Evidence map›Paper›PMID 41342926›Full record

ArticleAbdominal radiology (New York)2026

Development and validation: a whole-tumor histogram model based on intravoxel incoherent motion diffusion-weighted imaging for diagnosing tumor deposits in rectal cancer.

Hongyu Zhao, Yantong Sun, Longxia Xu, Haojie Sun, Dmytro Pylypenko, Zhengjun Dai, Aiyin Li, Gesheng Song

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in Abdominal radiology (New York), 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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8 authors.

Hongyu ZhaoSchool of Medical Imaging, Shandong Second Medical University, Weifang, China.
Yantong SunDepartment of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
Longxia XuSchool of Medical Imaging, Shandong Second Medical University, Weifang, China.
Haojie SunDepartment of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
Dmytro PylypenkoGE Healthcare, Beijing, China.
Zhengjun DaiScientific Research Department, Huiying Medical Technology Co., Ltd, Beijing, China.
Aiyin LiDepartment of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China. 13953101875@139.com.
Gesheng SongDepartment of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China. songgesheng921@126.com.

Funding

Youth Science Foundation Cultivation Support Program Project, Shandong First Medical University (Shandong Academy of Medical Sciences) 202201-080
6 · The paper itself

Abstract

purposeTo evaluate the value of the whole-tumor histogram model based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in diagnosing tumor deposits (TDs) in rectal cancer (RC) patients.

methodsA total of 101 RC patients, 39 TD-positive and 62 TD-negative cases, were enrolled. The whole-tumor volume was obtained by manually outlining the lesion on IVIM-DWI slices where the tumor was visible. Eighteen histogram features were extracted from the ADC, D, D*, and f maps derived from IVIM-DWI. Multivariate binary logistic regression analysis was used to develop a combined model for predicting TD status. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis and the area under the ROC curve (AUC). Internal validation was performed to evaluate model performance.

resultsADC energy, ADC total energy, D energy, D kurtosis, D maximum, D range, D total energy, D* maximum, D* mean, D* range, D* mean absolute deviation, D* root mean squared, D* variance, f energy, and f total energy differed significantly between the TD-positive and TD-negative groups. The combined model incorporating D range, D* mean absolute deviation, magnetic resonance T stage (mrT), and tumor thickness showed superior diagnostic performance for TD prediction, with an AUC of 0.854 (95% CI, 0.769-0.916), sensitivity of 0.923 (95% CI, 0.75-0.978), specificity of 0.645 (95% CI, 0.519-0.819), and accuracy of 0.752 (95% CI, 0.663-0.861). The results of internal validation indicated that the logistic regression model demonstrated good predictive performance, with an AUC of 0.841 (95% CI: 0.821-0.853), an accuracy of 0.743 (95% CI: 0.713-0.772), and sensitivity and specificity of 0.650 (95% CI: 0.487-0.795) and 0.802 (95% CI: 0.694-0.903), respectively.

conclusionWhole-tumor histogram analysis derived from IVIM-DWI provides a novel method for diagnosing TDs. The combined histogram-based model may aid the preoperative evaluation of TDs in RC patients.

Indexed as

Diffusion Magnetic Resonance ImagingImage Interpretation, Computer-AssistedRectal NeoplasmsAdultAgedAged, 80 and overFemaleHumansMaleMiddle AgedReproducibility of ResultsRetrospective StudiesSensitivity and SpecificityTumor BurdenHistogram analysisIntravoxel incoherent motion diffusion-weighted imagingRectal cancerTumor deposits

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