18F-FDG PET Radiomics as Predictor of Treatment Response in Oesophageal Cancer: A Systematic Review and Meta-Analysis

Deantonio, Letizia; Garo, Maria Luisa; Paone, Gaetano; Valli, M.; Cappio, Stefano; Regina, Davide La; Cefalì, Marco; Palmarocchi, Maria Celeste; Vannelli, Alberto; Dosso, Sara De

doi:10.3389/fonc.2022.861638

Cited by 9 publications

(8 citation statements)

References 37 publications

(110 reference statements)

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“…3 ). The meta-analyses in 6 systematic reviews were excluded due to unavailable data [ 34 , 42 , 43 , 48 , 60 , 64 ]. Up to 47 meta-analyses reached a stringent p -value of less than 10 −6 , and 6 meta-analyses presented p -values < 10 −3 .…”

Section: Resultsmentioning

confidence: 99%

An overview of meta-analyses on radiomics: more evidence is needed to support clinical translation

Zhong

Zhang

et al. 2023

Insights Imaging

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Objective To conduct an overview of meta-analyses of radiomics studies assessing their study quality and evidence level. Methods A systematical search was updated via peer-reviewed electronic databases, preprint servers, and systematic review protocol registers until 15 November 2022. Systematic reviews with meta-analysis of primary radiomics studies were included. Their reporting transparency, methodological quality, and risk of bias were assessed by PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) 2020 checklist, AMSTAR-2 (A MeaSurement Tool to Assess systematic Reviews, version 2) tool, and ROBIS (Risk Of Bias In Systematic reviews) tool, respectively. The evidence level supporting the radiomics for clinical use was rated. Results We identified 44 systematic reviews with meta-analyses on radiomics research. The mean ± standard deviation of PRISMA adherence rate was 65 ± 9%. The AMSTAR-2 tool rated 5 and 39 systematic reviews as low and critically low confidence, respectively. The ROBIS assessment resulted low, unclear and high risk in 5, 11, and 28 systematic reviews, respectively. We reperformed 53 meta-analyses in 38 included systematic reviews. There were 3, 7, and 43 meta-analyses rated as convincing, highly suggestive, and weak levels of evidence, respectively. The convincing level of evidence was rated in (1) T2-FLAIR radiomics for IDH-mutant vs IDH-wide type differentiation in low-grade glioma, (2) CT radiomics for COVID-19 vs other viral pneumonia differentiation, and (3) MRI radiomics for high-grade glioma vs brain metastasis differentiation. Conclusions The systematic reviews on radiomics were with suboptimal quality. A limited number of radiomics approaches were supported by convincing level of evidence. Clinical relevance statement The evidence supporting the clinical application of radiomics are insufficient, calling for researches translating radiomics from an academic tool to a practicable adjunct towards clinical deployment. Graphical Abstract

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Section: Resultsmentioning

confidence: 99%

An overview of meta-analyses on radiomics: more evidence is needed to support clinical translation

Zhong

Zhang

et al. 2023

Insights Imaging

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“…In addition, Deantonio et al . 13 included five studies and revealed the potential of 18F-FDG PET-based radiomics to predict pCR in EC NCRT. In comparison, the performance of radiomics using different imaging modalities, including PET, CECT, and MRI, were also described in our sub-analysis, all of which showed great potential in patient stratification with favorable sensitivity and specificity.…”

Section: Discussionmentioning

confidence: 99%

“…Although radiomic analysis seems promising for the management of EC patients by predicting pathological response after NCRT 13 , 14 , there is still a lack of comprehensive assessment of the methodological robustness and quality of EC radiomic studies in CCRT management before their clinical translation. In addition, there has been no report to fully assess the pooled predictive power and potential clinical value of the current radiomic model regarding EC CCRT.…”

Section: Introductionmentioning

confidence: 99%

The gap before real clinical application of imaging-based machine-learning and radiomic models for chemoradiation outcome prediction in esophageal cancer: a systematic review and meta-analysis

