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Call for papers - Radiomics in cancer diagnosis and treatment

Guest Editors:
Archya Dasgupta: Tata Memorial Centre, Homi Bhabha National Institute, India
Qingtao Qiu: Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, China
Stefania Volpe: European Institute of Oncology (Istituto Europeo di Oncologia, IEO) IRCCS and University of Milan, Italy

Submission Status: Open   |   Submission Deadline: 17 July 2024

BMC Cancer is calling for submissions to our Collection on radiomics in cancer diagnosis and treatment. Radiomics has emerged as a powerful and non-invasive approach for extracting quantitative data from medical images, providing valuable insights into tumor characterization, treatment response prediction, and prognostic assessment in cancer patients. The integration of radiomics into oncology has the potential to revolutionize cancer diagnosis, treatment planning, and monitoring, leading to more precise and personalized patient care.


New Content ItemThis Collection supports and amplifies research related to SDG 3: Good Health and Well-Being.

Meet the Guest Editors

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Archya Dasgupta: Tata Memorial Centre, Homi Bhabha National Institute, India

Dr Dasgupta is an Assistant Professor of Radiation Oncology at Tata Memorial Centre, Mumbai, India. He completed his residency training in Radiation Oncology at Tata Memorial Centre Mumbai, following which he was a clinical research fellow at the University of Toronto with specialized experience in CNS oncology, quantitative image analysis, and ablative radiotherapy (SRS and SBRT) for brain and spine malignancies. His research interests include clinical neuro-oncology, particle beam therapy, evolutionary biology, and quantitative image analysis. 

Qingtao Qiu: Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, China

Dr Qingtao Qiu, a medical physicist, is a senior researcher in the Department of Radiation Oncology and Physics, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, China. His area of research focuses on medical imaging processing, especially for radiomics, artificial intelligence in image segmentation, registration, and modelling. Over the past 5 years, he has published more than 40 journal articles in leading journals. His research projects have been funded by the National Natural Science Foundation of China and the Natural Science Foundation of Shandong Province. He also serves on the editorial board and as a reviewer for several professional journals.

Stefania Volpe: European Institute of Oncology (Istituto Europeo di Oncologia, IEO) IRCCS and University of Milan, Italy

Dr Volpe is a Radiation Oncologist working at the Radiation Oncology Department of the European Institute of Oncology (Istituto Europeo di Oncologia, IEO), Milan, Italy. She is currently a PhD Candidate in Computational Biology at the European School of Molecular Medicine (Scuola Europea di Medicina Molecolare, SEMM), in Milan, Italy. Her areas of reasearch include stereotactic body radiotherapy, head and neck and lung malignancies and quantitative imaging applications, with a dedicated focus on radiomics. Moreover, Dr Volpe holds the position of Principal Investigator of a 5-year prospective study on multi-omics for outcome prediction in early-stage non-small cell lung cancer candidates to curative-intent stereotactic body radiotherapy, funded by the Italian Association for Cancer Research (Associazione Italiana per la Ricerca sul Cancro, AIRC). 

About the Collection

BMC Cancer is calling for submissions to our Collection on radiomics in cancer diagnosis and treatment. Radiomics has emerged as a powerful and non-invasive approach for extracting quantitative data from medical images, providing valuable insights into tumor characterization, treatment response prediction, and prognostic assessment in cancer patients. The integration of radiomics into oncology has the potential to revolutionize cancer diagnosis, treatment planning, and monitoring, leading to more precise and personalized patient care.

Topics of interest for this Collection include, but are not limited to:

  • Radiomic analysis in cancer diagnosis: Advances in radiomics for distinguishing benign and malignant tumors, and differentiating various cancer types based on imaging characteristics.
  • Radiogenomics: Correlations between radiomic features and underlying genomic data to uncover imaging-based biomarkers and potential therapeutic targets.
  • Radiomics for treatment response monitoring and prediction: Predictive models and algorithms utilizing radiomics data to forecast tumor response to different treatment modalities, such as chemotherapy, immunotherapy, and radiation therapy.
  • Prognostic applications of radiomics: Identifying radiomic signatures associated with cancer prognosis and survival outcomes.
  • Radiomics in precision oncology: Integrating radiomics into personalized treatment approaches and decision-making processes for individual cancer patients.
  • Technical advancements in radiomics: Novel methodologies, algorithms, and software tools for radiomic feature extraction, analysis, and interpretation, including machine learning and artificial intelligence (AI).
  • Multi-modal and multi-parametric radiomics: Combining data from different imaging modalities and incorporating clinical and genomic data for comprehensive cancer characterization.


