Development and validation of novel radiomics-based nomograms for the prediction of EGFR mutations and Ki-67 proliferation index in non-small cell lung cancer

Dong, Yinping; Jiang, Zekun; Li, Chaowei; Dong, Shuai; Zhang, Shengdong; Lv, Yunhong; Sun, Fenghao

doi:10.21037/qims-21-980

Cited by 17 publications

(26 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar to previous findings, inverse variance and skewness were included among radiomics features in the EGFR prediction model, 25,36 and among clinical features, smoking history and sex were used in the EGFR mutation prediction model 32,37 . In the KRAS group, smoking history was excluded from the final model, which is inconsistent with some studies, 38,39 suggesting that smoking history may be an influencing factor when predicting KRAS mutation status in patients with NSCLC, but not in patients with EGFR − tumors.…”

Section: Discussionmentioning

confidence: 86%

See 1 more Smart Citation

CT‐based non‐invasive identification of the most common gene mutation status in patients with non‐small cell lung cancer

Chen,

Gao,

Ding

et al. 2023

Medical Physics

View full text Add to dashboard Cite

BackgroundEpidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homolog (KRAS) are mutually exclusive, and they are two important genes that are most prone to mutation in patients with non‐small cell lung cancer.PurposeThis retrospective study investigated the ability of radiomics to predict the mutation status of EGFR and KRAS in patients with non‐small cell lung cancer (NSCLC) and guide precision medicine.MethodsComputed tomography images of 1045 NSCLC patients from five different institutions were collected, and 1204 imaging features were extracted. In the training set (EGFR: 678, KRAS: 246), Max‐Relevance and Min‐Redundancy and least absolute shrinkage and selection operator logistic regression were used to screen radiomics features. The combination of selected radiomics features and clinical factors was used to establish the combined models in identifying EGFR and KRAS mutation status, respectively, through stepwise logistic regression. Then, on two independent external validation sets (EGFR: 203/164, KRAS: 123/95), the performance of each model was evaluated separately, and then the overall performance of predicting the two mutation states was calculated.ResultsIn the EGFR and KRAS groups, radiomics signatures comprised 14 and 10 radiomics features, respectively. They were mutually exclusive between the tumors with positive EGFR mutation and those with positive KRAS mutation in imaging phenotype. For the EGFR group, the area under the curve (AUC) of the combined model in the two validation sets was 0.871 (95% CI: 0.821–0.926) and 0.861 (95% CI: 0.802–0.911), respectively, whereas the AUC of the combined model in the two validation sets was 0.798 (95% CI: 0.739–0.850) and 0.778 (95% CI: 0.735–0.821), respectively, for the KRAS group. Considering both EGFR and KRAS, the overall precision, recall, and F1‐score of the combined model in the two validation sets were 0.704, 0.844, and 0.768, as well as 0.754, 0.693, and 0.722, respectively.ConclusionsOur study demonstrates the potential of radiomics in the non‐invasive identification of EGFR and KRAS mutation status, which may guide patients with non‐small cell lung cancer to choose the most appropriate personalized treatment. This method can be used when biopsy will bring unacceptable risk to patients with NSCLC.

show abstract

Section: Discussionmentioning

confidence: 86%

“…Recently, three multicenter studies [30][31][32] predicted EGFR mutation status using radiomics based on CT images. The number of patients enrolled was 132, 194, and 636, and the AUCs of the validation set were 0.80, 0.77, and 0.803, respectively.…”

Section: Discussionmentioning

confidence: 99%

CT‐based non‐invasive identification of the most common gene mutation status in patients with non‐small cell lung cancer

Chen,

Gao,

Ding

et al. 2023

Medical Physics

View full text Add to dashboard Cite

show abstract

“…4). Dong et al (16) achieved the highest RQS of 19 (52.78%), while Gu et al (21) scored the lowest with 1 (2.78%). These results underscore the importance of rigorous and standardized reporting in radiomics studies.…”

