Radiomics diagnostic performance in predicting lymph node metastasis of papillary thyroid carcinoma: A systematic review and meta-analysis

HajiEsmailPoor, Zanyar; Kargar, Zana; Tabnak, Peyman

doi:10.1016/j.ejrad.2023.111129

Cited by 9 publications

(6 citation statements)

References 46 publications

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“…These complexities are often not extensively covered in quality assessment tools. In line with our study, meta-analyses conducted on other clinical contexts have similarly identified methodological limitations in their included studies 58 , 59 , 61 , 66 .…”

Section: Discussionsupporting

confidence: 88%

“… 58 conducted a meta-analysis showing the performance of CT-based radiomics models predicting LNM in gastric cancer, with an impressive AUC of 0.85 (95% CI, 0.81–0.86). Similarly, in their study on papillary thyroid carcinoma 59 , radiomics models exhibited a notable AUC of 0.80 (95% CI, 0.73, 0.85), which closely aligns with the outcomes of our study in CRC.…”

Section: Discussionsupporting

confidence: 88%

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Application of radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis

Abbaspour,

Karimzadhagh,

Monsef

et al. 2024

International Journal of Surgery

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Background: Colorectal cancer (CRC) stands as the third most prevalent cancer globally, projecting 3.2 million new cases and 1.6 million deaths by 2040. Accurate lymph node metastasis (LNM) detection is critical for determining optimal surgical approaches, including preoperative neoadjuvant chemoradiotherapy and surgery, which significantly influence CRC prognosis. However, conventional imaging lacks adequate precision, prompting exploration into radiomics, which addresses this shortfall by converting medical images into reproducible, quantitative data. Methods: Following PRISMA, Supplemental Digital Content 1 (http://links.lww.com/JS9/C77) and Supplemental Digital Content 2 (http://links.lww.com/JS9/C78), and AMSTAR-2 guidelines, Supplemental Digital Content 3 (http://links.lww.com/JS9/C79), we systematically searched PubMed, Web of Science, Embase, Cochrane Library, and Google Scholar databases until 11 January 2024, to evaluate radiomics models’ diagnostic precision in predicting preoperative LNM in CRC patients. The quality and bias risk of the included studies were assessed using the Radiomics Quality Score (RQS) and the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Subsequently, statistical analyses were conducted. Results: Thirty-six studies encompassing 8039 patients were included, with a significant concentration in 2022–2023 (20/36). Radiomics models predicting LNM demonstrated a pooled area under the curve (AUC) of 0.814 (95% CI: 0.78–0.85), featuring sensitivity and specificity of 0.77 (95% CI: 0.69, 0.84) and 0.73 (95% CI: 0.67, 0.78), respectively. Subgroup analyses revealed similar AUCs for CT and MRI-based models, and rectal cancer models outperformed colon and colorectal cancers. Additionally, studies utilizing cross-validation, 2D segmentation, internal validation, manual segmentation, prospective design, and single-center populations tended to have higher AUCs. However, these differences were not statistically significant. Radiologists collectively achieved a pooled AUC of 0.659 (95% CI: 0.627, 0.691), significantly differing from the performance of radiomics models (P<0.001). Conclusion: Artificial intelligence-based radiomics shows promise in preoperative lymph node staging for CRC, exhibiting significant predictive performance. These findings support the integration of radiomics into clinical practice to enhance preoperative strategies in CRC management.

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Section: Discussionsupporting

confidence: 88%

Section: Discussionsupporting

confidence: 88%

Application of radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis

Abbaspour,

Karimzadhagh,

Monsef

et al. 2024

International Journal of Surgery

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“…These distinctions in radiomic processes yield distinct sets of features, complicating direct comparisons between studies ( 47 ). The superiority of PyRadiomics compared to other feature extraction software has also been mentioned in a previous meta-analysis ( 48 ). Lastly, using a Ki-67 cut-off of 14% could also cause interstudy heterogeneity.…”

Section: Discussionmentioning

confidence: 70%

CT-based radiomics for predicting Ki-67 expression in lung cancer: a systematic review and meta-analysis

Luo,

Zheng,

Zhang

et al. 2024

Front. Oncol.

