Evidence map›Paper›PMID 33816155›Full record

ArticleQuantitative imaging in medicine and surgery2021

Artificial intelligence-based vessel suppression for detection of sub-solid nodules in lung cancer screening computed tomography.

Ramandeep Singh, Mannudeep K Kalra, Fatemeh Homayounieh, Chayanin Nitiwarangkul, Shaunagh McDermott, Brent P Little, Inga T Lennes, Jo-Anne O Shepard, Subba R Digumarthy

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Article in Quantitative imaging in medicine and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 2 pooled it
–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

20 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  10. Artificial intelligence in automated detection of lung nodules: a narrative review.International journal of physiology, pathophysiology and pharmacology · 2025
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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

9 authors.

Ramandeep SinghDivision of Thoracic Imaging and Intervention, Massachusetts General Hospital, Boston, MA, USA.
Mannudeep K KalraDivision of Thoracic Imaging and Intervention, Massachusetts General Hospital, Boston, MA, USA.
Fatemeh HomayouniehDivision of Thoracic Imaging and Intervention, Massachusetts General Hospital, Boston, MA, USA.
Chayanin NitiwarangkulDivision of Thoracic Imaging and Intervention, Massachusetts General Hospital, Boston, MA, USA.
Shaunagh McDermottDivision of Thoracic Imaging and Intervention, Massachusetts General Hospital, Boston, MA, USA.
Brent P LittleDivision of Thoracic Imaging and Intervention, Massachusetts General Hospital, Boston, MA, USA.
Inga T LennesHarvard Medical School, Boston, MA, USA.
Jo-Anne O ShepardDivision of Thoracic Imaging and Intervention, Massachusetts General Hospital, Boston, MA, USA.
Subba R DigumarthyDivision of Thoracic Imaging and Intervention, Massachusetts General Hospital, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLung cancer screening (LCS) with low-dose computed tomography (LDCT) helps early lung cancer detection, commonly presenting as small pulmonary nodules. Artificial intelligence (AI)-based vessel suppression (AI-VS) and automatic detection (AI-AD) algorithm can improve detection of subsolid nodules (SSNs) on LDCT. We assessed the impact of AI-VS and AI-AD in detection and classification of SSNs [ground-glass nodules (GGNs) and part-solid nodules (PSNs)], on LDCT performed for LCS.

methodsFollowing regulatory approval, 123 LDCT examinations with sub-solid pulmonary nodules (average diameter ≥6 mm) were processed to generate three image series for each examination-unprocessed, AI-VS, and AI-AD series with annotated lung nodules. Two thoracic radiologists in consensus formed the standard of reference (SOR) for this study. Two other thoracic radiologists (R1 and R2; 5 and 10 years of experience in thoracic CT image interpretation) independently assessed the unprocessed images alone, then together with AI-VS series, and finally with AI-AD for detecting all ≥6 mm GGN and PSN. We performed receiver operator characteristics (ROC) and Cohen's Kappa analyses for statistical analyses.

resultsOn unprocessed images, R1 and R2 detected 232/310 nodules (R1: 114 GGN, 118 PSN) and 255/310 nodules (R2: 122 GGN, 133 PSN), respectively (P>0.05). On AI-VS images, they detected 249/310 nodules (119 GGN, 130 PSN) and 277/310 nodules (128 GGN, 149 PSN), respectively (P≥0.12). When compared to the SOR, accuracy (AUC) for detection of PSN on the AI-VS images (AUC 0.80-0.81) was greater than on the unprocessed images (AUC 0.70-0.76). AI-VS images enabled detection of solid components in five nodules deemed as GGN on the unprocessed images. Accuracy of AI-AD was lower than both the radiologists (AUC 0.60-0.72).

conclusionsAI-VS improved the detection and classification of SSN into GGN and PSN on LDCT of the chest for the two radiologist (R1 and R2) readers.

Indexed as

Artificial intelligence (AI)multidetector-row computed tomographypulmonary cancerpulmonary nodule

Identifiers

PMID33816155
PMCPMC7930659

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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.