Dataset Information

CT-based deep learning radiomics nomogram for the prediction of pathological grade in bladder cancer: a multicenter study.

ABSTRACT:

Background

To construct and assess a computed tomography (CT)-based deep learning radiomics nomogram (DLRN) for predicting the pathological grade of bladder cancer (BCa) preoperatively.

Methods

We retrospectively enrolled 688 patients with BCa (469 in the training cohort, 219 in the external test cohort) who underwent surgical resection. We extracted handcrafted radiomics (HCR) features and deep learning (DL) features from three-phase CT images (including corticomedullary-phase [C-phase], nephrographic-phase [N-phase] and excretory-phase [E-phase]). We constructed predictive models using 11 machine learning classifiers, and we developed a DLRN by combining the radiomic signature with clinical factors. We assessed performance and clinical utility of the models with reference to the area under the curve (AUC), calibration curve, and decision curve analysis (DCA).

Results

The support vector machine (SVM) classifier model based on HCR and DL combined features was the best radiomic signature, with AUC values of 0.953 and 0.943 in the training cohort and the external test cohort, respectively. The AUC values of the clinical model in the training cohort and the external test cohort were 0.752 and 0.745, respectively. DLRN performed well on both data cohorts (training cohort: AUC = 0.961; external test cohort: AUC = 0.947), and outperformed the clinical model and the optimal radiomic signature.

Conclusion

The proposed CT-based DLRN showed good diagnostic capability in distinguishing between high and low grade BCa.

SUBMITTER: Song H

PROVIDER: S-EPMC10507832 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Publications

CT-based deep learning radiomics nomogram for the prediction of pathological grade in bladder cancer: a multicenter study.

Song Hongzheng H Yang Shifeng S Yu Boyang B Li Na N Huang Yonghua Y Sun Rui R Wang Bo B Nie Pei P Hou Feng F Huang Chencui C Zhang Meng M Wang Hexiang H

Cancer imaging : the official publication of the International Cancer Imaging Society 20230918 1

<h4>Background</h4>To construct and assess a computed tomography (CT)-based deep learning radiomics nomogram (DLRN) for predicting the pathological grade of bladder cancer (BCa) preoperatively.<h4>Methods</h4>We retrospectively enrolled 688 patients with BCa (469 in the training cohort, 219 in the external test cohort) who underwent surgical resection. We extracted handcrafted radiomics (HCR) features and deep learning (DL) features from three-phase CT images (including corticomedullary-phase [C ...[more]

PMID: 37723572

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Dataset Information

CT-based deep learning radiomics nomogram for the prediction of pathological grade in bladder cancer: a multicenter study.

Background

Methods

Results

Conclusion

Publications

CT-based deep learning radiomics nomogram for the prediction of pathological grade in bladder cancer: a multicenter study.

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