Multiple deep learning models based on MRI images in discriminating glioblastoma from solitary brain metastases: a multicentre study DOI Creative Commons
Chao Kong, Ding Yan, Kai Liu

et al.

BMC Medical Imaging, Journal Year: 2025, Volume and Issue: 25(1)

Published: May 19, 2025

Development of a deep learning model for accurate preoperative identification glioblastoma and solitary brain metastases by combining multi-centre multi-sequence magnetic resonance images comparison the performance different models. Clinical data MR total 236 patients with pathologically confirmed single were retrospectively collected from January 2019 to May 2024 at Provincial Hospital Shandong First Medical University, randomly divided into training set test according ratio 8:2, in which contained 197 cases 39 cases; preprocessed labeled tumor regions. The pre-processed regions, MRI sequences input individually or combination train 3D ResNet-18, optimal sequence combinations obtained five-fold cross-validation enhancement inputs models Vision Transformer (3D Vit), DenseNet, VGG; working characteristic curves (ROCs) subjects plotted, area under curve (AUC) was calculated. (AUC), accuracy, precision, recall F1 score used evaluate discriminative In addition, 48 2020 December 2022 Affiliated Cancer University as an external compare performance, robustness generalization ability four effect sequences, three T1-CE, T2, T2-Flair gained effect, accuracy AUC values 0.8718 0.9305, respectively; after inputted aforementioned combinations, validation ResNet-18 0.8125, respectively, 0.8899, all are highest among A can efficiently identify preoperatively, has efficacy identifying two types tumours.

Language: Английский

Machine Learning Radiomics for Predicting Response to MR-Guided Radiotherapy in Unresectable Hepatocellular Carcinoma: A Multicenter Cohort Study DOI Creative Commons
Ke Su, Xin Liu, Yue‐Can Zeng

et al.

Journal of Hepatocellular Carcinoma, Journal Year: 2025, Volume and Issue: Volume 12, P. 933 - 947

Published: May 1, 2025

This study was conducted to assess the efficacy and safety of magnetic resonance (MR)-guided hypofractionated radiotherapy in patients with unresectable hepatocellular carcinoma (HCC). Machine learning-based radiomics utilized predict responses these patients. retrospective included 118 hCC who received MR-guided radiotherapy. The primary endpoint objective response rate (ORR). Radiomics features were based on gross tumor volume (GTV). K-means clustering performed differentiate cancer subtypes radiomics. Nine radiomics-utilizing machine learning models built validated internally through 5-fold cross-validation. ORR, median progression-free survival (mPFS), overall (mOS) 54.4%, 21.7 months, 40.7 respectively. No patient experienced Grade 3/4 adverse events. 1130 extracted from GTV, which 7 for further analysis. identified 2 selected features. Subtype 1 had significantly higher response, longer mPFS, mOS than 2. In both internal external validations, multi-layer perceptron (MLP) model demonstrated superior predictive performance achieving a receiver operating characteristic-area under curve (ROC-AUC) 0.804 0.842, proven be effective safe HCC. developed this could accurately radiotherapy-treated inoperable

Language: Английский

Citations

0
Multiple deep learning models based on MRI images in discriminating glioblastoma from solitary brain metastases: a multicentre study DOI Creative Commons
Chao Kong, Ding Yan, Kai Liu

et al.

BMC Medical Imaging, Journal Year: 2025, Volume and Issue: 25(1)

Published: May 19, 2025

Development of a deep learning model for accurate preoperative identification glioblastoma and solitary brain metastases by combining multi-centre multi-sequence magnetic resonance images comparison the performance different models. Clinical data MR total 236 patients with pathologically confirmed single were retrospectively collected from January 2019 to May 2024 at Provincial Hospital Shandong First Medical University, randomly divided into training set test according ratio 8:2, in which contained 197 cases 39 cases; preprocessed labeled tumor regions. The pre-processed regions, MRI sequences input individually or combination train 3D ResNet-18, optimal sequence combinations obtained five-fold cross-validation enhancement inputs models Vision Transformer (3D Vit), DenseNet, VGG; working characteristic curves (ROCs) subjects plotted, area under curve (AUC) was calculated. (AUC), accuracy, precision, recall F1 score used evaluate discriminative In addition, 48 2020 December 2022 Affiliated Cancer University as an external compare performance, robustness generalization ability four effect sequences, three T1-CE, T2, T2-Flair gained effect, accuracy AUC values 0.8718 0.9305, respectively; after inputted aforementioned combinations, validation ResNet-18 0.8125, respectively, 0.8899, all are highest among A can efficiently identify preoperatively, has efficacy identifying two types tumours.

Language: Английский

Citations

0