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Record W3195365965 · doi:10.30683/1929-2279.2020.09.05

Diagnostic and Prognostic DNA-Karyometry for Cancer Diagnostics

2021· article· en· W3195365965 on OpenAlex
Alfred Böcking, David Friedrich, Branko Palcic, Dietrich Meyer-Ebrech, Chen Jin

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueJournal of cancer research updates · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsFeulgen stainMalignancyGrading (engineering)PathologyNuclear DNACancerAneuploidyStainingBiologyStainDNAMedicineGenetics

Abstract

fetched live from OpenAlex

Diagnostic and prognostic DNA-karyometry represents an automated computerized microscopical procedure, designed to improve cancer diagnostics at three different aspects: Screening for cancer cells, e.g. in body cavity effusions, urines or mucosal smears Specifying the risk of dysplasias or borderline lesions to progress to manifest cancer, e.g. of oral, bronchial or cervical mucosa, or the ovary. Grading the malignancy of certain tumors, like prostate cancer. It combines an automated diagnostic classification of Feulgen-stained nuclei with precise nuclear DNA-measurements. DNA-aneuploidy is used as a specific marker of malignancy and its degree for grading. All types of cytological specimens can be used after (re-)staining specific for DNA according to Feulgen. Histological specimens are subjected to enzymatic cell separation before Feulgen-staining. A video-slide scanner is used for automated scanning of microscopical slides. Diagnostic nuclear classifiers have tissue-specifically been trained by an expert-cytopathologist (A. B.), based on Random Forest Classifiers, applying 18 different morphometric features. They achieve an overall accuracy of 91.1% to differentiate 8 differents types of objects/nuclei. Nuclear DNA-measurements of diploid nuclei achieve a CV of <3%. DNA-stemline-aneuploidy, applied as a 100% specific marker for malignancy, is detected and quantified, using internationally accepted algorithms (ESACP 1995-2001). Suspicion of malignancy is raised in the absence of DNA-aneuploidy but presence of >1% morphometrically abnormal nuclei. Time needed for loading, scanning and validation of results per slide is about 10 minutes. Results of digital diagnostic nuclear classification can be verified by a cytopathologist, using image galleries. Likewise automated diagnostic interpretation of nuclear DNA-distributions can be checked on the monitor, before a pathologists validated diagnoses are issued. Screening-results are presented for body cavity effusions and urines. Evaluations of dysplasias are reported for oral, bronchial and cervical smears. Results of grading malignancy are shown for prostate cancers.

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.

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)0.001
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.009
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 head0.034
GPT teacher head0.387
Teacher spread0.353 · 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