312 related articles for article (PubMed ID: 37085830)
41. Radiomics signature as a new biomarker for preoperative prediction of neoadjuvant chemoradiotherapy response in locally advanced rectal cancer.
Zhang Z; Jiang X; Zhang R; Yu T; Liu S; Luo Y
Diagn Interv Radiol; 2021 May; 27(3):308-314. PubMed ID: 34003118
[TBL] [Abstract][Full Text] [Related]
42. MRI-based delta-radiomics predicts pathologic complete response in high-grade soft-tissue sarcoma patients treated with neoadjuvant therapy.
Peeken JC; Asadpour R; Specht K; Chen EY; Klymenko O; Akinkuoroye V; Hippe DS; Spraker MB; Schaub SK; Dapper H; Knebel C; Mayr NA; Gersing AS; Woodruff HC; Lambin P; Nyflot MJ; Combs SE
Radiother Oncol; 2021 Nov; 164():73-82. PubMed ID: 34506832
[TBL] [Abstract][Full Text] [Related]
43. Pre-treatment ADC image-based random forest classifier for identifying resistant rectal adenocarcinoma to neoadjuvant chemoradiotherapy.
Yang C; Jiang ZK; Liu LH; Zeng MS
Int J Colorectal Dis; 2020 Jan; 35(1):101-107. PubMed ID: 31786652
[TBL] [Abstract][Full Text] [Related]
44. An investigation of machine learning methods in delta-radiomics feature analysis.
Chang Y; Lafata K; Sun W; Wang C; Chang Z; Kirkpatrick JP; Yin FF
PLoS One; 2019; 14(12):e0226348. PubMed ID: 31834910
[TBL] [Abstract][Full Text] [Related]
45. Predicting Neoadjuvant Treatment Response in Rectal Cancer Using Machine Learning: Evaluation of MRI-Based Radiomic and Clinical Models.
Peterson KJ; Simpson MT; Drezdzon MK; Szabo A; Ausman RA; Nencka AS; Knechtges PM; Peterson CY; Ludwig KA; Ridolfi TJ
J Gastrointest Surg; 2023 Jan; 27(1):122-130. PubMed ID: 36271199
[TBL] [Abstract][Full Text] [Related]
46. MR-based artificial intelligence model to assess response to therapy in locally advanced rectal cancer.
Ferrari R; Mancini-Terracciano C; Voena C; Rengo M; Zerunian M; Ciardiello A; Grasso S; Mare' V; Paramatti R; Russomando A; Santacesaria R; Satta A; Solfaroli Camillocci E; Faccini R; Laghi A
Eur J Radiol; 2019 Sep; 118():1-9. PubMed ID: 31439226
[TBL] [Abstract][Full Text] [Related]
47. MR Imaging of Rectal Cancer: Radiomics Analysis to Assess Treatment Response after Neoadjuvant Therapy.
Horvat N; Veeraraghavan H; Khan M; Blazic I; Zheng J; Capanu M; Sala E; Garcia-Aguilar J; Gollub MJ; Petkovska I
Radiology; 2018 Jun; 287(3):833-843. PubMed ID: 29514017
[TBL] [Abstract][Full Text] [Related]
48. A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy.
Sutton EJ; Onishi N; Fehr DA; Dashevsky BZ; Sadinski M; Pinker K; Martinez DF; Brogi E; Braunstein L; Razavi P; El-Tamer M; Sacchini V; Deasy JO; Morris EA; Veeraraghavan H
Breast Cancer Res; 2020 May; 22(1):57. PubMed ID: 32466777
[TBL] [Abstract][Full Text] [Related]
49. Delta-radiomics features for predicting the major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer.
Han X; Wang M; Zheng Y; Wang N; Wu Y; Ding C; Jia X; Yang R; Geng M; Chen Z; Zhang S; Zhang K; Li Y; Liu J; Gu J; Liao Y; Fan J; Shi H
Eur Radiol; 2024 Apr; 34(4):2716-2726. PubMed ID: 37736804
[TBL] [Abstract][Full Text] [Related]
50. MRI radiomics independent of clinical baseline characteristics and neoadjuvant treatment modalities predicts response to neoadjuvant therapy in rectal cancer.
