Evidence map›Paper›PMID 30103713›Full record

ArticleBMC cancer2018

MRI in predicting the response of gastrointestinal stromal tumor to targeted therapy: a patient-based multi-parameter study.

Lei Tang, Jian Li, Zi-Yu Li, Xiao-Ting Li, Ji-Fang Gong, Jia-Fu Ji, Ying-Shi Sun, Lin Shen

Abstract read
In one paragraph

Article in BMC cancer, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–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

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Lei TangDepartment of Radiology, Peking University Cancer Hospital & Institute, Key laboratory of Carcinogenesis and Translational Research (Ministry of Education), No. 52Fu Cheng Road, HaiDian District, Beijing, 100142, China.
Jian LiDepartment of Gastroenterology, Peking University Cancer Hospital & Institute, Key laboratory of Carcinogenesis and Translational Research (Ministry of Education), No.52 Fu Cheng Road, HaiDian District, Beijing, 100142, China.
Zi-Yu LiDepartment of Gastrointestinal Surgery, Peking University Cancer Hospital & Institute, Key laboratory of Carcinogenesis and Translational Research (Ministry of Education), No.52Fu Cheng Road, HaiDian District, Beijing, 100142, China.
Xiao-Ting LiDepartment of Radiology, Peking University Cancer Hospital & Institute, Key laboratory of Carcinogenesis and Translational Research (Ministry of Education), No. 52Fu Cheng Road, HaiDian District, Beijing, 100142, China.
Ji-Fang GongDepartment of Gastroenterology, Peking University Cancer Hospital & Institute, Key laboratory of Carcinogenesis and Translational Research (Ministry of Education), No.52 Fu Cheng Road, HaiDian District, Beijing, 100142, China.
Jia-Fu JiDepartment of Gastrointestinal Surgery, Peking University Cancer Hospital & Institute, Key laboratory of Carcinogenesis and Translational Research (Ministry of Education), No.52Fu Cheng Road, HaiDian District, Beijing, 100142, China.
Ying-Shi SunDepartment of Radiology, Peking University Cancer Hospital & Institute, Key laboratory of Carcinogenesis and Translational Research (Ministry of Education), No. 52Fu Cheng Road, HaiDian District, Beijing, 100142, China. sunys27@163.com.
Lin ShenDepartment of Gastroenterology, Peking University Cancer Hospital & Institute, Key laboratory of Carcinogenesis and Translational Research (Ministry of Education), No.52 Fu Cheng Road, HaiDian District, Beijing, 100142, China. linshenpku@163.com.

Funding

Beijing Municipal Administration of Hospital's Youth Program QML20161102Beijing Municipal Science & Technology Commission Z161100000516060National Key Research and Development Program of China 2017YFC1308900, 2017YFC0908400National Natural Science Foundation of China 81371715
6 · The paper itself

Abstract

backgroundTo investigate the performance of quantitative indicators of MRI in early prediction of the response of gastrointestinal stromal tumor (GIST) to targeted therapy in a patient-based study.

methodsMRI examinations were performed on 62 patients with GIST using 1.5 T scanners before and at two and 12 weeks after treatment with targeted agents. The longest diameter (LD) and contrast-to-noise ratio (CNR) of the tumors were measured by T2-weighted imaging (T2WI), and the apparent diffusion coefficient (ADC) was determined using diffusion-weighted imaging (DWI). The pre-therapy and early percentage changes (%Δ) of the three parameters were compared with regard to their abilities to differentiate responder and non- responder patients, using ROC curves.

resultsThere were 42 patients in responder and 20 in non-responder group. After two weeks of therapy, the percentage changes in the ADC and LD were significantly different between the two groups (ADC: responder 30% vs. non- responder 1%, Z = - 4.819, P < 0.001; LD: responder - 7% vs. non- responder - 2%, Z = - 3.238, P = 0.001), but not in T2WI-CNR (responder - 3% vs. non-responder 9%, Z = - 0.663, P = 0.508). The AUCs on ROC for %ΔLD, %ΔT2WI-CNR and %ΔADC after two weeks of therapy were 0.756, 0.552 and 0.881, respectively, for response differentiation. When %ΔADC ≥15% was used to predict responder, the PPV was 93.3%.

conclusionsThe percentage change of the ADC after two weeks of therapy outperformed T2WI-CNR and longest diameter in predicting the early response of GIST to targeted therapy.

Indexed as

Diffusion Magnetic Resonance ImagingAdultAgedFemaleGastrointestinal Stromal TumorsHumansImatinib MesylateIndolesMaleMiddle AgedPyrrolesSunitinibTreatment OutcomeImatinib MesylateIndolesPyrrolesSunitinibApparent diffusion coefficientDiffusion-weighted imagingGastrointestinal stromal tumorMagnetic resonance imagingResponse evaluationTargeted therapy

Identifiers

PMID30103713
PMCPMC6088415

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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.