Abstract
ObjectiveThe aim of this study was to develop and validate a radiomics model to predict treatment response in patients with advanced gastric cancer (AGC) sensitive to neoadjuvant therapies and verify its generalization among different regimens, including neoadjuvant chemotherapy (NAC) and molecular targeted therapy.Materials and MethodsA total of 373 patients with AGC receiving neoadjuvant therapies were enrolled from five cohorts. Four cohorts of patients received different regimens of NAC, including three retrospective cohorts (training cohort and internal and external validation cohorts) and a prospective Dragon III cohort (NCT03636893). Another prospective SOXA (apatinib in combination with S-1 and oxaliplatin) cohort received neoadjuvant molecular targeted therapy (ChiCTR-OPC-16010061). All patients underwent computed tomography before treatment, and thereafter, tumor regression grade (TRG) was assessed. The primary tumor was delineated, and 2,452 radiomics features were extracted for each patient. Mutual information and random forest were used for dimensionality reduction and modeling. The performance of the radiomics model to predict TRG under different neoadjuvant therapies was evaluated.ResultsThere were 28 radiomics features selected. The radiomics model showed generalization to predict TRG for AGC patients across different NAC regimens, with areas under the curve (AUCs) (95% interval confidence) of 0.82 (0.76~0.90), 0.77 (0.63~0.91), 0.78 (0.66~0.89), and 0.72 (0.66~0.89) in the four cohorts, with no statistical difference observed (all p > 0.05). However, the radiomics model showed poor predictive value on the SOXA cohort [AUC, 0.50 (0.27~0.73)], which was significantly worse than that in the training cohort (p = 0.010).ConclusionRadiomics is generalizable to predict TRG for AGC patients receiving NAC treatments, which is beneficial to transform appropriate treatment, especially for those insensitive to NAC.
Highlights
Gastric cancer (GC) is a serious health problem in the world, causing an estimated 783,000 deaths in 2018 [1]
The results indicated a similar trend observed for the radiomics models, where a similar predictive power was found for patients receiving neoadjuvant chemotherapy (NAC) regimens, while significantly declined performance was found in the SOXA regimen
We developed and validated an artificial intelligence (AI) method using quantitative imaging named radiomics and successfully predicted tumor regression grade (TRG) for advanced gastric cancer (AGC) patients treated with NAC, which showed generalized power among different NAC regimens by validating its predictive value in the Dragon III cohort and an external validation cohort
Summary
Gastric cancer (GC) is a serious health problem in the world, causing an estimated 783,000 deaths in 2018 [1]. Despite surgery being the only curative approach, more than half of cases are initially diagnosed as advanced disease, with a limited 5-year survival of 20%–30% [2]. Neoadjuvant chemotherapy (NAC) is beneficial to improving R0 resection and prognosis in patients with advanced gastric cancer (AGC) by downstaging the tumor, eradicating micrometastasis, and reducing the risk of recurrence [3]. Accumulated studies have been made to investigate regimens with more safety and effectiveness since the landmark MAGIC study launched in 2006, a considerable proportion of cases are insensitive to NAC, leaving unnecessary cytotoxicity to those patients [4–9]. Even for the newly reported triplet FLOT regimen (docetaxel, oxaliplatin, fluorouracil, and leucovorin), which is under impassioned discussion as the new standard for NAC, completed or subtotal pathological regression was achieved in only 37% of cases [6]. It is of urgent need to find an easy-to-use and noninvasive tool to predict tumor sensitivity to different neoadjuvant regimens
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