Incremental feature fusion based time series forecasting with cumulative risk constraint for longitudinal overall survival prediction.
Saved in:
| Title: | Incremental feature fusion based time series forecasting with cumulative risk constraint for longitudinal overall survival prediction. |
|---|---|
| Authors: | Tang, Zhenyu1,2 (AUTHOR), Lin, Jingfeng1 (AUTHOR), Li, Jiannan1 (AUTHOR), Pan, Junjun1,2 (AUTHOR) pan_junjun@buaa.edu.cn, Yan, Jing3 (AUTHOR) fccyanj@zzu.edu.cn |
| Source: | Medical Physics. May2026, Vol. 53 Issue 5, p1-17. 17p. |
| Subjects: | Longitudinal method, Survival analysis (Biometry), Forecasting, Artificial intelligence in medicine, Gliomas, Spatiotemporal processes |
| Abstract: | Background: Overall survival (OS) prediction methods usually adopt pre‐operative data which lack important prognosis‐related information, such as post‐operative lesion status and evolution during treatment, leading to unsatisfactory performance. Incorporating longitudinal data into OS prediction, however, introduces two main challenges: (1) variable time span; and (2) implicit spatiotemporal information. Purpose: This study aims to break the limitation of pre‐operative data based OS prediction by addressing the aforementioned two main challenges and leveraging longitudinal data to achieve accurate OS prediction. Methods: We propose a novel longitudinal data based OS prediction method. Specifically, a new incremental feature fusion (IFF) based time series forecasting module is presented to derive accumulated features up to each time point and fill missing time points in longitudinal data. It addresses the challenge of variable time span with high computational efficiency compared to the widely applied decoder‐only transformer with causal‐attention (DoT‐CA). Based on the accumulated features in the IFF module, corresponding survival risks up to each time point are predicted under a cumulative survival risk (CSR) constraint, where the survival risks are encouraged to be monotonically increased over time, effectively exploring the spatiotemporal information. Results: In the experiment, both in‐house and public multimodal MR datasets (BraTS2020) containing 1678 patients of diffuse glioma are used to evaluate our method, and the experimental results show that our method outperforms all state‐of‐the‐art (SOTA) methods with statistical significance. Further ablation study shows that both proposed IFF module and CSR constraint are effective in longitudinal OS prediction. Moreover, the proposed IFF module is more efficient than DoT‐CA, enabling scalable offline longitudinal analysis on large patient cohorts. Conclusion: Longitudinal data contain important spatiotemporal information related to prognosis, based on which more accurate OS prediction can be achieved comparing with existing pre‐operative data based methods. For longitudinal data with variable time span, the evolution pattern of lesions can be effectively learned and used to fill up the missing time points. Codes of our method are available at https://github.com/BH‐MICom/OSTimes. [ABSTRACT FROM AUTHOR] |
| Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 194450983 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Incremental feature fusion based time series forecasting with cumulative risk constraint for longitudinal overall survival prediction. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Tang%2C+Zhenyu%22">Tang, Zhenyu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lin%2C+Jingfeng%22">Lin, Jingfeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Jiannan%22">Li, Jiannan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pan%2C+Junjun%22">Pan, Junjun</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> pan_junjun@buaa.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Yan%2C+Jing%22">Yan, Jing</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> fccyanj@zzu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. May2026, Vol. 53 Issue 5, p1-17. 17p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Longitudinal+method%22">Longitudinal method</searchLink><br /><searchLink fieldCode="DE" term="%22Survival+analysis+%28Biometry%29%22">Survival analysis (Biometry)</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence+in+medicine%22">Artificial intelligence in medicine</searchLink><br /><searchLink fieldCode="DE" term="%22Gliomas%22">Gliomas</searchLink><br /><searchLink fieldCode="DE" term="%22Spatiotemporal+processes%22">Spatiotemporal processes</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background: Overall survival (OS) prediction methods usually adopt pre‐operative data which lack important prognosis‐related information, such as post‐operative lesion status and evolution during treatment, leading to unsatisfactory performance. Incorporating longitudinal data into OS prediction, however, introduces two main challenges: (1) variable time span; and (2) implicit spatiotemporal information. Purpose: This study aims to break the limitation of pre‐operative data based OS prediction by addressing the aforementioned two main challenges and leveraging longitudinal data to achieve accurate OS prediction. Methods: We propose a novel longitudinal data based OS prediction method. Specifically, a new incremental feature fusion (IFF) based time series forecasting module is presented to derive accumulated features up to each time point and fill missing time points in longitudinal data. It addresses the challenge of variable time span with high computational efficiency compared to the widely applied decoder‐only transformer with causal‐attention (DoT‐CA). Based on the accumulated features in the IFF module, corresponding survival risks up to each time point are predicted under a cumulative survival risk (CSR) constraint, where the survival risks are encouraged to be monotonically increased over time, effectively exploring the spatiotemporal information. Results: In the experiment, both in‐house and public multimodal MR datasets (BraTS2020) containing 1678 patients of diffuse glioma are used to evaluate our method, and the experimental results show that our method outperforms all state‐of‐the‐art (SOTA) methods with statistical significance. Further ablation study shows that both proposed IFF module and CSR constraint are effective in longitudinal OS prediction. Moreover, the proposed IFF module is more efficient than DoT‐CA, enabling scalable offline longitudinal analysis on large patient cohorts. Conclusion: Longitudinal data contain important spatiotemporal information related to prognosis, based on which more accurate OS prediction can be achieved comparing with existing pre‐operative data based methods. For longitudinal data with variable time span, the evolution pattern of lesions can be effectively learned and used to fill up the missing time points. Codes of our method are available at https://github.com/BH‐MICom/OSTimes. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194450983 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/mp.70495 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 1 Subjects: – SubjectFull: Longitudinal method Type: general – SubjectFull: Survival analysis (Biometry) Type: general – SubjectFull: Forecasting Type: general – SubjectFull: Artificial intelligence in medicine Type: general – SubjectFull: Gliomas Type: general – SubjectFull: Spatiotemporal processes Type: general Titles: – TitleFull: Incremental feature fusion based time series forecasting with cumulative risk constraint for longitudinal overall survival prediction. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Tang, Zhenyu – PersonEntity: Name: NameFull: Lin, Jingfeng – PersonEntity: Name: NameFull: Li, Jiannan – PersonEntity: Name: NameFull: Pan, Junjun – PersonEntity: Name: NameFull: Yan, Jing IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00942405 Numbering: – Type: volume Value: 53 – Type: issue Value: 5 Titles: – TitleFull: Medical Physics Type: main |
| ResultId | 1 |
