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. 2024 Feb 7:14:1329801.
doi: 10.3389/fonc.2024.1329801. eCollection 2024.

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

Xinmin Luo #  1 , Renying Zheng #  2 , Jiao Zhang  1 , Juan He  1 , Wei Luo  1 , Zhi Jiang  1 , Qiang Li  3
Affiliations

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

Xinmin Luo et al. Front Oncol. .

Abstract

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.

Keywords: CT-scan; Ki-67; artificial intelligence; lung cancer; machine learning; radiomics.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
PRISMA flowchart of the study.
Figure 2
Figure 2
QUADAS quality assessment per study (A) and per domain (B).
Figure 3
Figure 3
Coupled forest plot of the diagnostic performance in training cohorts.
Figure 4
Figure 4
Summary ROC curve with confidence and prediction regions in training (A) and validation (B) cohorts.
Figure 5
Figure 5
Coupled forest plot of the diagnostic performance in validation cohorts.
Figure 6
Figure 6
Deeks’ funnel plots for training (A) and validation (B) cohorts.
See this image and copyright information in PMC

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