This collection supports and amplifies research related to SDG #3: Good Health and Well-Being.
 

Image credit: sudok1 / Stock.adobe.com

  1. Accurate assessment of axillary status after neoadjuvant therapy for breast cancer patients with axillary lymph node metastasis is important for the selection of appropriate subsequent axillary treatment decis...

    Authors: Jia Wang, Cong Tian, Bing-Jie Zheng, Jiao Zhang, De-Chuang Jiao, Jin-Rong Qu and Zhen-Zhen Liu
    Citation: BMC Cancer 2024 24:549
    Content type: Research Published on:

  2. Cervical lymph node metastasis (LNM) is an important prognostic factor for patients with non-small cell lung cancer (NSCLC). We aimed to develop and validate machine learning models that use ultrasound radiomi...

    Authors: Zhiqiang Deng, Xiaoling Liu, Renmei Wu, Haoji Yan, Lingyun Gou, Wenlong Hu, Jiaxin Wan, Chenwanqiu Song, Jing Chen, Daiyuan Ma, Haining Zhou and Dong Tian
    Citation: BMC Cancer 2024 24:536
    Content type: Research Published on:

  3. To predict pathological complete response (pCR) in patients receiving neoadjuvant immunochemotherapy (nICT) for esophageal squamous cell carcinoma (ESCC), we explored the factors that influence pCR after nICT ...

    Authors: Yu Yang, Yan Yi, Zhongtang Wang, Shanshan Li, Bin Zhang, Zheng Sang, Lili Zhang, Qiang Cao and Baosheng Li
    Citation: BMC Cancer 2024 24:460
    Content type: Research Published on:

  4. The identification of survival predictors is crucial for early intervention to improve outcome in acute myeloid leukemia (AML). This study aim to identify chest computed tomography (CT)-derived features to pre...

    Authors: Xiaoping Yi, Huien Zhan, Jun Lyu, Juan Du, Min Dai, Min Zhao, Yu Zhang, Cheng Zhou, Xin Xu, Yi Fan, Lin Li, Baoxia Dong, Xinya Jiang, Zeyu Xiao, Jihao Zhou, Minyi Zhao…
    Citation: BMC Cancer 2024 24:458
    Content type: Research Published on:

  6. To establish and validate a predictive model combining pretreatment multiparametric MRI-based radiomic signatures and clinical characteristics for the risk evaluation of early rapid metastasis in nasopharyngea...

    Authors: Xiujuan Cao, Xiaowen Wang, Jian Song, Ya Su, Lizhen Wang and Yong Yin
    Citation: BMC Cancer 2024 24:435
    Content type: Research Published on:

  7. This study aimed to develop and validate a machine learning (ML)-based fusion model to preoperatively predict Ki-67 expression levels in patients with head and neck squamous cell carcinoma (HNSCC) using multip...

    Authors: Weiyue Chen, Guihan Lin, Yongjun Chen, Feng Cheng, Xia Li, Jiayi Ding, Yi Zhong, Chunli Kong, Minjiang Chen, Shuiwei Xia, Chenying Lu and Jiansong Ji
    Citation: BMC Cancer 2024 24:418
    Content type: Research Published on:

  9. The presence of heterogeneity is a significant attribute within the context of ovarian cancer. This study aimed to assess the predictive accuracy of models utilizing quantitative 18F-FDG PET/CT derived inter-tumo...

    Authors: Dianning He, Xin Zhang, Zhihui Chang, Zhaoyu Liu and Beibei Li
    Citation: BMC Cancer 2024 24:337
    Content type: Research Published on:

  10. The existing staging system cannot meet the needs of accurate survival prediction. Accurate survival prediction for locally advanced cervical cancer (LACC) patients who have undergone concurrent radiochemother...

    Authors: Huiling Liu, Yongbin Cui, Cheng Chang, Zichun Zhou, Yalin Zhang, Changsheng Ma, Yong Yin and Ruozheng Wang
    Citation: BMC Cancer 2024 24:150
    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 in cancer diagnosis and treatment" 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.