Prediction of platinum resistance for advanced high-grade serous ovarian carcinoma using MRI-based radiomics nomogram

Li, Haiming; Cai, Songqi; Deng, Lin; Xiao, Zebin; Guo, Qinhao; Qiang, Jin Wei; Gong, Jing; Gu, Yajia; Liu, Zaiyi

doi:10.1007/s00330-023-09552-w

Cited by 8 publications

(3 citation statements)

References 38 publications

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“…Nomograms have been used for data visualization in recent years [ 21 ], and several studies have constructed nomograms [ [22] , [23] , [24] , [25] ] to assess the survival prognosis of ovarian cancer. In our study, we included surgical compliance, constructed OS and CSS nomograms, validated the constructed nomograms, and found that the ROC curves of the model incorporating compliance were superior to those of the traditional AJCC staging, grade classification, and the time-ROC index.…”

Section: Discussionmentioning

confidence: 99%

Impact of surgical compliance on survival prognosis of patients with ovarian cancer and associated influencing factors: A propensity score matching analysis of the SEER database

Zhang,

Yao,

Zhou

et al. 2024

Heliyon

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Section: Discussionmentioning

confidence: 99%

Impact of surgical compliance on survival prognosis of patients with ovarian cancer and associated influencing factors: A propensity score matching analysis of the SEER database

Zhang,

Yao,

Zhou

et al. 2024

Heliyon

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“…In a study including 186 patients, Wang et al [160] reported that the radiomics model based on T2WI had the highest performance among different MRI sequences of the prediction of prognosis in patients with epithelial ovarian cancers. MRI-based radiomics models have also been tested and validated for the prediction of postoperative residual tumour, peritoneal metastases, or chemotherapy response in patients with ovarian cancers [161][162][163].…”

Section: Mrimentioning

confidence: 99%

Radiomics in Gynaecological Imaging: A State-of-the-Art Review

Franco,

Vernuccio,

Maino

et al. 2023

Applied Sciences

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Radiomics is an emerging field of research based on extracting mathematical descriptive features from medical images with the aim of improving diagnostic performance and providing increasing support to clinical decisions. In recent years, a number of studies have been published regarding different possible applications of radiomics in gynaecological imaging. Many fields have been explored, such as tumour diagnosis and staging, differentiation of histological subtypes, assessment of distant metastases, prediction of response to therapy, recurrence, and patients’ outcome. However, several studies are not robust, do not include validation cohorts, or lack reproducibility. On these bases, the purpose of this narrative review is to provide an overview of the most relevant studies in the literature on radiomics in gynaecological imaging. We focused on gynaecological malignancies, particularly endometrial, cervical, mesenchymal, and ovarian malignant pathologies.

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“…DWI has high sensitivity for distinguishing between benign and malignant tumors based on their shape and texture information (21). However, previous studies using MRI-based radiomics have predominantly focused on improving precision diagnostics and the classification of histologic subtypes, and only a few studies have explored the utility of this imaging tool in developing models for predicting platinum sensitivity (22)(23)(24). In this study, we used multimodal MRI to comprehensively assess ovarian-cancer-related information, specifically focusing on platinum sensitivity.…”

Section: Introductionmentioning

confidence: 99%

Combined radiomics-clinical model to predict platinum-sensitivity in advanced high-grade serous ovarian carcinoma using multimodal MRI

Na,

Noh,

Kim

et al. 2024

Front. Oncol.

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IntroductionWe aimed to predict platinum sensitivity using routine baseline multimodal magnetic resonance imaging (MRI) and established clinical data in a radiomics framework.MethodsWe evaluated 96 patients with ovarian cancer who underwent multimodal MRI and routine laboratory tests between January 2016 and December 2020. The patients underwent diffusion-weighted, contrast-enhanced T1-weighted, and T2-weighted MRI. Subsequently, 293 radiomic features were extracted by manually identifying tumor regions of interest. The features were subjected to the least absolute shrinkage and selection operators, leaving only a few selected features. We built the first prediction model with a tree-based classifier using selected radiomics features. A second prediction model was built by combining the selected radiomic features with four established clinical factors: age, disease stage, initial tumor marker level, and treatment course. Both models were built and tested using a five-fold cross-validation.ResultsOur radiomics model predicted platinum sensitivity with an AUC of 0.65 using a few radiomics features related to heterogeneity. The second combined model had an AUC of 0.77, confirming the incremental benefits of the radiomics model in addition to models using established clinical factors.ConclusionOur combined radiomics-clinical data model was effective in predicting platinum sensitivity in patients with advanced ovarian cancer.

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Prediction of platinum resistance for advanced high-grade serous ovarian carcinoma using MRI-based radiomics nomogram

Cited by 8 publications

References 38 publications

Impact of surgical compliance on survival prognosis of patients with ovarian cancer and associated influencing factors: A propensity score matching analysis of the SEER database

Impact of surgical compliance on survival prognosis of patients with ovarian cancer and associated influencing factors: A propensity score matching analysis of the SEER database

Radiomics in Gynaecological Imaging: A State-of-the-Art Review

Combined radiomics-clinical model to predict platinum-sensitivity in advanced high-grade serous ovarian carcinoma using multimodal MRI

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