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Record W1969968894 · doi:10.1109/embc.2012.6346634

Diffuse optical imaging for monitoring treatment response in breast cancer patients

2012· article· en· W1969968894 on OpenAlexaff
Omar Falou, Ali Sadeghi‐Naini, Heba Soliman, Martin J. Yaffe, Gregory J. Czarnota

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsCredit Valley HospitalHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsBreast cancerMedicineBreast-conserving surgeryOptical imagingMastectomyNuclear medicineCancerBreast imagingRadiologyBiomedical engineeringOncologyMammographyInternal medicinePhysics

Abstract

fetched live from OpenAlex

The necessity for a non-invasive and inexpensive imaging modality to both diagnose and monitor treatment response has lead to renewed interest in the potential of optical imaging. The aim of this study was to investigate the potential of diffuse optical spectroscopy for monitoring of patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy. Fifteen women receiving neoadjuvant treatment for breast cancer had the affected breast scanned 5 times: before, 1 week, 4 weeks, and 8 weeks following initiation of the treatment and prior to surgery. Data was collected using a commercial optical system at four different wavelengths (690 nm, 730 nm, 780 nm, and 830 nm) and used to create three dimensional tomographic images. Mean measured values of deoxyhemoglobin (Hb), oxyhemoglobin (HbO(2)), and water in the entire breast were obtained and integrated over the entire breast volume to calculate the integrated optical index for each parameter. Volume-of-interest weighted tissue Hb, HbO(2), and water corresponding to the tumor were also calculated. Patient response to the treatment was evaluated from clinical and pathological response using whole-mount pathology after mastectomy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.357
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2012
Admission routes1
Has abstractyes

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