Evidence map›Paper›PMID 41102259›Full record

ArticleScientific reports2025

Modelling of immune infiltration in prostate cancer treated with HDR-brachytherapy using Raman spectroscopy and machine learning.

Sandra N Popescu, Kirsty Milligan, Mitchell Wiebe, Alejandra Fuentes, Joan M Brewer, Christina K Haston, Julian J Lum, Samantha Punch, Alejandra Raudales, Alexandre G Brolo and 3 more

Abstract read
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In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

13 authors.

Sandra N PopescuDepartment of Physics, University of British Columbia, Kelowna, BC, Canada.
Kirsty MilliganDepartment of Physics, University of British Columbia, Kelowna, BC, Canada.
Mitchell WiebeDepartment of Physics, University of British Columbia, Kelowna, BC, Canada.
Alejandra FuentesDepartment of Physics, University of British Columbia, Kelowna, BC, Canada.
Joan M BrewerDepartment of Physics, University of British Columbia, Kelowna, BC, Canada.
Christina K HastonDepartment of Physics, University of British Columbia, Kelowna, BC, Canada.
Julian J LumTrev and Joyce Deeley Research Centre, BC Cancer, Victoria, BC, Canada.
Samantha PunchTrev and Joyce Deeley Research Centre, BC Cancer, Victoria, BC, Canada.
Alejandra RaudalesTrev and Joyce Deeley Research Centre, BC Cancer, Victoria, BC, Canada.
Alexandre G BroloDepartment of Chemistry, University of Victoria, Victoria, BC, Canada.
Juanita M CrookDepartment of Radiation Oncology, University of British Columbia, Kelowna, BC, Canada.
Jeffrey L AndrewsDepartment of Statistics, University of British Columbia, Kelowna, Canada.
Andrew JirasekDepartment of Physics, University of British Columbia, Kelowna, BC, Canada. andrew.jirasek@ubc.ca.

Funding

CIHR PJT 162279National Sciences and Engineering Research Council of Canada Discovery Grants RGPIN-2020-04646National Sciences and Engineering Research Council of Canada Discovery Grants RGPIN-2020-07232
6 · The paper itself

Abstract

Prostate cancer is characterized by an immunosuppressive tumour environment. This work combines Raman spectroscopy with group-and-bases-restricted non-negative matrix factorization (GBR-NMF) and machine learning to assemble models of immune cell densities within the needle-core biopsies of patients undergoing high-dose-rate brachytherapy (HDR-BT). Raman spectral acquisition, as well as immunohistochemistry staining of CD68[Formula: see text], CD3[Formula: see text], and [Formula: see text] cells, was completed for biopsies collected before and 2 weeks following the first fraction of HDR-BT. Regression techniques, constructed using GBR-NMF scores, that produced the most accurate predictions of immune cell density by metrics of root mean-squared error (RMSE) and R[Formula: see text] were the gradient-boosted trees model of [Formula: see text] density (RMSE: 163 counts[Formula: see text], [Formula: see text]: 0.65) and the elastic net model of [Formula: see text]/ [Formula: see text] (RMSE: 0.25, [Formula: see text]: 0.82). The accuracy of these models, herein defined as the fraction of patient predictions within [Formula: see text] standard deviation of their measured values was 11/16 and 12/16, for CD68[Formula: see text] CD3[Formula: see text] and CD68[Formula: see text]/ CD8[Formula: see text] models, respectively. To further delineate which metabolites were most important in the CD68[Formula: see text]/ CD8[Formula: see text] model, this ratio was further predicted in stromal and epithelial tissues within the biopsies, and resulting models utilized the GBR-NMF scores of glutathione, collagen, palmitic acid, and the pre- or post-HDR-BT label to produce an optimal performance level according to RMSE and R[Formula: see text]. In summary, this study illustrates a novel methodology in which supervised machine learning techniques are used to model immune cells, which are prognostic indicators of disease progression.

Indexed as

BrachytherapyMachine LearningProstatic NeoplasmsSpectrum Analysis, RamanAgedAntigens, CDAntigens, Differentiation, MyelomonocyticHumansMaleMiddle AgedAntigens, CDAntigens, Differentiation, Myelomonocytic

Identifiers

PMID41102259
PMCPMC12533066

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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 Socratic graph.