ArticleBiology2022
The Utility of ADC First-Order Histogram Features for the Prediction of Metachronous Metastases in Rectal Cancer: A Preliminary Study.
Article in Biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 13 citations in OpenAlex.
- Development and validation: a whole-tumor histogram model based on intravoxel incoherent motion diffusion-weighted imaging for diagnosing tumor deposits in rectal cancer.Abdominal radiology (New York) · 2026Article
- Preoperative prediction of tumor budding grade in rectal cancer by combining APT histogram analysis and ADC MRI.European radiology experimental · 2026Article
- Comparison of reduced FOV diffusion-weighted imaging of rectal cancer at 5.0T ultra-high field versus 3.0T MRI: image quality and histopathological T staging.BMC medical imaging · 2025Article
- Whole-liver histogram analysis of hepatocyte-specific contrast-enhanced magnetic resonance imaging for predicting progression in patients with cirrhosis.Quantitative imaging in medicine and surgery · 2024Article
- Histogram analysis based on intravoxel incoherent motion diffusion-weighted imaging for determining the perineural invasion status of rectal cancer.Quantitative imaging in medicine and surgery · 2024Article
- Predicting Response to Exclusive Combined Radio-Chemotherapy in Naso-Oropharyngeal Cancer: The Role of Texture Analysis.Diagnostics (Basel, Switzerland) · 2024Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aims the ability of first-order histogram-based features, derived from ADC maps, to predict the occurrence of metachronous metastases (MM) in rectal cancer. A total of 52 patients with pathologically confirmed rectal adenocarcinoma were retrospectively enrolled and divided into two groups: patients who developed metachronous metastases (n = 15) and patients without metachronous metastases (n = 37). We extracted 17 first-order (FO) histogram-based features from the pretreatment ADC maps. Student's t-test and Mann-Whitney U test were used for the association between each FO feature and presence of MM. Statistically significant features were combined into a model, using the binary regression logistic method. The receiver operating curve analysis was used to determine the diagnostic performance of the individual parameters and combined model. There were significant differences in ADC 90th percentile, interquartile range, entropy, uniformity, variance, mean absolute deviation, and robust mean absolute deviation in patients with MM, as compared to those without MM (
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