A Machine Learning Model for Predicting a Major Response to Neoadjuvant Chemotherapy in Advanced Gastric Cancer

Chen, Yonghe; Wei, Kaikai; Liú, Dan; Xiang, Jun; Wang, Gang; Meng, Xiaochun; Peng, Junsheng

doi:10.3389/fonc.2021.675458

Cited by 18 publications

(7 citation statements)

References 32 publications

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“…5.6%-16% (16, 42, 43) and 21.2%-27.6% (44,45). In the present study, we found that the pooled pCR rate is 21.7% and that the pooled MPR rate is 44.0% in patients with LAGC who received NACT combined with immunotherapy, which seemed superior to that in patients who received NACT alone in previous studies.…”

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confidence: 48%

“…Therefore, pCR and MPR could be used as key indicators in the selection of optimal regimens. Previous studies have demonstrated that the pCR rate and MPR rate for patients with LAGC who received NACT was mostly 5.6%–16% ( 16 , 42 , 43 ) and 21.2%–27.6% ( 44 , 45 ). In the present study, we found that the pooled pCR rate is 21.7% and that the pooled MPR rate is 44.0% in patients with LAGC who received NACT combined with immunotherapy, which seemed superior to that in patients who received NACT alone in previous studies.…”

Section: Discussionmentioning

confidence: 99%

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Combining neoadjuvant chemotherapy with PD-1/PD-L1 inhibitors for locally advanced, resectable gastric or gastroesophageal junction adenocarcinoma: A systematic review and meta-analysis

et al. 2023

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BackgroundImmune checkpoint inhibitors (ICIs) have shown promising prospects in locally advanced, resectable gastric or gastroesophageal junction adenocarcinoma (GC/GEJC) immunotherapy, but their efficacy in neoadjuvant settings remains unclear. This study aimed to assess the efficacy and safety of integrating programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) inhibitors into neoadjuvant chemotherapy (NACT) of GC/GEJC treatment.MethodsPubMed, Cochrane Library, Embase, ClinicalTrials.gov, and main oncology conference databases were systematically searched up to 19 November 2022, and randomized controlled trials (RCTs) and cohort studies that evaluated the efficacy and safety of PD-1/PD-L1 inhibitors plus NACT were included. The main outcomes were pathological complete response (pCR), major pathological response (MPR), R0 resection rate, and treatment-related adverse events (TRAEs).ResultsA total of 753 patients from 20 prospective studies were included in this meta-analysis. The pooled pCR and MPR rates from studies reporting were 21.7% [95% confidence interval (CI), 18.1%–25.5%] and 44.0% (95% CI, 34.1%–53.8%), respectively. The pooled incidence rate of total TRAEs was 89.1% (95% CI, 82.7%–94.3%), and the incidence rate of grade 3 to 4 TRAEs was 34.4% (95% CI, 17.8%–66.5%). The pooled R0 resection rate was reported to be 98.9% (95% CI, 97.0%–99.9%). Subgroup analysis has not found significant differences in efficacy and safety among different PD-1/PD-L1 inhibitors. Moreover, the efficacy in patients with positive PD-L1 expression (combined positive score ≥1) was comparable with that in the entire study population [pCR, 22.5% vs. 21.2% (p > 0.05); MPR, 48.6% vs. 43.7% (p > 0.05)].ConclusionThis systematic review and meta-analysis found that PD-1/PD-L1 inhibitors combined with NACT for locally advanced GC/GEJC were well tolerated and may confer therapeutic advantages. The integration of ICIs into NACT has shown the potential for application in any PD-L1 expression population.

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confidence: 48%

Section: Discussionmentioning

confidence: 99%

Combining neoadjuvant chemotherapy with PD-1/PD-L1 inhibitors for locally advanced, resectable gastric or gastroesophageal junction adenocarcinoma: A systematic review and meta-analysis

et al. 2023

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“…By training a radiomics model, Chen et al aimed to predict response to nCRT in patients with advanced gastric cancer [ 45 ]. Out of all clinical factors, only tumor differentiation was significant for this response.…”