Section: Resultsmentioning

confidence: 97%

“…Most studies in our review demonstrated a strong preference for logistic regression models in their radiomic analysis, with some incorporating additional methodologies for enhanced robustness and precision (12–14, 16–18, 20). In contrast, Zhu et al and Sun et al diversified their approach by integrating logistic regression with decision trees, support vector machines (SVM), and adaboost (15, 19).…”

Section: Resultsmentioning

confidence: 99%

Diagnostic Performance of Radiomics in Prediction of Ki-67 Index Status in Non-small Cell Lung Cancer: A Systematic Review and Meta-Analysis

Shahidi,

Hassannejad,

Baradaran

et al. 2024

Preprint

View full text Add to dashboard Cite

Background: Lung cancer is a global health concern, in part due to its high prevalence and invasiveness. The Ki-67 index, indicating cellular proliferation, is pivotal for assessing lung cancer aggressiveness. Radiomics is the inference of quantifiable data features from medical images through algorithms and may offer insights into tumor behavior. Here, we perform a systematic review and meta-analysis to assess the performance of radiomics for predicting Ki-67 status in Non-small Cell Lung Cancer (NSCLC) on CT scan. Methods and materials: A comprehensive search of the current literature was conducted using relevant keywords in PubMed/MEDLINE, Embase, Scopus, and Web of Science databases from inception to November 16, 2023. Original studies discussing the performance of CT-based radiomics for predicting Ki-67 status in NSCLC cohorts were included. The quality assessment involved quality assessment of diagnostic accuracy studies (QUADAS-2) and radiomics quality score (RQS). Quantitative meta-analysis, using R, assessed pooled sensitivity and specificity in NSCLC cohorts. Results: We identified 10 studies that met the inclusion criteria, involving 2279 participants, with 9 of these studies included in quantitative meta-analysis. The overall quality of the included studies was moderate to high based on QUADAS-2 and RQS assessment. The pooled sensitivity and specificity of radiomics-based models for predicting the Ki-67 status of NSCLC training cohorts were 0.78 (95% CI [0.73; 0.83]) and 0.76 (95% CI [0.70; 0.82]), respectively. The pooled sensitivity and specificity of radiomics-based models for predicting the Ki-67 status of NSCLC validation cohorts were 0.79 (95% CI [0.73; 0.84]) and 0.69 (95% CI [0.61; 0.76]), respectively. Substantial heterogeneity was noted in the pooled sensitivity and specificity of training cohorts and the pooled specificity of validation cohorts (I2 > 40%). It was identified that utilizing ITK-SNAP as a segmentation software contributed to a significantly higher pooled sensitivity. Conclusion: This meta-analysis indicates promising diagnostic accuracy of radiomics in predicting Ki-67 in NSCLC. The study underscores radiomics' potential in personalized lung cancer management, advocating for prospective studies with standardized methodologies and larger samples. Keywords: Lung cancer, Ki-67 index, radiomics, CT scan

show abstract

“…All of the included studies were designed retrospectively, conducted in China, and used CT scans as the only imaging modality. Seven studies were conducted at single-center institutions (29, 30,[33][34][35][36]38), while three studies adopted a multicenter approach (31,32,37). Three studies used contrast-enhanced CT scans for their images (30,33,35).…”

Section: Characteristics Of the Included Studiesmentioning

confidence: 99%

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.

View full text Add to dashboard Cite

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.

show abstract

Development and validation of novel radiomics-based nomograms for the prediction of EGFR mutations and Ki-67 proliferation index in non-small cell lung cancer

Cited by 17 publications

References 53 publications

CT‐based non‐invasive identification of the most common gene mutation status in patients with non‐small cell lung cancer

CT‐based non‐invasive identification of the most common gene mutation status in patients with non‐small cell lung cancer

Diagnostic Performance of Radiomics in Prediction of Ki-67 Index Status in Non-small Cell Lung Cancer: A Systematic Review and Meta-Analysis

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

Contact Info

Product

Resources

About