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Development and validation of a deep learning radiomics model with clinical-radiological characteristics for the identification of occult peritoneal metastases in patients with pancreatic ductal adenocarcinoma.
Shi, Siya; Lin, Chuxuan; Zhou, Jian; Wei, Luyong; Chen, Mingjie; Zhang, Jian; Cao, Kangyang; Fan, Yaheng; Huang, Bingsheng; Luo, Yanji; Feng, Shi-Ting.
Afiliação
  • Shi S; Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University.
  • Lin C; Medical AI Lab, School of Biomedical Engineering.
  • Zhou J; Marshall Laboratory of Biomedical Engineering, Shenzhen University.
  • Wei L; State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou.
  • Chen M; South China Hospital, Medical School, Shenzhen University.
  • Zhang J; Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University.
  • Cao K; Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University.
  • Fan Y; Shenzhen University Medical School.
  • Huang B; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, People's Republic of China.
  • Luo Y; Medical AI Lab, School of Biomedical Engineering.
  • Feng ST; Marshall Laboratory of Biomedical Engineering, Shenzhen University.
Int J Surg ; 110(5): 2669-2678, 2024 May 01.
Article em En | MEDLINE | ID: mdl-38445459
ABSTRACT

BACKGROUND:

Occult peritoneal metastases (OPM) in patients with pancreatic ductal adenocarcinoma (PDAC) are frequently overlooked during imaging. The authors aimed to develop and validate a computed tomography (CT)-based deep learning-based radiomics (DLR) model to identify OPM in PDAC before treatment.

METHODS:

This retrospective, bicentric study included 302 patients with PDAC (training n =167, OPM-positive, n =22; internal test n =72, OPM-positive, n =9 external test, n =63, OPM-positive, n =9) who had undergone baseline CT examinations between January 2012 and October 2022. Handcrafted radiomics (HCR) and DLR features of the tumor and HCR features of peritoneum were extracted from CT images. Mutual information and least absolute shrinkage and selection operator algorithms were used for feature selection. A combined model, which incorporated the selected clinical-radiological, HCR, and DLR features, was developed using a logistic regression classifier using data from the training cohort and validated in the test cohorts.

RESULTS:

Three clinical-radiological characteristics (carcinoembryonic antigen 19-9 and CT-based T and N stages), nine HCR features of the tumor, 14 DLR features of the tumor, and three HCR features of the peritoneum were retained after feature selection. The combined model yielded satisfactory predictive performance, with an area under the curve (AUC) of 0.853 (95% CI 0.790-0.903), 0.845 (95% CI 0.740-0.919), and 0.852 (95% CI 0.740-0.929) in the training, internal test, and external test cohorts, respectively (all P <0.05). The combined model showed better discrimination than the clinical-radiological model in the training (AUC=0.853 vs. 0.612, P <0.001) and the total test (AUC=0.842 vs. 0.638, P <0.05) cohorts. The decision curves revealed that the combined model had greater clinical applicability than the clinical-radiological model.

CONCLUSIONS:

The model combining CT-based DLR and clinical-radiological features showed satisfactory performance for predicting OPM in patients with PDAC.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Pancreáticas / Neoplasias Peritoneais / Tomografia Computadorizada por Raios X / Carcinoma Ductal Pancreático / Aprendizado Profundo Limite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Int J Surg Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Pancreáticas / Neoplasias Peritoneais / Tomografia Computadorizada por Raios X / Carcinoma Ductal Pancreático / Aprendizado Profundo Limite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Int J Surg Ano de publicação: 2024 Tipo de documento: Article