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A miRNA-based diagnostic model predicts resectable lung cancer in humans with high accuracy.

ABSTRACT: Lung cancer, the leading cause of cancer death worldwide, is most frequently detected through imaging tests. In this study, we investigated serum microRNAs (miRNAs) as a possible early screening tool for resectable lung cancer. First, we used serum samples from participants with and without lung cancer to comprehensively create 2588 miRNAs profiles; next, we established a diagnostic model based on the combined expression levels of two miRNAs (miR-1268b and miR-6075) in the discovery set (208 lung cancer patients and 208 non-cancer participants). The model displayed a sensitivity of 99% and specificity of 99% in the validation set (1358 patients and 1970 non-cancer participants) and exhibited high sensitivity regardless of histological type and pathological TNM stage of the cancer. Moreover, the diagnostic index markedly decreased after lung cancer resection. Thus, the model we developed has the potential to markedly improve screening for resectable lung cancer.

SUBMITTER: Asakura K

PROVIDER: S-EPMC7081195 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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A miRNA-based diagnostic model predicts resectable lung cancer in humans with high accuracy.

Asakura Keisuke K Kadota Tsukasa T Matsuzaki Juntaro J Yoshida Yukihiro Y Yamamoto Yusuke Y Nakagawa Kazuo K Takizawa Satoko S Aoki Yoshiaki Y Nakamura Eiji E Miura Junichiro J Sakamoto Hiromi H Kato Ken K Watanabe Shun-Ichi SI Ochiya Takahiro T

Communications biology 20200319 1

Lung cancer, the leading cause of cancer death worldwide, is most frequently detected through imaging tests. In this study, we investigated serum microRNAs (miRNAs) as a possible early screening tool for resectable lung cancer. First, we used serum samples from participants with and without lung cancer to comprehensively create 2588 miRNAs profiles; next, we established a diagnostic model based on the combined expression levels of two miRNAs (miR-1268b and miR-6075) in the discovery set (208 lun ...[more]

PMID: 32193503

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Project description:BackgroundSpread through air space (STAS) has been identified as a pathological pattern associated with lung cancer progression. Patients with STAS were related to a worse prognosis compared with patients without STAS. The objective of this study was to establish a radiomics model capable of forecasting STAS before surgery, which can assist surgeons in selecting the most appropriate operation type for patients with STAS.MethodThere were 537 eligible patients retrospectively included in this study. ROI segmentation was performed manually on all CT images to identify the region of interest. From each segmented lesion, a total of 1688 features were extracted. The tumor size, maximum tumor diameters, and tumor type were also recorded. Using Spearman's correlation coefficient to calculate the correlation and redundancy of elements, and redundant features less than 0.80 were removed. In order to reduce the level of overfitting and avoid statistical biases, a dimension reduction process of the dataset was conducted to decrease the number of features. Finally, a radiomics model included 44 features was established to predict STAS. To evaluate the performance of the model, the receiver operating characteristic (ROC) curve was used, and the area under the curve (AUC) was calculated, and the accuracy of the model was verified by 10-fold cross-validation.ResultsThe incidence of STAS was 38.2% (205/537). The tumor type, maximum tumor diameters, and consolidation tumor ratio were significantly different between STAS group and non-STAS group. The training group included 430 patients, while the test group was consisted with 107. The training group achieved an AUC of 0.825 (sensitivity, 0.875; specificity, 0.621; and accuracy, 0.749) and the test group had an AUC of 0.802 (sensitivity, 0.797; specificity,0.688; and accuracy, 0.748). The 10-fold cross-validation had an AUC of 0.834.ConclusionCT-based radiomic model can predict STAS effectively, which is of great importance to guide the selection of operation types before surgery.

Dataset Information

A miRNA-based diagnostic model predicts resectable lung cancer in humans with high accuracy.

Publications

A miRNA-based diagnostic model predicts resectable lung cancer in humans with high accuracy.

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