Incremental value of PET primary lesion-based radiomics signature to conventional metabolic parameters and traditional risk factors for preoperative prediction of lymph node metastases in gastric cancer

Xue, Xiu-Qing; Yu, Wenji; Shao, Xiaoliang; Wang, Yuetao

doi:10.1007/s00261-022-03738-4

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…Xue et al ( 66 , 72 ) published two more studies in 2022 and 2023, respectively. In the first study, the authors enrolled 127 patients with pathologically confirmed GC who underwent preoperative 18F-fluorodeoxyglucose (18F-FDG) PET/CT, with the aim of building and validating an 18F-FDG PET-based radiomics nomogram to predict N2-3b LNM.…”

Section: Resultsmentioning

confidence: 99%

Role of radiomics in predicting lymph node metastasis in gastric cancer: a systematic review

Miccichè,

Rizzo,

Casà

et al. 2023

Front. Med.

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IntroductionGastric cancer (GC) is an aggressive and clinically heterogeneous tumor, and better risk stratification of lymph node metastasis (LNM) could lead to personalized treatments. The role of radiomics in the prediction of nodal involvement in GC has not yet been systematically assessed. This study aims to assess the role of radiomics in the prediction of LNM in GC.MethodsA PubMed/MEDLINE systematic review was conducted to assess the role of radiomics in LNM. The inclusion criteria were as follows: i. original articles, ii. articles on radiomics, and iii. articles on LNM prediction in GC. All articles were selected and analyzed by a multidisciplinary board of two radiation oncologists and one surgeon, under the supervision of one radiation oncologist, one surgeon, and one medical oncologist.ResultsA total of 171 studies were obtained using the search strategy mentioned on PubMed. After the complete selection process, a total of 20 papers were considered eligible for the analysis of the results. Radiomics methods were applied in GC to assess the LNM risk. The number of patients, imaging modalities, type of predictive models, number of radiomics features, TRIPOD classification, and performances of the models were reported.ConclusionsRadiomics seems to be a promising approach for evaluating the risk of LNM in GC. Further and larger studies are required to evaluate the clinical impact of the inclusion of radiomics in a comprehensive decision support system (DSS) for GC.

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Section: Resultsmentioning

confidence: 99%

Role of radiomics in predicting lymph node metastasis in gastric cancer: a systematic review

Miccichè,

Rizzo,

Casà

et al. 2023

Front. Med.

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Radiomics in Oesogastric Cancer: Staging and Prediction of Preoperative Treatment Response: A Narrative Review and the Results of Personal Experience

Garbarino,

Polici,

Caruso

et al. 2024

Cancers

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Background: Oesophageal, gastroesophageal, and gastric malignancies are often diagnosed at locally advanced stage and multimodal therapy is recommended to increase the chances of survival. However, given the significant variation in treatment response, there is a clear imperative to refine patient stratification. The aim of this narrative review was to explore the existing evidence and the potential of radiomics to improve staging and prediction of treatment response of oesogastric cancers. Methods: The references for this review article were identified via MEDLINE (PubMed) and Scopus searches with the terms “radiomics”, “texture analysis”, “oesophageal cancer”, “gastroesophageal junction cancer”, “oesophagogastric junction cancer”, “gastric cancer”, “stomach cancer”, “staging”, and “treatment response” until May 2024. Results: Radiomics proved to be effective in improving disease staging and prediction of treatment response for both oesophageal and gastric cancer with all imaging modalities (TC, MRI, and 18F-FDG PET/CT). The literature data on the application of radiomics to gastroesophageal junction cancer are very scarce. Radiomics models perform better when integrating different imaging modalities compared to a single radiology method and when combining clinical to radiomics features compared to only a radiomics signature. Conclusions: Radiomics shows potential in noninvasive staging and predicting response to preoperative therapy among patients with locally advanced oesogastric cancer. As a future perspective, the incorporation of molecular subgroup analysis to clinical and radiomic features may even increase the effectiveness of these predictive and prognostic models.

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Incremental value of PET primary lesion-based radiomics signature to conventional metabolic parameters and traditional risk factors for preoperative prediction of lymph node metastases in gastric cancer

Cited by 2 publications

References 29 publications

Role of radiomics in predicting lymph node metastasis in gastric cancer: a systematic review

Role of radiomics in predicting lymph node metastasis in gastric cancer: a systematic review

Radiomics in Oesogastric Cancer: Staging and Prediction of Preoperative Treatment Response: A Narrative Review and the Results of Personal Experience

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