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Radiomics approaches to lung cancers

Guest Editor

Kim Lori Sandler, MD, Vanderbilt University Medical Center, US

BMC Pulmonary Medicine called for submissions to our Collection on Radiomics approaches to lung cancers. Lung cancer remains a significant global health challenge, necessitating continuous advancements in diagnostic and therapeutic strategies. Radiomics, an emerging field at the nexus of imaging and data science, holds immense promise in revolutionizing our understanding and management of lung cancers. By extracting a wealth of quantitative features from medical images, radiomics empowers clinicians to decipher these images, offering unprecedented insights into tumor characteristics and behavior.


New Content ItemThis Collection supports and amplifies research related to SDG 3: Good Health & Wellbeing.

Meet the Guest Editors

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Kim Lori Sandler, MD, Vanderbilt University Medical Center, US

Dr Kim Sandler completed her undergraduate education at Emory University and both medical school and residency at Vanderbilt University. She trained as a cardiothoracic radiologist and is currently an Associate Professor of Radiology and Radiological Sciences at Vanderbilt University Medical Center. Dr Sandler is a clinician-scientist and has served as the Co-Director of the Vanderbilt Lung Screening Program since 2016. She is a women’s health advocate who is working to leverage the success of screening for breast cancer to improve enrollment of women in lung screening.  In addition to women’s health, Dr Sandler continues to focus her academic efforts on improving lung screening through machine learning and both imaging and blood-based biomarkers.

About the Collection

BMC Pulmonary Medicine called for submissions to our Collection on Radiomics approaches to lung cancers. Lung cancer remains a significant global health challenge, necessitating continuous advancements in diagnostic and therapeutic strategies. Radiomics, an emerging field at the nexus of imaging and data science, holds immense promise in revolutionizing our understanding and management of lung cancers. By extracting a wealth of quantitative features from medical images, radiomics empowers clinicians to decipher these images, offering unprecedented insights into tumor characteristics and behavior.

In support of SDG 3: Good Health & Wellbeing, this Collection aims to explore the dynamic intersection of radiomics and oncology, shedding light on the potential of cutting-edge imaging techniques in the diagnosis, prognosis, and treatment of lung cancers.

Topics of interest include, but are not limited to, the following:
• Quantitative Imaging in Lung Cancer
• Radiomic Biomarkers for Prognosis
• Advanced Radiology Techniques in Lung Cancer
• Machine Learning in Radiomics
• Tumor Heterogeneity and Radiomics
• Radiomics for Treatment Response Assessment
• Integrating Radiomics into Clinical Practice
• Molecular Correlates in Radiogenomics
• PET-CT Applications in Lung Cancer Radiomics
•MRI and Radiomics in Lung Cancer Characterization


Image credit: [M] RAJCREATIONZS / stock.adobe.com

  1. Pulmonary mucous gland adenoma (MGA) is an exceptionally rare benign tumor. Even with the assistance of 18 F-FDG PET/CT, the accurate diagnosis of MGA as lung cancer remains challenging. Only one case of fluor...

    Authors: Chen Xiaomei, Zhou Jiahui, Zhang Fangbiao and Zheng Chunhui
    Citation: BMC Pulmonary Medicine 2025 25:137
    Content type: Case Report Published on:

  2. Lymphovascular invasion (LVI) was histological factor that was closely related to prognosis of lung adenocarcinoma (LAC).The primary aim was to investigate the value of a nomogram incorporating clinical and co...

    Authors: Miaomaio Lin, Xiang Zhao, Haipeng Huang, Huashan Lin and Kai Li
    Citation: BMC Pulmonary Medicine 2024 24:588
    Content type: Research Published on:

  3. Lung cancer continues to pose a serious risk to human health. With a high mortality rate, non-small cell lung cancer (NSCLC) is the major type of lung cancer, making up to 85% of all cases of lung cancer. Lung...

    Authors: Xingxing Zheng, Hongzhe Tian, Wei Li, Jun Li, Kai Xu, Chenwang Jin and Yuhui Pang
    Citation: BMC Pulmonary Medicine 2024 24:545
    Content type: Research Published on:

  4. To develop and validate a radiomic model for differentiating pulmonary invasive adenocarcinomas from benign lesions based on follow-up longitudinal CT images.

    Authors: Zhengming Wang, Fei Wang, Yan Yang, Weijie Fan, Li Wen and Dong Zhang
    Citation: BMC Pulmonary Medicine 2024 24:534
    Content type: Research Published on:

  5. A 47-year-old Asian woman was admitted with worsening chest tightness and dyspnea for 10 days. Computed tomography (CT) showed changes in the trachea and segmental bronchi. Pulmonary function results suggestiv...

    Authors: Ji-Wei Zhu, Guo-Hong Cao, Fu-Quan Gao, Zhang Cao, Yan Feng, Hong-Kun Sun, Lu Liu, Pan Xu, Chang-Jun Lv and Lei Pan
    Citation: BMC Pulmonary Medicine 2024 24:514
    Content type: Case Report Published on:

  6. Currently, deep learning methods for the classification of benign and malignant lung nodules encounter challenges encompassing intricate and unstable algorithmic models, limited data adaptability, and an abund...

    Authors: Wenju Wang, Shuya Yin, Fang Ye, Yinan Chen, Lin Zhu and Hong Yu
    Citation: BMC Pulmonary Medicine 2024 24:465
    Content type: Research Published on:

Submission Guidelines

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This Collection welcomes submission of original Research Articles. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. Articles for this Collection should be submitted via our submission system, Snapp. During the submission process you will be asked whether you are submitting to a Collection, please select "Radiomics approaches to lung cancers" from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.