497 related articles for article (PubMed ID: 34112180)
21. [A prediction model of pathological complete response in patients with locally advanced rectal cancer after PD-1 antibody combined with total neoadjuvant chemoradiotherapy based on MRI radiomics].
Zhang XY; Zhu HT; Li XT; Li YJ; Li ZW; Wang WH; Wu AW; Sun YS; Zhang L
Zhonghua Wei Chang Wai Ke Za Zhi; 2022 Mar; 25(3):228-234. PubMed ID: 35340172
[No Abstract] [Full Text] [Related]
22. Radiomics Approach Outperforms Diameter Criteria for Predicting Pathological Lateral Lymph Node Metastasis After Neoadjuvant (Chemo)Radiotherapy in Advanced Low Rectal Cancer.
Nakanishi R; Akiyoshi T; Toda S; Murakami Y; Taguchi S; Oba K; Hanaoka Y; Nagasaki T; Yamaguchi T; Konishi T; Matoba S; Ueno M; Fukunaga Y; Kuroyanagi H
Ann Surg Oncol; 2020 Oct; 27(11):4273-4283. PubMed ID: 32767224
[TBL] [Abstract][Full Text] [Related]
23. Using clinical and radiomic feature-based machine learning models to predict pathological complete response in patients with esophageal squamous cell carcinoma receiving neoadjuvant chemoradiation.
Wang J; Zhu X; Zeng J; Liu C; Shen W; Sun X; Lin Q; Fang J; Chen Q; Ji Y
Eur Radiol; 2023 Dec; 33(12):8554-8563. PubMed ID: 37439939
[TBL] [Abstract][Full Text] [Related]
24. Development and validation of an MRI-based radiomic nomogram to distinguish between good and poor responders in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiotherapy.
Wang J; Liu X; Hu B; Gao Y; Chen J; Li J
Abdom Radiol (NY); 2021 May; 46(5):1805-1815. PubMed ID: 33151359
[TBL] [Abstract][Full Text] [Related]
25. A novel radiomic nomogram for predicting epidermal growth factor receptor mutation in peripheral lung adenocarcinoma.
Lu X; Li M; Zhang H; Hua S; Meng F; Yang H; Li X; Cao D
Phys Med Biol; 2020 Mar; 65(5):055012. PubMed ID: 31978901
[TBL] [Abstract][Full Text] [Related]
26. Multi-modal radiomics model to predict treatment response to neoadjuvant chemotherapy for locally advanced rectal cancer.
Li ZY; Wang XD; Li M; Liu XJ; Ye Z; Song B; Yuan F; Yuan Y; Xia CC; Zhang X; Li Q
World J Gastroenterol; 2020 May; 26(19):2388-2402. PubMed ID: 32476800
[TBL] [Abstract][Full Text] [Related]
27. A radiomic signature model to predict the chemoradiation-induced alteration in tumor-infiltrating CD8
Jeon SH; Lim YJ; Koh J; Chang WI; Kim S; Kim K; Chie EK
Radiother Oncol; 2021 Sep; 162():124-131. PubMed ID: 34265357
[TBL] [Abstract][Full Text] [Related]
28. A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study.
Sun R; Limkin EJ; Vakalopoulou M; Dercle L; Champiat S; Han SR; Verlingue L; Brandao D; Lancia A; Ammari S; Hollebecque A; Scoazec JY; Marabelle A; Massard C; Soria JC; Robert C; Paragios N; Deutsch E; Ferté C
Lancet Oncol; 2018 Sep; 19(9):1180-1191. PubMed ID: 30120041
[TBL] [Abstract][Full Text] [Related]
29. MRI-Based Radiomic Signature as a Prognostic Biomarker for HER2-Positive Invasive Breast Cancer Treated with NAC.
Li Q; Xiao Q; Li J; Duan S; Wang H; Gu Y
Cancer Manag Res; 2020; 12():10603-10613. PubMed ID: 33149669
[TBL] [Abstract][Full Text] [Related]
30. Pre-Treatment Computed Tomography Radiomics for Predicting the Response to Neoadjuvant Chemoradiation in Locally Advanced Rectal Cancer: A Retrospective Study.
