Evidence map›Paper›PMID 31395503›Full record

ArticleEBioMedicine2019

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

Caixia Sun, Xin Tian, Zhenyu Liu, Weili Li, Pengfei Li, Jiaming Chen, Weifeng Zhang, Ziyu Fang, Peiyan Du, Hui Duan and 4 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in EBioMedicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
71citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

71 citing papers in PubMed, 1 synthesis or guideline pooled it.

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11 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors.

Caixia SunKey Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, School of Computer Science and Technology, Guizhou University, Guiyang, China; CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Xin TianDepartment of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Zhenyu LiuCAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
Weili LiDepartment of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Pengfei LiDepartment of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Jiaming ChenDepartment of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Weifeng ZhangDepartment of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Ziyu FangDepartment of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Peiyan DuDepartment of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Hui DuanDepartment of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China.
Ping LiuDepartment of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China. Electronic address: lpivy@126.com.
Lihui WangKey Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, School of Computer Science and Technology, Guizhou University, Guiyang, China. Electronic address: wlh1984@gmail.com.
Chunlin ChenDepartment of Gynaecology and Obstetrics, Nanfang Hospital, Southern Medical University, Guangzhou, China. Electronic address: ccl1@smu.edu.cn.
Jie TianCAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing, China; Engineering Research Center of Molecular and NeSuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi, China. Electronic address: jie.tian@ia.ac.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWe 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).

methodsA 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.

findingsThe 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.

interpretationThis 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.

Indexed as

Diagnostic ImagingImage Processing, Computer-AssistedAdultChemotherapy, AdjuvantFemaleHumansMagnetic Resonance ImagingMiddle AgedNeoadjuvant TherapyNeoplasm MetastasisNeoplasm StagingPrognosisReproducibility of ResultsRetrospective StudiesROC CurveTreatment OutcomeLocally advanced cervical cancerMagnetic resonance imagingNeoadjuvant chemotherapyRadiomics

Identifiers

PMID31395503
PMCPMC6712288

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.