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Radiomics in cancer diagnosis and treatment

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

BMC Cancer has published this 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, MD: 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, MD: 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, MD, PhD: 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 has published this 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 included, but were 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. Identifying effective predictive strategies to assess the response of immune checkpoint inhibitors (ICIs)-based combination therapy in advanced hepatocellular carcinoma (HCC) is crucial. This study presents a ...

    Authors: Jun Xu, Junjun Li, Tengfei Wang, Xin Luo, Zhangxiang Zhu, Yimou Wang, Yong Wang, Zhenglin Zhang, Ruipeng Song, Li-Zhuang Yang, Hongzhi Wang, Stephen T. C. Wong and Hai Li
    Citation: BMC Cancer 2025 25:602
    Content type: Research Published on:

  2. To establish prediction models to predict 2-year overall survival (OS) and stratify patients with different risks based on radiomics features extracted from magnetic resonance imaging (MRI) and computed tomogr...

    Authors: Nuo Yu, Yidong Wan, Lijing Zuo, Ying Cao, Dong Qu, Wenyang Liu, Lei Deng, Tao Zhang, Wenqing Wang, Jianyang Wang, Jima Lv, Zefen Xiao, Qinfu Feng, Zongmei Zhou, Nan Bi, Tianye Niu…
    Citation: BMC Cancer 2025 25:596
    Content type: Research Published on:

  3. Distinguishing between benign and malignant testicular lesions on clinical magnetic resonance imaging (MRI) is crucial for guiding treatment planning. However, conventional MRI-based radiomics to identify test...

    Authors: Liang Wang, PeiPei Zhang, Yanhui Feng, Wenzhi Lv, Xiangde Min, Zhiyong Liu, Jin Li and Zhaoyan Feng
    Citation: BMC Cancer 2025 25:563
    Content type: Research Published on:

  5. Prognostic prediction plays a pivotal role in guiding personalized treatment for patients with locoregionally advanced nasopharyngeal carcinoma (LANPC). However, few studies have investigated the incremental v...

    Authors: Lian Jian, Cai Sheng, Huaping Liu, Handong Li, Pingsheng Hu, Zhaodong Ai, Xiaoping Yu and Huai Liu
    Citation: BMC Cancer 2025 25:519
    Content type: Research Published on:

  6. Early and accurate identification of epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients with brain metastases is critical for guiding targeted therapy. This ...

    Authors: Pingdong Cao, Xiao Jia, Xi Wang, Liyuan Fan, Zheng Chen, Yuanyuan Zhao, Jian Zhu and Qiang Wen
    Citation: BMC Cancer 2025 25:443
    Content type: Research Published on:

  7. We aimed to develop a preoperative clinical radiomics survival prediction model based on the radiomics features via deep learning to provide a reference basis for preoperative assessment and treatment decision...

    Authors: Zhechuan Jin, Chen Chen, Dong Zhang, Min Yang, Qiuping Wang, Zhiqiang Cai, Shubin Si, Zhimin Geng and Qi Li
    Citation: BMC Cancer 2025 25:341
    Content type: Research Published on:

  8. Although concurrent chemoradiotherapy (CCRT) is the standard treatment strategy for locally advanced cervical squamous carcinoma (LACSC), there are still individual differences. It is of vital importance to es...

    Authors: Yuan Wang, Yanyan Yu, Lina Gu, Yunfeng Sun, Jiazhuo Yan, Hongxia Zhang and Yunyan Zhang
    Citation: BMC Cancer 2025 25:230
    Content type: Research Published on:

  9. To use intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) to evaluate the masseter muscle and tumors in patients with head and neck cancer (HNC) and to compare them with those of normal contr...

    Authors: Kai-Lun Cheng, Hsueh-Ju Lu, Chao-Yu Shen, Chia-Wei Lin, Hui-Yu Wang, Ying-Hsiang Chou, Yeu-Sheng Tyan and Ping-Huei Tsai
    Citation: BMC Cancer 2025 25:184
    Content type: Research Published on:

  10. To construct a prediction model based on deep learning (DL) and radiomics features of diffusion weighted imaging (DWI), and clinical variables for evaluating TP53 mutations in endometrial cancer (EC).

    Authors: Lei Shen, Bo Dai, Shewei Dou, Fengshan Yan, Tianyun Yang and Yaping Wu
    Citation: BMC Cancer 2025 25:45
    Content type: Research Published on:

  11. Upper urinary tract urothelial carcinoma (UTUC) is a rare and highly aggressive malignancy characterized by poor prognosis, making the accurate identification of high-grade (HG) UTUC essential for subsequent t...

    Authors: Yanghuang Zheng, Hongjin Shi, Shi Fu, Haifeng Wang, Xin Li, Zhi Li, Bing Hai and Jinsong Zhang
    Citation: BMC Cancer 2024 24:1546
    Content type: Research Published on:

  12. The management of complex renal cysts is guided by the Bosniak classification system, which may be inadequate for risk stratification of patients to determine the appropriate intervention. Radiomics models bas...

