Evidence map›Paper›PMID 39943622›Full record

ArticleSensors (Basel, Switzerland)2025

Advancing Near-Infrared Probes for Enhanced Breast Cancer Assessment.

Mohammad Pouriayevali, Ryley McWilliams, Avner Bachar, Parmveer Atwal, Ramani Ramaseshan, Farid Golnaraghi

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
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.

Mohammad PouriayevaliSchool of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, Canada.
Ryley McWilliamsSchool of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, Canada.
Avner BacharSchool of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, Canada.
Parmveer AtwalDepartment of Medical Physics, BC Cancer, Abbotsford, BC V2S 0C2, Canada.
Ramani RamaseshanSchool of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, Canada.
Farid GolnaraghiSchool of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC V3T 0A3, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer remains a leading cause of cancer-related deaths among women, emphasizing the critical need for early detection and monitoring techniques. Conventional imaging modalities such as mammography, MRI, and ultrasound have face sensitivity, specificity, cost, and patient comfort limitations. This study introduces a handheld Near-Infrared Diffuse Optical Tomography (NIR DOT) probe for breast cancer imaging. The NIRscan probe utilizes multi-wavelength light-emitting diodes (LEDs) and a linear charge-coupled device (CCD) sensor to acquire real-time optical data, reconstructing cross-sectional images of breast tissue based on scattering and absorption coefficients. With wavelengths optimized for the differential optical properties of tissue components, the probe enables functional imaging, distinguishing between healthy and malignant tissues. Clinical evaluations have demonstrated its potential for precise tumor localization and monitoring therapeutic responses, achieving a sensitivity of 94.7% and specificity of 84.2%. By incorporating machine learning algorithms and a modified diffusion equation (MDE), the system enhances the accuracy and speed of image reconstruction, supporting rapid, non-invasive diagnostics. This development represents a significant step forward in portable, cost-effective solutions for breast cancer detection, with potential applications in low-resource settings and diverse clinical environments.

Indexed as

Breast NeoplasmsTomography, OpticalAlgorithmsFemaleHumansImage Processing, Computer-AssistedMachine LearningSpectroscopy, Near-Infraredbiosensorbreast cancerchemotherapymachine learningresidual cancer burden

Identifiers

PMID39943622
PMCPMC11820052

What OpenQuestion holds

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Registered trials

None linked

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.