Multiparameter MRI Radiomics Model Predicts Preoperative Peritoneal Carcinomatosis in Ovarian Cancer

Yu, Xiao; Ren, Jialiang; Jia, Yushan; Wu, Hui; Guang-ming, Niu; Liu, Aishi; Gao, Yang; Hao, Fene; Xie, Lizhi

doi:10.3389/fonc.2021.765652

Cited by 16 publications

(21 citation statements)

References 33 publications

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“…Many studies have attempted to combine imaging histology with clinical parameters to improve the predictive power of the model. The nomogram was developed by Yu et al (35) With the combination of radiomics features and clinical parameters was able to predict peritoneal metastases in ovarian cancer preoperatively well, and its efficacy was superior to that of a single model with radiomics and the clinic. We also developed a hybrid model to plot a nomogram combining Rad scores and important clinical features for the assessment of 3-year PFS in PCa patients.…”

Section: Discussionmentioning

confidence: 99%

MRI radiomics predicts progression-free survival in prostate cancer

Jia

Quan²,

Ren³

et al. 2022

Front. Oncol.

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ObjectiveTo assess the predictive value of magnetic resonance imaging (MRI) radiomics for progression-free survival (PFS) in patients with prostate cancer (PCa).Methods191 patients with prostate cancer confirmed by puncture biopsy or surgical pathology were included in this retrospective study, including 133 in the training group and 58 in the validation group. All patients underwent T2WI and DWI serial scans. Three radiomics models were constructed using univariate logistic regression and Gradient Boosting Decision Tree(GBDT) for feature screening, followed by Cox risk regression to construct a mixed model combining radiomics features and clinicopathological risk factors and to draw a nomogram. The performance of the models was evaluated by receiver operating characteristic curve (ROC), calibration curve and decision curve analysis. The Kaplan-Meier method was applied for survival analysis.ResultsCompared with the radiomics model, the hybrid model consisting of a combination of radiomics features and clinical data performed the best in predicting PFS in PCa patients, with AUCs of 0.926 and 0.917 in the training and validation groups, respectively. Decision curve analysis showed that the radiomics nomogram had good clinical application and the calibration curve proved to have good stability. Survival curves showed that PFS was shorter in the high-risk group than in the low-risk group.ConclusionThe hybrid model constructed from radiomics and clinical data showed excellent performance in predicting PFS in prostate cancer patients. The nomogram provides a non-invasive diagnostic tool for risk stratification of clinical patients.

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Section: Discussionmentioning

confidence: 99%

MRI radiomics predicts progression-free survival in prostate cancer

Jia

Quan²,

Ren³

et al. 2022

Front. Oncol.

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“…Two recent studies showed that the MRI‐based radiomics nomogram had AUCs greater than 0.90 in predicting the PM status of EOC patients 16,17 . While these results are promising, these mentioned studies all used small sample sizes (the total sample size was less than 100), which may have introduced bias into the results.…”

Section: Discussionmentioning

confidence: 99%

“…12,13 Studies have confirmed that DL and radiomics open new horizons in ovarian cancer research. 14,15 Two recent MRI-based radiomics models have been established to predict the PM status of EOC patients, 16,17 but their clinical utility is limited due to small sample sizes (N < 100) and lack of external validation. Furthermore, these aforementioned studies did not employ the DL method, which has been shown to accurately predict PM of gastric cancer.…”

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confidence: 99%

Associating Peritoneal Metastasis With T2‐Weighted MRI Images in Epithelial Ovarian Cancer Using Deep Learning and Radiomics: A Multicenter Study

