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Deep Learning on Enhanced CT Images Can Predict the Muscular Invasiveness of Bladder Cancer
- Source :
- Frontiers in Oncology, Frontiers in Oncology, Vol 11 (2021)
- Publication Year :
- 2021
-
Abstract
- BackgroundClinical treatment decision making of bladder cancer (BCa) relies on the absence or presence of muscle invasion and tumor staging. Deep learning (DL) is a novel technique in image analysis, but its potential for evaluating the muscular invasiveness of bladder cancer remains unclear. The purpose of this study was to develop and validate a DL model based on computed tomography (CT) images for prediction of muscle-invasive status of BCa.MethodsA total of 441 BCa patients were retrospectively enrolled from two centers and were divided into development (n=183), tuning (n=110), internal validation (n=73) and external validation (n=75) cohorts. The model was built based on nephrographic phase images of preoperative CT urography. Receiver operating characteristic (ROC) curves were performed and the area under the ROC curve (AUC) for discrimination between muscle-invasive BCa and non-muscle-invasive BCa was calculated. The performance of the model was evaluated and compared with that of the subjective assessment by two radiologists.ResultsThe DL model exhibited relatively good performance in all cohorts [AUC: 0.861 in the internal validation cohort, 0.791 in the external validation cohort] and outperformed the two radiologists. The model yielded a sensitivity of 0.733, a specificity of 0.810 in the internal validation cohort and a sensitivity of 0.710 and a specificity of 0.773 in the external validation cohort.ConclusionThe proposed DL model based on CT images exhibited relatively good prediction ability of muscle-invasive status of BCa preoperatively, which may improve individual treatment of BCa.
- Subjects :
- Cancer Research
Enhanced ct
diagnosis
Computed tomography
Ct urography
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Medicine
skin and connective tissue diseases
Clinical treatment
RC254-282
Original Research
Bladder cancer
Receiver operating characteristic
medicine.diagnostic_test
business.industry
External validation
Neoplasms. Tumors. Oncology. Including cancer and carcinogens
deep learning
computed tomography
medicine.disease
artificial intelligence
Oncology
030220 oncology & carcinogenesis
Cohort
bladder cancer
business
Nuclear medicine
computed-assisted
Subjects
Details
- ISSN :
- 2234943X
- Volume :
- 11
- Database :
- OpenAIRE
- Journal :
- Frontiers in oncology
- Accession number :
- edsair.doi.dedup.....b94fca7d6d93b788b750e5703f0d4a33