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Record W2522970486 · doi:10.7759/cureus.795

Image-Guided High-Dose-Rate (HDR) Boost Localization Using MRI/MR Spectroscopy: A Correlation Study with Biopsy

2016· article· en· W2522970486 on OpenAlexaff
Éric Vigneault, Khaly Mbodji, Louis G Racine, Éric Chevrette, marie claude lavallee, André‐Guy Martin, Philippe Després, Luc Beaulieu

Bibliographic record

VenueCureus · 2016
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsHotel Dieu HospitalCentre hospitalier universitaire de QuébecUniversité LavalCentre hospitalier de l'Université Laval
Fundersnot available
KeywordsMedicineBiopsyProstate cancerMagnetic resonance imagingEffective diffusion coefficientProstateRadiologyReceiver operating characteristicNuclear medicineProstate biopsyDiffusion MRITransrectal ultrasonographyGold standard (test)BrachytherapyCancerRadiation therapy

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study is to compare the blind interpretations of magnetic resonance imaging (MRI) sequences, diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC), mapping, and magnetic resonance spectroscopy (MRS) of the prostate, in comparison to prostate biopsy to identify a valid dominant intraprostatic lesion (DIL) for dose escalation using high-dose rate brachytherapy. METHODS: MRI/MRS were performed on 20 patients with intermediate risk adenocarcinoma of the prostate. T1W, T2W, DWI-ADC, and MRS sequences were performed at 1.5 T with pelvic and endorectal coils. An experienced radiologist rated the presence of cancer in each sextant by using a dichotomic approach, first on MR standard acquisitions (T1W and T2W), then on DWI-ADC mapping, and later on MRS images. Areas under the receiver's operating characteristic curve were calculated using a sextant as the unit of analysis. The transrectal ultrasonography-guided biopsy results were used as the reference standard. A table summarizing the MRI/MRS findings was made and compared to the corresponding area in the prostate biopsy report. A perfect match was defined to be the presence of cancer in the same sextant of the MRI/MRS exam and the prostate biopsy. RESULTS: The interpretation of the MRI/MRS exams per sextant was compared to the diagnostic biopsy report. MRI readings were compared with the biopsy as a surrogate for the complete pathology specimen of the prostate. A sensitivity (Sn) of 98.6% (95% confidence interval, 92.2% - 99.9%) and specificity (Sp) of 60.8% (46.1% - 74.2%) were found. The positive and negative predictive values (PPV, NPV) were 77.3% (67.1% - 85.5%) and 96.9% (83.8% - 99.9%), respectively. When MRS readings were compared with biopsy, we found a Sn of 96.4% (87.7% - 99.6%) and Sp of 54.8% (38.7% - 70.2%). The PPV and NPV were 74% (62.4% - 83.6%) and 92% (74% - 99%), respectively. DWI-ADC mapping results were also compared with biopsy. We found a Sn and Sp of 93.7% (84.5% - 98.2%) and 82.1% (66.5% - 92.5%), respectively, and a PPV and NPV of 89.4% (79.4% - 95.6%) and 88.9% (73.9% - 96.9%), respectively. Finally, after combining MRI, MRS, and DWI-ADC mapping, compared with biopsy, we obtained a Sn, Sp, PPV, and NPV of 100% (94.8% - 100%), 49% (34.8% - 63.4%), 72.6% (62.5% - 81.3%), and 100% (86.3% - 100%), respectively. CONCLUSIONS: The combination of MRI/MRS is a sensitive tool for both the structural and metabolic evaluation of prostate cancer location. MRI/MRS exams are useful to delineate a DIL for high-dose-rate (HDR) intraprostatic boost.

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)0.002
metaresearch head score (Gemma)0.013
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.025
GPT teacher head0.304
Teacher spread0.279 · 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".

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Citations1
Published2016
Admission routes1
Has abstractyes

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