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Luo X, Zheng R, Zhang J, He J, Luo W, Jiang Z, Li Q. CT-based radiomics for predicting Ki-67 expression in lung cancer: a systematic review and meta-analysis. Front Oncol 2024; 14:1329801. [PMID: 38384802 PMCID: PMC10879429 DOI: 10.3389/fonc.2024.1329801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 01/22/2024] [Indexed: 02/23/2024] Open
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.
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Affiliation(s)
- Xinmin Luo
- Department of Radiology, People’s Hospital of Yuechi County, Guang’an, Sichuan, China
| | - Renying Zheng
- Department of Oncology, People’s Hospital of Yuechi County, Guang’an, Sichuan, China
| | - Jiao Zhang
- Department of Radiology, People’s Hospital of Yuechi County, Guang’an, Sichuan, China
| | - Juan He
- Department of Radiology, People’s Hospital of Yuechi County, Guang’an, Sichuan, China
| | - Wei Luo
- Department of Radiology, People’s Hospital of Yuechi County, Guang’an, Sichuan, China
| | - Zhi Jiang
- Department of Radiology, People’s Hospital of Yuechi County, Guang’an, Sichuan, China
| | - Qiang Li
- Department of Radiology, Yuechi County Traditional Chinese Medicine Hospital in Sichuan Province, Guang’an, Sichuan, China
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Yin Y, Liu J, Sun R, Liu X, Zhou Z, Zhang H, Li D. Exploring the efficacy of 18F-FDG PET/CT in hepatocellular carcinoma diagnosis: role of Ki-67 index and tumor differentiation. Abdom Radiol (NY) 2023; 48:3408-3419. [PMID: 37682282 PMCID: PMC10556170 DOI: 10.1007/s00261-023-04027-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 08/13/2023] [Accepted: 08/14/2023] [Indexed: 09/09/2023]
Abstract
PURPOSE The sensitivity of [18F] fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET/CT) for detecting hepatocellular carcinoma (HCC) has not been clarified thoroughly. Our study seeks to explore the association between the Ki-67 index and FDG-avidity in HCC tumors using 18F-FDG PET/CT. METHODS 112 HCC lesions from 109 patients detected by 18F-FDG PET/CT were included retrospectively between August 2017 and May 2022, comprising 82 lesions in the training cohort and 30 in the validation cohort to simulate prospective studies. In the training cohort, lesions were stratified by a lesion-to-liver maximum standardized uptake value (SUVmax) ratio cut-off of 1.59. The relationships between lesion-to-liver SUVmax ratios and several clinical factors including tumor differentiation, alpha fetoprotein (AFP), carcinoembryonic antigen (CEA), hepatitis B virus (HBV) infection, Ki-67 index et al. were assessed. These findings were subsequently validated in the independent validation cohort. RESULTS In the training cohort, group A1 lesions demonstrated a higher Ki-67 index (%, 40.00 [30.00, 57.50] vs. 10.00 [5.00, 28.75], p<0.001) than group A0, the positive correlation between FDG-avidity and Ki-67 index was revealed by multivariate analysis, OR=1.040, 95% CI of OR [1.004-1.077], p=0.030. The calculated cut-off value was 17.5% using the receiver operating characteristic (ROC) curve, with an area under curve (AUC) of 0.834 and 95% CI [0.742-0.926], p<0.001. These findings were further validated in the independent validation cohort, with similar results (AUC=0.875, 95% CI [0.750-1.000], p<0.001). CONCLUSION In comparison to tumor differentiation, Ki-67 index demonstrates a stronger association with FDG-avidity in HCC tumors, and when the Ki-67 index exceeds 17.5%, 18F-FDG PET/CT might serve as a useful indicator for HCC.
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Affiliation(s)
- Yuping Yin
- Department of Nuclear Medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Jiachen Liu
- Department of Nuclear Medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Runlu Sun
- Department of Cardiology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xuming Liu
- Department of Pathology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhangchi Zhou
- Department of Nuclear Medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Hong Zhang
- Department of Nuclear Medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
- Department of Nuclear Medicine, Sun Yat-sen Memorial Hospital, No. 107, The West of Yanjiang Road, Guangzhou, 510120, China.
| | - Dan Li
- Department of Nuclear Medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
- Department of Nuclear Medicine, Sun Yat-sen Memorial Hospital, No. 107, The West of Yanjiang Road, Guangzhou, 510120, China.