Yang

Gong

et al. 2023

International Journal of Surgery

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Background: Due to tumoral heterogeneity and the lack of robust biomarkers, the prediction of chemoradiotherapy response and prognosis in patients with esophageal cancer (EC) is challenging. The goal of this study was to assess the study quality and clinical value of machine learning and radiomic-based quantitative imaging studies for predicting outcomes of EC patients after chemoradiotherapy. Materials and Methods: PubMed, Embase and Cochrane were searched for eligible articles. The methodological quality and risk of bias were evaluated using the RQS, IBSI guideline and TRIPOD statement, as well as the modified QUADAS-2 tool. A meta-analysis of the evidence focusing on predicting chemoradiotherapy response and outcome in EC patients was implemented. Results: Forty-six studies were eligible for qualitative synthesis. The mean RQS score was 9.07, with an adherence rate of 42.52%. The adherence rates of the TRIPOD and IBSI were 61.70% and 43.17%, respectively. Ultimately, twenty-four studies were included in the meta-analysis, of which sixteen studies had a pooled sensitivity, specificity and AUC of 0.83 (0.76-0.89), 0.83 (0.79-0.86) and 0.84 (0.81-0.87) in NCRT datasets, as well as 0.84 (0.75-0.93), 0.89 (0.83-0.93) and 0.93 (0.90-0.95) in DCRT datasets, respectively. Moreover, radiomics could distinguish patients from the low- and high-risk groups with different DFS (pooled HR: 3.43, 95% CI 2.39-4.92) and OS (pooled HR: 2.49, 95% CI 1.91-3.25). The results of subgroup and regression analyses showed that some of the heterogeneity was explained by the combination with clinical factors, sample size and usage of DL signature. Conclusions: Noninvasive radiomics offers promising potential for optimizing treatment decision-making in EC patients. However, it is necessary to make scientific advancements in EC radiomics regarding reproducibility, clinical usefulness analysis and open science categories. Improved model reporting of study objectives, blind assessment and image processing steps are required to help promote real clinical applications of radiomics in EC research.

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“…In the literature, only a few studies are available on PET-CT radiomics and oesophageal cancer. A recent systematic review by Deantonio et al, summarising the impact of 18F-FDG PET-CT-based radiomics in predicting the response to neoadjuvant chemoradiotherapy in oesophageal cancer, identified only five studies where radiomics was used to predict a pCR [ 36 ]. These authors concluded that radiomic models exhibited a good performance in predicting pathological complete responses (pCRs), with a pooled area under the curve (AUC) of 0.82 (95% CI: 0.74–0.9).…”

Section: Discussionmentioning

confidence: 99%

Could 18-FDG PET-CT Radiomic Features Predict the Locoregional Progression-Free Survival in Inoperable or Unresectable Oesophageal Cancer?

Bari

Lefevre

Henriques

et al. 2022

Cancers

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Background: We evaluated the value of pre-treatment positron-emission tomography–computed tomography (PET-CT)-based radiomic features in predicting the locoregional progression-free survival (LR-PFS) of patients with inoperable or unresectable oesophageal cancer. Material and Methods: Forty-six patients were included and 230 radiomic parameters were extracted. After a principal component analysis (PCA), we identified the more robust radiomic parameters, and we used them to develop a heatmap. Finally, we correlated these radiomic features with LR-PFS. Results: The median follow-up time was 17 months. The two-year LR-PFS and PFS rates were 35.9% (95% CI: 18.9–53.3) and 21.6% (95%CI: 10.0–36.2), respectively. After the correlation analysis, we identified 55 radiomic parameters that were included in the heatmap. According to the results of the hierarchical clustering, we identified two groups of patients presenting statistically different median LR-PFSs (22.8 months vs. 9.9 months; HR = 2.64; 95% CI 0.97–7.15; p = 0.0573). We also identified two radiomic features (“F_rlm_rl_entr_per” and “F_rlm_2_5D_rl_entr”) significantly associated with LR-PFS. Patients expressing a “F_rlm_2_5D_rl_entr” of <3.3 had a better median LR- PFS (29.4 months vs. 8.2 months; p = 0.0343). Patients presenting a “F_rlm_rl_entr_per” of <4.7 had a better median LR-PFS (50.4 months vs. 9.9 months; p = 0.0132). Conclusion: We identified two radiomic signatures associated with a lower risk of locoregional relapse after CRT.

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18F-FDG PET Radiomics as Predictor of Treatment Response in Oesophageal Cancer: A Systematic Review and Meta-Analysis

Cited by 9 publications

References 37 publications

An overview of meta-analyses on radiomics: more evidence is needed to support clinical translation

An overview of meta-analyses on radiomics: more evidence is needed to support clinical translation

The gap before real clinical application of imaging-based machine-learning and radiomic models for chemoradiation outcome prediction in esophageal cancer: a systematic review and meta-analysis

Could 18-FDG PET-CT Radiomic Features Predict the Locoregional Progression-Free Survival in Inoperable or Unresectable Oesophageal Cancer?

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