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BackgroundRadiomics, an emerging field, presents a promising avenue for the accurate prediction of biomarkers in different solid cancers. Lung cancer remains a significant global health challenge, contributing substantially to cancer-related mortality. Accurate assessment of Ki-67, a marker reflecting cellular proliferation, is crucial for evaluating tumor aggressiveness and treatment responsiveness, particularly in non-small cell lung cancer (NSCLC).MethodsA systematic review and meta-analysis conducted following the preferred reporting items for systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA) guidelines. Two authors independently conducted a literature search until September 23, 2023, in PubMed, Embase, and Web of Science. The focus was on identifying radiomics studies that predict Ki-67 expression in lung cancer. We evaluated quality using both Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and the Radiomics Quality Score (RQS) tools. For statistical analysis in the meta-analysis, we used STATA 14.2 to assess sensitivity, specificity, heterogeneity, and diagnostic values.ResultsTen retrospective studies were pooled in the meta-analysis. The findings demonstrated that the use of computed tomography (CT) scan-based radiomics for predicting Ki-67 expression in lung cancer exhibited encouraging diagnostic performance. Pooled sensitivity, specificity, and area under the curve (AUC) in training cohorts were 0.78, 0.81, and 0.85, respectively. In validation cohorts, these values were 0.78, 0.70, and 0.81. Quality assessment using QUADAS-2 and RQS indicated generally acceptable study quality. Heterogeneity in training cohorts, attributed to factors like contrast-enhanced CT scans and specific Ki-67 thresholds, was observed. Notably, publication bias was detected in the training cohort, indicating that positive results are more likely to be published than non-significant or negative results. Thus, journals are encouraged to publish negative results as well.ConclusionIn summary, CT-based radiomics exhibit promise in predicting Ki-67 expression in lung cancer. While the results suggest potential clinical utility, additional research efforts should concentrate on enhancing diagnostic accuracy. This could pave the way for the integration of radiomics methods as a less invasive alternative to current procedures like biopsy and surgery in the assessment of Ki-67 expression.

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“…Threshold effect was analyzed by the Spearman correlation coefficient (r), which was calculated between the logit of the true positive rate and the false positive rate. 13 When p > 0.05, the threshold effect can be ignored when analyzing the source of heterogeneity.…”

Section: Discussionmentioning

confidence: 99%

Gene mutations as predictors of central lymph mode metastasis in cN0 PTC: A meta‐analysis

Ji,

Shi

2023

Clinical Genetics

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Gene mutations could predict the tumor progression and prognosis, which are us to predict CLNM in patients with cN0 PTC, however, these results are not consistent. This meta‐analysis tried to identify gene mutations which could predict CLNM in patients with cN0 PTC. A systematic search was performed for identifying relevant literature published prior to July 2023 in three search engines: PubMed, EMBASE and Web of Science. Studies that investigated the gene mutations for CLNM in patients with cN0 PTC were included in our meta‐analysis. Sixteen studies, including 6095 cN0 PTC with BRAF mutations were include in our meta‐analysis. The prevalence of CLNM in cN0 PTC ranged from 13.7% to 50.6%. The pooled analysis demonstrated that BRAFV600E mutation is significantly associated with CLNM (OR = 2.01, 95% CI: 1.55–2.60, p < 0.001) in PTC and PTMC (OR = 1.70, 95% CI: 0.51–1.81, p < 0.001). Whereas, cN0 PTC with TERT (OR = 1.94, 95% CI: 0.51–7.36, p = 0.33) and KRAS (OR = 0.57, 95% CI: 0.51–1.81, p = 0.34) mutations might not contribute to predict CLNM. Our analysis identified that BRAF mutation was a predictive factor for cN0 PTC, as well as for cN0 PTMC, which could be useful for clinician to accurately choose prophylactic CLND and better manage cN0 PTC.

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Radiomics diagnostic performance in predicting lymph node metastasis of papillary thyroid carcinoma: A systematic review and meta-analysis

Cited by 9 publications

References 46 publications

Application of radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis

Application of radiomics for preoperative prediction of lymph node metastasis in colorectal cancer: a systematic review and meta-analysis

CT-based radiomics for predicting Ki-67 expression in lung cancer: a systematic review and meta-analysis

Gene mutations as predictors of central lymph mode metastasis in cN0 PTC: A meta‐analysis

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