Evidence map›Paper›PMID 35717344›Full record

ArticleScientific reports2022

Machine learning-enabled cancer diagnostics with widefield polarimetric second-harmonic generation microscopy.

Kamdin Mirsanaye, Leonardo Uribe Castaño, Yasmeen Kamaliddin, Ahmad Golaraei, Renaldas Augulis, Lukas Kontenis, Susan J Done, Edvardas Žurauskas, Vuk Stambolic, Brian C Wilson and 1 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
9.2field-weighted citation impact, top 2% of its field
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

18 citing papers in PubMed, 36 citations in OpenAlex.

  1. Understanding and targeting the tumour matrisome.Nature reviews. Clinical oncology · 2026
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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

11 authors at 3 institutions in 2 countries.

Kamdin MirsanayeDepartment of Physics, University of Toronto, Toronto, Canada.
Leonardo Uribe CastañoDepartment of Physics, University of Toronto, Toronto, Canada.
Yasmeen KamaliddinDepartment of Physics, University of Toronto, Toronto, Canada.
Ahmad GolaraeiDepartment of Physics, University of Toronto, Toronto, Canada.
Renaldas AugulisNational Centre of Pathology, Vilnius, Lithuania.
Lukas KontenisLaser Research Centre, Faculty of Physics, Vilnius University, Vilnius, Lithuania.
Susan J DonePrincess Margaret Cancer Centre, University Health Network, Toronto, Canada.
Edvardas ŽurauskasDepartment of Pathology, Forensic Medicine and Pharmacology, Faculty of Medicine, Vilnius University, Vilnius, Lithuania.
Vuk StambolicPrincess Margaret Cancer Centre, University Health Network, Toronto, Canada.
Brian C WilsonPrincess Margaret Cancer Centre, University Health Network, Toronto, Canada.
Virginijus BarzdaDepartment of Physics, University of Toronto, Toronto, Canada. virgis.barzda@utoronto.ca.
University of Toronto · CAVilnius University · LTUniversity Health Network · CA

Funding

CIHR CPG-134752
6 · The paper itself

Abstract

The extracellular matrix (ECM) collagen undergoes major remodeling during tumorigenesis. However, alterations to the ECM are not widely considered in cancer diagnostics, due to mostly uniform appearance of collagen fibers in white light images of hematoxylin and eosin-stained (H&E) tissue sections. Polarimetric second-harmonic generation (P-SHG) microscopy enables label-free visualization and ultrastructural investigation of non-centrosymmetric molecules, which, when combined with texture analysis, provides multiparameter characterization of tissue collagen. This paper demonstrates whole slide imaging of breast tissue microarrays using high-throughput widefield P-SHG microscopy. The resulting P-SHG parameters are used in classification to differentiate tumor from normal tissue, resulting in 94.2% for both accuracy and F1-score, and 6.3% false discovery rate. Subsequently, the trained classifier is employed to predict tumor tissue with 91.3% accuracy, 90.7% F1-score, and 13.8% false omission rate. As such, we show that widefield P-SHG microscopy reveals collagen ultrastructure over large tissue regions and can be utilized as a sensitive biomarker for cancer diagnostics and prognostics studies.

Indexed as

NeoplasmsSecond Harmonic Generation MicroscopyCollagenExtracellular MatrixMachine LearningPrognosisCollagen

Identifiers

PMID35717344
PMCPMC9206659
OpenAlexW4283072525

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.