Evidence map›Paper›PMID 40206677›Full record

ArticleMayo Clinic proceedings. Digital health2024

Long-Term Survival and CANARY-Based Artificial Intelligence for Multifocal Lung Adenocarcinoma.

Sahar A Saddoughi, Chelsea Powell, Gregory R Stroh, Srinivasan Rajagopalan, Brian J Bartholmai, Jennifer M Boland, Marie Christine Aubry, William S Harmsen, Shanda H Blackmon, Stephen D Cassivi and 7 more

Registry-linked trialAbstract read
In one paragraph

Article in Mayo Clinic proceedings. Digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01946100 (A Clinical Pathway for the Treatment of Multifocal Lung Adenocarcinoma Using Genome Sequencing), which is not on this 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.

NCT01946100 naterminatednot on this map

A Clinical Pathway for the Treatment of Multifocal Lung Adenocarcinoma Using Genome Sequencing

TypeinterventionalSponsorMayo ClinicRan2013 to 2025Enrolled100ConditionsLung Neoplasms, Carcinoma Non-small-cell Lung, AdenocarcinomaArmsMultifocal Lung Adenocarcinoma
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

17 authors.

Sahar A SaddoughiDivision of Thoracic Surgery, Department of Surgery, Mayo Clinic, Rochester, MN.
Chelsea PowellDivision of Thoracic Surgery, Department of Surgery, Mayo Clinic, Rochester, MN.
Gregory R StrohDepartment of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN.
Srinivasan RajagopalanBiomedical Imaging Resource, Mayo Clinic, Rochester, MN.
Brian J BartholmaiDepartment of Radiology, Mayo Clinic, Rochester, MN.
Jennifer M BolandDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN.
Marie Christine AubryDepartment of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN.
William S HarmsenDivision of Health Science Research, Mayo Clinic, Rochester, MN.
Shanda H BlackmonDivision of Thoracic Surgery, Department of Surgery, Mayo Clinic, Rochester, MN.
Stephen D CassiviDivision of Thoracic Surgery, Department of Surgery, Mayo Clinic, Rochester, MN.
Francis C NicholsDivision of Thoracic Surgery, Department of Surgery, Mayo Clinic, Rochester, MN.
Janani S ReisenauerDivision of Thoracic Surgery, Department of Surgery, Mayo Clinic, Rochester, MN.
K Robert ShenDivision of Thoracic Surgery, Department of Surgery, Mayo Clinic, Rochester, MN.
Aaron S MansfieldDivision of Medical Oncology, Mayo Clinic, Rochester, MN.
Fabien MaldonadoDivision of Pulmonary and Critical Care Medicine, Vanderbilt University, Nashville, TN.
Tobias PeikertDepartment of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN.
Dennis A WigleDivision of Thoracic Surgery, Department of Surgery, Mayo Clinic, Rochester, MN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To investigate whether an artificial intelligence (AI)-based model can predict tumor invasiveness in patients with multifocal lung adenocarcinoma (MFLA). Patients and Methods: Patients with MFLA who underwent surgical resection were enrolled to a prospective registry trial (NCT01946100). Each identified nodule underwent retrospective computer-aided nodule assessment and risk yield (CANARY)-based AI to determine a quantitative degree of invasiveness. Data regarding age, sex, medical and surgical management, and survival were collected and analyzed. Pathologic review was performed by a pulmonary pathologist with comprehensive histologic subtyping. Results: From January 1, 2013, through December 31, 2018, 68 patients with MFLA underwent at least 1 surgical resection. Five-year survival for the cohort was 91%, and 10-year survival was 73.6%. No significant differences in survival were observed when separated by sex, number, or size of the nodules. A 10-year survival trend was seen when comparing patients with unilateral (100% survival) vs bilateral disease (66%). Retrospective CANARY-based AI analysis demonstrated that the majority of the nodules present at the time of diagnosis (229/302; 75.8%) were classified good, with an average score of 0.19, suggesting indolent clinical behavior and noninvasive pathology. However, AI-CANARY scores of the surgically removed nodules were significantly higher compared with those of the nonresected nodules ( Conclusion: The long-term survival for patients with N0, M0 MFLA who have undergone surgical resection may approach those of stage I non-small cell lung cancer. CANARY-based AI has the potential to stratify individual nodules to help guide surgical intervention versus observation of nodules. Trial Registration: clinicaltrials.gov Identifier: NCT01946100.

Identifiers

PMID40206677
PMCPMC11975651

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