Evidence map›Paper›PMID 42381014›Full record

ArticleBMC medical informatics and decision making2026

Multitask learning of longitudinal circulating biomarkers and clinical outcomes: identification of optimal machine-learning and deep-learning models.

Min Yuan, Shixin Su, Haolun Ding, Yaning Yang, Manish Gupta, Xu Steven Xu

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00981058 (A Randomized, Multicenter, Open-Label Phase 3 Study of Gemcitabine-Cisplatin Chemotherapy Plus Necitumumab), which is not on this map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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.

NCT00981058 phase3completednot on this map

A Randomized, Multicenter, Open-Label Phase 3 Study of Gemcitabine-Cisplatin Chemotherapy Plus Necitumumab (IMC-11F8) Versus Gemcitabine-Cisplatin Chemotherapy Alone in the First-Line Treatment of Patients With Stage IV Squamous Non-Small Cell Lung Cancer (NSCLC)

TypeinterventionalSponsorEli Lilly and CompanyRan2010 to 2024Enrolled1,093ConditionsNon Small Cell Lung CancerArmsNecitumumab, Gemcitabine, Cisplatin
3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. 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

6 authors.

Min Yuan *Department of Health Data Science, Anhui Medical University, Hefei, Anhui, 230032, China. myuan@ustc.edu.cn.
Shixin Su *Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei, Anhui, 230036, China.
Haolun DingDepartment of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei, Anhui, 230036, China.
Yaning YangDepartment of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei, Anhui, 230036, China.
Manish GuptaClinical Pharmacology and Quantitative Science, Genmab Inc., Princeton, NJ, 08540, USA.
Xu Steven XuClinical Pharmacology and Quantitative Science, Genmab Inc., Princeton, NJ, 08540, USA. sxu@genmab.com.

Funding

Anhui Provincial Department of Education 2024AH050693National Natural Science Foundation of China 11671375
6 · The paper itself

Abstract

backgroundMany circulating biomarkers are assessed at different time intervals during clinical studies. Despite of the success of standard joint models in predicting clinical outcomes using low-dimensional longitudinal data (1-2 biomarkers), significant computational challenges are encountered when applying these techniques to high-dimensional biomarker datasets. Modern machine- or deep-learning models show potential for multiple biomarker processes, but systematic evaluations and applications to high-dimensional data in the clinical settings have yet to be reported. We aimed to enhance the scalability of joint modeling and provide guidance on optimal approaches for high-dimensional biomarker data and outcomes.

methodsWe evaluated multiple deep-learning and machine-learning models using 24 clinical biomarkers and survival data from the SQUIRE trial, a phase 3 randomized clinical trial investigating necitumumab and standard gemcitabine/cisplatin treatment in patients with squamous non-small-cell lung cancer (NSCLC).

resultsOverall, we confirmed that longitudinal models enabled more accurate prediction of patients' survival compared to those solely based on baseline information. Coupling multivariate functional principal component analysis (MFPCA) with Cox regression (MFPCA-Cox) provided the highest predictive discrimination and accuracy for the NSCLC patients with AUC values of 0.7 - >0.8 at various landmark time points and prediction timeframes, outperforming recent advanced Transformer and convolutional neural network deep-learning algorithms (TransformerJM and Match-Net, respectively).

conclusionsIn conclusion, we identified that MFPCA-Cox represents a robust and versatile joint modeling algorithm for high-dimensional biomarker longitudinal data with irregular and missing data, capturing complex relationships within the data, yielding accurate predictions for both longitudinal biomarkers and survival outcomes, and gaining insights into the underlying dynamics.

trial registrationClinicalTrials.gov (NCT00981058; first posted on September 22, 2009).

Indexed as

Biomarkers, TumorCarcinoma, Non-Small-Cell LungDeep LearningLung NeoplasmsMachine LearningCisplatinClinical Trials, Phase III as TopicHumansPredictive Learning ModelsRandomized Controlled Trials as TopicBiomarkers, TumorCisplatinDeep learningLongitudinal biomarkersLung cancerMachine learningSurvival

Identifiers

PMID42381014
PMCPMC13584259

What OpenQuestion holds

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Registered trials

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.