112 related articles for article (PubMed ID: 36762417)
1. Deep learning-extracted CT imaging phenotypes predict response to total resection in colorectal cancer.
Pan X; Cong H; Wang X; Zhang H; Ge Y; Hu S
Acta Radiol; 2023 May; 64(5):1783-1791. PubMed ID: 36762417
[TBL] [Abstract][Full Text] [Related]
2. Development and validation of machine learning models for predicting prognosis and guiding individualized postoperative chemotherapy: A real-world study of distal cholangiocarcinoma.
Wang D; Pan B; Huang JC; Chen Q; Cui SP; Lang R; Lyu SC
Front Oncol; 2023; 13():1106029. PubMed ID: 37007095
[TBL] [Abstract][Full Text] [Related]
3. Deep learning radiomics model related with genomics phenotypes for lymph node metastasis prediction in colorectal cancer.
Zhao J; Wang H; Zhang Y; Wang R; Liu Q; Li J; Li X; Huang H; Zhang J; Zeng Z; Zhang J; Yi Z; Zeng F
Radiother Oncol; 2022 Feb; 167():195-202. PubMed ID: 34968471
[TBL] [Abstract][Full Text] [Related]
4. Special issue "The advance of solid tumor research in China": Prognosis prediction for stage II colorectal cancer by fusing computed tomography radiomics and deep-learning features of primary lesions and peripheral lymph nodes.
Li M; Gong J; Bao Y; Huang D; Peng J; Tong T
Int J Cancer; 2023 Jan; 152(1):31-41. PubMed ID: 35484979
[TBL] [Abstract][Full Text] [Related]
5. Preoperative prediction of regional lymph node metastasis of colorectal cancer based on
He J; Wang Q; Zhang Y; Wu H; Zhou Y; Zhao S
Ann Nucl Med; 2021 May; 35(5):617-627. PubMed ID: 33738763
[TBL] [Abstract][Full Text] [Related]
6. Noninvasive KRAS mutation estimation in colorectal cancer using a deep learning method based on CT imaging.
He K; Liu X; Li M; Li X; Yang H; Zhang H
BMC Med Imaging; 2020 Jun; 20(1):59. PubMed ID: 32487083
[TBL] [Abstract][Full Text] [Related]
7. Multi-Size Deep Learning Based Preoperative Computed Tomography Signature for Prognosis Prediction of Colorectal Cancer.
Li CH; Cai D; Zhong ME; Lv MY; Huang ZP; Zhu Q; Hu C; Qi H; Wu X; Gao F
Front Genet; 2022; 13():880093. PubMed ID: 35646105
[No Abstract] [Full Text] [Related]
8. Deep Learning Features Improve the Performance of a Radiomics Signature for Predicting KRAS Status in Patients with Colorectal Cancer.
Wu X; Li Y; Chen X; Huang Y; He L; Zhao K; Huang X; Zhang W; Huang Y; Li Y; Dong M; Huang J; Xia T; Liang C; Liu Z
Acad Radiol; 2020 Nov; 27(11):e254-e262. PubMed ID: 31982342
[TBL] [Abstract][Full Text] [Related]
9. CT texture analysis for the prediction of KRAS mutation status in colorectal cancer via a machine learning approach.
Taguchi N; Oda S; Yokota Y; Yamamura S; Imuta M; Tsuchigame T; Nagayama Y; Kidoh M; Nakaura T; Shiraishi S; Funama Y; Shinriki S; Miyamoto Y; Baba H; Yamashita Y
Eur J Radiol; 2019 Sep; 118():38-43. PubMed ID: 31439256
[TBL] [Abstract][Full Text] [Related]
10. Development of a novel combined nomogram integrating deep-learning-assisted CT texture and clinical-radiological features to predict the invasiveness of clinical stage IA part-solid lung adenocarcinoma: a multicentre study.
Zuo Z; Zeng W; Peng K; Mao Y; Wu Y; Zhou Y; Qi W
Clin Radiol; 2023 Oct; 78(10):e698-e706. PubMed ID: 37487842
[TBL] [Abstract][Full Text] [Related]
11. Development and validation of survival prediction model for gastric adenocarcinoma patients using deep learning: A SEER-based study.
Zeng J; Li K; Cao F; Zheng Y
Front Oncol; 2023; 13():1131859. PubMed ID: 36959782
[TBL] [Abstract][Full Text] [Related]
12. Deep learning models for predicting the survival of patients with chondrosarcoma based on a surveillance, epidemiology, and end results analysis.
Yan L; Gao N; Ai F; Zhao Y; Kang Y; Chen J; Weng Y
Front Oncol; 2022; 12():967758. PubMed ID: 36072795
[TBL] [Abstract][Full Text] [Related]
13. Prediction of Microsatellite Instability in Colorectal Cancer Using a Machine Learning Model Based on PET/CT Radiomics.
Kim S; Lee JH; Park EJ; Lee HS; Baik SH; Jeon TJ; Lee KY; Ryu YH; Kang J
Yonsei Med J; 2023 May; 64(5):320-326. PubMed ID: 37114635
[TBL] [Abstract][Full Text] [Related]
14. CT-based deep learning model for the prediction of DNA mismatch repair deficient colorectal cancer: a diagnostic study.
Cao W; Hu H; Guo J; Qin Q; Lian Y; Li J; Wu Q; Chen J; Wang X; Deng Y
J Transl Med; 2023 Mar; 21(1):214. PubMed ID: 36949511
[TBL] [Abstract][Full Text] [Related]
15. Development and validation of a radiomics-based nomogram for the preoperative prediction of microsatellite instability in colorectal cancer.
Ying M; Pan J; Lu G; Zhou S; Fu J; Wang Q; Wang L; Hu B; Wei Y; Shen J
BMC Cancer; 2022 May; 22(1):524. PubMed ID: 35534797
[TBL] [Abstract][Full Text] [Related]
16. Survival outcome prediction in cervical cancer: Cox models vs deep-learning model.
Matsuo K; Purushotham S; Jiang B; Mandelbaum RS; Takiuchi T; Liu Y; Roman LD
Am J Obstet Gynecol; 2019 Apr; 220(4):381.e1-381.e14. PubMed ID: 30582927
[TBL] [Abstract][Full Text] [Related]
17. Radiomics approach with deep learning for predicting T4 obstructive colorectal cancer using CT image.
Pan L; He T; Huang Z; Chen S; Zhang J; Zheng S; Chen X
Abdom Radiol (NY); 2023 Apr; 48(4):1246-1259. PubMed ID: 36859730
[TBL] [Abstract][Full Text] [Related]
18. Preoperative CT-based Deep Learning Model for Predicting Disease-Free Survival in Patients with Lung Adenocarcinomas.
Kim H; Goo JM; Lee KH; Kim YT; Park CM
Radiology; 2020 Jul; 296(1):216-224. PubMed ID: 32396042
[TBL] [Abstract][Full Text] [Related]
19. CT Texture Analysis: A Potential Biomarker for Evaluating KRAS Mutational Status in Colorectal Cancer.
Cao J; Wang GR; Wang ZW; Jin ZY
Chin Med Sci J; 2020 Dec; 35(4):306-314. PubMed ID: 33413746
[TBL] [Abstract][Full Text] [Related]
20. Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer.
Wang S; Liu Z; Rong Y; Zhou B; Bai Y; Wei W; Wei W; Wang M; Guo Y; Tian J
Radiother Oncol; 2019 Mar; 132():171-177. PubMed ID: 30392780
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]