Radiomic signature of ¹⁸F fluorodeoxyglucose PET/CT for prediction of gastric cancer survival and chemotherapeutic benefits

Abstract

We aimed to evaluate whether radiomic feature-based fluorine 18 (18F) fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging signatures allow prediction of gastric cancer (GC) survival and chemotherapy benefits.Methods: A total of 214 GC patients (training (n = 132) or validation (n = 82) cohort) were subjected to radiomic feature extraction (80 features). Radiomic features of patients in the training cohort were subjected to a LASSO cox analysis to predict disease-free survival (DFS) and overall survival (OS) and were validated in the validation cohort. A radiomics nomogram with the radiomic signature incorporated was constructed to demonstrate the incremental value of the radiomic signature to the TNM staging system for individualized survival estimation, which was then assessed with respect to calibration, discrimination, and clinical usefulness. The performance was assessed with concordance index (C-index) and integrated Brier scores.Results: Significant differences were found between the high- and low-radiomic score (Rad-score) patients in 5-year DFS and OS in training and validation cohorts. Multivariate analysis revealed that the Rad-score was an independent prognostic factor. Incorporating the Rad-score into the radiomics-based nomogram resulted in better performance (C-index: DFS, 0.800; OS, 0.786; in the training cohort) than TNM staging system and clinicopathologic nomogram. Further analysis revealed that patients with higher Rad-scores were prone to benefit from chemotherapy.Conclusion: The newly developed radiomic signature was a powerful predictor of OS and DFS. Moreover, the radiomic signature could predict which patients could benefit from chemotherapy.

Young age increases risk for lymph node positivity in gastric cancer: A Chinese multi-institutional database and US SEER database study

“…Clinically, accurate prediction of lymph node involvement is essential in helping doctors make decisions more reasonably [20,[25][26][27][28][29]. Treatment recommendations for a patient with GC would vary depending on lymph node status.…”

A predictive nomogram for individualized recurrence stratification of bladder cancer using multiparametric MRI and clinical risk factors

“…Whether these image signs could be used for TFTY BCa recurrence prediction remains inconclusive to date. In addition, a preoperative radiomics strategy with nomogram models are reported to be capable of individualized recurrence risk stratification of patients with lung, hepatic, and gastric cancer diseases, as well as predicting mental disorders schizophrenia …”

“…14,15 Whether these image signs could be used for TFTY BCa recurrence prediction remains inconclusive to date. In addition, a preoperative radiomics strategy with nomogram models are reported to be capable of individualized recurrence risk stratification of patients with lung, hepatic, and gastric cancer diseases, [16][17][18] as well as predicting mental disorders schizophrenia. 19 Based on these findings above, we hypothesized that: 1) the radiomics features extracted from preoperative mpMRI to characterize the subtle variations of tissue distribution within the lesion [20][21][22] might be potential in predicting BCa recurrence; 2) the combination of the radiomics strategy with important clinical factors, mainly including age, gender, histological grade, and MIS of the archived tumor with the maximal size in bladder lumen, tumor size, NoT, operation choice, together with the imaging signs like stalk and SLE, 13,[23][24][25] might add the incremental value for TFTY BCa prediction.…”

Radiomics Features of 18F-fluorodeoxyglucose Positron-Emission Tomography as a Novel Prognostic Signature in Colorectal Cancer

“…A radiomics signature-based nomogram has been developed and validated for preoperative prediction of survival in breast cancer, gastric cancer, and early-stage non-small cell lung cancer patients [28][29][30]. In our study, the developed nomogram incorporated a radiomics score derived from two components of the PET-based features, pN and LVI.…”

“…First, various computer algorithms have been used for feature extraction, and the types of features extracted by each algorithm were not uniform. Moreover, we applied standardization of features as a preprocessing step before entering the data into the LASSO COX model, following a method that has already been adopted for radiomics analysis in patients with breast or gastric cancer [28,30]. However, most previous studies of PETderived radiomics analysis in patients with CRC did not use standardization as a preprocessing step [17][18][19]43].…”