CT-based radiomics for predicting Ki-67 expression in lung cancer: a systematic review and meta-analysis
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 reflecti...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-02-01
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| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2024.1329801/full |
| _version_ | 1797322411927928832 |
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| author | Xinmin Luo Renying Zheng Jiao Zhang Juan He Wei Luo Zhi Jiang Qiang Li |
| author_facet | Xinmin Luo Renying Zheng Jiao Zhang Juan He Wei Luo Zhi Jiang Qiang Li |
| author_sort | Xinmin Luo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T05:12:59Z |
| format | Article |
| id | doaj.art-3798fb5faca0460d815f379d27529f02 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-03-08T05:12:59Z |
| publishDate | 2024-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-3798fb5faca0460d815f379d27529f022024-02-07T05:23:12ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2024-02-011410.3389/fonc.2024.13298011329801CT-based radiomics for predicting Ki-67 expression in lung cancer: a systematic review and meta-analysisXinmin Luo0Renying Zheng1Jiao Zhang2Juan He3Wei Luo4Zhi Jiang5Qiang Li6Department of Radiology, People’s Hospital of Yuechi County, Guang’an, Sichuan, ChinaDepartment of Oncology, People’s Hospital of Yuechi County, Guang’an, Sichuan, ChinaDepartment of Radiology, People’s Hospital of Yuechi County, Guang’an, Sichuan, ChinaDepartment of Radiology, People’s Hospital of Yuechi County, Guang’an, Sichuan, ChinaDepartment of Radiology, People’s Hospital of Yuechi County, Guang’an, Sichuan, ChinaDepartment of Radiology, People’s Hospital of Yuechi County, Guang’an, Sichuan, ChinaDepartment of Radiology, Yuechi County Traditional Chinese Medicine Hospital in Sichuan Province, Guang’an, Sichuan, ChinaBackgroundRadiomics, 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.https://www.frontiersin.org/articles/10.3389/fonc.2024.1329801/fullradiomicsCT-scanartificial intelligenceKi-67lung cancermachine learning |
| spellingShingle | Xinmin Luo Renying Zheng Jiao Zhang Juan He Wei Luo Zhi Jiang Qiang Li CT-based radiomics for predicting Ki-67 expression in lung cancer: a systematic review and meta-analysis Frontiers in Oncology radiomics CT-scan artificial intelligence Ki-67 lung cancer machine learning |
| title | CT-based radiomics for predicting Ki-67 expression in lung cancer: a systematic review and meta-analysis |
| title_full | CT-based radiomics for predicting Ki-67 expression in lung cancer: a systematic review and meta-analysis |
| title_fullStr | CT-based radiomics for predicting Ki-67 expression in lung cancer: a systematic review and meta-analysis |
| title_full_unstemmed | CT-based radiomics for predicting Ki-67 expression in lung cancer: a systematic review and meta-analysis |
| title_short | CT-based radiomics for predicting Ki-67 expression in lung cancer: a systematic review and meta-analysis |
| title_sort | ct based radiomics for predicting ki 67 expression in lung cancer a systematic review and meta analysis |
| topic | radiomics CT-scan artificial intelligence Ki-67 lung cancer machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2024.1329801/full |
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