ArticleBMC medical informatics and decision making2020
The role of histogram analysis in diffusion-weighted imaging in the differential diagnosis of benign and malignant breast lesions.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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.
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.
Who cites it
12 citing papers in PubMed, 21 citations in OpenAlex.
- SynQ-Breast: a flexible deep learning framework for quantitative MRI of breast tumors via synthetic data with IVIM-MRI validation.Quantitative imaging in medicine and surgery · 2026Article
- Added value of intravoxel incoherent motion histogram analysis in differentiating HER2-zero, HER2-low, and HER2-positive breast cancer.La Radiologia medica · 2026Article
- Impact of deep learning image reconstruction on ADC quantification and histogram metrics: a phantom study.European radiology experimental · 2026Article
- Whole-lesion histogram analysis of multi-model diffusion-weighted imaging for characterization and molecular classification of breast lesions.La Radiologia medica · 2026Article
- Characterizing Breast Tumor Heterogeneity Through IVIM-DWI Parameters and Signal Decay Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- Incidence of deep venous thrombosis following medial opening-wedge high tibial osteotomy for varus knee osteoarthritis: a retrospective study.BMC musculoskeletal disorders · 2025Article
- Review
- Diagnostic value of multiple b-value diffusion-weighted imaging in discriminating the malignant from benign breast lesions.BMC medical imaging · 2023Article
- The utility of diffusion-weighted imaging for differentiation of phyllodes tumor from fibroadenoma and breast cancer.Frontiers in oncology · 2023Article
- Computed tomography-based texture assessment for the differentiation of benign, borderline, and early-stage malignant ovarian neoplasms.The Journal of international medical research · 2023Article
- Quantitative Measurement of Breast Tumors Using Intravoxel Incoherent Motion (IVIM) MR Images.Journal of personalized medicine · 2021Article
- The Value of Magnetic Resonance Imaging Histograms in the Preoperative Differential Diagnosis of Endometrial Stromal Sarcoma and Degenerative Hysteromyoma.Frontiers in surgery · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
backgroundThe present study aims to investigate the role of histogram analysis of intravoxel incoherent motion (IVIM) in the differential diagnosis of benign and malignant breast lesions.
methodsThe magnetic resonance imaging and clinical data of 55 patients (63 lesions) were retrospectively analyzed. The multi-b-valued diffusion-weighted imaging image was processed using the MADC software to obtain the gray-scaled maps of apparent diffusion coefficient (ADC)-slow, ADC-fast and f. The MaZda software was used to extract the histogram metrics of these maps. Combined with the conventional sequence images, the region of interest (ROI) was manually drawn along the edge of the lesion at the maximum level of the gray-scale image, and the difference of the data was analyzed between the benign and malignant breast lesions.
resultsThere were 29 patients with 37 benign lesions, which included 23 fibroadenomas, 6 adenosis, 1 breast cysts, 4 intraductal papillomas, and 3 inflammations of breast. Furthermore, 26 malignant lesions in 26 patients, which included 20 non-specific invasive ductal carcinomas, 5 intraductal carcinomas and 1 patient with squamous cell carcinoma. The ADC-slow (mean and the 50th percentile) and f (minimum, mean, kurtosis, the 10th percentile and 50th percentile) of these malignant breast lesions were significantly lower than those of benign lesions (P < 0.05), while ADC-fast (kurtosis) and f (variance, skewness) of these malignant breast lesions were significantly higher than those of benign lesions (P < 0.05).
conclusionThe histogram analysis of ADC-slow (mean and the 50th percentile), ADC-fast (kurtosis) and f (minimum, mean, kurtosis, the 10th percentile and 50th percentile. Variance, skewness) can provide a more objective and accurate basis for the differential diagnosis of benign and malignant breast lesions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.