Evidence map›Paper›PMID 40751030›Full record

ArticleBreast cancer research and treatment2025

Diffuse reflectance and fluorescence spectroscopy for breast conserving surgery.

Dhurka Shanthakumar, Vadzim Chalau, Yufeng Shi, Ria Ranjitkar, Anna Silvanto, Ara Darzi, Daniel R Leff, Daniel S Elson

Abstract read
In one paragraph

Article in Breast cancer research and treatment, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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

2 citing papers in PubMed.

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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

8 authors.

Dhurka ShanthakumarDepartment of Surgery & Cancer, Imperial College London, London, UK. dshanthakumar08@gmail.com.ORCID http://orcid.org/0000-0001-6263-7080
Vadzim ChalauDepartment of Surgery & Cancer, Imperial College London, London, UK.
Yufeng ShiDepartment of Surgery & Cancer, Imperial College London, London, UK.
Ria RanjitkarDepartment of Surgery & Cancer, Imperial College London, London, UK.
Anna SilvantoDepartment of Surgery & Cancer, Imperial College Healthcare NHS Trust, London, UK.
Ara DarziDepartment of Surgery & Cancer, Imperial College London, London, UK.
Daniel R Leff *Department of Surgery & Cancer, Imperial College London, London, UK.
Daniel S Elson *Department of Surgery & Cancer, Imperial College London, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe major challenge in breast conserving surgery is the high rates of re-excision due to positive resection margins. This study evaluates whether a combined diffuse reflectance spectroscopy (DRS) and laser induced intrinsic fluorescence spectroscopy (IFS) technique can differentiate breast tissue sample types, towards the development of an intraoperative margin assessment tool.

methodsBreast tissue samples were collected from patients undergoing breast cancer surgery. A handheld DRS-IFS probe was used on the frozen thawed ex-vivo  breast samples to acquire spectral data. Machine learning classifiers were used to determine sensitivity, specificity, overall diagnostic accuracy, and the area under the curve (AUC) against "gold-standard" histopathology ground truth.

results181 breast tissue samples from 138 patients were interrogated using DRS-IFS. All patients were female, with median age (range) of 56.8 (20-94) years The total number of spectra acquired was 18,349. Following five-fold cross validation for normal versus cancer tissue, extreme gradient boost classifier achieved a sensitivity of 84% (SD ± 13), specificity of 61% (SD ± 16), overall diagnostic accuracy of 75% (SD ± 3), and AUC of 84%.

conclusionThe results suggests that DRS-IFS can distinguish normal breast tissue from breast cancer with high diagnostic accuracy. For DRS-IFS to be translated into the operating theatre to aid a surgeon's real-time visualisation for oncologic margin control assessment of intraoperative, the in vivo diagnostic accuracy needs to be determined.

Indexed as

Breast NeoplasmsMastectomy, SegmentalAdultAgedAged, 80 and overFemaleHumansMargins of ExcisionMiddle AgedSensitivity and SpecificitySpectrometry, FluorescenceYoung AdultBreast cancer, marginsDiffuse reflectance spectroscopyIntraoperative margin assessment, spectroscopyIntrinsic fluorescence

Identifiers

PMID40751030
PMCPMC12398437

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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.