Radiomics signature based on computed tomography images for the preoperative prediction of lymph node metastasis at individual stations in gastric cancer: A multicenter study

Sun, Zhiwei; Jiang, Yuming; Chen, Chuanli; Zheng, Huan; Huang, Weicai; Xu, Benjamin; Tang, Weijing; Yuan, Qingyu; Zhou, Kangneng; Liang, Xiaokun; Chen, Hao; Han, Zhen; Feng, Hao; Yu, Shitong; Hu, Yanfeng; Yu, Jiang; Zhou, Zhiwei; Wang, Wei; Xu, Yu; Li, Guoxin

doi:10.1016/j.radonc.2021.11.003

Cited by 13 publications

(19 citation statements)

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“…There were 35 studies (85.4%) that were conducted in China ( 32 , 33 , 35 – 43 , 45 – 49 , 51 – 54 , 56 – 63 , 65 – 69 , 71 , 72 ), 5 (12.2%) in Korea ( 34 , 44 , 50 , 55 , 64 ) and 1 (2.4%) in Germany ( 70 ), with the publication date ranged from 2004 to 2022. The number of studies using ML for LNM prediction has gradually increased since 2018 ( Figure 2 ).…”

Section: Resultsmentioning

confidence: 99%

“…The number of studies using ML for LNM prediction has gradually increased since 2018 ( Figure 2 ). There were 35 retrospective studies ( 33 , 35 – 53 , 56 – 59 , 61 – 70 , 72 ) and 6 prospective studies ( 32 , 34 , 54 , 55 , 60 , 71 ), with a total of 56182 participants, in which the number of patients with LNM was 12031. Characteristics of included studies are presented in Table 1 .…”

Section: Resultsmentioning

confidence: 99%

“…All the included studies were of low risk of bias with respect to the domain of participants and outcome, 19 (46.34%) studies had low risk of bias in predictors ( 32 , 34 , 37 , 38 , 40 , 42 , 44 , 54 – 56 , 60 , 62 , 63 , 66 – 71 ), 22 (53.66%) had unclear risk of bias due to that the prediction assessment was performed in the know of outcome data ( 33 , 35 , 36 , 39 , 41 , 43 , 45 – 53 , 57 – 59 , 61 , 64 , 65 , 72 ). As for the domain of analysis, the risk of bias in 16 studies was considered high ( 33 , 35 , 41 , 43 , 47 , 50 , 51 , 53 , 58 , 59 , 61 , 63 , 65 , 68 , 70 , 71 ), and the reasons were that (1): Insufficient sample size.…”

Section: Resultsmentioning

confidence: 99%

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Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis

Xie

Xiong

et al. 2022

Front. Oncol.

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ObjectiveTo evaluate the diagnostic performance of machine learning (ML) in predicting lymph node metastasis (LNM) in patients with gastric cancer (GC) and to identify predictors applicable to the models.MethodsPubMed, EMBASE, Web of Science, and Cochrane Library were searched from inception to March 16, 2022. The pooled c-index and accuracy were used to assess the diagnostic accuracy. Subgroup analysis was performed based on ML types. Meta-analyses were performed using random-effect models. Risk of bias assessment was conducted using PROBAST tool.ResultsA total of 41 studies (56182 patients) were included, and 33 of the studies divided the participants into a training set and a test set, while the rest of the studies only had a training set. The c-index of ML for LNM prediction in training set and test set was 0.837 [95%CI (0.814, 0.859)] and 0.811 [95%CI (0.785-0.838)], respectively. The pooled accuracy was 0.781 [(95%CI (0.756-0.805)] in training set and 0.753 [95%CI (0.721-0.783)] in test set. Subgroup analysis for different ML algorithms and staging of GC showed no significant difference. In contrast, in the subgroup analysis for predictors, in the training set, the model that included radiomics had better accuracy than the model with only clinical predictors (F = 3.546, p = 0.037). Additionally, cancer size, depth of cancer invasion and histological differentiation were the three most commonly used features in models built for prediction.ConclusionML has shown to be of excellent diagnostic performance in predicting the LNM of GC. One of the models covering radiomics and its ML algorithms showed good accuracy for the risk of LNM in GC. However, the results revealed some methodological limitations in the development process. Future studies should focus on refining and improving existing models to improve the accuracy of LNM prediction.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022320752

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis

Xie

Xiong

et al. 2022

Front. Oncol.

