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Development and Validation of CT-Based Radiomics Signature for Overall Survival Prediction in Multi-organ Cancer

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Abstract

The malignant tumors in nature share some common morphological characteristics. Radiomics is not only images but also data; we think that a probability exists in a set of radiomics signatures extracted from CT scan images of one cancer tumor in one specific organ also be utilized for overall survival prediction in different types of cancers in different organs. The retrospective study enrolled four data sets of cancer patients in three different organs (420, 157, 137, and 191 patients for lung 1 training, lung 2 testing, and two external validation set: kidney and head and neck, respectively). In the training set, radiomics features were obtained from CT scan images, and essential features were chosen by LASSO algorithm. Univariable and multivariable analyses were then conducted to find a radiomics signature via Cox proportional hazard regression. The Kaplan–Meier curve was performed based on the risk score. The integrated time-dependent area under the ROC curve (iAUC) was calculated for each predictive model. In the training set, Kaplan–Meier curve classified patients as high or low-risk groups (p-value < 0.001; log-rank test). The risk score of radiomics signature was locked and independently evaluated in the testing set, and two external validation sets showed significant differences (p-value < 0.05; log-rank test). A combined model (radiomics + clinical) showed improved iAUC in lung 1, lung 2, head and neck, and kidney data set are 0.621 (95% CI 0.588, 0.654), 0.736 (95% CI 0.654, 0.819), 0.732 (95% CI 0.655, 0.809), and 0.834 (95% CI 0.722, 0.946), respectively. We believe that CT-based radiomics signatures for predicting overall survival in various cancer sites may exist.

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Data Availability

All datasets are publicly available at The Cancer Imaging Archive (TCIA) (https://www.cancerimagingarchive.net/). The first cohort, Lung 1 (NSCLC-Radiomics): https://doi.org/10.7937/K9/TCIA.2015.PF0M9REI. The second cohort, Lung 2 (NSCLC Radiogenomics): http://doi.org/10.7937/K9/TCIA.2017.7hs46erv. The third cohort, Head-Neck-Radiomics-HN1: https://doi.org/10.7937/tcia.2019.8kap372n. The fourth cohort, Training Set of the 2019 Kidney and Kidney Tumor Segmentation Challenge (KiTS19): https://doi.org/10.7937/TCIA.2019.IX49E8NX Source codes for analyses are made available online at https://github.com/huanlevietMD/Overall-survival-prediction-in-multi-organ-cancer.

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Funding

This work was supported by the National Science and Technology Council, Taiwan (grant numbers MOST110-2221-E-038–001-MY2 and MOST111-2628-E-038–002-MY3), and the Taiwan Higher Education Sprout Project by the Ministry of Education (grant number DP2-111–21121-01-A-12).

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Authors and Affiliations

  1. International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, 110, Taiwan

    Viet Huan Le, Quang Hien Kha, Tran Nguyen Tuan Minh, Van Hiep Nguyen & Van Long Le

  2. Department of Thoracic Surgery, Khanh Hoa General Hospital, Nha Trang, 65000, Vietnam

    Viet Huan Le

  3. Oncology Center, Bai Chay Hospital, Quang Ninh, 20000, Vietnam

    Van Hiep Nguyen

  4. Department of Anesthesiology and Critical Care, Hue University of Medicine and Pharmacy, Hue University, Hue City, 52000, Vietnam

    Van Long Le

  5. Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, 106, Taiwan

    Nguyen Quoc Khanh Le

  6. Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, 106, Taiwan

    Nguyen Quoc Khanh Le

  7. Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, 110, Taiwan

    Nguyen Quoc Khanh Le

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  1. Viet Huan Le
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Contributions

Viet Huan Le: conceptualization, methodology, validation, formal analysis, investigation, writing—original draft preparation, visualization, Quang Hien Kha: validation, investigation, data curation, writing—original draft preparation, visualization, Tran Nguyen Tuan Minh: investigation, data curation, Van Hiep Nguyen: validation, data curation, Van Long Le: validation, visualization, Nguyen Quoc Khanh Le: conceptualization, methodology, validation, writing—review and editing, supervision, funding acquisition, All authors have read and agreed to the published version of the manuscript.

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Correspondence to Nguyen Quoc Khanh Le.

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Le, V.H., Kha, Q.H., Minh, T.N.T. et al. Development and Validation of CT-Based Radiomics Signature for Overall Survival Prediction in Multi-organ Cancer. J Digit Imaging 36, 911–922 (2023). https://doi.org/10.1007/s10278-023-00778-0

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  • DOI: https://doi.org/10.1007/s10278-023-00778-0

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