Enhancing the Accuracy of Lymph-Node-Metastasis Prediction in Gynecologic Malignancies Using Multimodal Federated Learning: Integrating CT, MRI, and PET/CT DOI Open Access
Zhijun Hu, Ling Ma, Yue Ding

et al.

Cancers, Journal Year: 2023, Volume and Issue: 15(21), P. 5281 - 5281

Published: Nov. 3, 2023

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, bridge this gap through more holistic innovative approach. By developing comprehensive framework that integrates both non-image data detailed MRI image analyses, harnessed the capabilities of multimodal federated-learning model. Employing composite neural network within environment, adeptly merged diverse sources enhance prediction accuracy. further complemented by sophisticated deep convolutional an enhanced U-NET architecture for meticulous processing. Traditional yielded sensitivities ranging from 32.63% 57.69%. In contrast, model, without incorporating data, achieved impressive sensitivity approximately 0.9231, which soared 0.9412 integration data. Such advancements underscore significant potential approach, suggesting federated learning, especially when combined assessment can revolutionize lymph-node-metastasis detection in gynecological malignancies. paves way precise patient care, potentially transforming current paradigm resulting improved outcomes.

Language: Английский

Hypercoagulation after neoadjuvant immunochemotherapy as a new prognostic indicator in patients with locally advanced gastric cancer undergoing surgery DOI
Tianhao Li, Xiong Sun, Chengguo Li

et al.

World Journal of Gastrointestinal Oncology, Journal Year: 2025, Volume and Issue: 17(3)

Published: Feb. 13, 2025

BACKGROUND Coagulation status is closely related to the progression of malignant tumors. In era neoadjuvant immunochemotherapy (NICT), prognostic utility coagulation indicators in patients with locally advanced gastric cancer (LAGC) undergoing new treatments remains be determined. AIM To determine whether hypercoagulation an effective indicator LAGC who underwent radical resection after NICT. METHODS A retrospective analysis clinical data from 104 LAGC, NICT between 2020 and 2023, was performed. D-dimer fibrinogen concentrations were measured one week before NICT, again surgery, analyze association these two their combined indices [non-hypercoagulation (D-dimer within upper limit normal) vs or above normal)] prognosis. After resection, followed-up periodically. The median follow-up duration 21 months. RESULTS Data collected revealed that three-year overall survival (OS) disease-free (DFS) rates non-hypercoagulation group significantly better than those [94.4% 78.0% (P = 0.019) 87.0% 68.0% 0.027), respectively]. Multivariate indicated independent factor for poor postoperative OS [hazard ratio (HR) 4.436, P 0.023] DFS (HR 2.551, 0.039). Pre-NICT demonstrated no statistically significant difference groups (88.3% 84.1%, respectively; 0.443). CONCLUSION Hypercoagulation gastrectomy.

Language: Английский

Citations

0
Machine Learning Models for Risk Prediction of Cancer Associated Thrombosis: A Systematic Review and Meta-Analysis DOI

Keya Chen,

Ying Zhang, Lufang Zhang

et al.

Journal of Thrombosis and Haemostasis, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 1, 2024

Language: Английский

Citations

1
Enhancing the Accuracy of Lymph-Node-Metastasis Prediction in Gynecologic Malignancies Using Multimodal Federated Learning: Integrating CT, MRI, and PET/CT DOI Open Access
Zhijun Hu, Ling Ma, Yue Ding

et al.

Cancers, Journal Year: 2023, Volume and Issue: 15(21), P. 5281 - 5281

Published: Nov. 3, 2023

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, bridge this gap through more holistic innovative approach. By developing comprehensive framework that integrates both non-image data detailed MRI image analyses, harnessed the capabilities of multimodal federated-learning model. Employing composite neural network within environment, adeptly merged diverse sources enhance prediction accuracy. further complemented by sophisticated deep convolutional an enhanced U-NET architecture for meticulous processing. Traditional yielded sensitivities ranging from 32.63% 57.69%. In contrast, model, without incorporating data, achieved impressive sensitivity approximately 0.9231, which soared 0.9412 integration data. Such advancements underscore significant potential approach, suggesting federated learning, especially when combined assessment can revolutionize lymph-node-metastasis detection in gynecological malignancies. paves way precise patient care, potentially transforming current paradigm resulting improved outcomes.

Language: Английский

Citations

3