Evidence map›Paper›PMID 37428248›Full record

ArticleJournal of cancer research and clinical oncology2023

Deep learning model for predicting the survival of patients with primary gastrointestinal lymphoma based on the SEER database and a multicentre external validation cohort.

Feifan Wang, Lu Chen, Lihong Liu, Yitao Jia, Wei Li, Lianjing Wang, Jie Zhi, Wei Liu, Weijing Li, Zhongxin Li

Abstract readMulticenter StudyValidation Study
PubMed Full text at PMC Publisher
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Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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3 · Its place in the literature

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4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Feifan Wang *Gastrointestinal Disease Diagnosis and Treatment Center, The First Hospital of Hebei Medical University, 89 Donggang Road, Shijiazhuang, 050000, China.
Lu Chen *Department of Medical Oncology and Radiation Sickness, Peking University Third Hospital, Beijing, 100191, China.
Lihong LiuDepartment of Hematology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, China.
Yitao JiaDepartment of Oncology, Hebei General Hospital, Shijiazhuang, 050051, China.
Wei LiGastrointestinal Disease Diagnosis and Treatment Center, The First Hospital of Hebei Medical University, 89 Donggang Road, Shijiazhuang, 050000, China.
Lianjing WangDepartment of Hematology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, China.
Jie ZhiDepartment of Oncology, Hebei General Hospital, Shijiazhuang, 050051, China.
Wei LiuDepartment of Hematology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, China.
Weijing LiDepartment of Hematology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, China.
Zhongxin LiGastrointestinal Disease Diagnosis and Treatment Center, The First Hospital of Hebei Medical University, 89 Donggang Road, Shijiazhuang, 050000, China. lizhongxin99@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeDue to the rarity of primary gastrointestinal lymphoma (PGIL), the prognostic factors and optimal management of PGIL have not been clearly defined. We aimed to establish prognostic models using a deep learning algorithm for survival prediction.

methodsWe collected 11,168 PGIL patients from the Surveillance, Epidemiology, and End Results (SEER) database to form the training and test cohorts. At the same time, we collected 82 PGIL patients from three medical centres to form the external validation cohort. We constructed a Cox proportional hazards (CoxPH) model, random survival forest (RSF) model, and neural multitask logistic regression (DeepSurv) model to predict PGIL patients' overall survival (OS).

resultsThe 1-, 3-, 5-, and 10-year OS rates of PGIL patients in the SEER database were 77.1%, 69.4%, 63.7%, and 50.3%, respectively. The RSF model based on all variables showed that the top three most important variables for predicting OS were age, histological type, and chemotherapy. The independent risk factors for PGIL patient prognosis included sex, age, race, primary site, Ann Arbor stage, histological type, symptom, radiotherapy, and chemotherapy, according to the Lasso regression analysis. Using these factors, we built the CoxPH and DeepSurv models. The DeepSurv model's C-index values were 0.760 in the training cohort, 0.742 in the test cohort, and 0.707 in the external validation cohort, which demonstrated that the DeepSurv model performed better compared to the RSF model (0.728) and the CoxPH model (0.724). The DeepSurv model accurately predicted 1-, 3-, 5- and 10-year OS. Both calibration curves and decision curve analysis curves demonstrated the superior performance of the DeepSurv model. We developed the DeepSurv model as an online web calculator for survival prediction, which can be accessed at http://124.222.228.112:8501/ .

conclusionsThis DeepSurv model with external validation is superior to previous studies in predicting short-term and long-term survival and can help us make better-individualized decisions for PGIL patients.

Indexed as

Deep LearningGastrointestinal NeoplasmsLymphomaSurvival AnalysisAgedFemaleHumansLogistic ModelsMaleMiddle AgedPrognosisProportional Hazards ModelsRandom ForestSEER ProgramDeep learningDeepSurvMachine learningprimary gastrointestinal lymphomaSurvival analysis

Identifiers

PMID37428248
PMCPMC11796771

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