Enhancing the Accuracy of Lymph-Node-Metastasis Prediction in Gynecologic Malignancies Using Multimodal Federated Learning: Integrating CT, MRI, and PET/CT.
Simple Summary: Lymph node metastasis is a crucial factor in determining the treatment and prognosis of patients with gynecologic malignancies. Traditionally, medical imaging, including CT, MRI, and PET-CT, are used to detect these metastases. This research introduces a novel approach called "multim...
| Published in: | Cancers Vol. 15; no. 21; pp. 5281 - 5301 |
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| Main Authors: | , , , , , , |
| Format: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Published: |
MDPI
Nov2023
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=173570009&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173570009 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: Nov2023 vid: 15 iid: 21 pid: 97109 pub: MDPI artinfo: ui: 173570009 173570009 173570009 10.3390/cancers15215281 173570009 ppf: 5281 ppct: 20 formats: tig: atl: Enhancing the Accuracy of Lymph-Node-Metastasis Prediction in Gynecologic Malignancies Using Multimodal Federated Learning: Integrating CT, MRI, and PET/CT. aug: au: Hu, Zhijun Ma, Ling Ding, Yue Zhao, Xuanxuan Shi, Xiaohua Lu, Hongtao Liu, Kaijiang affil: Department of Gynecologic Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200001, China sug: subj: Lymph Nodes Neoplasm Metastasis Risk Assessment Learning Methods Tomography, X-Ray Computed Magnetic Resonance Imaging Positron-Emission Tomography Validity Genital Neoplasms, Female Risk Factors Human Algorithms Genital Neoplasms, Female Diagnosis Genital Neoplasms, Female Therapy Neural Networks (Computer) Descriptive Statistics Funding Source ab: Simple Summary: Lymph node metastasis is a crucial factor in determining the treatment and prognosis of patients with gynecologic malignancies. Traditionally, medical imaging, including CT, MRI, and PET-CT, are used to detect these metastases. This research introduces a novel approach called "multimodal federated learning" that combines information from these imaging methods to improve the accuracy of lymph-node-metastasis prediction. In simpler terms, this research merges the strengths of multiple-imaging techniques, using advanced computer algorithms to provide a clearer picture of cancer spread. This merger of techniques can provide a more precise diagnosis, facilitate the accurate formulation of treatment plans for patients, and pave the way for similar improvements in other areas of medical imaging. Gynecological malignancies, particularly lymph node metastasis, have presented a diagnostic challenge, even with traditional imaging techniques such as CT, MRI, and PET/CT. This study was conceived to explore and, subsequently, to bridge this diagnostic gap through a more holistic and innovative approach. By developing a comprehensive framework that integrates both non-image data and detailed MRI image analyses, this study harnessed the capabilities of a multimodal federated-learning model. Employing a composite neural network within a federated-learning environment, this study adeptly merged diverse data sources to enhance prediction accuracy. This was further complemented by a sophisticated deep convolutional neural network with an enhanced U-NET architecture for meticulous MRI image processing. Traditional imaging yielded sensitivities ranging from 32.63% to 57.69%. In contrast, the federated-learning model, without incorporating image data, achieved an impressive sensitivity of approximately 0.9231, which soared to 0.9412 with the integration of MRI data. Such advancements underscore the significant potential of this approach, suggesting that federated learning, especially when combined with MRI assessment data, can revolutionize lymph-node-metastasis detection in gynecological malignancies. This paves the way for more precise patient care, potentially transforming the current diagnostic paradigm and resulting in improved patient outcomes. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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