Evidence map›Paper›PMID 39787436›Full record

ArticleJCO clinical cancer informatics2025

ImpACT Project: Improving Access to Clinical Trials in Victoria, an Artificial Intelligence-Based Approach.

Maria L Bechelli, Kris Ivanova, Suan Siang Tan, Beena Kumar, Dayna Swiatek, Surein Arulananda, Sue M Evans

Abstract read
In one paragraph

Article in JCO clinical cancer informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Maria L BechelliVictorian Cancer Registry, Cancer Council Victoria, Victoria, Australia.ORCID 0009-0004-9157-2051
Kris IvanovaVictorian Cancer Registry, Cancer Council Victoria, Victoria, Australia.
Suan Siang TanDepartment of Medical Oncology, Monash Health, Victoria, Australia.ORCID 0009-0006-8020-731X
Beena KumarDepartment of Medical Oncology, Monash Health, Victoria, Australia.
Dayna SwiatekOlivia Newton-John Cancer Research Institute, Victoria, Australia.ORCID 0009-0004-1378-7276
Surein ArulanandaDepartment of Medical Oncology, Monash Health, Victoria, Australia.
Sue M EvansVictorian Cancer Registry, Cancer Council Victoria, Victoria, Australia.ORCID 0000-0003-2962-8400

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeEnhancing the speed and efficiency of clinical trial recruitment is a key objective across international health systems. This study aimed to use artificial intelligence (AI) applied in the Victorian Cancer Registry (VCR), a population-based cancer registry, to assess (1) if VCR received all relevant pathology reports for three clinical trials, (2) AI accuracy in auto-extracting information from pathology reports for recruitment, and (3) the number of participants approached for trial enrollment using the AI approach compared with standard hospital-based recruitment.

methodsTo verify pathology report accessibility for VCR trial enrollment, reports from the laboratory were cross-referenced. To determine the accuracy of a Rapid Case Ascertainment (RCA) module of the AI software in extracting key clinical variables from the pathology report, data were compared with manually reviewed reports. To examine the effectiveness of the AI recruitment approach, the number of patients approached for recruitment was compared with standard practice.

resultsOf the 195 reports provided by the pathology laboratory, 185 (94.9%) were received by VCR, 73 of 195 (37.4%) were eligible for the studies, and 5 of 73 (6.8%) eligible cases had not been received by the VCR. The RCA module demonstrated an accuracy of 93% and an F1 score of 0.94 in extracting key clinical variables. However, the RCA false-positive rate was 10% and the false-negative rate was 5%. The standard hospital approach selected fewer cases for approach to clinical trials compared with the RCA module approach, 8 of 336 (2.4%) versus 12 of 336 (3.6%), respectively.

conclusionUsing AI to screen potentially eligible cases for recruitment to three clinical trials resulted in a 50% increase in eligible cases being approached for enrollment.

Indexed as

Artificial IntelligenceClinical Trials as TopicPatient SelectionRegistriesFemaleHumansMaleNeoplasmsVictoria

Identifiers

PMID39787436
PMCPMC11732263

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.