Elaboration of a multisequence MRI-based radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer: a double-center study

Wang, Huanjun; Xu, Xiaopan; Zhang, Xi; Liu, Yang; Ouyang, Longyuan; Du, Peng; Li, Shurong; Tian, Qiang; Ling, Jian; Guo, Yan; Lu, Hongbing

doi:10.1007/s00330-020-06796-8

Cited by 55 publications

(69 citation statements)

References 40 publications

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“…This method allows us to combine radiomics features into a radiomics signature [31][32][33]. Multi-factor analysis that incorporates individual factors into a factor panel has been widely used in recent studies [34][35][36]. For example, Wang et al [34] constructed an MRI-based radiomics model to predict the muscle-invasive status of bladder cancer and confirmed that the radiomics could be an efficient tool for preoperative prediction.…”

Section: Discussionmentioning

confidence: 99%

“…Multi-factor analysis that incorporates individual factors into a factor panel has been widely used in recent studies [34][35][36]. For example, Wang et al [34] constructed an MRI-based radiomics model to predict the muscle-invasive status of bladder cancer and confirmed that the radiomics could be an efficient tool for preoperative prediction. Similarly, Xu et al [35] developed a radiomics nomogram to predict intracerebral hematoma expansion and found that the nomogram could serve as a convenient measurement.…”

Section: Discussionmentioning

confidence: 99%

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A radiomics-based nomogram for preoperative T staging prediction of rectal cancer

et al. 2021

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Purpose To investigate the value of a radiomics-based nomogram in predicting preoperative T staging of rectal cancer. Methods A total of 268 eligible rectal cancer patients from August 2012 to December 2018 were enrolled and allocated into two datasets: training (n = 188) and validation datasets (n = 80). Another set of 32 patients from January 2019 to July 2019 was included in a prospective analysis. Pretreatment T2-weighted images were used to radiomics features extraction. Feature selection and radiomics score (Rad-score) construction were performed through a least absolute shrinkage and selection operator regression analysis. The nomogram, which included Rad-scores and clinical factors, was built using multivariate logistic regression. Discrimination, calibration, and clinical utility were used to evaluate the performance of the nomogram. Results The Rad-score containing nine selected features was significantly related to T staging. Patients who had locally advanced rectal cancer (LARC) generally had higher Rad-scores than patients with early-stage rectal cancer. The nomogram incorporated Rad-scores and carcinoembryonic antigen levels and showed good discrimination, with an area under the curve (AUC) of 0.882 (95% confidence interval [CI] 0.835–0.930) in the training dataset and 0.846 (95% CI 0.757–0.936) in the validation dataset. The calibration curves confirmed high goodness of fit, and the decision curve analysis revealed the clinical value. A prospective analysis demonstrated that the AUC of the nomogram to predict LARC was 0.859 (95% CI 0.730–0.987). Conclusion A radiomics-based nomogram is a novel method for predicting LARC and can provide support in clinical decision making.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A radiomics-based nomogram for preoperative T staging prediction of rectal cancer

et al. 2021

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“…AI can be leveraged on clinical data, molecular and genetic biomarkers, and imaging for several narrow tasks in BC. While there are currently no studies published on the use of AI on 18F-FDG PET images for BC, promising results of its application using CT and MRI images have been reported in terms of predicting the depth of invasion of the primary tumor (83), grade (84), local and systemic staging (85), and assessment of treatment response (86). Additionally, AI based on PET/CT images has been used in other malignancies to predict nodal disease (87), risk stratification (88), treatment response (89), and patient outcomes (90).…”

Section: Rationale For Artificial Intelligencementioning

confidence: 99%

The Role of 18F-FDG PET/CT in Guiding Precision Medicine for Invasive Bladder Carcinoma

et al. 2020

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Bladder cancer (BC) is the 10th most common cancer worldwide. Approximately one quarter of patients with BC have muscle-invasive disease (MIBC). Muscle-invasive disease carries a poor prognosis and choosing the optimal treatment option is critical to improve patients’ outcomes. Ongoing research supports the role of 2-deoxy-2-(18F)fluoro- D -glucose positron emission tomography (18F-FDG PET) in guiding patient-specific management decisions throughout the course of MIBC. As an imaging modality, 18F-FDG PET is acquired simultaneously with either computed tomography (CT) or MRI to offer a hybrid approach combining anatomical and metabolic information that complement each other. At initial staging, 18F-FDG PET/CT enhances the detection of extravesical disease, particularly in patients classified as oligometastatic by conventional imaging. 18F-FDG PET/CT has value in monitoring response to neoadjuvant and systemic chemotherapy, as well as in localizing relapse after treatment. In the new era of immunotherapy, 18F-FDG PET/CT may also be useful to monitor treatment efficacy as well as to detect immune-related adverse events. With the advent of artificial intelligence techniques such as radiomics and deep learning, these hybrid medical images can be mined for quantitative data, providing incremental value over current standard-of-care clinical and biological data. This approach has the potential to produce a major paradigm shift toward data-driven precision medicine with the ultimate goal of personalized medicine. In this review, we highlight current literature reporting the role of 18F-FDG PET in supporting personalized management decisions for patients with MIBC. Specific topics reviewed include the incremental value of 18F-FDG PET in prognostication, pre-operative planning, response assessment, prediction of recurrence, and diagnosing drug toxicity.

