MétaCan
Menu
Back to cohort
Record W2004190766 · doi:10.1118/1.2768866

Investigating the low‐dose limits of multidetector CT in lung nodule surveillance

2007· article· en· W2004190766 on OpenAlexaff
Narinder Paul, J. H. Siewerdsen, Demetris Patsios, TaeBong Chung

Bibliographic record

VenueMedical Physics · 2007
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health NetworkToronto General HospitalUniversity of TorontoOntario Institute for Cancer ResearchMount Sinai Hospital
FundersNational Cancer InstituteNational Institutes of Health
KeywordsNodule (geology)Imaging phantomNuclear medicineSolitary pulmonary noduleAttenuationReceiver operating characteristicDosimetryMedicineLung cancer screeningRadiologyScannerComputed tomographyComputer sciencePhysicsArtificial intelligenceOptics

Abstract

fetched live from OpenAlex

The purpose of this study was to evaluate the factors limiting nodule detection in thoracic computed tomography (CT) and to determine whether prior knowledge of nodule size and attenuation, available from a baseline CT study, influences the minimum radiation dose at which nodule surveillance CT scans can be performed while maintaining current levels of nodule detectability. Multiple nodules varying in attenuation (-509 to + 110 HU) and diameter (1.6 to 9.5 mm) were layered in random and ordered sequences within 2 lung cylinders made of Rando lung material and suspended within a custom-built CT phantom. Multiple CT scans were performed at varying kVp (120, 100, and 80), mA (200, 150, 100, 50, 20, and 10), and beam collimation (5, 2.5, and 1.25 mm) on a four-row multidetector scanner (Lightspeed, General Electric, Milwaukee, WI) using 0.8 s gantry rotation. The corresponding range of radiation dose over which images were acquired was 0.3-26.4 mGy. Nine observers independently performed three specific tasks, namely: (1) To detect a 3.2 mm nodule of 23 HU; (2) To detect 3.2 mm nodules of varying attenuation (-509 to -154 HU); and (3) To detect nodules varying in size (1.6-9 mm) and attenuation (-509 to 110 HU). A two-alternative forced-choice test was used in order to determine the limits of nodule detection in terms of the proportion of correct responses (Pcorr, related to the area under the ROC curve) as a summary metric of observer performance. The radiation dose levels for detection of 99% of nodules in each task were as follows: Task 1 (1 mGy); Task 2 (5 mGy); and Task 3 (7 mGy). The corresponding interobserver confidence limits were 1, 5, and 10 mGy for Tasks 1, 2, and 3, respectively. There was a fivefold increase in the radiation dose required for detection of lower-density nodules (Tasks 1 to 2). Absence of prior knowledge of the nodule size and density (Task 3) corresponds to a significant increase in the minimum required radiation dose. Significant image degradation and reduction in observer performance for all tasks occur at a dose of < or = 1 mGy. It is concluded that the size and attenuation of a nodule strongly influence the radiation dose required for confident evaluation with a minimum threshold value of 1-2 mGy (minimum dose CT). A prior knowledge of nodule size and attenuation is available from the baseline CT scan and is an important consideration in minimizing the radiation exposure required for nodule detection with surveillance CT.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.027
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.309
Teacher spread0.289 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueMedical PhysicsSame topicLung Cancer Diagnosis and TreatmentFrench-language works237,207