Evidence map›Paper›PMID 39187900›Full record

ArticleCancer imaging : the official publication of the International Cancer Imaging Society2024

Growth dynamics of lung nodules: implications for classification in lung cancer screening.

Beatriz Ocaña-Tienda, Alba Eroles-Simó, Julián Pérez-Beteta, Estanislao Arana, Víctor M Pérez-García

Abstract read
In one paragraph

Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

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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

8 citing papers in PubMed.

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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

5 authors.

Beatriz Ocaña-TiendaMathematical Oncology Laboratory, University of Castilla-La Mancha, Ciudad Real, Spain. Beatriz.Ocana@uclm.es.
Alba Eroles-SimóInstituto de Instrumentación para la Imagen Molecular (i3M), Universitat Politécnica de València, Consejo Superior de Investigaciones Científicas (CSIC), València, Spain.
Julián Pérez-BetetaMathematical Oncology Laboratory, University of Castilla-La Mancha, Ciudad Real, Spain.
Estanislao Arana *Department of Radiology, Fundación Instituto Valenciano de Oncología, Valencia, Spain.
Víctor M Pérez-García *Mathematical Oncology Laboratory, University of Castilla-La Mancha, Ciudad Real, Spain.

Funding

Junta de Comunidades de Castilla-La Mancha SBPLY/21/180501/000145Ministerio de Ciencia e Innovación PDC2022-133520-I00Ministerio de Ciencia e Innovación PRE2020-092178
6 · The paper itself

Abstract

backgroundLung nodules observed in cancer screening are believed to grow exponentially, and their associated volume doubling time (VDT) has been proposed for nodule classification. This retrospective study aimed to elucidate the growth dynamics of lung nodules and determine the best classification as either benign or malignant.

methodsData were analyzed from 180 participants (73.7% male) enrolled in the I-ELCAP screening program (140 primary lung cancer and 40 benign) with three or more annual CT examinations before resection. Attenuation, volume, mass and growth patterns (decelerated, linear, subexponential, exponential and accelerated) were assessed and compared as classification methods.

resultsMost lung cancers (83/140) and few benign nodules (11/40) exhibited an accelerated, faster than exponential, growth pattern. Half (50%) of the benign nodules versus 26.4% of the malignant ones displayed decelerated growth. Differences in growth patterns allowed nodule malignancy to be classified, the most effective individual variable being the increase in volume between two-year-interval scans (ROC-AUC = 0.871). The same metric on the first two follow-ups yielded an AUC value of 0.769. Further classification into solid, part-solid or non-solid, improved results (ROC-AUC of 0.813 in the first year and 0.897 in the second year).

conclusionsIn our dataset, most lung cancers exhibited accelerated growth in contrast to their benign counterparts. A measure of volumetric growth allowed discrimination between benign and malignant nodules. Its classification power increased when adding information on nodule compactness. The combination of these two meaningful and easily obtained variables could be used to assess malignancy of lung cancer nodules.

Indexed as

Early Detection of CancerLung NeoplasmsSolitary Pulmonary NoduleTomography, X-Ray ComputedAgedFemaleHumansMaleMiddle AgedMultiple Pulmonary NodulesRetrospective StudiesCTGrowth dynamicsLung cancerLung nodulesScreening

Identifiers

PMID39187900
PMCPMC11346294

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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.