Evidence map›Paper›PMID 39851321›Full record

ArticleBioengineering (Basel, Switzerland)2025

Haralick Texture Analysis for Differentiating Suspicious Prostate Lesions from Normal Tissue in Low-Field MRI.

Dang Bich Thuy Le, Ram Narayanan, Meredith Sadinski, Aleksandar Nacev, Yuling Yan, Srirama S Venkataraman

Abstract read
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

6 authors.

Dang Bich Thuy LePromaxo Inc., Oakland, CA 94607, USA.ORCID 0009-0004-6952-5566
Ram NarayananPromaxo Inc., Oakland, CA 94607, USA.
Meredith SadinskiPromaxo Inc., Oakland, CA 94607, USA.
Aleksandar NacevPromaxo Inc., Oakland, CA 94607, USA.
Yuling YanDepartment of Bioengineering, School of Engineering, Santa Clara University, Santa Clara, CA 95050, USA.
Srirama S VenkataramanPromaxo Inc., Oakland, CA 94607, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study evaluates the feasibility of using Haralick texture analysis on low-field, T2-weighted MRI images for detecting prostate cancer, extending current research from high-field MRI to the more accessible and cost-effective low-field MRI. A total of twenty-one patients with biopsy-proven prostate cancer (Gleason score 4+3 or higher) were included. Before transperineal biopsy guided by low-field (58-74mT) MRI, a radiologist annotated suspicious regions of interest (ROIs) on high-field (3T) MRI. Rigid image registration was performed to align corresponding regions on both high- and low-field images, ensuring an accurate propagation of annotations to the co-registered low-field images for texture feature calculations. For each cancerous ROI, a matching ROI of identical size was drawn in a non-suspicious region presumed to be normal tissue. Four Haralick texture features (Energy, Correlation, Contrast, and Homogeneity) were extracted and compared between cancerous and non-suspicious ROIs. Two extraction methods were used: the direct computation of texture measures within the ROIs and a sliding window technique generating texture maps across the prostate from which average values were derived. The results demonstrated statistically significant differences in texture features between cancerous and non-suspicious regions. Specifically, Energy and Homogeneity were elevated (

Indexed as

feature extractionHaralick texturelow-fieldMRIprostate cancer

Identifiers

PMID39851321
PMCPMC11760443

What OpenQuestion holds

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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.