Mao Y; Pei Q; Fu Y; Liu H; Chen C; Li H; Gong G; Yin H; Pang P; Lin H; Xu B; Zai H; Yi X; Chen BT
Front Oncol; 2022; 12():850774. PubMed ID: 35619922
[TBL] [Abstract][Full Text] [Related]
31. Radiomics Based on Dynamic Contrast-Enhanced MRI to Early Predict Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Therapy.
Zeng Q; Ke M; Zhong L; Zhou Y; Zhu X; He C; Liu L
Acad Radiol; 2023 Aug; 30(8):1638-1647. PubMed ID: 36564256
[TBL] [Abstract][Full Text] [Related]
32. Radiomics for lung adenocarcinoma manifesting as pure ground-glass nodules: invasive prediction.
Sun Y; Li C; Jin L; Gao P; Zhao W; Ma W; Tan M; Wu W; Duan S; Shan Y; Li M
Eur Radiol; 2020 Jul; 30(7):3650-3659. PubMed ID: 32162003
[TBL] [Abstract][Full Text] [Related]
33. Preoperative Assessment for Event-Free Survival With Hepatoblastoma in Pediatric Patients by Developing a CT-Based Radiomics Model.
Jiang Y; Sun J; Xia Y; Cheng Y; Xie L; Guo X; Guo Y
Front Oncol; 2021; 11():644994. PubMed ID: 33937051
[No Abstract] [Full Text] [Related]
34. Radiomic features derived from pretherapeutic MRI predict chemoradiation response in locally advanced rectal cancer.
Chou Y; Peng SH; Lin HY; Lan TL; Jiang JK; Liang WY; Hu YW; Wang LW
J Chin Med Assoc; 2023 Apr; 86(4):399-408. PubMed ID: 36727777
[TBL] [Abstract][Full Text] [Related]
35. Homology-based radiomic features for prediction of the prognosis of lung cancer based on CT-based radiomics.
Kadoya N; Tanaka S; Kajikawa T; Tanabe S; Abe K; Nakajima Y; Yamamoto T; Takahashi N; Takeda K; Dobashi S; Takeda K; Nakane K; Jingu K
Med Phys; 2020 Jun; 47(5):2197-2205. PubMed ID: 32096876
[TBL] [Abstract][Full Text] [Related]
36. MRI-based radiomics signature for pretreatment prediction of pathological response to neoadjuvant chemotherapy in osteosarcoma: a multicenter study.
Chen H; Zhang X; Wang X; Quan X; Deng Y; Lu M; Wei Q; Ye Q; Zhou Q; Xiang Z; Liang C; Yang W; Zhao Y
Eur Radiol; 2021 Oct; 31(10):7913-7924. PubMed ID: 33825032
[TBL] [Abstract][Full Text] [Related]
37. Develop and validate a radiomics space-time model to predict the pathological complete response in patients undergoing neoadjuvant treatment of rectal cancer: an artificial intelligence model study based on machine learning.
Peng J; Wang W; Jin H; Qin X; Hou J; Yang Z; Shu Z
BMC Cancer; 2023 Apr; 23(1):365. PubMed ID: 37085830
[TBL] [Abstract][Full Text] [Related]
38. Deep learning-based radiomic features for improving neoadjuvant chemoradiation response prediction in locally advanced rectal cancer.
Fu J; Zhong X; Li N; Van Dams R; Lewis J; Sung K; Raldow AC; Jin J; Qi XS
Phys Med Biol; 2020 Apr; 65(7):075001. PubMed ID: 32092710
[TBL] [Abstract][Full Text] [Related]
39. Value of pre-therapy
Zhang J; Zhao X; Zhao Y; Zhang J; Zhang Z; Wang J; Wang Y; Dai M; Han J
Eur J Nucl Med Mol Imaging; 2020 May; 47(5):1137-1146. PubMed ID: 31728587
[TBL] [Abstract][Full Text] [Related]
40. A Comprehensive Prediction Model Based on MRI Radiomics and Clinical Factors to Predict Tumor Response After Neoadjuvant Chemoradiotherapy in Rectal Cancer.
Jiang H; Guo W; Yu Z; Lin X; Zhang M; Jiang H; Zhang H; Sun Z; Li J; Yu Y; Zhao S; Hu H
Acad Radiol; 2023 Sep; 30 Suppl 1():S185-S198. PubMed ID: 37394412
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]