The value of MRI in predicting hepatocellular carcinoma with cytokeratin 19 expression: a systematic review and meta-analysis

CCL26 as a prognostic biomarker in hepatocellular carcinoma: integrating bioinformatics analysis, clinical validation, and radiomics score

BACKGROUND

CCL26 has been identified as a potential prognostic biomarker in hepatocellular carcinoma (HCC). This study aimed to assess the prognostic significance of CCL26 and develop a radiomics score (Rad-score) for predicting outcomes in HCC patients.

METHODS

Data from 316 HCC patients, including genomic information, computed tomography (CT) images, and clinicopathological data, were analyzed. The prognostic value of CCL26 was evaluated in 295 TCGA patients using Kaplan-Meier and Cox regression analyses, and validated in 21 patients from Jiujiang No. 1 People's Hospital. Gene set variation and immune cell infiltration analyses were conducted to elucidate the biological functions of CCL26. Radiomic models for predicting CCL26 expression were constructed using CT images and genomic data from 34 TCGA patients. Radiomic features were extracted from tumor regions and screened using maximum relevance minimum redundancy (mRMR) and recursive feature elimination (RFE). Two Rad-scores were generated via logistic regression and validated using internal fivefold cross-validation. A prognostic nomogram incorporating the optimal Rad-score, gender, and hepatic inflammation was developed using Cox proportional hazards regression.

RESULTS

Elevated CCL26 levels correlated with poor prognosis, as confirmed by immunohistochemistry. The optimal Rad-score, combined with gender and hepatic inflammation, accurately predicted overall survival (OS), with areas under the receiver operating characteristic curve (AUCs) of 0.819, 0.902, and 0.982 for 24-, 36-, and 48 month survival, respectively. Calibration curves and decision curve analysis (DCA) demonstrated the accuracy and clinical utility of the model.

CONCLUSIONS

CCL26 serves as a significant prognostic biomarker in HCC. The developed Rad-score provides an effective, non-invasive tool for predicting patient outcomes and enhancing clinical decision-making. This study not only highlights the prognostic role of CCL26 but also offers a novel approach for evaluating HCC patient prognosis through radiomics.

A Bayesian meta-analysis on MRI-based radiomics for predicting EGFR mutation in brain metastasis of lung cancer

OBJECTIVES

This study aimed to investigate the diagnostic test accuracy of MRI-based radiomics studies for predicting EGFR mutation in brain metastasis originating from lung cancer.

METHODS

This meta-analysis, conducted following PRISMA guidelines, involved a systematic search in PubMed, Embase, and Web of Science up to November 3, 2024. Eligibility criteria followed the PICO framework, assessing population, intervention, comparison, and outcome. The RQS and QUADAS-2 tools were employed for quality assessment. A Bayesian model determined summary estimates, and statistical analysis was conducted using R and STATA software.

RESULTS

Eleven studies consisting of nine training and ten validation cohorts were included in the meta-analysis. In the training cohorts, MRI-based radiomics showed robust predictive performance for EGFR mutations in brain metastases, with an AUC of 0.90 (95% CI: 0.82-0.93), sensitivity of 0.84 (95% CI: 0.80-0.88), specificity of 0.86 (95% CI: 0.80-0.91), and a diagnostic odds ratio (DOR) of 34.17 (95% CI: 19.16-57.49). Validation cohorts confirmed strong performance, with an AUC of 0.91 (95% CI: 0.69-0.95), sensitivity of 0.79 (95% CI: 0.73-0.84), specificity of 0.88 (95% CI: 0.83-0.93), and a DOR of 31.33 (95% CI: 15.50-58.3). Subgroup analyses revealed notable trends: the T1C + T2WI sequences and 3.0 T scanners showed potential superiority, machine learning-based radiomics and manual segmentation exhibited higher diagnostic accuracy, and PyRadiomics emerged as the preferred feature extraction software.

CONCLUSION

This meta-analysis suggests that MRI-based radiomics holds promise for the non-invasive prediction of EGFR mutations in brain metastases of lung cancer.

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

Background

Radiomics, 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).

Methods

A 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.

Results

Ten 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.

Conclusion

In 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.