteristics of CC [13].…”

Section: Introductionmentioning

confidence: 99%

Identifying pathological differentiation of cervical squamous cell carcinoma with APTw and IVIM

Zhong-hong

Peng

Lian

et al. 2023

Preprint

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Background Cervix Squamous cell carcinoma(CSCC) is the most common pathological subtypes of cervix carcinoma(CC). CSCC can be divided into poorly differentiated, moderately differentiated and well-differentiated types. The pathological differentiation is essential for the treatment and prognosis of CSCC. Compared with the well-differentiated CSCC patients, poorly differentiated CSCC patients have poor clinical prognosis. The biopsy is the golden standard for identifying pathological differentiation with the disadvantages including invasive. Therefore, an imaging method is needed to determine the degree of tumor differentiation before surgery. Purpose The objective is to explore APTw and IVIM values in diagnosing the differentiation degree of cervical squamous cell carcinoma (CSCC). Methods APTw was scanned by using 3D Multi-shot TSE for obtaining APT signal intensity (APT SI). IVIM was scanned by using 12 b values (0, 20, 100, 150, 200, 300, 400, 500, 600, 800, 1000 and 1200 s/mm2) to calculate parameters: D, D*, and f. ADC was calculated based on 2 b values (0, 800 s/mm2). The parameters among different groups were compared by t-tests. Diagnostic performance was evaluated with a ROC analysis. Results 56 patients and 30 healthy volunteers were included in study. Patients were divided into: a well-moderately differentiated group (n = 34) and a poorly differentiated group (n = 22). The parameters (APT SI, ADC, D, f) were statistically significantly different between CSCC and normal cervix. APT SI of the CSCC was higher than that of normal cervix (P < 0.001). The ADC, D, and f of the CSCC were lower than those of normal cervix (P < 0.001). Significant differences were found in APT SI and D between the well-moderately differentiated and poorly differentiated group (P < 0.001). Comparing the well-moderately differentiated and poorly differentiated group, AUC of APT SI, D and f were 0.789, 0.775 ,and 0.670, sensitivity were 72.73%, 68.18%, 77.27%, and specificity were 79.41%, 82.35%, 64.71%, respectively (P < 0.05). Conclusion APTw and IVIM can be used to diagnose CSCC and provide accurate quantitative information. Compared with IVIM, APTw has higher diagnostic performance in identifying the differentiation degree of CSCC.

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Section: Introductionmentioning

confidence: 99%

Identifying pathological differentiation of cervical squamous cell carcinoma with APTw and IVIM

Zhong-hong

Peng

Lian

et al. 2023

Preprint

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18F-FDG PET radiomics-based machine learning model for differentiating pathological subtypes in locally advanced cervical cancer

Liu

Lao

Chang

et al. 2023

Preprint

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Purpose To determine diagnostic performance of 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) and computed tomography (CT) radiomics-based machine learning (ML) for classification of cervical adenocarcinoma (AC) and squamous cell carcinoma (SCC). Methods A total of 195 patients with locally advanced cervical cancer were enrolled in this study, and randomly allocated to training cohort (n = 136) and validation cohort (n = 59) in a ratio of 7:3. Radiomics features were extracted from pretreatment 18F-FDG PET/CT and selected by the Pearson correlation coefficient and the least absolute shrinkage and selection operator regression analysis. Six ML classifiers were trained and validated, and the best-performing classifier was selected based on accuracy, sensitivity, specificity, and area under the curve (AUC). The performance of different models was assessed and compared using the DeLong test. Results Five PET and one CT radiomics features were selected and incorporated into the ML classifiers. The PET radiomics model constructed based on the lightGBM algorithm had an accuracy of 0.915 and an AUC of 0.851 (95% CI, 0.715–0.986) in the validation cohort, which were higher than that of the CT radiomics model (accuracy: 0.661; AUC: 0.513 [95% CI, 0.339–0.688]). The DeLong test revealed no significant difference in AUC between the combined radiomics model and the PET radiomics model in both the training cohort (P = 0.347) and the validation cohort (P = 0.776). Conclusions The 18F-FDG PET radiomics model can be used as a clinically applicable tool for differentiating pathological subtypes in patients with locally advanced cervical cancer.

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Cervical Carcinoma: Evaluation Using Diffusion MRI With a Fractional Order Calculus Model and its Correlation With Histopathologic Findings

Cited by 2 publications

References 36 publications

Identifying pathological differentiation of cervical squamous cell carcinoma with APTw and IVIM

Identifying pathological differentiation of cervical squamous cell carcinoma with APTw and IVIM

18F-FDG PET radiomics-based machine learning model for differentiating pathological subtypes in locally advanced cervical cancer

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