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Evidence Synthesis in Cancer Imaging

Guest Editors:
Qi Yong H. AI: The Hong Kong Polytechnic University, Hong Kong
Valerio Di Paola: Fondazione Policlinico Universitario Agostino Gemelli, Italy
Natale Quartuccio: A.R.N.A.S. Ospedali Civico Di Cristina e Benfratelli, Italy

BMC Medical Imaging  welcomed submissions to the Collection which gave researchers the opportunity to publish articles presenting evidence synthesis in the field of cancer imaging.

Meet the Guest Editors

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Qi Yong H. AI: The Hong Kong Polytechnic University, Hong Kong

Dr Qi Yong H. AI is an assistant professor for the Department of Health Technology and Informatics at The Hong Kong Polytechnic University. His research mainly focuses on clinical applications of head and neck radiology, particularly imaging markers on staging head and neck magnetic resonance imaging (MRI) of nasopharyngeal carcinoma (NPC) for outcome prediction. He also has applied new functional MRI techniques to clinical applications and is one of the first researchers to apply T1rho imaging to clinical head and neck cancer research. He has accumulated valuable experiences in the adoption of artificial intelligence (AI) to the head and neck imaging and oral and maxillofacial radiology for clinical applications and collaborating with bioengineers to develop AI algorithms that are practicable for clinical practice.

Valerio Di Paola: Fondazione Policlinico Universitario Agostino Gemelli, Italy
Dr Valerio Di Paola is a medical doctor working as radiologist at Fondazione Universitario Agostino Gemelli – IRCCS, Rome, Italy since 2017. His main clinical activity consists of diagnostic CT and MRI imaging, with particular regard to genitourinary pathology. His research activity is focused on MRI abdominal imaging of urologic and gynecological diseases, with particular interest in recent technical developments and applications, such as Diffusion Tensor Imaging (DTI) and Radiomics.


Natale Quartuccio: A.R.N.A.S. Ospedali Civico Di Cristina e Benfratelli, Italy
Dr Natale Quartuccio has been working as a visiting researcher and clinical research fellow in various research institutes in Italy (University of Turin), USA (Bradley-Alavi Student Fellowship at the Memorial Sloan Kettering Cancer Center, New York, USA) and UK (MD, Wolfson Molecular Imaging Centre - University of Manchester). He is currently working as a Nuclear Medicine Consultant in Palermo, Italy and the main topics of his research include nuclear medicine, PET imaging, and oncology.

About the collection

BMC Medical Imaging is calling for submissions to our Collection on evidence-based medicine in cancer imaging.

Evidence-based medicine is crucial, as clinical practice should not be based on findings from a single primary study without insight on their reproducibility.

Cancer imaging is an essential tool to guide clinicians in patient management, from diagnosis to treatment planning and follow-up. It involves specialists, including radiologists, nuclear medicine physicians, physicists, biomedical engineers, oncologists, radiation therapists and surgeons, representing the base for a multidisciplinary approach. Consequently, multidisciplinary approach based   The choice of the most appropriate imaging technique to investigate cancer among the multiple available options is not always straightforward for the clinicians.

This collection gives researchers the opportunity to publish articles presenting evidence synthesis in the field of cancer imaging. This includes systematic reviews and meta-analysis studies focusing on novel imaging and tracking methods, as well as on the evaluation of less cutting-edge or less recent methods applied to cancer research and clinical practice.

Please find below a non-exhaustive list of topics that will be considered:

  • Conventional and functional imaging in cancer management, which includes cancer detection, differentiation, treatment monitoring, radiation treatment planning and outcome prediction (short-term and long-term).
  • Clinical application of medical imaging to identify biological biomarkers (such as histological, immunological markers) in cancer management.
  • Cost-effectiveness analysis and change of management by means of imaging techniques.
  • Machine-learning based techniques, such as radiomics analysis and artificial intelligence, applied to medical imaging for cancer management.
  1. The purpose of our study was to differentiate uterine carcinosarcoma (UCS) from endometrioid adenocarcinoma (EAC) by the multiparametric magnetic resonance imaging (MRI) features.

    Authors: Xiaodan Chen, Qingyong Guo, Xiaorong Chen, Wanjing Zheng, Yaqing Kang and Dairong Cao
    Citation: BMC Medical Imaging 2024 24:48
  2. To construct a gadoxetic acid-enhanced MRI (EOB-MRI) -based multivariable model to predict Ki-67 expression levels in hepatocellular carcinoma (HCC) using LI-RADS v2018 imaging features.

    Authors: Yingying Liang, Fan Xu, Qiuju Mou, Zihua Wang, Chuyin Xiao, Tingwen Zhou, Nianru Zhang, Jing Yang and Hongzhen Wu
    Citation: BMC Medical Imaging 2024 24:27
  3. Non-invasive identification of breast cancer (BCa) patients with pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) is critical to determine appropriate surgical strategies and guide th...

    Authors: Guangying Zheng, Jie Hou, Zhenyu Shu, Jiaxuan Peng, Lu Han, Zhongyu Yuan, Xiaodong He and Xiangyang Gong
    Citation: BMC Medical Imaging 2024 24:22
  4. Numerous previous studies have assessed the prognostic role of 18F-fluorodeoxyglucose positron-emission tomography (18F FDG PET) in patients with biliary tract cancer (BTC), but those results were inconsistent...

