Evidence map›Paper›PMID 40771018›Full record

ArticleCancer reports (Hoboken, N.J.)2025

Development and Validation of Survival Prediction Models for Patients With Pineoblastomas Using Deep Learning: A SEER-Based Study.

Xuanzi Li, Shuai Yang, Yingpeng Peng, Xueqiang You, Shunli Peng, Siyang Wang, Dasong Zha, Shuyuan Zhang, Chuntao Deng

Abstract readValidation Study
In one paragraph

Article in Cancer reports (Hoboken, N.J.), 2025. 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.

Xuanzi LiThe Cancer Center, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guang dong Province, China.
Shuai YangDepartment of Radiotherapy of The Cancer Center, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guang dong Province, China.
Yingpeng PengThe Cancer Center, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guang dong Province, China.
Xueqiang YouSchool of Innovative Engineering, Macao University of Science and Technology, Macao, Macao Special Administrative Region, China.
Shunli PengThe Cancer Center, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guang dong Province, China.
Siyang WangThe Cancer Center, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guang dong Province, China.
Dasong ZhaThe Cancer Center, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guang dong Province, China.
Shuyuan ZhangThe Cancer Center, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guang dong Province, China.
Chuntao DengDepartment of Radiotherapy of The Cancer Center, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, Guang dong Province, China.ORCID 0009-0002-0049-8497

Funding

Basic and Applied Basic Research Foundation of Guangdong province 2020A1515110599
6 · The paper itself

Abstract

purposePineoblastomas (PBs) are rare central nervous system tumors primarily affecting children and adolescents, with limited data on clinical characteristics and survival outcomes. Prognosis prediction models for this disease are lacking. The purpose of this study was to develop deep learning (DL) models for predicting 3-year survival in patients with pineoblastoma.

methodsPatients with pineoblastomas of all ages were identified from the Surveillance, Epidemiology, and End Results (SEER) database (1975-2019). Deep neural networks (DNN) were trained and tested at a ratio of 7:3 in a 5-fold cross-validated fashion. Multivariate CPH models were constructed for comparison. The primary outcomes were 3-year overall survival (OS) and disease-specific survival (DSS). All the variables were included in the analysis. Receiver operating characteristic (ROC) curve analysis and calibration plots were used to evaluate the model performance.

resultsA total of 145 patients were included in this study. The area under the curve (AUC) for the DNN models was 0.92, 0.91, and 0.749 for OS and 0.76 for DSS. The DNN models exhibited good calibration: the OS model (slope = 0.94, intercept = 0.07) and DSS model (slope = 0.81, intercept = 0.20).

conclusionOur DNN models showed a more accurate prediction of survival outcomes in patients with pineoblastoma than the widely used CPH models. These results indicate the potential of DL algorithms to improve outcome prediction in patients with rare tumors.

Indexed as

Brain NeoplasmsDeep LearningPineal GlandPinealomaAdolescentAdultChildChild, PreschoolFemaleHumansInfantMaleMiddle AgedPrognosisRetrospective StudiesROC Curveartificial intelligencedeep learningmachine learningpineoblastomaSEER

Identifiers

PMID40771018
PMCPMC12329240

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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.