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.
Highlights
Bladder cancer (BCa) is one of the most common and lethal malignancies worldwide [1, 2]
The proposed Deep learning (DL) model based on Computed tomography (CT) images exhibited relatively good prediction ability of muscle-invasive status of BCa preoperatively, which may improve individual treatment of BCa
We divided the patients into three cohorts: 293 patients treated between May 2014 and September 2017 in Center 1 were allocated to the training cohort, 73 patients treated between October 2017 and July 2018 in Center 1 were allocated to the internal validation cohort, and all 75 patients treated in Center 2 constituted the external cohort
Summary
Bladder cancer (BCa) is one of the most common and lethal malignancies worldwide [1, 2]. Clinical treatment decision making primarily relies on the absence or presence of muscle invasion and tumor staging [3]. Computed tomography (CT) imaging has been widely used to preoperatively evaluate BCa patients and assist in tumor staging, especially for T3 and T4 tumors [7]. Developing a technique that could provide additional information about the status of muscular invasion of BCa would enable traditional CT to play a larger role in BCa evaluation and assist in patient management. Clinical treatment decision making of bladder cancer (BCa) relies on the absence or presence of muscle invasion and tumor staging. 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
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