Evidence map›Paper›PMID 42523629›Full record

ReviewFrontiers in oncology2026

Predictive modeling for survival-related outcomes in lung cancer patients with brain metastases: a mini-review.

Sifat Jahan Shorna, Sreya Majumder, Diya Rahman, Fariha Jahan, Sheak Rashed Haider Noori, Liew Tze Hui, Dip Nandi, Mashiour Rahman

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the 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.

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

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

8 authors.

Sifat Jahan Shorna *Department of Computer Science and Engineering, Shahjalal University of Science and Technology (SUST), Sylhet, Bangladesh.
Sreya Majumder *ELITE Research Lab, New York, NY, United States.
Diya RahmanDepartment of Computer Science, American International University-Bangladesh, Dhaka, Bangladesh.
Fariha JahanELITE Research Lab, New York, NY, United States.
Sheak Rashed Haider NooriHealth Informatics Research Lab (HIRL), Department of Computer Science & Engineering, Daffodil International University, Dhaka, Bangladesh.
Liew Tze HuiFaculty of Information Science and Technology, Multimedia University, Melaka, Malaysia.
Dip NandiELITE Research Lab, New York, NY, United States.
Mashiour RahmanELITE Research Lab, New York, NY, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Predicting survival in lung cancer patients, particularly those with brain metastases, is crucial for personalizing treatment plans and estimating patient prognosis. This minireview synthesizes findings from fifteen recent studies published between 2020 and 2026, focusing on survival prediction in lung cancer patients with brain metastases, with key outcomes including overall survival, progression-free survival, and intracranial progressionfree survival. Traditional and current approaches for predicting survival are discussed, along with comparisons between unimodal and multimodal approaches. Moreover, the strengths and limitations of existing methods are critically analyzed. The findings emphasize the potential of advanced predictive modeling to inform personalized treatment plans and improve survival outcomes in this high-risk population.

Indexed as

brain metastasesdeep learninglung cancermachine learningmultimodal learningsurvival prediction

Identifiers

PMID42523629
PMCPMC13407106

What OpenQuestion holds

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Registered trials

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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.