Evidence map›Paper›PMID 41489694›Full record

ArticleJapanese journal of radiology2026

Time-dependent diffusion-weighted imaging assessment of tumor grading and isocitrate dehydrogenase genotypes in adult-type diffuse gliomas.

Kiyohisa Kamimura, Tsubasa Nakano, Masanori Nakajo, Junki Kamizono, Tomohito Hasegawa, Daiki Tobo, Akie Mukai, Yoshiki Kamimura, Fumitaka Ejima, Hiroaki Nagano and 12 more

Abstract read
In one paragraph

Article in Japanese journal of radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

22 authors.

Kiyohisa KamimuraDepartment of Advanced Radiological Imaging, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1, Sakuragaoka, Kagoshima, 890-8544, Japan. kiyohisa@m2.kufm.kagoshima-u.ac.jp.ORCID http://orcid.org/0000-0003-4191-9358
Tsubasa NakanoDepartment of Radiology, Kagoshima University Graduate School of Medical and DentalSciences, 8-35-1, Sakuragaoka, Kagoshima, 890-8544, Japan.
Masanori NakajoDepartment of Radiology, Kagoshima University Graduate School of Medical and DentalSciences, 8-35-1, Sakuragaoka, Kagoshima, 890-8544, Japan.
Junki KamizonoDepartment of Radiology, Kagoshima University Graduate School of Medical and DentalSciences, 8-35-1, Sakuragaoka, Kagoshima, 890-8544, Japan.
Tomohito HasegawaDepartment of Radiology, Kagoshima University Graduate School of Medical and DentalSciences, 8-35-1, Sakuragaoka, Kagoshima, 890-8544, Japan.
Daiki ToboDepartment of Radiology, Kagoshima University Graduate School of Medical and DentalSciences, 8-35-1, Sakuragaoka, Kagoshima, 890-8544, Japan.
Akie MukaiDepartment of Radiology, Kagoshima University Graduate School of Medical and DentalSciences, 8-35-1, Sakuragaoka, Kagoshima, 890-8544, Japan.
Yoshiki KamimuraDepartment of Radiology, Kagoshima University Graduate School of Medical and DentalSciences, 8-35-1, Sakuragaoka, Kagoshima, 890-8544, Japan.
Fumitaka EjimaDepartment of Radiology, Kagoshima University Graduate School of Medical and DentalSciences, 8-35-1, Sakuragaoka, Kagoshima, 890-8544, Japan.
Hiroaki NaganoDepartment of Radiology, Kagoshima University Graduate School of Medical and DentalSciences, 8-35-1, Sakuragaoka, Kagoshima, 890-8544, Japan.
Koji TakumiDepartment of Radiology, Kagoshima University Graduate School of Medical and DentalSciences, 8-35-1, Sakuragaoka, Kagoshima, 890-8544, Japan.
Masatoyo NakajoDepartment of Radiology, Kagoshima University Graduate School of Medical and DentalSciences, 8-35-1, Sakuragaoka, Kagoshima, 890-8544, Japan.
Nayuta HigaDepartmentof Neurosurgery, Kagoshima University Graduate School of Medicaland Dental Sciences, 8-35-1, Sakuragaoka, Kagoshima, 890-8544, Japan.
Hajime YonezawaDepartmentof Neurosurgery, Kagoshima University Graduate School of Medicaland Dental Sciences, 8-35-1, Sakuragaoka, Kagoshima, 890-8544, Japan.
Ryosuke HanayaDepartmentof Neurosurgery, Kagoshima University Graduate School of Medicaland Dental Sciences, 8-35-1, Sakuragaoka, Kagoshima, 890-8544, Japan.
Mari KirishimaDepartment of Pathology, KagoshimaUniversity Graduate School of Medical and Dental Sciences, 8-35-1, Sakuragaoka, Kagoshima, 890-8544, Japan.
Akihide TanimotoDepartment of Pathology, KagoshimaUniversity Graduate School of Medical and Dental Sciences, 8-35-1, Sakuragaoka, Kagoshima, 890-8544, Japan.
Hirokazu OtsukaDepartment of Radiological Technology, Kagoshima University Hospital, 8-35-1, Sakuragaoka, Kagoshima, 890-8544, Japan.
Daisuke HiraharaDepartment of AI Research Lab, Harada Academy, 2-54-4, Higashi-Tanayama, Kagoshima, 891-0113, Japan.
Hiroshi ImaiSiemens Healthcare K.K., Gate City Osaki West Tower, 1-11-1, Shinagawa-ku, Tokyo, Osaki, 141-8644, Japan.
Thorsten FeiweierResearch & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG, Allee am Roethelheimpark 2, Erlangen, 91052, Germany.
Takashi YoshiuraDepartment of Advanced Radiological Imaging, Kagoshima University Graduate School of Medical and Dental Sciences, 8-35-1, Sakuragaoka, Kagoshima, 890-8544, Japan.

Funding

JSPS KAKENHI JP23K07113
6 · The paper itself

Abstract

backgroundThis study aimed to investigate the usefulness of time-dependent diffusion magnetic resonance imaging (MRI) parameters compared with the conventional apparent diffusion coefficient (ADC) in distinguishing tumor grade and isocitrate dehydrogenase (IDH) genotypes of adult-type diffuse gliomas.

methodsThis retrospective study included 102 patients with adult-type diffuse gliomas. ADC maps obtained using diffusion-weighted imaging at short (7.1 ms) and long (44.5 ms) diffusion times (ADC7.1ms and ADC44.5ms) and maps of ADC changes (cADC) and relative ADC changes (rcADC) between the two diffusion times were generated. The mean, 5th, and 95th percentile values of each parameter were compared between low-grade (LGGs) and high-grade gliomas (HGGs) and between IDH-mutant and IDH-wildtype gliomas. The discriminative performance was assessed using receiver operating characteristic (ROC) analysis, and correlation with Ki-67 labeling index (Ki-67LI) was assessed using Spearman's rank correlation. Multivariable logistic regression analyses were conducted to predict HGGs and IDH-wildtype gliomas.

resultsIn HGGs, the mean and 5th percentile values of ADC44.5ms and ADC7.1ms were significantly lower, whereas cADC and rcADC indices were significantly higher than those in LGGs. Performance of the mean rcADC (area under the ROC curve: 0.925; 95% confidence interval: 0.855-0.967) was significantly better than any index of conventional ADCs for tumor grade classification. The mean rcADC demonstrated the strongest correlation with Ki-67LI (ρ = 0.542, p < 0.0001). Moreover, the 95th percentile of rcADC was an independent predictor of IDH-wildtype gliomas after adjustment for age and sex, was useful for distinguishing IDH-wildtype from IDH-mutant gliomas

conclusionsThe mean rcADC showed the strongest correlation with the Ki-67 LI and achieved better diagnostic performance than conventional PGSE-based ADC for differentiating LGGs from HGGs. In multivariable analyses, the mean and 95th percentile of rcADC were identified as independent predictors of HGGs and IDH-wildtype gliomas, respectively.

Indexed as

Brain NeoplasmsDiffusion Magnetic Resonance ImagingGliomaIsocitrate DehydrogenaseAdultAgedBrainFemaleGenotypeHumansMaleMiddle AgedNeoplasm GradingRetrospective StudiesYoung AdultIsocitrate DehydrogenaseAdult-type diffuse gliomaIsocitrate dehydrogenase genotypesMRITime-dependent diffusionTumor grading

Identifiers

PMID41489694
PMCPMC13144230

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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.