Radiomics nomogram for preoperative prediction of progression-free survival using diffusion-weighted imaging in patients with muscle-invasive bladder cancer

Zhang, Shenghai; Song, Mengfan; Zhao, Yuanshen; Xu, Shuaishuai; Sun, Qinjun; Zhai, Guangtao; Liang, Dong; Wu, Guangyu; Li, Zhicheng

doi:10.1016/j.ejrad.2020.109219

Cited by 25 publications

(30 citation statements)

References 39 publications

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“… 53 have been made to explore the ability mpMRI to further stratify MR-visible lesions based on their progressive potential in patients otherwise suitable for AS. Adding this extra risk-stratification element at baseline, which showed promise in other tumour types such as bladder 54 and ovarian 55 cancers, may improve clinical decision-making and help refine and personalise follow-up protocols. For these purposes, MRI-derived radiomics presents a promising approach due to its quantitative nature and the rapid development of novel machine learning algorithms for feature selection that may help overcome the known poor cross-system reproducibility of the technique 56 , 57 .…”

Section: Discussionmentioning

confidence: 99%

MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance

Sushentsev

Rundo

Blyuss

et al. 2021

Sci Rep

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Nearly half of patients with prostate cancer (PCa) harbour low- or intermediate-risk disease considered suitable for active surveillance (AS). However, up to 44% of patients discontinue AS within the first five years, highlighting the unmet clinical need for robust baseline risk-stratification tools that enable timely and accurate prediction of tumour progression. In this proof-of-concept study, we sought to investigate the added value of MRI-derived radiomic features to standard-of-care clinical parameters for improving baseline prediction of PCa progression in AS patients. Tumour T2-weighted imaging (T2WI) and apparent diffusion coefficient radiomic features were extracted, with rigorous calibration and pre-processing methods applied to select the most robust features for predictive modelling. Following leave-one-out cross-validation, the addition of T2WI-derived radiomic features to clinical variables alone improved the area under the ROC curve for predicting progression from 0.61 (95% confidence interval [CI] 0.481–0.743) to 0.75 (95% CI 0.64–0.86). These exploratory findings demonstrate the potential benefit of MRI-derived radiomics to add incremental benefit to clinical data only models in the baseline prediction of PCa progression on AS, paving the way for future multicentre studies validating the proposed model and evaluating its impact on clinical outcomes.

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Section: Discussionmentioning

confidence: 99%

MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance

Sushentsev

Rundo

Blyuss

et al. 2021

Sci Rep

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“…Due to the nature of regularized regression, the features selected and their weights estimated depend on the involved data samples (Table 1 and Figure 4). This kind of inconsistency imposes difficulties on model explanation and generalization [23,24,25,26]. To address this problem, the proposed framework takes advantages of elastic net to identify important features, and uses the frequency of selected features for importance ranking.…”

Section: Discussionmentioning

confidence: 99%

“…It is the consistency that makes possible to retrieve discriminative features for data representation, and it is also crucial for improving model explanation and generalization [23,24,25,26,27]. In intelligent diagnosis, inconsistent feature selection might account for cannot-be-repeated experiments, and how to improve the consistency of FS has been a long-standing problem.…”

Section: Discussionmentioning

confidence: 99%

Elastic Net based Feature Ranking and Selection

Yu¹,

Chen²,

Yu³

et al. 2020

Preprint

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Feature selection is important in data representation and intelligent diagnosis.Elastic net is one of the most widely used feature selectors. However, the features selected are dependant on the training data, and their weights dedicated for regularized regression are irrelevant to their importance if used for feature ranking, that degrades the model interpretability and extension. In this study, an intuitive idea is put at the end of multiple times of data splitting and elastic net based feature selection. It concerns the frequency of selected features and uses the frequency as an indicator of feature importance. After features are sorted according to their frequency, linear support vector machine performs the classification in an incremental manner. At last, a compact subset of discriminative features is selected by comparing the prediction performance. Experimental results on breast cancer data sets (BCDR-F03, WDBC, GSE 10810, and GSE

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“…( 18 ) indicates that the diagnostic of VI-RADS score 5 in predicting locally advanced bladder cancer is of great accuracy, and it can help to identify those patients who could avoid the morbidity of deep TURBT in favor of histologic sampling-TUR before radical cystectomy (RC). It is also found that multiparametric MRI has good performance in predicting the recurrence risk of patients with bladder cancer ( 19 , 20 ). In the study conducted by Xu et al.…”

Section: Introductionmentioning

confidence: 96%

Feasibility Study on Predicting Recurrence Risk of Bladder Cancer Based on Radiomics Features of Multiphase CT Images

Qian

Yang

et al. 2022

Front. Oncol.

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BackgroundPredicting the recurrence risk of bladder cancer is crucial for the individualized clinical treatment of patients with bladder cancer.ObjectiveTo explore the radiomics based on multiphase CT images combined with clinical risk factors, and to further construct a radiomics-clinical model to predict the recurrence risk of bladder cancer within 2 years after surgery.MethodsPatients with bladder cancer who underwent surgical treatment at the First Affiliated Hospital of Soochow University from January 2016 to December 2019 were retrospectively included and followed up to record the disease recurrence. A total of 183 patients were included in the study, and they were randomly divided into training group and validation group in a ratio of 7: 3. The three basic models which are plain scan, corticomedullary phase, and nephrographic phase as well as two combination models, namely, corticomedullary phase + nephrographic phase and plain scan + corticomedullary phase + nephrographic phase, were built with the logistic regression algorithm, and we selected the model with higher performance and calculated the Rad-score (radiomics score) of each patient. The clinical risk factors and Rad-score were screened by Cox univariate and multivariate proportional hazard models in turn to obtain the independent risk factors, then the radiomics-clinical model was constructed, and their performance was evaluated.ResultsOf the 183 patients included, 128 patients constituted the training group and 55 patients constituted the validation group. In terms of the radiomics-clinical model constructed by three independent risk factors—number of tumors, tumor grade, and Rad-score—the AUCs of the training group and validation group were 0.813 (95% CI 0.740–0.886) and 0.838 (95% CI 0.733–0.943), respectively. In the validation group, the diagnostic accuracy, sensitivity, and specificity were 0.727, 0.739, and 0.719, respectively.ConclusionCombining with radiomics based on multiphase CT images and clinical risk factors, the radiomics-clinical model constructed to predict the recurrence risk of bladder cancer within 2 years after surgery had a good performance.

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Radiomics nomogram for preoperative prediction of progression-free survival using diffusion-weighted imaging in patients with muscle-invasive bladder cancer

Cited by 25 publications

References 39 publications

MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance

MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance

Elastic Net based Feature Ranking and Selection

Feasibility Study on Predicting Recurrence Risk of Bladder Cancer Based on Radiomics Features of Multiphase CT Images

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