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Pancreatic imaging findings from the PRECEDE study: A large high-risk heritable cohort for pancreatic cancer.

2023· article· en· W4317891151 on OpenAlexaff
Ido Haimi, George Zogopoulos, Shenin A. Dettwyler, Jessica N. Everett, Yan Bi, Randall E. Brand, Daniel C. Chung, James J. Farrell, Aaron J. Grossberg, Fay Kastrinos, Bryson W. Katona, Kelsey Klute, Sonia S. Kupfer, Aimee L. Lucas, Salvatore Paiella, Giovanni Parmigiani, Jennifer B. Permuth, Rosalie C. Sears, Daniel A. Sussman, Diane M. Simeone

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineCohortPancreatic cancerPancreasInternal medicineCancerOncologyPancreatic ductal adenocarcinoma

Abstract

fetched live from OpenAlex

689 Background: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer typically discovered at incurable stages. The PRECEDE Consortium was established to accelerate early detection by using a large-scale, collaborative, innovative model, predicated on standardized collection of demographic, clinical, and imaging data from high-risk individuals (HRI). Here we report the initial pancreas imaging findings in Cohort 1, representing HRI with familial pancreatic cancer (FPC) or pathogenic germline variants (PGV) in PDAC susceptibility genes with a 1 st or 2 nd degree relative with PDAC. Methods: The PRECEDE Consortium (NCT04970056) began enrollment in 5/2020. HRI enrolled prospectively at centers worldwide into one of 7 cohorts based on personal and/or family history of PDAC and PGV status. PRECEDE’s planned enrollment is 10,000 patients. Data sharing is required to join PRECEDE, facilitated by a standardized data collection system and central database (PRECEDELink). Imaging (MRI/MRCP and EUS) is performed using standardized image acquisition and reporting templates. Imaging and clinical sequencing data are stored and analyzed in the PRECEDE data cloud. Results: By 9/16/2022, 26 sites enrolled 3156 patients in 7 cohorts, with 1716 in Cohort 1. Cohort 1 was 60% female, 80% white; 48% met FPC criteria, and 52% were PGV carriers. Within Cohort 1, 658 FPC and 965 PGV (1353 total, 79%) underwent imaging (68% MRI; 32% EUS). Overall, 573/1353 (42%) had pancreas abnormalities: 320/573 (49%) FPC, and 253/573 (36%) PGV (OR [95% CI] 1.65 [1.32–2.06], P < 0.001). Cysts were the most common abnormality, present in 549/1353 (41%) and accounting for 310/320 (97%) FPC and 239/253 (94%) PGV HRI abnormalities. Of 549 HRI with cysts, 262 (48%) had 1 cyst, including 137/310 (44%) FPC and 125/239 (52%) PGV HRI (OR 1.43 [1.07–1.92], P = .012) The remaining 287/549 (52%) had ≥2 cysts, including 173/310 (56%) FPC and 114/239 (48%) PGV HRI (OR 1.98 [1.49–2.64], P < 0.001). Worrisome features occurred in 83/1353 (6.1%) including: 14 (1%) cyst > 2 cm, 7 (0.5%) cyst ≥3 cm, 35 (2.6%) main pancreatic duct (MPD) diameter ≥5 mm; 2 (0.15%) duct strictures; and 25 (1.8%) solid masses. Solid masses included 1 (0.07%) PDAC, 9 (0.7%) neuroendocrine tumors, and 15 (1.1%) benign lesions (e.g. lipoma, splenule). Conclusions: Pancreatic abnormalities are common in a cohort of 1353 HRI enrolled in PRECEDE; 6.1% of HRI had findings with worrisome features and clinical implications. Multiple cysts were significantly more common in FPC HRI (OR 1.98); worrisome findings did not differ between FPC and PGV groups. The longitudinal study of this growing HRI cohort with standardized imaging and matched comprehensive epidemiological, clinical, and laboratory data, along with germline testing, will provide critical information and understanding of PDAC risk, and augment existing clinical decision-making models governing surveillance and treatment.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.117
GPT teacher head0.498
Teacher spread0.381 · 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
Published2023
Admission routes1
Has abstractyes

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