Jincheng Wang
Xudong Zhang
Jinhui Liu
Yin Yin- 1Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
- 2Department of Hepatobiliary Surgery, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
- 3The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- 4Department of Hepato-Biliary-Pancreatic Surgery, The Affiliated Changzhou, No.2 People’s Hospital of Nanjing Medical University, Changzhou, China
Editorial on the Research Topic
The use of data mining in radiological-pathological images for personal medicine
The use of data mining in radiological-pathological images for personalized medicine is a promising and rapidly developing field. This innovative approach involves the integration of large amounts of data from medical images, pathology reports, and clinical records to improve patient care and treatment outcomes.
Currently, there are mainly two methods for data mining on medical images: image findings (subjectively summarized factors) (Renzulli et al., 2016; Yue et al., 2022) and high-order features (objectively calculated features, such as radiomics and pathomics) (Lambin et al., 2017; Chen et al., 2022). Image findings can be easily obtained and explained by pathophysiological mechanisms (Segal et al., 2007). However, they are determined by readers and stability need to be improved. Fortunately, deep learning techniques can behave like an experienced reader and compute more robust evaluation results (Zhao et al., 2020). High-order features depend on image analysis techniques which attracted great attention in the last few years. Most research focused on tumor characteristics and prognosis (Xu et al., 2019; Ji et al., 2020; Hu et al., 2022; Xu et al., 2022), and chronic disease can also be fully detected (Wang et al., 2020; Zhang et al., 2022a; Zhang et al., 2022b; Wang et al., 2022). Different from image findings, it is hard to explain biological role of each high-order feature which is worthy of more exploration.
We believe articles published in this Research Topic can provide more evidence for data mining on medical image in personalized medicine. Despite the many benefits of data mining in radiological-pathological images, there are also some potential challenges and risks to be aware of. One of the biggest challenges is ensuring that the data being used is accurate, reliable, and representative of the patient population being studied. Additionally, there are concerns about privacy and data security, particularly as medical records and images contain sensitive information about patients.
Overall, the use of data mining in radiological-pathological images for personalized medicine is an exciting development that has the potential to transform the way we diagnose and treat diseases. With careful attention to data quality and patient privacy, this approach has the potential to help clinical decision-making and improve patient outcomes.
Author contributions
JW and XZ wrote the manuscript. JL and YY edited the language.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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References
Chen, D., Fu, M., Chi, L., Lin, L., Cheng, J., Xue, W., et al. (2022). Prognostic and predictive value of a pathomics signature in gastric cancer. Nat. Commun. 13, 6903. doi:10.1038/s41467-022-34703-w
Hu, S., Lyu, X., Li, W., Cui, X., Liu, Q., Xu, X., et al. (2022). Radiomics analysis on noncontrast CT for distinguishing hepatic hemangioma (HH) and hepatocellular carcinoma (HCC). Contrast Media Mol. Imaging 2022, 7693631. doi:10.1155/2022/7693631
Ji, G. W., Zhu, F. P., Xu, Q., Wang, K., Wu, M. Y., Tang, W. W., et al. (2020). Radiomic features at contrast-enhanced ct predict recurrence in early stage hepatocellular carcinoma: A multi-institutional study. Radiology 294, 568–579. doi:10.1148/radiol.2020191470
Lambin, P., Leijenaar, R. T. H., Deist, T. M., Peerlings, J., de Jong, E. E. C., van Timmeren, J., et al. (2017). Radiomics: The bridge between medical imaging and personalized medicine. Nat. Rev. Clin. Oncol. 14, 749–762. doi:10.1038/nrclinonc.2017.141
Renzulli, M., Brocchi, S., Cucchetti, A., Mazzotti, F., Mosconi, C., Sportoletti, C., et al. (2016). Can current preoperative imaging Be used to detect microvascular invasion of hepatocellular carcinoma? Radiology 279, 432–442. doi:10.1148/radiol.2015150998
Segal, E., Sirlin, C. B., Ooi, C., Adler, A. S., Gollub, J., Chen, X., et al. (2007). Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat. Biotechnol. 25, 675–680. doi:10.1038/nbt1306
Wang, J. C., Fu, R., Tao, X. W., Mao, Y. F., Wang, F., Zhang, Z. C., et al. (2020). A radiomics-based model on non-contrast CT for predicting cirrhosis: Make the most of image data. Biomark. Res. 8, 47. doi:10.1186/s40364-020-00219-y
Wang, J., Tang, S., Mao, Y., Wu, J., Xu, S., Yue, Q., et al. (2022). Radiomics analysis of contrast-enhanced CT for staging liver fibrosis: An update for image biomarker. Hepatol. Int. 16, 627–639. doi:10.1007/s12072-022-10326-7
Xu, X., Mao, Y., Tang, Y., Liu, Y., Xue, C., Yue, Q., et al. (2022). Classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on radiomic analysis. Comput. Math. Methods Med. 2022, 5334095. doi:10.1155/2022/5334095
Xu, X., Zhang, H. L., Liu, Q. P., Sun, S. W., Zhang, J., Zhu, F. P., et al. (2019). Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. J. Hepatol. 70, 1133–1144. doi:10.1016/j.jhep.2019.02.023
Yue, Q., Zhou, Z., Zhang, X., Xu, X., Liu, Y., Wang, K., et al. (2022). Contrast-enhanced CT findings-based model to predict MVI in patients with hepatocellular carcinoma. BMC Gastroenterol. 22, 544. doi:10.1186/s12876-022-02586-2
Zhang, X., Wang, J., Wu, B., Li, T., Jin, L., Wu, Y., et al. (2022). A nomogram-based model and ultrasonic radiomic features for gallbladder polyp classification. J. Gastroenterol. Hepatol. 37, 1380–1388. doi:10.1111/jgh.15841
Zhang, X., Wang, J., Wu, B., Li, T., Jin, L., Wu, Y., et al. (2022). A nomogram-based model to predict neoplastic risk for patients with gallbladder polyps. J. Clin. Transl. Hepatol. 10, 263–272. doi:10.14218/JCTH.2021.00078
Keywords: data mining, medical image, deep learning, image finding, personalized medicine
Citation: Wang J, Zhang X, Liu J and Yin Y (2023) Editorial: The use of data mining in radiological-pathological images for personal medicine. Front. Genet. 14:1187040. doi: 10.3389/fgene.2023.1187040
Received: 15 March 2023; Accepted: 21 March 2023;
Published: 27 March 2023.
Edited and reviewed by:
Quan Zou, University of Electronic Science and Technology of China, ChinaCopyright © 2023 Wang, Zhang, Liu and Yin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Yin Yin, jyinjyin@sina.com; Jinhui Liu, jinhuiliu@njmu.edu.cn; Xudong Zhang, zhangxudong@njmu.edu.cn; Jincheng Wang, jcwang_med@hotmail.com