Wei

Ding

Jia

et al. 2023

Magnetic Resonance Imaging

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BackgroundThe preoperative diagnosis of peritoneal metastasis (PM) in epithelial ovarian cancer (EOC) is challenging and can impact clinical decision‐making.PurposeTo investigate the performance of T2‐weighted (T2W) MRI‐based deep learning (DL) and radiomics methods for PM evaluation in EOC patients.Study TypeRetrospective.PopulationFour hundred seventy‐nine patients from five centers, including one training set (N = 297 [mean, 54.87 years]), one internal validation set (N = 75 [mean, 56.67 years]), and two external validation sets (N = 53 [mean, 55.58 years] and N = 54 [mean, 58.22 years]).Field Strength/Sequence1.5 or 3 T/fat‐suppression T2W fast or turbo spin‐echo sequence.AssessmentResNet‐50 was used as the architecture of DL. The largest orthogonal slices of the tumor area, radiomics features, and clinical characteristics were used to construct the DL, radiomics, and clinical models, respectively. The three models were combined using decision‐level fusion to create an ensemble model. Diagnostic performances of radiologists and radiology residents with and without model assistance were evaluated.Statistical TestsReceiver operating characteristic analysis was used to assess the performances of models. The McNemar test was used to compare sensitivity and specificity. A two‐tailed P < 0.05 was considered significant.ResultsThe ensemble model had the best AUCs, outperforming the DL model (0.844 vs. 0.743, internal validation set; 0.859 vs. 0.737, external validation set I) and clinical model (0.872 vs. 0.730, external validation set II). After model assistance, all readers had significantly improved sensitivity, especially for those with less experience (junior radiologist1, from 0.639 to 0.820; junior radiologist2, from 0.689 to 0.803; resident1, from 0.623 to 0.803; resident2, from 0.541 to 0.738). One resident also had significantly improved specificity (from 0.633 to 0.789).Data ConclusionsT2W MRI‐based DL and radiomics approaches have the potential to preoperatively predict PM in EOC patients and assist in clinical decision‐making.Evidence Level4Technical EfficacyStage 2

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“…Radiomics involves the use of a variety of algorithms to convert the image data of the region of interest into radiomics features include rst-order, second-order or higher-order data, and further improve the accuracy of clinical diagnosis and prognostic value by mining and analyzing the deep level relationships between data (12)(13)(14). The use of radiomics can detect subtle structures and reveal potential image information that is invisible to naked eyes, thereby further strengthening diagnostic and prognostic utility (15)(16)(17)(18).…”

Section: Introductionmentioning

confidence: 99%

Radiomics based on enhanced CT for the preoperative prediction of metastasis in epithelial ovarian cancer

Leng

Wang

Zheng

et al. 2023

Preprint

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Purpose: To develop and evaluate an enhanced CT-based radiomics nomogram for predicting preoperative metastasis in epithelial ovarian cancer (EOC). Materials and Methods: 109 patients with histopathology-confirmed EOC were retrospectively enrolled. The volume of interest (VOI) was delineated in preoperative enhanced CT images, and 851 radiomics features were extracted. The radiomics features were filtered by the least absolute shrinkage and selection operator (LASSO), and the radiomics score was calculated using the formula of the radiomics label. A clinical radiomics model and nomogram were constructed by multivariate logistic regression. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) was used to evaluate the prediction effect. Results: 75 patients (68.8%) were histologically confirmed to have metastasis. Eleven nonzero LASSO coefficient radiomics features were selected to develop radiomic model, and four clinical charac-teristics were selected to develop clinical model. The clinical radiomics model for prediction metastasis of EOC achieved areas under the curve (AUCs) of 0.929 (95% CI, 0.8593-0.9996) in the training cohort and 0.909 (95% CI, 0.7921-1.0000) in the test cohort. To facilitate clinical use, a radiomic nomogram was builtedby combined the clinical charac-teristics with Rad-score. The DCAs confirmed that the nomogram could predict metastasis. Conclusions: The radiomics nomogram had significantly superior prediction ability than the clinical model and the radiomics model, which could be suggested as a useful and convenient tool to help clinicians formulate personalized treatment plans for EOC patients.

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Multiparameter MRI Radiomics Model Predicts Preoperative Peritoneal Carcinomatosis in Ovarian Cancer

Cited by 16 publications

References 33 publications

MRI radiomics predicts progression-free survival in prostate cancer

MRI radiomics predicts progression-free survival in prostate cancer

Associating Peritoneal Metastasis With T2‐Weighted MRI Images in Epithelial Ovarian Cancer Using Deep Learning and Radiomics: A Multicenter Study

Radiomics based on enhanced CT for the preoperative prediction of metastasis in epithelial ovarian cancer

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