Song M; Li S; Wang H; Hu K; Wang F; Teng H; Wang Z; Liu J; Jia AY; Cai Y; Li Y; Zhu X; Geng J; Zhang Y; Wan X; Wang W
Br J Cancer; 2022 Jul; 127(2):249-257. PubMed ID: 35368044
[TBL] [Abstract][Full Text] [Related]
51. microRNAs combined to radiomic features as a predictor of complete clinical response after neoadjuvant radio-chemotherapy for locally advanced rectal cancer: a preliminary study.
Losurdo P; Gandin I; Belgrano M; Fiorese I; Verardo R; Zanconati F; Cova MA; de Manzini N
Surg Endosc; 2023 May; 37(5):3676-3683. PubMed ID: 36639577
[TBL] [Abstract][Full Text] [Related]
52. Multiparameter MRI-based radiomics for preoperative prediction of extramural venous invasion in rectal cancer.
Shu Z; Mao D; Song Q; Xu Y; Pang P; Zhang Y
Eur Radiol; 2022 Feb; 32(2):1002-1013. PubMed ID: 34482429
[TBL] [Abstract][Full Text] [Related]
53. [Application value of texture analysis of magnetic resonance images in prediction of neoadjuvant chemoradiotherapy efficacy for rectal cancer].
Shu Z; Fang S; Ding Z; Mao D; Pang P; Gong X
Zhonghua Wei Chang Wai Ke Za Zhi; 2018 Sep; 21(9):1051-1058. PubMed ID: 30269327
[TBL] [Abstract][Full Text] [Related]
54. [Application of Automated Machine Learning Based on Radiomics Features of T2WI and RS-EPI DWI to Predict Preoperative T Staging of Rectal Cancer].
Wen DG; Hu SX; Li ZL; Deng XB; Tian C; Li X; Wang XR; Leng Q; Xia CC
Sichuan Da Xue Xue Bao Yi Xue Ban; 2021 Jul; 52(4):698-705. PubMed ID: 34323052
[TBL] [Abstract][Full Text] [Related]
55. Machine Learning-based Analysis of Rectal Cancer MRI Radiomics for Prediction of Metachronous Liver Metastasis.
Liang M; Cai Z; Zhang H; Huang C; Meng Y; Zhao L; Li D; Ma X; Zhao X
Acad Radiol; 2019 Nov; 26(11):1495-1504. PubMed ID: 30711405
[TBL] [Abstract][Full Text] [Related]
56. MRI Radiomics Model Predicts Pathologic Complete Response of Rectal Cancer Following Chemoradiotherapy.
Shin J; Seo N; Baek SE; Son NH; Lim JS; Kim NK; Koom WS; Kim S
Radiology; 2022 May; 303(2):351-358. PubMed ID: 35133200
[TBL] [Abstract][Full Text] [Related]
57. Developing a prediction model based on MRI for pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer.
Wan L; Zhang C; Zhao Q; Meng Y; Zou S; Yang Y; Liu Y; Jiang J; Ye F; Ouyang H; Zhao X; Zhang H
Abdom Radiol (NY); 2019 Sep; 44(9):2978-2987. PubMed ID: 31327039
[TBL] [Abstract][Full Text] [Related]
58. 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]
59. Selecting Candidates for Organ-Preserving Strategies After Neoadjuvant Chemoradiotherapy for Rectal Cancer: Development and Validation of a Model Integrating MRI Radiomics and Pathomics.
Wan L; Sun Z; Peng W; Wang S; Li J; Zhao Q; Wang S; Ouyang H; Zhao X; Zou S; Zhang H
J Magn Reson Imaging; 2022 Oct; 56(4):1130-1142. PubMed ID: 35142001
[TBL] [Abstract][Full Text] [Related]
60. CT-based radiomic features to predict pathological response in rectal cancer: A retrospective cohort study.
Yuan Z; Frazer M; Zhang GG; Latifi K; Moros EG; Feygelman V; Felder S; Sanchez J; Dessureault S; Imanirad I; Kim RD; Harrison LB; Hoffe SE; Frakes JM
J Med Imaging Radiat Oncol; 2020 Jun; 64(3):444-449. PubMed ID: 32386109
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
