<scp>MRI</scp>‐based radiomic signatures for pretreatment prognostication in cervical cancer

Wagner‐Larsen, Kari S.; Hodneland, Erlend; Fasmer, Kristine E.; Lura, Njål; Woie, Kathrine; Bertelsen, Bjørn I.; Salvesen, Øyvind; Halle, Mari K.; Smit, Noeska; Krakstad, Camilla; Haldorsen, Ingfrid S.

doi:10.1002/cam4.6526

Cancer Medicine

2023

DOI: 10.1002/cam4.6526

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MRI‐based radiomic signatures for pretreatment prognostication in cervical cancer

Kari S. Wagner‐Larsen,

Erlend Hodneland,

Kristine E. Fasmer

et al.

Abstract: BackgroundAccurate pretherapeutic prognostication is important for tailoring treatment in cervical cancer (CC).PurposeTo investigate whether pretreatment MRI‐based radiomic signatures predict disease‐specific survival (DSS) in CC.Study TypeRetrospective.PopulationCC patients (n = 133) allocated into training(T) (nT = 89)/validation(V) (nV = 44) cohorts.Field Strength/SequenceT2‐weighted imaging (T2WI) and diffusion‐weighted imaging (DWI) at 1.5T or 3.0T.AssessmentRadiomic features from segmented tumors were ex… Show more

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Cited by 2 publications

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A MRI radiomics-based model for prediction of pelvic lymph node metastasis in cervical cancer

Wang,

Li,

et al. 2024

World J Surg Onc

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Background Cervical cancer (CC) is a common malignancy of the female reproductive tract, and preoperative prediction of lymph node metastasis (LNM) is essential. This study aims to design and validate a magnetic resonance imaging (MRI) radiomics-based predictive model capable of detecting LNM in patients diagnosed with CC. Methods This retrospective analysis incorporated 86 and 38 CC patients into the training and testing groups, respectively. Radiomics features were extracted from MRI T2WI, T2WI-SPAIR, and axial apparent diffusion coefficient (ADC) sequences. Selected features identified in the training group were then used to construct a radiomics scoring model, with relevant LNM-related risk factors having been identified through univariate and multivariate logistic regression analyses. The resultant predictive model was then validated in the testing cohort. Results In total, 16 features were selected for the construction of a radiomics scoring model. LNM-related risk factors included worse differentiation (P < 0.001), more advanced International Federation of Gynecology and Obstetrics (FIGO) stages (P = 0.03), and a higher radiomics score from the combined MRI sequences (P = 0.01). The equation for the predictive model was as follows: −0.0493–2.1410 × differentiation level + 7.7203 × radiomics score of combined sequences + 1.6752 × FIGO stage. The respective area under the curve (AUC) values for the T2WI radiomics score, T2WI-SPAIR radiomics score, ADC radiomics score, combined sequence radiomics score, and predictive model were 0.656, 0.664, 0.658, 0.835, and 0.923 in the training cohort, while these corresponding AUC values were 0.643, 0.525, 0.513, 0.826, and 0.82 in the testing cohort. Conclusions This MRI radiomics-based model exhibited favorable accuracy when used to predict LNM in patients with CC. Relative to the use of any individual MRI sequence-based radiomics score, this predictive model yielded superior diagnostic accuracy.

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A MRI radiomics-based model for prediction of pelvic lymph node metastasis in cervical cancer

Wang,

Li,

et al. 2024

World J Surg Onc

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Integrating MRI-based radiomics and clinicopathological features for preoperative prognostication of early-stage cervical adenocarcinoma patients: in comparison to deep learning approach

Qiu,

Wang,

Wang

et al. 2024

Cancer Imaging

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Objectives The roles of magnetic resonance imaging (MRI) -based radiomics approach and deep learning approach in cervical adenocarcinoma (AC) have not been explored. Herein, we aim to develop prognosis-predictive models based on MRI-radiomics and clinical features for AC patients. Methods Clinical and pathological information from one hundred and ninety-seven patients with cervical AC was collected and analyzed. For each patient, 107 radiomics features were extracted from T2-weighted MRI images. Feature selection was performed using Spearman correlation and random forest (RF) algorithms, and predictive models were built using support vector machine (SVM) technique. Deep learning models were also trained with T2-weighted MRI images and clinicopathological features through Convolutional Neural Network (CNN). Kaplan-Meier curve was analyzed using significant features. In addition, information from another group of 56 AC patients was used for the independent validation. Results A total of 107 radiomics features and 6 clinicopathological features (age, FIGO stage, differentiation, invasion depth, lymphovascular space invasion (LVSI), and lymph node metastasis (LNM) were included in the analysis. When predicting the 3-year, 4-year, and 5-year DFS, the model trained solely on radiomics features achieved AUC values of 0.659 (95%CI: 0.620–0.716), 0.791 (95%CI: 0.603–0.922), and 0.853 (95%CI: 0.745–0.912), respectively. However, the combined model, incorporating both radiomics and clinicopathological features, outperformed the radiomics model with AUC values of 0.934 (95%CI: 0.885–0.981), 0.937 (95%CI: 0.867–0.995), and 0.916 (95%CI: 0.857–0.970), respectively. For deep learning models, the MRI-based models achieved an AUC of 0.857, 0.777 and 0.828 for 3-year DFS, 4-year DFS and 5-year DFS prediction, respectively. And the combined deep learning models got a improved performance, the AUCs were 0.903. 0.862 and 0.969. In the independent test set, the combined model achieved an AUC of 0.873, 0.858 and 0.914 for 3-year DFS, 4-year DFS and 5-year DFS prediction, respectively. Conclusions We demonstrated the prognostic value of integrating MRI-based radiomics and clinicopathological features in cervical adenocarcinoma. Both radiomics and deep learning models showed improved predictive performance when combined with clinical data, emphasizing the importance of a multimodal approach in patient management.

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MRI‐based radiomic signatures for pretreatment prognostication in cervical cancer

Cited by 2 publications

References 38 publications

A MRI radiomics-based model for prediction of pelvic lymph node metastasis in cervical cancer

A MRI radiomics-based model for prediction of pelvic lymph node metastasis in cervical cancer

Integrating MRI-based radiomics and clinicopathological features for preoperative prognostication of early-stage cervical adenocarcinoma patients: in comparison to deep learning approach

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