Context-Aware Saliency Guided Radiomics: Application to Prediction of Outcome and HPV-Status from Multi-Center PET/CT Images of Head and Neck Cancer.

HPV PET/CT head and neck cancer outcome radiomics saliency

Journal

Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829

Informations de publication

Date de publication:
25 Mar 2022
Historique:
received: 18 02 2022
revised: 19 03 2022
accepted: 21 03 2022
entrez: 12 4 2022
pubmed: 13 4 2022
medline: 13 4 2022
Statut: epublish

Résumé

This multi-center study aims to investigate the prognostic value of context-aware saliency-guided radiomics in 806 HNC patients (training vs. validation vs. external testing: 500 vs. 97 vs. 209) from 9 centers were collected from The Cancer Imaging Archive (TCIA). There were 100/384 and 60/123 oropharyngeal carcinoma (OPC) patients with human papillomavirus (HPV) status in training and testing cohorts, respectively. Six types of images were used for radiomics feature extraction and further model construction, namely (i) the original image (Origin), (ii) a context-aware saliency map (SalMap), (iii, iv) high- or low-saliency regions in the original image (highSal or lowSal), (v) a saliency-weighted image (SalxImg), and finally, (vi) a fused PET-CT image (FusedImg). Four outcomes were evaluated, i.e., recurrence-free survival (RFS), metastasis-free survival (MFS), overall survival (OS), and disease-free survival (DFS), respectively. Multivariate Cox analysis and logistic regression were adopted to construct radiomics scores for the prediction of outcome (Rad_Ocm) and HPV-status (Rad_HPV), respectively. Besides, the prognostic value of their integration (Rad_Ocm_HPV) was also investigated. In the external testing cohort, compared with the Origin model, SalMap and SalxImg achieved the highest C-indices for RFS (0.621 vs. 0.559) and MFS (0.785 vs. 0.739) predictions, respectively, while FusedImg performed the best for both OS (0.685 vs. 0.659) and DFS (0.641 vs. 0.582) predictions. In the OPC HPV testing cohort, FusedImg showed higher AUC for HPV-status prediction compared with the Origin model (0.653 vs. 0.484). In the OPC testing cohort, compared with Rad_Ocm or Rad_HPV alone, Rad_Ocm_HPV performed the best for OS and DFS predictions with C-indices of 0.702 ( Saliency-guided radiomics showed enhanced performance for both outcome and HPV-status predictions relative to conventional radiomics. The radiomics-predicted HPV status also showed complementary prognostic value.

Identifiants

pubmed: 35406449
pii: cancers14071674
doi: 10.3390/cancers14071674
pmc: PMC8996849
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : National Natural Science Foundation of China
ID : 81871437
Organisme : National Natural Science Foundation of China
ID : 12026601
Organisme : Guangdong Basic and Applied Basic Research Foundation
ID : 2019A1515011104
Organisme : Guangdong Basic and Applied Basic Research Foundation
ID : 2020A1515110683
Organisme : Guangdong Basic and Applied Basic Research Foundation
ID : 2021A1515011676
Organisme : China Postdoctoral Science Foundation
ID : 2020M682792

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Auteurs

Wenbing Lv (W)

School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China.
Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China.
Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China.
Pazhou Lab, Guangzhou 510330, China.

Hui Xu (H)

School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China.
Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China.
Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China.
Pazhou Lab, Guangzhou 510330, China.

Xu Han (X)

School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China.
Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China.
Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China.
Pazhou Lab, Guangzhou 510330, China.

Hao Zhang (H)

Department of Medical Imaging, Nanfang Hospital, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China.

Jianhua Ma (J)

School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China.
Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China.
Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China.
Pazhou Lab, Guangzhou 510330, China.

Arman Rahmim (A)

Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada.
Department of Radiology, University of British Columbia, 2211 Wesbrook Mall, Vancouver, BC V6T 1Z1, Canada.
Department of Physics, University of British Columbia, 6224 Agricultural Road, Vancouver, BC V6T 1Z1, Canada.

Lijun Lu (L)

School of Biomedical Engineering, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China.
Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China.
Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Road, Guangzhou 510515, China.
Pazhou Lab, Guangzhou 510330, China.

Classifications MeSH