    Authors: Xun Zhao, Ye Yan, Wanfang Xie, Zijian Qin, Litao Zhao, Cheng Liu, Shudong Zhang, Jiangang Liu and Lulin Ma
    Citation: BMC Cancer 2024 24:1508
    Content type: Research Published on:

  13. This study aimed to develop and validate a predictive model for assessing the efficacy of neoadjuvant chemotherapy (NACT) in nasopharyngeal carcinoma (NPC) by integrating radiomics and pathomics features using...

    Authors: Yiren Wang, Huaiwen Zhang, Huan Wang, Yiheng Hu, Zhongjian Wen, Hairui Deng, Delong Huang, Li Xiang, Yun Zheng, Lu Yang, Lei Su, Yunfei Li, Fang Liu, Peng Wang, Shengmin Guo, Haowen Pang…
    Citation: BMC Cancer 2024 24:1501
    Content type: Research Published on:

  14. To build and validate a periprostatic fat magnetic resonance imaging (MRI) based radiomics nomogram for prediction of biochemical recurrence-free survival (bRFS) of patients with non-metastatic prostate cancer...

    Authors: Xiao-Hui Wu, Zhi-Bin Ke, Ze-Jia Chen, Wen-Qi Liu, Yu-Ting Xue, Shao-Hao Chen, Dong-Ning Chen, Qing-Shui Zheng, Xue-Yi Xue, Yong Wei and Ning Xu
    Citation: BMC Cancer 2024 24:1459
    Content type: Research Published on:

  15. Colon cancer, a frequently encountered malignancy, exhibits a comparatively poor survival prognosis. Perineural invasion (PNI), highly correlated with tumor progression and metastasis, is a substantial effecti...

    Authors: Tairan Guo, Bing Cheng, Yunlong Li, Yaqing Li, Shaojie Chen, Guoda Lian, Jiajia Li, Ming Gao, Kaihong Huang and Yuzhou Huang
    Citation: BMC Cancer 2024 24:1226
    Content type: Research Published on:

  16. In recent years, clinicians often encounter patients with multiple pulmonary nodules in their clinical practices. As most of these ground glass nodules (GGNs) are small in volume and show no spicule sign, it i...

    Authors: Xiaodan Zhu, Changxing Shen and Jingcheng Dong
    Citation: BMC Cancer 2024 24:1225
    Content type: Research Published on:

  17. The brachytherapy (BT) and radical prostatectomy (RP) are two methods recommended in current guidelines for the treatment of localized prostate cancer (PCa). It is difficult to compare the oncological results ...

    Authors: Zaisheng Zhu, Yiyi Zhu, Hongqi Shi, Penfei Zhou, Yadong Xue, Ke Dong and Shengye Hu
    Citation: BMC Cancer 2024 24:1177
    Content type: Research Published on:

  18. This study explores integrating clinical features with radiomic and dosiomic characteristics into AI models to enhance the prediction accuracy of radiation dermatitis (RD) in breast cancer patients undergoing ...

    Authors: Tsair-Fwu Lee, Chu-Ho Chang, Chih-Hsuan Chi, Yen-Hsien Liu, Jen-Chung Shao, Yang-Wei Hsieh, Pei-Ying Yang, Chin-Dar Tseng, Chien-Liang Chiu, Yu-Chang Hu, Yu-Wei Lin, Pei-Ju Chao, Shen-Hao Lee and Shyh-An Yeh
    Citation: BMC Cancer 2024 24:965
    Content type: Research Published on:

  19. The recurrence of papillary thyroid carcinoma (PTC) is not unusual and associated with risk of death. This study is aimed to construct a nomogram that combines clinicopathological characteristics and ultrasoun...

    Authors: Binqian Zhou, Jianxin Liu, Yaqin Yang, Xuewei Ye, Yang Liu, Mingfeng Mao, Xiaofeng Sun, Xinwu Cui and Qin Zhou
    Citation: BMC Cancer 2024 24:810
    Content type: Research Published on:

  20. Oral Squamous Cell Carcinoma (OSCC) presents significant diagnostic challenges in its early and late stages. This study aims to utilize preoperative MRI and biochemical indicators of OSCC patients to predict t...

    Authors: Wen Li, Yang Li, Shiyu Gao, Nengwen Huang, Ikuho Kojima, Taro Kusama, Yanjing Ou, Masahiro Iikubo and Xuegang Niu
    Citation: BMC Cancer 2024 24:795
    Content type: Research Published on:

  21. An accurate and non-invasive approach is urgently needed to distinguish tuberculosis granulomas from lung adenocarcinomas. This study aimed to develop and validate a nomogram based on contrast enhanced-compute...

    Authors: Liping Yang, Zhiyun Jiang, Jinlong Tong, Nan Li, Qing Dong and Kezheng Wang
    Citation: BMC Cancer 2024 24:670
    Content type: Research Published on:

  23. 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:

  24. 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:

  25. 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:

  26. 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:

    The Correction to this article has been published in BMC Cancer 2024 24:562

  28. 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:

  29. 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:

  31. 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:

  32. 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.