Section: Resultsmentioning

confidence: 99%

Machine learning applications in upper gastrointestinal cancer surgery: a systematic review

et al. 2022

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Background Machine learning (ML) has seen an increase in application, and is an important element of a digital evolution. The role of ML within upper gastrointestinal surgery for malignancies has not been evaluated properly in the literature. Therefore, this systematic review aims to provide a comprehensive overview of ML applications within upper gastrointestinal surgery for malignancies. Methods A systematic search was performed in PubMed, EMBASE, Cochrane, and Web of Science. Studies were only included when they described machine learning in upper gastrointestinal surgery for malignancies. The Cochrane risk-of-bias tool was used to determine the methodological quality of studies. The accuracy and area under the curve were evaluated, representing the predictive performances of ML models. Results From a total of 1821 articles, 27 studies met the inclusion criteria. Most studies received a moderate risk-of-bias score. The majority of these studies focused on neural networks (n = 9), multiple machine learning (n = 8), and random forests (n = 3). Remaining studies involved radiomics (n = 3), support vector machines (n = 3), and decision trees (n = 1). Purposes of ML included predominantly prediction of metastasis, detection of risk factors, prediction of survival, and prediction of postoperative complications. Other purposes were predictions of TNM staging, chemotherapy response, tumor resectability, and optimal therapy. Conclusions Machine Learning algorithms seem to contribute to the prediction of postoperative complications and the course of disease after upper gastrointestinal surgery for malignancies. However, due to the retrospective character of ML studies, these results require trials or prospective studies to validate this application of ML. Graphical abstract

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“…Radiomics integrates meaningful quantitative imaging features for modeling, which is a major difference from methods utilizing the traditional visual interpretation of images [35][36][37][38][39]. Chen et al [40] used the features of the CT venous phase and established a predictive model to distinguish between advanced gastric cancer patients with potentially pathologically significant reactions and those with mild reactions and were able to effectively stratify patients according to their response to NAC. Mazzei et al [41] performed a multicenter study to predict NAC response by delta radiomics in locally progressive gastric cancer.…”

Section: Discussionmentioning

confidence: 99%

Enhanced CT-based radiomics predicts pathological complete response after neoadjuvant chemotherapy for advanced adenocarcinoma of the esophagogastric junction: a two-center study

Huang

Liu³

et al. 2022

Insights Imaging

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Purpose This study aimed to develop and validate CT-based models to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) for advanced adenocarcinoma of the esophagogastric junction (AEG). Methods Pre-NAC clinical and imaging data of AEG patients who underwent surgical resection after preoperative-NAC at two centers were retrospectively collected from November 2014 to September 2020. The dataset included training (n = 60) and external validation groups (n = 32). Three models, including CT-based radiomics, clinical and radiomics–clinical combined models, were established to differentiate pCR (tumor regression grade (TRG) = grade 0) and nonpCR (TRG = grade 1–3) patients. For the radiomics model, tumor-region-based radiomics features in the arterial and venous phases were extracted and selected. The naïve Bayes classifier was used to establish arterial- and venous-phase radiomics models. The selected candidate clinical factors were used to establish a clinical model, which was further incorporated into the radiomics–clinical combined model. ROC analysis, calibration and decision curves were used to assess the model performance. Results For the radiomics model, the AUC values obtained using the venous data were higher than those obtained using the arterial data (training: 0.751 vs. 0.736; validation: 0.768 vs. 0.750). Borrmann typing, tumor thickness and degree of differentiation were utilized to establish the clinical model (AUC-training: 0.753; AUC-validation: 0.848). The combination of arterial- and venous-phase radiomics and clinical factors further improved the discriminatory performance of the model (AUC-training: 0.838; AUC-validation: 0.902). The decision curve reflects the higher net benefit of the combined model. Conclusion The combination of CT imaging and clinical factors pre-NAC for advanced AEG could help stratify potential responsiveness to NAC.

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A Machine Learning Model for Predicting a Major Response to Neoadjuvant Chemotherapy in Advanced Gastric Cancer

Cited by 18 publications

References 32 publications

Combining neoadjuvant chemotherapy with PD-1/PD-L1 inhibitors for locally advanced, resectable gastric or gastroesophageal junction adenocarcinoma: A systematic review and meta-analysis

Combining neoadjuvant chemotherapy with PD-1/PD-L1 inhibitors for locally advanced, resectable gastric or gastroesophageal junction adenocarcinoma: A systematic review and meta-analysis

Machine learning applications in upper gastrointestinal cancer surgery: a systematic review

Enhanced CT-based radiomics predicts pathological complete response after neoadjuvant chemotherapy for advanced adenocarcinoma of the esophagogastric junction: a two-center study

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