Deep Learning Radiomics Nomogram Based on Magnetic Resonance Imaging for Differentiating Type I/II Epithelial Ovarian Cancer

Academic Radiology, Год журнала: 2023, Номер 31(6), С. 2391 - 2401

Опубликована: Авг. 27, 2023

Язык: Английский

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Intratumoral and Peritumoral Radiomics for Predicting the Prognosis of High-grade Serous Ovarian Cancer Patients Receiving Platinum-Based Chemotherapy

Academic Radiology, Год журнала: 2024, Номер unknown

Опубликована: Сен. 1, 2024

This study aimed to develop a deep learning (DL) prognostic model evaluate the significance of intra- and peritumoral radiomics in predicting outcomes for high-grade serous ovarian cancer (HGSOC) patients receiving platinum-based chemotherapy.

Язык: Английский

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A Hybrid Machine Learning CT-Based Radiomics Nomogram for Predicting Cancer-Specific Survival in Curatively Resected Colorectal Cancer

Academic Radiology, Год журнала: 2025, Номер unknown

Опубликована: Фев. 1, 2025

Язык: Английский

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TRANS: a prediction model for EGFR mutation status in NSCLC based on radiomics and clinical features

Respiratory Research, Год журнала: 2025, Номер 26(1)

Опубликована: Июнь 5, 2025

Язык: Английский

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Radiomics analysis of dual-layer spectral-detector CT-derived iodine maps for predicting tumor deposits in colorectal cancer

European Radiology, Год журнала: 2024, Номер 35(1), С. 105 - 116

Опубликована: Июль 11, 2024

Язык: Английский

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Multiparametric MRI-Based Deep Learning Models for Preoperative Prediction of Tumor Deposits in Rectal Cancer and Prognostic Outcome

Academic Radiology, Год журнала: 2024, Номер unknown

Опубликована: Окт. 1, 2024

Язык: Английский

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Predicting tumor deposits in rectal cancer: a combined deep learning model using T2-MR imaging and clinical features

Insights into Imaging, Год журнала: 2023, Номер 14(1)

Опубликована: Дек. 20, 2023

Abstract Background Tumor deposits (TDs) are associated with poor prognosis in rectal cancer (RC). This study aims to develop and validate a deep learning (DL) model incorporating T2-MR image clinical factors for the preoperative prediction of TDs RC patients. Methods methods A total 327 patients pathologically confirmed status from January 2016 December 2019 were retrospectively recruited, images variables collected. Patients randomly split into development dataset ( n = 246) an independent testing 81). single-channel DL model, multi-channel hybrid constructed. The performance these predictive models was assessed by using receiver operating characteristics (ROC) analysis decision curve (DCA). Results areas under curves (AUCs) clinical, single-DL, multi-DL, hybrid-DL 0.734 (95% CI, 0.674–0.788), 0.710 0.649–0.766), 0.767 0.710–0.819), 0.857 0.807–0.898) dataset. AUC significantly higher than single-DL multi-DL (both p < 0.001) dataset, 0.028) Decision demonstrated had net benefit other across majority range threshold probabilities. Conclusions proposed achieved good efficacy could be used predict tumor cancer. Critical relevance statement Key points • Preoperative non-invasive identification is great significance. combined nomogram provides gastroenterologist accurate effective tool. Graphical

Язык: Английский

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Research on Predicting PD-L1 Expression Levels in Colorectal Cancer Patients Based on CT Radiomics Features

Advances in Clinical Medicine, Год журнала: 2024, Номер 14(12), С. 158 - 169

Опубликована: Янв. 1, 2024

Язык: Английский

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