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“…DCE reflects the microvessel permeability and issue vascularity of lesions, and the slight submucosal linear enhancement is regarded as a useful characteristic for the nonmuscle invasiveness condition of BCa ( 8 ). Our study used the DCE for radiomics signature development and muscle-invasive status identification in BCa and showed that the radiomics signature based on the T2WI and DCE radiomics features had a better discriminatory power compared with previous research based on MRI ( 23 , 35 , 36 ). Fourth, considering that clinical characteristics, such as sex, age, tumor size, and tumor number are commonly applied for the preoperative diagnosis of BCa patients, and given that the mpMRI-based VI-RADS score has been reported to be closely related to muscle-invasive status ( 37 ), we evaluated the diagnostic value of incorporating these clinical characteristics and the radiomics signature for the preoperative discrimination of muscle-invasive status.…”

Section: Discussionmentioning

confidence: 74%

“…Calibration plots and DCA plots demonstrated good calibration and favorable clinical net benefit of the nomogram. The performance of this nomogram was superior to previous nomograms based on MRI images and clinical factors (23,36). Finally, the software we used is publicly available and the PyRadiomics platform is open source for the radiomics procedure so that other institutions can apply and validate the proposed nomogram.…”

Section: Discussionmentioning

confidence: 94%

Combining Multiparametric MRI Radiomics Signature With the Vesical Imaging-Reporting and Data System (VI-RADS) Score to Preoperatively Differentiate Muscle Invasion of Bladder Cancer

Zheng

et al. 2021

Front. Oncol.

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BackgroundThe treatment and prognosis for muscle-invasive bladder cancer (MIBC) and non-muscle-invasive bladder cancer (NMIBC) are different. We aimed to construct a nomogram based on the multiparametric MRI (mpMRI) radiomics signature and the Vesical Imaging-Reporting and Data System (VI-RADS) score for the preoperative differentiation of MIBC from NMIBC.MethodThe retrospective study involved 185 pathologically confirmed bladder cancer (BCa) patients (training set: 129 patients, validation set: 56 patients) who received mpMRI before surgery between August 2014 to April 2020. A total of 2,436 radiomics features were quantitatively extracted from the largest lesion located on the axial T2WI and from dynamic contrast-enhancement images. The minimum redundancy maximum relevance (mRMR) algorithm was used for feature screening. The selected features were introduced to construct radiomics signatures using three classifiers, including least absolute shrinkage and selection operator (LASSO), support vector machines (SVM) and random forest (RF) in the training set. The differentiation performances of the three classifiers were evaluated using the area under the curve (AUC) and accuracy. Univariable and multivariable logistic regression were used to develop a nomogram based on the optimal radiomics signature and clinical characteristics. The performance of the radiomics signatures and the nomogram was assessed and validated in the validation set.ResultsCompared to the RF and SVM classifiers, the LASSO classifier had the best capacity for muscle invasive status differentiation in both the training (accuracy: 90.7%, AUC: 0.934) and validation sets (accuracy: 87.5%, AUC: 0.906). Incorporating the radiomics signature and VI-RADS score, the nomogram demonstrated better discrimination and calibration both in the training set (accuracy: 93.0%, AUC: 0.970) and validation set (accuracy: 89.3%, AUC: 0.943). Decision curve analysis showed the clinical usefulness of the nomogram.ConclusionsThe mpMRI radiomics signature may be useful for the preoperative differentiation of muscle-invasive status in BCa. The proposed nomogram integrating the radiomics signature with the VI-RADS score may further increase the differentiation power and improve clinical decision making.

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Elaboration of a multisequence MRI-based radiomics signature for the preoperative prediction of the muscle-invasive status of bladder cancer: a double-center study

Cited by 55 publications

References 40 publications

A radiomics-based nomogram for preoperative T staging prediction of rectal cancer

A radiomics-based nomogram for preoperative T staging prediction of rectal cancer

The Role of 18F-FDG PET/CT in Guiding Precision Medicine for Invasive Bladder Carcinoma

Combining Multiparametric MRI Radiomics Signature With the Vesical Imaging-Reporting and Data System (VI-RADS) Score to Preoperatively Differentiate Muscle Invasion of Bladder Cancer

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