Evidence map›Paper›PMID 41492270›Full record

ArticleHealth information science and systems2026

Predicting lung cancer survival with attention-based CT slices combination.

Domenico Paolo, Carlo Greco, Edy Ippolito, Michele Fiore, Sara Ramella, Paolo Soda, Matteo Tortora, Alessandro Bria, Rosa Sicilia

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Health information science and systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Domenico PaoloUnit of Artificial Intelligence and Computer Systems, Department of Engineering, University Campus Bio-Medico of Rome, Rome, Italy.
Carlo GrecoDepartment of Medicine and Surgery, Research Unit of Radiation Oncology, Università Campus Bio-Medico di Roma, Rome, Italy.
Edy IppolitoDepartment of Medicine and Surgery, Research Unit of Radiation Oncology, Università Campus Bio-Medico di Roma, Rome, Italy.
Michele FioreDepartment of Medicine and Surgery, Research Unit of Radiation Oncology, Università Campus Bio-Medico di Roma, Rome, Italy.
Sara RamellaDepartment of Medicine and Surgery, Research Unit of Radiation Oncology, Università Campus Bio-Medico di Roma, Rome, Italy.
Paolo SodaUnit of Artificial Intelligence and Computer Systems, Department of Engineering, University Campus Bio-Medico of Rome, Rome, Italy.ORCID 0000-0003-2621-072X
Matteo TortoraDepartment of Naval, Electrical, Electronics and Telecommunications Engineering, University of Genoa, Genoa, Italy.
Alessandro BriaDepartment of Electrical and Information Engineering, University of Cassino and Southern Latium, Cassino, Italy.
Rosa SiciliaUnit of Artificial Intelligence and Computer Systems, Department of Engineering, University Campus Bio-Medico of Rome, Rome, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate prognosis of Non-Small Cell Lung Cancer (NSCLC) is crucial for enhancing patient care and treatment outcomes. Despite the advancements in deep learning, the task of overall survival prediction in NSCLC has not fully leveraged these techniques, yet. This study introduces a novel methodology for predicting 2-year overall survival (OS) in NSCLC patients using CT scans. Our approach integrates CT scan representations produced by EfficientNetB0 with a soft attention mechanism to identify the most relevant slices for survival risk prediction, which are then analyzed by a risk-assessment network. To validate our method and ensure reproducibility, we employed the public LUNG1 dataset and a smaller private dataset. Our approach was compared to benchmark 3D networks and two variants of our methodology: on the LUNG1 it outperformed the competitors achieving a mean

Indexed as

Deep learningNon-small cell lung cancerOverall survivalRisk analysisSoft-attention

Identifiers

PMID41492270
PMCPMC12764710

What OpenQuestion holds

Textmetadata
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.