Predicting tumor deposits in rectal cancer: a combined deep learning model using T2-MR imaging and clinical features.
Insights Imaging
; 14(1): 221, 2023 Dec 20.
Article
em En
| MEDLINE
| ID: mdl-38117396
ABSTRACT
BACKGROUND:
Tumor deposits (TDs) are associated with poor prognosis in rectal cancer (RC). This study aims to develop and validate a deep learning (DL) model incorporating T2-MR image and clinical factors for the preoperative prediction of TDs in RC patients. METHODS ANDMETHODS:
A total of 327 RC patients with pathologically confirmed TDs status from January 2016 to December 2019 were retrospectively recruited, and the T2-MR images and clinical variables were collected. Patients were randomly split into a development dataset (n = 246) and an independent testing dataset (n = 81). A single-channel DL model, a multi-channel DL model, a hybrid DL model, and a clinical model were constructed. The performance of these predictive models was assessed by using receiver operating characteristics (ROC) analysis and decision curve analysis (DCA).RESULTS:
The areas under the curves (AUCs) of the clinical, single-DL, multi-DL, and hybrid-DL models were 0.734 (95% CI, 0.674-0.788), 0.710 (95% CI, 0.649-0.766), 0.767 (95% CI, 0.710-0.819), and 0.857 (95% CI, 0.807-0.898) in the development dataset. The AUC of the hybrid-DL model was significantly higher than the single-DL and multi-DL models (both p < 0.001) in the development dataset, and the single-DL model (p = 0.028) in the testing dataset. Decision curve analysis demonstrated the hybrid-DL model had higher net benefit than other models across the majority range of threshold probabilities.CONCLUSIONS:
The proposed hybrid-DL model achieved good predictive efficacy and could be used to predict tumor deposits in rectal cancer. CRITICAL RELEVANCE STATEMENT The proposed hybrid-DL model achieved good predictive efficacy and could be used to predict tumor deposits in rectal cancer. KEY POINTS ⢠Preoperative non-invasive identification of TDs is of great clinical significance. ⢠The combined hybrid-DL model achieved good predictive efficacy and could be used to predict tumor deposits in rectal cancer. ⢠A preoperative nomogram provides gastroenterologist with an accurate and effective tool.
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Base de dados:
MEDLINE
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article