Evidence map›Paper›PMID 38927969›Full record

ArticleCancers2024

Breast Cancer Screening Using Inverse Modeling of Surface Temperatures and Steady-State Thermal Imaging.

Nithya Sritharan, Carlos Gutierrez, Isaac Perez-Raya, Jose-Luis Gonzalez-Hernandez, Alyssa Owens, Donnette Dabydeen, Lori Medeiros, Satish Kandlikar, Pradyumna Phatak

Abstract read
In one paragraph

Article in Cancers, 2024. 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. AI in Medical Imaging and Image Processing.Journal of clinical medicine · 2025
    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

9 authors.

Nithya SritharanDepartment of Hematology-Oncology, Rochester Regional Health, Rochester, NY 14621, USA.
Carlos GutierrezDepartment of Mechanical Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA.ORCID 0009-0000-4159-9820
Isaac Perez-RayaDepartment of Mechanical Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA.
Jose-Luis Gonzalez-HernandezDepartment of Mechanical Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA.
Alyssa OwensDepartment of Mechanical Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA.
Donnette DabydeenDepartment of Hematology-Oncology, Rochester Regional Health, Rochester, NY 14621, USA.
Lori MedeirosDepartment of Hematology-Oncology, Rochester Regional Health, Rochester, NY 14621, USA.
Satish KandlikarDepartment of Mechanical Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA.ORCID 0000-0001-5246-9431
Pradyumna PhatakDepartment of Hematology-Oncology, Rochester Regional Health, Rochester, NY 14621, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer is characterized by increased metabolic activity and vascularity, leading to temperature changes in cancerous tissues compared to normal cells. This study focused on patients with abnormal mammogram findings or a clinical suspicion of breast cancer, exclusively those confirmed by biopsy. Utilizing an ultra-high sensitivity thermal camera and prone patient positioning, we measured surface temperatures integrated with an inverse modeling technique based on heat transfer principles to predict malignant breast lesions. Involving 25 breast tumors, our technique accurately predicted all tumors, with maximum errors below 5 mm in size and less than 1 cm in tumor location. Predictive efficacy was unaffected by tumor size, location, or breast density, with no aberrant predictions in the contralateral normal breast. Infrared temperature profiles and inverse modeling using both techniques successfully predicted breast cancer, highlighting its potential in breast cancer screening.

Indexed as

breast cancer screeningheat generation mapinverse modelingphysics-informed neural network

Identifiers

PMID38927969
PMCPMC11201981

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.