TY - JOUR
T1 - Prognostic Value of [18F]FDG PET Radiomics to Detect Peritoneal and Distant Metastases in Locally Advanced Gastric Cancer
T2 - A Side Study of the Prospective Multicentre PLASTIC Study
AU - Pullen, Lieke C.E.
AU - Noortman, Wyanne A.
AU - Triemstra, Lianne
AU - de Jongh, Cas
AU - Rademaker, Fenna J.
AU - Spijkerman, Romy
AU - Kalisvaart, Gijsbert M.
AU - Gertsen, Emma C.
AU - de Geus-Oei, Lioe Fee
AU - Tolboom, Nelleke
AU - de Steur, Wobbe O.
AU - Dantuma, Maura
AU - Slart, Riemer H.J.A.
AU - van Hillegersberg, Richard
AU - Siersema, Peter D.
AU - Ruurda, Jelle P.
AU - van Velden, Floris H.P.
AU - Vegt, Erik
AU - on behalf of the PLASTIC Study Group
N1 - Funding Information:
The PLASTIC-study was funded by ZonMW (The Netherlands Organization for Health Research and Development), project number 843004103. This research received no additional funding.
Publisher Copyright:
© 2023 by the authors.
PY - 2023/6/1
Y1 - 2023/6/1
N2 - Aim: To improve identification of peritoneal and distant metastases in locally advanced gastric cancer using [18F]FDG-PET radiomics. Methods: [18F]FDG-PET scans of 206 patients acquired in 16 different Dutch hospitals in the prospective multicentre PLASTIC-study were analysed. Tumours were delineated and 105 radiomic features were extracted. Three classification models were developed to identify peritoneal and distant metastases (incidence: 21%): a model with clinical variables, a model with radiomic features, and a clinicoradiomic model, combining clinical variables and radiomic features. A least absolute shrinkage and selection operator (LASSO) regression classifier was trained and evaluated in a 100-times repeated random split, stratified for the presence of peritoneal and distant metastases. To exclude features with high mutual correlations, redundancy filtering of the Pearson correlation matrix was performed (r = 0.9). Model performances were expressed by the area under the receiver operating characteristic curve (AUC). In addition, subgroup analyses based on Lauren classification were performed. Results: None of the models could identify metastases with low AUCs of 0.59, 0.51, and 0.56, for the clinical, radiomic, and clinicoradiomic model, respectively. Subgroup analysis of intestinal and mixed-type tumours resulted in low AUCs of 0.67 and 0.60 for the clinical and radiomic models, and a moderate AUC of 0.71 in the clinicoradiomic model. Subgroup analysis of diffuse-type tumours did not improve the classification performance. Conclusion: Overall, [18F]FDG-PET-based radiomics did not contribute to the preoperative identification of peritoneal and distant metastases in patients with locally advanced gastric carcinoma. In intestinal and mixed-type tumours, the classification performance of the clinical model slightly improved with the addition of radiomic features, but this slight improvement does not outweigh the laborious radiomic analysis.
AB - Aim: To improve identification of peritoneal and distant metastases in locally advanced gastric cancer using [18F]FDG-PET radiomics. Methods: [18F]FDG-PET scans of 206 patients acquired in 16 different Dutch hospitals in the prospective multicentre PLASTIC-study were analysed. Tumours were delineated and 105 radiomic features were extracted. Three classification models were developed to identify peritoneal and distant metastases (incidence: 21%): a model with clinical variables, a model with radiomic features, and a clinicoradiomic model, combining clinical variables and radiomic features. A least absolute shrinkage and selection operator (LASSO) regression classifier was trained and evaluated in a 100-times repeated random split, stratified for the presence of peritoneal and distant metastases. To exclude features with high mutual correlations, redundancy filtering of the Pearson correlation matrix was performed (r = 0.9). Model performances were expressed by the area under the receiver operating characteristic curve (AUC). In addition, subgroup analyses based on Lauren classification were performed. Results: None of the models could identify metastases with low AUCs of 0.59, 0.51, and 0.56, for the clinical, radiomic, and clinicoradiomic model, respectively. Subgroup analysis of intestinal and mixed-type tumours resulted in low AUCs of 0.67 and 0.60 for the clinical and radiomic models, and a moderate AUC of 0.71 in the clinicoradiomic model. Subgroup analysis of diffuse-type tumours did not improve the classification performance. Conclusion: Overall, [18F]FDG-PET-based radiomics did not contribute to the preoperative identification of peritoneal and distant metastases in patients with locally advanced gastric carcinoma. In intestinal and mixed-type tumours, the classification performance of the clinical model slightly improved with the addition of radiomic features, but this slight improvement does not outweigh the laborious radiomic analysis.
KW - gastric cancer
KW - machine learning
KW - radiomics
KW - [F]FDG-PET/CT
UR - http://www.scopus.com/inward/record.url?scp=85163062091&partnerID=8YFLogxK
U2 - 10.3390/cancers15112874
DO - 10.3390/cancers15112874
M3 - Article
AN - SCOPUS:85163062091
SN - 2072-6694
VL - 15
JO - Cancers
JF - Cancers
IS - 11
M1 - 2874
ER -
