Evidence map›Paper›PMID 40227695›Full record

ReviewCancers2025

Incidental Pulmonary Nodule (IPN) Programs Working Together with Lung Cancer Screening and Artificial Intelligence to Increase Lung Cancer Detection.

Luv Purohit, Amy Kiamos, Sundas Ali, Andres M Alvarez-Pinzon, Luis Raez

Erratum issuedAbstract readReview
In one paragraph

Review in Cancers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Review
  6. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Luv PurohitMemorial Cancer Institute, Memorial Healthcare System, Pembroke Pines, FL 33026, USA.
Amy KiamosMemorial Cancer Institute, Memorial Healthcare System, Pembroke Pines, FL 33026, USA.ORCID 0000-0003-2156-8917
Sundas AliMemorial Cancer Institute, Memorial Healthcare System, Pembroke Pines, FL 33026, USA.
Andres M Alvarez-PinzonMemorial Cancer Institute, Memorial Healthcare System, Pembroke Pines, FL 33026, USA.ORCID 0000-0003-3488-8020
Luis RaezMemorial Cancer Institute, Memorial Healthcare System, Pembroke Pines, FL 33026, USA.ORCID 0000-0003-2669-5771

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Current lung cancer screening guidelines in the United States fail to identify many individuals at risk of developing the disease. Additionally, existing healthcare infrastructure has been leveraged to establish IPN clinics, a promising approach to addressing the limitations of current screening guidelines. Early-stage lung cancer is frequently diagnosed because of the incidental detection of pulmonary nodules on clinically indicated chest CT scans, particularly in the absence of formal screening programs. While artificial intelligence (AI) systems for lung cancer detection have demonstrated significant advancements in medicine, their clinical validation in screening settings remains limited. This review will discuss the pivotal trials underpinning the United States Preventive Services Task Force (USPSTF) recommendations for lung cancer screening, which have shaped the current guidelines for at-risk populations. We will explore recent studies investigating the role of AI in enhancing lung cancer screening efforts, highlighting how AI has the potential to improve early detection, streamline workflows, and reduce false positives and negatives in screening processes. This review will present the lung cancer screening rates at our institution, with a specific focus on the validation and integration of AI-driven technologies into our established screening programs. Using AI algorithms, we have validated enhanced detection capabilities through retrospective analysis of historical patient data, demonstrating significant improvements in identifying high-risk individuals and early-stage malignancies. These AI models, validated through rigorous cross-validation methods and clinical trials, have proven to outperform traditional screening approaches in sensitivity and specificity. The integration of these AI technologies within the lung cancer screening framework not only optimizes existing programs but also expands access to screening, improving early detection rates and ultimately leading to better patient outcomes. Through continuous validation and refinement, we aim to solidify AI's role in transforming lung cancer detection and patient care. Through ongoing validation and implementation, AI can play a crucial role in transforming lung cancer screening practices, ultimately contributing to earlier diagnosis and improved patient survival.

Indexed as

artificial intelligenceincidental pulmonary noduleIPN cliniclung cancer detectionlung cancer screening

Identifiers

PMID40227695
PMCPMC11987850

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.