Development of MRI-Based Radiomics Model to Predict the Risk of Recurrence in Patients With Advanced High-Grade Serous Ovarian Carcinoma-Reference-Cited by-同舟云学术

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Development of MRI-Based Radiomics Model to Predict the Risk of Recurrence in Patients With Advanced High-Grade Serous Ovarian Carcinoma

Published:2021-09 Issue:3 Volume:217 Page:664-675
ISSN:0361-803X
Container-title:American Journal of Roentgenology
language:en
Short-container-title:American Journal of Roentgenology

Author:

Li Hai Ming¹²,Gong Jing¹²,Li Rui Min¹²,Xiao Ze Bin³,Qiang Jin Wei⁴,Peng Wei Jun¹²,Gu Ya Jia¹²

Affiliation:

1. Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Rd, Shanghai 200032, China

2. Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China

3. Department of Biomedical Sciences, University of Pennsylvania, Philadelphia, PA

4. Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China

Publisher

American Roentgen Ray Society

Subject

Radiology Nuclear Medicine and imaging,General Medicine

Link

https://www.ajronline.org/doi/pdf/10.2214/AJR.20.23195

Reference41 articles.

1. Ovarian Carcinoma Subtypes Are Different Diseases: Implications for Biomarker Studies

2. Rethinking ovarian cancer II: reducing mortality from high-grade serous ovarian cancer

3. Final Overall Survival of a Randomized Trial of Bevacizumab for Primary Treatment of Ovarian Cancer

4. HOXA4/HOXB3 gene expression signature as a biomarker of recurrence in patients with high-grade serous ovarian cancer following primary cytoreductive surgery and first-line adjuvant chemotherapy

5. Antitumor activity and safety of pembrolizumab in patients with advanced recurrent ovarian cancer: results from the phase II KEYNOTE-100 study

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1. Radiomics combined with clinical and MRI features may provide preoperative evaluation of suboptimal debulking surgery for serous ovarian carcinoma;Abdominal Radiology;2024-07-14

2. Prediction of Histological Grade of Oral Squamous Cell Carcinoma Using Machine Learning Models Applied to 18F-FDG-PET Radiomics;Biomedicines;2024-06-25

3. Predicting CD27 expression and clinical prognosis in serous ovarian cancer using CT-based radiomics;Journal of Ovarian Research;2024-06-22

4. Role of radiomics as a predictor of disease recurrence in ovarian cancer: a systematic review;Abdominal Radiology;2024-05-15

5. MRI-based radiomics model to preoperatively predict mesenchymal transition subtype in high-grade serous ovarian cancer;Clinical Radiology;2024-05

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