163 related articles for article (PubMed ID: 35704090)
1. Predicting pathologic complete response in locally advanced rectal cancer patients after neoadjuvant therapy: a machine learning model using XGBoost.
Chen X; Wang W; Chen J; Xu L; He X; Lan P; Hu J; Lian L
Int J Colorectal Dis; 2022 Jul; 37(7):1621-1634. PubMed ID: 35704090
[TBL] [Abstract][Full Text] [Related]
2. [Analysis on efficacy and safety of total neoadjuvant therapy in patients with locally advanced rectal cancer with high risk factors].
Ouyang GL; Meng WJ; Shu P; Deng XB; Wu B; Jiang D; Zhuang H; Shen YL; Zhou ZG; Wang ZQ; Wang X
Zhonghua Wei Chang Wai Ke Za Zhi; 2019 Apr; 22(4):349-356. PubMed ID: 31054549
[No Abstract] [Full Text] [Related]
3. Nomograms for predicting pathological response to neoadjuvant treatments in patients with rectal cancer.
Ren DL; Li J; Yu HC; Peng SY; Lin WD; Wang XL; Ghoorun RA; Luo YX
World J Gastroenterol; 2019 Jan; 25(1):118-137. PubMed ID: 30643363
[TBL] [Abstract][Full Text] [Related]
4. Machine learning for predicting pathological complete response in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy.
Huang CM; Huang MY; Huang CW; Tsai HL; Su WC; Chang WC; Wang JY; Shi HY
Sci Rep; 2020 Jul; 10(1):12555. PubMed ID: 32724164
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. Deep learning of endoscopic features for the assessment of neoadjuvant therapy response in locally advanced rectal cancer.
Wang A; Zhou J; Wang G; Zhang B; Xin H; Zhou H
Asian J Surg; 2023 Sep; 46(9):3568-3574. PubMed ID: 37062601
[TBL] [Abstract][Full Text] [Related]
7. [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]
8. Predicting pathological complete response by comparing MRI-based radiomics pre- and postneoadjuvant radiotherapy for locally advanced rectal cancer.
Li Y; Liu W; Pei Q; Zhao L; Güngör C; Zhu H; Song X; Li C; Zhou Z; Xu Y; Wang D; Tan F; Yang P; Pei H
Cancer Med; 2019 Dec; 8(17):7244-7252. PubMed ID: 31642204
[TBL] [Abstract][Full Text] [Related]
9. Machine Learning of Histomorphological Features Predict Response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer.
Wang A; Ding R; Zhang J; Zhang B; Huang X; Zhou H
J Gastrointest Surg; 2023 Jan; 27(1):162-165. PubMed ID: 35915376
[TBL] [Abstract][Full Text] [Related]
10. Predictive Factors for Pathologic Complete Response Following Neoadjuvant Chemoradiotherapy for Rectal Cancer.
Zhang Q; Liang J; Chen J; Mei S; Wang Z
Asian Pac J Cancer Prev; 2021 May; 22(5):1607-1611. PubMed ID: 34048192
[TBL] [Abstract][Full Text] [Related]
11. [Short-term outcome of programmed cell death protein1 (PD-1) antibody combined with total neoadjuvant chemoradiotherapy in the treatment of locally advanced middle-low rectal cancer with high risk factors].
Li YJ; Zhang L; Dong QS; Cai Y; Zhang YZ; Wang L; Yao YF; Zhang XY; Li ZW; Li YH; Sun YS; Wang WH; Wu AW
Zhonghua Wei Chang Wai Ke Za Zhi; 2021 Nov; 24(11):998-1007. PubMed ID: 34823301
[No Abstract] [Full Text] [Related]
12. Utility of ctDNA in predicting response to neoadjuvant chemoradiotherapy and prognosis assessment in locally advanced rectal cancer: A prospective cohort study.
Wang Y; Yang L; Bao H; Fan X; Xia F; Wan J; Shen L; Guan Y; Bao H; Wu X; Xu Y; Shao Y; Sun Y; Tong T; Li X; Xu Y; Cai S; Zhu J; Zhang Z
PLoS Med; 2021 Aug; 18(8):e1003741. PubMed ID: 34464382
[TBL] [Abstract][Full Text] [Related]
13. [Predictive value of combination of MRI tumor regression grade and apparent diffusion coefficient for pathological complete remission after neoadjuvant treatment of locally advanced rectal cancer].
Xu N; Huang FC; Li WL; Luan X; Jiang YM; He B
Zhonghua Wei Chang Wai Ke Za Zhi; 2021 Apr; 24(4):359-365. PubMed ID: 33878826
[No Abstract] [Full Text] [Related]
14. Comparison of pathological complete response rates after neoadjuvant short-course radiotherapy or chemoradiation followed by delayed surgery in locally advanced rectal cancer.
Hoendervangers S; Couwenberg AM; Intven MPW; van Grevenstein WMU; Verkooijen HM
Eur J Surg Oncol; 2018 Jul; 44(7):1013-1017. PubMed ID: 29650419
[TBL] [Abstract][Full Text] [Related]
15. Beneficiaries of radical surgery among clinical complete responders to neoadjuvant chemoradiotherapy in rectal cancer.
Zhang S; Zhang R; Li RZ; Wang QX; Chang H; Ding PR; Li LR; Wu XJ; Chen G; Zeng ZF; Xiao WW; Gao YH
Cancer Sci; 2021 Sep; 112(9):3607-3615. PubMed ID: 34146368
[TBL] [Abstract][Full Text] [Related]
16. Local tuning of radiomics-based model for predicting pathological response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer.
Tang B; Lenkowicz J; Peng Q; Boldrini L; Hou Q; Dinapoli N; Valentini V; Diao P; Yin G; Orlandini LC
BMC Med Imaging; 2022 Mar; 22(1):44. PubMed ID: 35287607
[TBL] [Abstract][Full Text] [Related]
17. Radiomics of MRI for pretreatment prediction of pathologic complete response, tumor regression grade, and neoadjuvant rectal score in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiation: an international multicenter study.
Shaish H; Aukerman A; Vanguri R; Spinelli A; Armenta P; Jambawalikar S; Makkar J; Bentley-Hibbert S; Del Portillo A; Kiran R; Monti L; Bonifacio C; Kirienko M; Gardner KL; Schwartz L; Keller D
Eur Radiol; 2020 Nov; 30(11):6263-6273. PubMed ID: 32500192
[TBL] [Abstract][Full Text] [Related]
18. Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set.
Cain EH; Saha A; Harowicz MR; Marks JR; Marcom PK; Mazurowski MA
Breast Cancer Res Treat; 2019 Jan; 173(2):455-463. PubMed ID: 30328048
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Complete Pathologic Response Prediction by Radiomics Wavelets Features of Unenhanced CT Simulation Images in Locally Advanced Rectal Cancer Patients after Neoadjuvant Chemoradiation.
Lutsyk M; Gourevich K; Keidar Z
Isr Med Assoc J; 2021 Dec; 23(12):805-810. PubMed ID: 34954921
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