    Authors: Xia Zheng, Yue Shi, Delida Kulabieke, Zihao Wang, Ying Cheng and Jun Qian
    Citation: BMC Medical Imaging 2024 24:9
  5. This study aimed to develop and validate radiomics models on the basis of computed tomography (CT) and clinical features for the prediction of pulmonary metastases (MT) in patients with Ewing sarcoma (ES) with...

    Authors: Ying Liu, Ping Yin, Jingjing Cui, Chao Sun, Lei Chen, Nan Hong and Zhentao Li
    Citation: BMC Medical Imaging 2023 23:147

    The Correction to this article has been published in BMC Medical Imaging 2023 23:157

  6. Radical concurrent chemoradiotherapy (CCRT) is frequently used as the first-line treatment for patients with locally advanced esophageal cancer. Unfortunately, some patients respond poorly. To predict response...

    Authors: Xu Cheng, Yuxin Zhang, Min Zhu, Ruixia Sun, Lingling Liu and Xueling Li
    Citation: BMC Medical Imaging 2023 23:145
  7. This study seeks to evaluate the value of MRI (Magnetic resonance imaging) diffusion weighted images (DWI), diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) in the diagnosis of cervical...

    Authors: Heng Meng, Xin Guo and Duo Zhang
    Citation: BMC Medical Imaging 2023 23:144
  8. The WHO grade and Ki-67 index are independent indices used to evaluate the malignant biological behavior of meningioma. This study aims to develop MRI-based machine learning models to predict the malignant bio...

    Authors: Maoyuan Li, Luzhou Liu, Jie Qi, Ying Qiao, Hanrui Zeng, Wen Jiang, Rui Zhu, Fujian Chen, Huan Huang and Shaoping Wu
    Citation: BMC Medical Imaging 2023 23:141
  9. Cervical cell segmentation is a fundamental step in automated cervical cancer cytology screening. The aim of this study was to develop and evaluate a deep ensemble model for cervical cell segmentation includin...

    Authors: Jie Ji, Weifeng Zhang, Yuejiao Dong, Ruilin Lin, Yiqun Geng and Liangli Hong
    Citation: BMC Medical Imaging 2023 23:137
  10. Brain extraction is an essential prerequisite for the automated diagnosis of intracranial lesions and determines, to a certain extent, the accuracy of subsequent lesion recognition, location, and segmentation....

    Authors: Dingyuan Hu, Hongbin Liang, Shiya Qu, Chunyu Han and Yuhang Jiang
    Citation: BMC Medical Imaging 2023 23:124
  11. In the present study, we mainly aimed to predict the expression of androgen receptor (AR) in breast cancer (BC) patients by combing radiomic features and clinicopathological factors in a non-invasive machine l...

    Authors: Tongtong Jia, Qingfu Lv, Bin Zhang, Chunjing Yu, Shibiao Sang and Shengming Deng
    Citation: BMC Medical Imaging 2023 23:93
  12. To predict the malignancy of 1–5 cm gastric gastrointestinal stromal tumors (GISTs) by machine learning (ML) on CT images using three models - Logistic Regression (LR), Decision Tree (DT) and Gradient Boosting...

    Authors: Cui Zhang, Jian Wang, Yang Yang, Bailing Dai, Zhihua Xu, Fangmei Zhu and Huajun Yu
    Citation: BMC Medical Imaging 2023 23:90
  13. Prediction of locoregional treatment response is important for further therapeutic strategy in patients with hepatocellular carcinoma. This study aimed to investigate the role of MRI-based radiomics and nomogr...

    Authors: Yuxin Wang, Zhenhao Liu, Hui Xu, Dawei Yang, Jiahui Jiang, Himeko Asayo and Zhenghan Yang
    Citation: BMC Medical Imaging 2023 23:67
  14. To develop machine learning-based radiomics models derive from different MRI sequences for distinction between benign and malignant PI-RADS 3 lesions before intervention, and to cross-institution validate the ...

    Authors: Pengfei Jin, Junkang Shen, Liqin Yang, Ji Zhang, Ao Shen, Jie Bao and Ximing Wang
    Citation: BMC Medical Imaging 2023 23:47
  15. Osteosarcoma (OS) is the most common primary malignant bone tumor in adolescents. Lung metastasis (LM) occurs in more than half of patients at different stages of the disease course, which is one of the import...

    Authors: Ping Yin, Junwen Zhong, Ying Liu, Tao Liu, Chao Sun, Xiaoming Liu, Jingjing Cui, Lei Chen and Nan Hong
    Citation: BMC Medical Imaging 2023 23:40
  16. To compare the diagnostic accuracy of diffusion-weighted imaging (DWI) and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) for differentiating pulmonary nodules and mas...

    Authors: Jieqiong Liu, Xiaoying Xia, Qiao Zou, Xiaobin Xie, Yongxia Lei, Qi Wan and Xinchun Li
    Citation: BMC Medical Imaging 2023 23:37

Submission Guidelines

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This Collection welcomes submission of Research Articles. Before submitting your manuscript, please ensure you have read our submission guidelines. 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 "Evidence Synthesis in Cancer Imaging" 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 Guest 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 Guest Editors have competing interests is handled by another Editorial Board Member who has no competing interests.