218 related articles for article (PubMed ID: 37772478)
1. An MRI-based machine learning radiomics can predict short-term response to neoadjuvant chemotherapy in patients with cervical squamous cell carcinoma: A multicenter study.
Xin Z; Yan W; Feng Y; Yunzhi L; Zhang Y; Wang D; Chen W; Peng J; Guo C; Chen Z; Wang X; Zhu J; Lei J
Cancer Med; 2023 Oct; 12(19):19383-19393. PubMed ID: 37772478
[TBL] [Abstract][Full Text] [Related]
2. The MRI radiomics signature can predict the pathologic response to neoadjuvant chemotherapy in locally advanced esophageal squamous cell carcinoma.
Lu S; Wang C; Liu Y; Chu F; Jia Z; Zhang H; Wang Z; Lu Y; Wang S; Yang G; Qu J
Eur Radiol; 2024 Jan; 34(1):485-494. PubMed ID: 37540319
[TBL] [Abstract][Full Text] [Related]
3. Predicting pathological complete response to neoadjuvant chemotherapy in breast cancer patients: use of MRI radiomics data from three regions with multiple machine learning algorithms.
Zheng G; Peng J; Shu Z; Jin H; Han L; Yuan Z; Qin X; Hou J; He X; Gong X
J Cancer Res Clin Oncol; 2024 Mar; 150(3):147. PubMed ID: 38512406
[TBL] [Abstract][Full Text] [Related]
4. Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study.
Sun C; Tian X; Liu Z; Li W; Li P; Chen J; Zhang W; Fang Z; Du P; Duan H; Liu P; Wang L; Chen C; Tian J
EBioMedicine; 2019 Aug; 46():160-169. PubMed ID: 31395503
[TBL] [Abstract][Full Text] [Related]
5. Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: A machine learning, multicenter study.
Yu Y; He Z; Ouyang J; Tan Y; Chen Y; Gu Y; Mao L; Ren W; Wang J; Lin L; Wu Z; Liu J; Ou Q; Hu Q; Li A; Chen K; Li C; Lu N; Li X; Su F; Liu Q; Xie C; Yao H
EBioMedicine; 2021 Jul; 69():103460. PubMed ID: 34233259
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Prediction of the Ki-67 expression level in head and neck squamous cell carcinoma with machine learning-based multiparametric MRI radiomics: a multicenter study.
Chen W; Lin G; Chen Y; Cheng F; Li X; Ding J; Zhong Y; Kong C; Chen M; Xia S; Lu C; Ji J
BMC Cancer; 2024 Apr; 24(1):418. PubMed ID: 38580939
[TBL] [Abstract][Full Text] [Related]
8. Preoperative MRI-Based Radiomic Machine-Learning Nomogram May Accurately Distinguish Between Benign and Malignant Soft-Tissue Lesions: A Two-Center Study.
Wang H; Zhang J; Bao S; Liu J; Hou F; Huang Y; Chen H; Duan S; Hao D; Liu J
J Magn Reson Imaging; 2020 Sep; 52(3):873-882. PubMed ID: 32112598
[TBL] [Abstract][Full Text] [Related]
9. Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods.
Wang X; Wan Q; Chen H; Li Y; Li X
Eur Radiol; 2020 Aug; 30(8):4595-4605. PubMed ID: 32222795
[TBL] [Abstract][Full Text] [Related]
10. Machine learning applied to MRI evaluation for the detection of lymph node metastasis in patients with locally advanced cervical cancer treated with neoadjuvant chemotherapy.
Arezzo F; Cormio G; Mongelli M; Cazzato G; Silvestris E; Kardhashi A; Cazzolla A; Lombardi C; Venerito V; Loizzi V
Arch Gynecol Obstet; 2023 Jun; 307(6):1911-1919. PubMed ID: 36370209
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Machine Learning-Based Radiomics Nomogram Using Magnetic Resonance Images for Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer Patients.
Chen S; Shu Z; Li Y; Chen B; Tang L; Mo W; Shao G; Shao F
Front Oncol; 2020; 10():1410. PubMed ID: 32923392
[No Abstract] [Full Text] [Related]
13. A machine learning approach using
Qi WX; Li S; Xiao J; Li H; Chen J; Zhao S
Front Immunol; 2024; 15():1351750. PubMed ID: 38352868
[TBL] [Abstract][Full Text] [Related]
14. Application and Clinical Value of Machine Learning-Based Cervical Cancer Diagnosis and Prediction Model in Adjuvant Chemotherapy for Cervical Cancer: A Single-Center, Controlled, Non-Arbitrary Size Case-Control Study.
Wang Y; Shen L; Jin J; Wang G
Contrast Media Mol Imaging; 2022; 2022():2432291. PubMed ID: 35821886
[TBL] [Abstract][Full Text] [Related]
15. Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma.
Zhang XF; Wu HY; Liang XW; Chen JL; Li J; Zhang S; Liu Z
BMC Womens Health; 2024 Mar; 24(1):182. PubMed ID: 38504245
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. MRI radiomics may predict early tumor recurrence in patients with sinonasal squamous cell carcinoma.
Park CJ; Choi SH; Kim D; Kim SB; Han K; Ahn SS; Lee WH; Choi EC; Keum KC; Kim J
Eur Radiol; 2024 May; 34(5):3151-3159. PubMed ID: 37926740
[TBL] [Abstract][Full Text] [Related]
18. Prediction of unenhanced lesion evolution in multiple sclerosis using radiomics-based models: a machine learning approach.
Peng Y; Zheng Y; Tan Z; Liu J; Xiang Y; Liu H; Dai L; Xie Y; Wang J; Zeng C; Li Y
Mult Scler Relat Disord; 2021 Aug; 53():102989. PubMed ID: 34052741
[TBL] [Abstract][Full Text] [Related]
19. A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: A multicenter cohort study.
Cui Y; Zhang J; Li Z; Wei K; Lei Y; Ren J; Wu L; Shi Z; Meng X; Yang X; Gao X
EClinicalMedicine; 2022 Apr; 46():101348. PubMed ID: 35340629
[TBL] [Abstract][Full Text] [Related]
20. Endorectal ultrasound radiomics in locally advanced rectal cancer patients: despeckling and radiotherapy response prediction using machine learning.
Abbaspour S; Abdollahi H; Arabalibeik H; Barahman M; Arefpour AM; Fadavi P; Ay M; Mahdavi SR
Abdom Radiol (NY); 2022 Nov; 47(11):3645-3659. PubMed ID: 35951085
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