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“…We systematically explored the signature of pLNMs in relation to tumor location, tumor size, T stage, and lymphatic invasion, and showed that the combinations of tumor location with tumor size, T stage, and lymphatic invasion may determine the risk of pLNM, and may provide evidence to aid the appropriate selection of surgical procedures. Although various previous methods, including CT [ 5 ], DNA methylation markers [ 6 ], and the MCP [ 11 ], have been used to predict LNMs in patients with GC, the predicted LNMs were not station-specific and further investigations are therefore required. Here, we found that tumor size, T stage, lymphatic invasion, and the novel predictive factor, sDPD, were important predictors of pLNM.…”

Section: Discussionmentioning

confidence: 99%

“…However, the precise preoperative prediction of station-specific LNMs remains a challenge. Various procedures, including computed tomography (CT) [ 5 ], ultrasonography, magnetic resonance imaging with ferumoxtran-10 [ 6 ], and use of DNA methylation markers [ 7 ], have been explored to predict pLNMs prior to surgery; however, these have been inaccurate or complex, or have been unable to predict station-specific LNM [ 8 , 9 , 10 ]. Furthermore, the Maruyama Computer Program (MCP) was applied to predict LNMs in GC patients treated with neoadjuvant chemotherapy followed by gastrectomy [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Signature and Prediction of Perigastric Lymph Node Metastasis in Patients with Gastric Cancer and Total Gastrectomy: Is Total Gastrectomy Always Necessary?

Zhang

Yamashita

Okumura

et al. 2022

Cancers

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Background: A growing number of studies suggest that the current indications for partial gastrectomy, including proximal gastrectomy and pylorus-preserving gastrectomy (PPG), may be expanded, but evidence is still lacking. Methods: We retrospectively analyzed 300 patients with gastric cancer (GC) who underwent total gastrectomy. We analyzed the incidence of pLNMs in relation to tumor location, tumor size and T stage. We further identified predictive factors for perigastric lymph node metastasis (pLNM) in stations 1, 2, 3, 4sa, 4sb, 4d, 5, and 6. Results: No patients with upper-third T1–T2 stage GC had pLNMs in stations 4sa, 4sb, 4d, 5, or 6, but 3.8% of patients with stage T3 had 4d pLNM. No patients with upper-third GC < 4 cm in diameter had pLNMs in 2, 4sa, 4d, 5, or 6, and 2.3% of patients had pLNMs in 4sb. For middle-third GCs, 2.9% of patients with T1 stage had pLNMs in 4sa and 5, but no patients with T2 stage or tumors < 4 cm had pLNMs in 2, 4sa, or 5. The shortest distance from pylorus ring to distal edge of tumor (sDPD) was a new predictive factor for pLNMs in 2, 4d, 5, and 6. Conclusions: Proximal gastrectomy may be expanded to patients with stage T1–T2 GC and/or tumor diameter < 4 cm in the upper-third stomach, whereas PPG may be expanded to include T1–T2/N0 and/or tumors < 4 cm in the middle-third stomach. A new predictive factor, sDPD, showed good predictive performance for pLNMs, especially in stations 4d, 5, and 6.

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Radiomic applications in upper gastrointestinal cancer surgery

Doyle

Patel

Petrou

et al. 2023

Langenbecks Arch Surg

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Radiomics signature based on computed tomography images for the preoperative prediction of lymph node metastasis at individual stations in gastric cancer: A multicenter study

Cited by 13 publications

References 35 publications

Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis

Machine learning for lymph node metastasis prediction of in patients with gastric cancer: A systematic review and meta-analysis

Signature and Prediction of Perigastric Lymph Node Metastasis in Patients with Gastric Cancer and Total Gastrectomy: Is Total Gastrectomy Always Necessary?

Radiomic applications in upper gastrointestinal cancer surgery

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