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Liu F, Li Q, Xiang Z, Li X, Li F, Huang Y, Zeng Y, Lin H, Fang X, Yang Q. CT radiomics model for predicting the Ki-67 proliferation index of pure-solid non-small cell lung cancer: a multicenter study. Front Oncol 2023; 13:1175010. [PMID: 37706180 PMCID: PMC10497212 DOI: 10.3389/fonc.2023.1175010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 08/07/2023] [Indexed: 09/15/2023] Open
Abstract
Purpose This study aimed to explore the efficacy of the computed tomography (CT) radiomics model for predicting the Ki-67 proliferation index (PI) of pure-solid non-small cell lung cancer (NSCLC). Materials and methods This retrospective study included pure-solid NSCLC patients from five centers. The radiomics features were extracted from thin-slice, non-enhanced CT images of the chest. The minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) were used to reduce and select radiomics features. Logistic regression analysis was employed to build predictive models to determine Ki-67-high and Ki-67-low expression levels. Three prediction models were established: the clinical model, the radiomics model, and the nomogram model combining the radiomics signature and clinical features. The prediction efficiency of different models was evaluated using the area under the curve (AUC). Results A total of 211 NSCLC patients with pure-solid nodules or masses were included in the study (N=117 for the training cohort, N=49 for the internal validation cohort, and N=45 for the external validation cohort). The AUC values for the clinical models in the training, internal validation, and external validation cohorts were 0.73 (95% CI: 0.64-0.82), 0.75 (95% CI:0.62-0.89), and 0.72 (95% CI: 0.57-0.86), respectively. The radiomics models showed good predictive ability in diagnosing Ki-67 expression levels in the training cohort (AUC, 0.81 [95% CI: 0.73-0.89]), internal validation cohort (AUC, 0.81 [95% CI: 0.69-0.93]) and external validation cohort (AUC, 0.78 [95% CI: 0.64-0.91]). Compared to the clinical and radiomics models, the nomogram combining both radiomics signatures and clinical features had relatively better diagnostic performance in all three cohorts, with the AUC of 0.83 (95% CI: 0.76-0.90), 0.83 (95% CI: 0.71-0.94), and 0.81 (95% CI: 0.68-0.93), respectively. Conclusion The nomogram combining the radiomics signature and clinical features may be a potential non-invasive method for predicting Ki-67 expression levels in patients with pure-solid NSCLC.
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Affiliation(s)
- Fen Liu
- Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
| | - Qingcheng Li
- Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
| | - Zhiqiang Xiang
- Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
| | - Xiaofang Li
- Department of Radiology, The Affiliated Huaihua Hospital, Hengyang Medical School, University of South China, Huaihua, China
| | - Fangting Li
- Department of Radiology, People’s Hospital of Zhengzhou, Zhengzhou, China
| | - Yingqiong Huang
- Department of Radiology, The Second Affiliated Hospital of Hainan Medical University, Haikou, China
| | - Ye Zeng
- Department of Radiology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
| | - Huashan Lin
- Department of Pharmaceutical Diagnosis, GE Healthcare, Changsha, China
| | - Xiangjun Fang
- Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China
| | - Qinglai Yang
- Center for Molecular Imaging Probe, Hunan Province Key Laboratory of Tumor Cellular and Molecular Pathology, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, Hunan, China
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Guo S, Huang X, Xu C, Yu M, Li Y, Wu Z, Zhou A, Xu P. Multiregional radiomic model for breast cancer diagnosis: value of ultrasound-based peritumoral and parenchymal radiomics. Quant Imaging Med Surg 2023; 13:3127-3139. [PMID: 37179905 PMCID: PMC10167447 DOI: 10.21037/qims-22-939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 03/10/2023] [Indexed: 05/15/2023]
Abstract
Background Breast cancer consists not only of neoplastic cells but also of significant changes in the surrounding and parenchymal stroma, which can be reflected in radiomics. This study aimed to perform breast lesion classification through an ultrasound-based multiregional (intratumoral, peritumoral, and parenchymal) radiomic model. Methods We retrospectively reviewed ultrasound images of breast lesions from institution #1 (n=485) and institution #2 (n=106). Radiomic features were extracted from different regions (intratumoral, peritumoral, and ipsilateral breast parenchymal) and selected to train the random forest classifier with the training cohort (n=339, a subset of the institution #1 dataset). Then, the intratumoral, peritumoral, and parenchymal, intratumoral & peritumoral (In&Peri), intratumoral & parenchymal (In&P), and intratumoral & peritumoral & parenchymal (In&Peri&P) models were developed and validated on the internal (n=146, another subset of institution 1) and external (n=106, institution #2 dataset) test cohorts. Discrimination was evaluated using the area under the curve (AUC). Calibration curve and Hosmer-Lemeshow test assessed calibration. Integrated discrimination improvement (IDI) was used to assess performance improvement. Results The performance of the In&Peri (AUC values 0.892 and 0.866), In&P (0.866 and 0.863), and In&Peri&P (0.929 and 0.911) models was significantly better than that of the intratumoral model (0.849 and 0.838) in the internal and external test cohorts (IDI test, all P<0.05). The intratumoral, In&Peri and In&Peri&P models showed good calibration (Hosmer-Lemeshow test, all P>0.05). The multiregional (In&Peri&P) model had the highest discrimination among the 6 radiomic models in the test cohorts, respectively. Conclusions The multiregional model combining radiomic information of intratumoral, peritumoral, and ipsilateral parenchymal regions yielded better performance than the intratumoral model in distinguishing malignant breast lesions from benign lesions.
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Affiliation(s)
- Suping Guo
- Department of Ultrasonography, First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xingzhi Huang
- Department of Ultrasonography, First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Chao Xu
- Department of Ultrasonography, Jiangxi Tumor Hospital, Nanchang, China
| | - Meiqin Yu
- Department of Ultrasonography, First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yaohui Li
- Department of Ultrasonography, First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zhenghua Wu
- Department of Ultrasonography, First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Aiyun Zhou
- Department of Ultrasonography, First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Pan Xu
- Department of Ultrasonography, First Affiliated Hospital of Nanchang University, Nanchang, China
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Bao J, Liu Y, Ping X, Zha X, Hu S, Hu C. Preoperative Ki-67 Proliferation Index Prediction with a Radiomics Nomogram in Stage T1a-b Lung Adenocarcinoma. Eur J Radiol 2022; 155:110437. [DOI: 10.1016/j.ejrad.2022.110437] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 06/04/2022] [Accepted: 07/04/2022] [Indexed: 11/03/2022]
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