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      Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study

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          Abstract

          Background

          We aimed to investigate whether pre-therapeutic radiomic features based on magnetic resonance imaging (MRI) can predict the clinical response to neoadjuvant chemotherapy (NACT) in patients with locally advanced cervical cancer (LACC).

          Methods

          A total of 275 patients with LACC receiving NACT were enrolled in this study from eight hospitals, and allocated to training and testing sets (2:1 ratio). Three radiomic feature sets were extracted from the intratumoural region of T1-weighted images, intratumoural region of T2-weighted images, and peritumoural region of T2-weighted images before NACT for each patient. With a feature selection strategy, three single sequence radiomic models were constructed, and three additional combined models were constructed by combining the features of different regions or sequences. The performance of all models was assessed using receiver operating characteristic curve.

          Findings

          The combined model of the intratumoural zone of T1-weighted images, intratumoural zone of T2-weighted images,and peritumoural zone of T2-weighted images achieved an AUC of 0.998 in training set and 0.999 in testing set, which was significantly better ( p < .05) than the other radiomic models. Moreover, no significant variation in performance was found if different training sets were used.

          Interpretation

          This study demonstrated that MRI-based radiomic features hold potential in the pretreatment prediction of response to NACT in LACC, which could be used to identify rightful patients for receiving NACT avoiding unnecessary treatment.

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          Most cited references43

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          Index for rating diagnostic tests.

          W J YOUDEN, Michele Teng, Nancy Chabot … (1950)
          Article 0 comments Cited 952 times – based on 0 reviews     Review now
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            Cancer of the cervix uteri

            Neerja Bhatla, Daisuke Aoki, Daya Nand Sharma … (2018)
            Since the publication of the last FIGO Cancer Report there have been giant strides in the global effort to reduce the burden of cervical cancer, with WHO announcing a call for elimination. In over 80 countries, including LMICs, HPV vaccination is now included in the national program. Screening has also seen major advances with implementation of HPV testing on a larger scale. However, these interventions will take a few years to show their impact. Meanwhile, over half a million new cases are added each year. Recent developments in imaging and increased use of minimally invasive surgery have changed the paradigm for management of these cases. The FIGO Gynecologic Oncology Committee has revised the staging system based on these advances. This chapter discusses the management of cervical cancer based on the stage of disease, including attention to palliation and quality of life issues.
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              A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities

              I El Naqa, M Vallières, C R Freeman … (2015)
              This study aims at developing a joint FDG-PET and MRI texture-based model for the early evaluation of lung metastasis risk in soft-tissue sarcomas (STSs). We investigate if the creation of new composite textures from the combination of FDG-PET and MR imaging information could better identify aggressive tumours. Towards this goal, a cohort of 51 patients with histologically proven STSs of the extremities was retrospectively evaluated. All patients had pre-treatment FDG-PET and MRI scans comprised of T1-weighted and T2-weighted fat-suppression sequences (T2FS). Nine non-texture features (SUV metrics and shape features) and forty-one texture features were extracted from the tumour region of separate (FDG-PET, T1 and T2FS) and fused (FDG-PET/T1 and FDG-PET/T2FS) scans. Volume fusion of the FDG-PET and MRI scans was implemented using the wavelet transform. The influence of six different extraction parameters on the predictive value of textures was investigated. The incorporation of features into multivariable models was performed using logistic regression. The multivariable modeling strategy involved imbalance-adjusted bootstrap resampling in the following four steps leading to final prediction model construction: (1) feature set reduction; (2) feature selection; (3) prediction performance estimation; and (4) computation of model coefficients. Univariate analysis showed that the isotropic voxel size at which texture features were extracted had the most impact on predictive value. In multivariable analysis, texture features extracted from fused scans significantly outperformed those from separate scans in terms of lung metastases prediction estimates. The best performance was obtained using a combination of four texture features extracted from FDG-PET/T1 and FDG-PET/T2FS scans. This model reached an area under the receiver-operating characteristic curve of 0.984 ± 0.002, a sensitivity of 0.955 ± 0.006, and a specificity of 0.926 ± 0.004 in bootstrapping evaluations. Ultimately, lung metastasis risk assessment at diagnosis of STSs could improve patient outcomes by allowing better treatment adaptation.
              Article 0 comments Cited 295 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Ping Liu
                Lihui Wang
                Chunlin Chen
                Jie Tian
                Journal
                Journal ID (nlm-ta): EBioMedicine
                Journal ID (iso-abbrev): EBioMedicine
                Title: EBioMedicine
                Publisher: Elsevier
                ISSN (Electronic): 2352-3964
                Publication date PMC-release: 06 August 2019
                Publication date Collection: August 2019
                Publication date (Electronic): 06 August 2019
                Volume: 46
                Pages: 160-169
                Affiliations
                [a ]Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, School of Computer Science and Technology, Guizhou University, Guiyang, China
                [b ]CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
                [c ]Department of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China
                [d ]School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
                [e ]Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China
                [f ]Engineering Research Center of Molecular and NeSuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China
                Author notes
                [* ]Correspondence to: J. Tian, CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China. jie.tian@ 123456ia.ac.cn
                [** ]Corresponding author at: Department of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, 1838 Guangzhou Avenue North, Guangzhou, 510515, China. lpivy@ 123456126.com ccl1@ 123456smu.edu.cn
                [*** ]Corresponding author at: Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, School of Computer Science and Technology, Guizhou University, No.2708 South Section of Huaxi Avenue, Guiyang, 550025, China. wlh1984@ 123456gmail.com
                [1]

                These authors contributed equally to this work, and should be considered as co-first authors.

                Article
                Publisher ID: S2352-3964(19)30491-8
                DOI: 10.1016/j.ebiom.2019.07.049
                PMC ID: 6712288
                PubMed ID: 31395503
                SO-VID: 5f87e661-6d02-44da-aa1e-4a6225b52ead
                Copyright © © 2019 The Authors
                License:

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Date received : 4 June 2019
                Date revision received : 5 July 2019
                Date accepted : 18 July 2019
                Categories
                Subject: Research paper

                Keywords: radiomics,magnetic resonance imaging,neoadjuvant chemotherapy,locally advanced cervical cancer
                Data availability:
                Keywords: radiomics, magnetic resonance imaging, neoadjuvant chemotherapy, locally advanced cervical cancer

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