215 related articles for article (PubMed ID: 34219704)
21. Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients.
Tahmassebi A; Wengert GJ; Helbich TH; Bago-Horvath Z; Alaei S; Bartsch R; Dubsky P; Baltzer P; Clauser P; Kapetas P; Morris EA; Meyer-Baese A; Pinker K
Invest Radiol; 2019 Feb; 54(2):110-117. PubMed ID: 30358693
[TBL] [Abstract][Full Text] [Related]
22. Deep Learning Model Based on Dual-Modal Ultrasound and Molecular Data for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer.
Huang JX; Shi J; Ding SS; Zhang HL; Wang XY; Lin SY; Xu YF; Wei MJ; Liu LZ; Pei XQ
Acad Radiol; 2023 Sep; 30 Suppl 2():S50-S61. PubMed ID: 37270368
[TBL] [Abstract][Full Text] [Related]
23. Early Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer Sonography Using Siamese Convolutional Neural Networks.
Byra M; Dobruch-Sobczak K; Klimonda Z; Piotrzkowska-Wroblewska H; Litniewski J
IEEE J Biomed Health Inform; 2021 Mar; 25(3):797-805. PubMed ID: 32749986
[TBL] [Abstract][Full Text] [Related]
24. Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset.
Ha R; Chin C; Karcich J; Liu MZ; Chang P; Mutasa S; Pascual Van Sant E; Wynn RT; Connolly E; Jambawalikar S
J Digit Imaging; 2019 Oct; 32(5):693-701. PubMed ID: 30361936
[TBL] [Abstract][Full Text] [Related]
25. Apriori prediction of chemotherapy response in locally advanced breast cancer patients using CT imaging and deep learning: transformer versus transfer learning.
Moslemi A; Osapoetra LO; Dasgupta A; Alberico D; Trudeau M; Gandhi S; Eisen A; Wright F; Look-Hong N; Curpen B; Kolios MC; Czarnota GJ
Front Oncol; 2024; 14():1359148. PubMed ID: 38756659
[TBL] [Abstract][Full Text] [Related]
26. Prediction of response to neoadjuvant chemotherapy in breast cancer with recurrent neural networks and raw ultrasound signals.
Byra M; Dobruch-Sobczak K; Piotrzkowska-Wroblewska H; Klimonda Z; Litniewski J
Phys Med Biol; 2022 Sep; 67(18):. PubMed ID: 36001984
[No Abstract] [Full Text] [Related]
27. Early prediction of neoadjuvant chemotherapy response for advanced breast cancer using PET/MRI image deep learning.
Choi JH; Kim HA; Kim W; Lim I; Lee I; Byun BH; Noh WC; Seong MK; Lee SS; Kim BI; Choi CW; Lim SM; Woo SK
Sci Rep; 2020 Dec; 10(1):21149. PubMed ID: 33273490
[TBL] [Abstract][Full Text] [Related]
28. Imaging-proteomic analysis for prediction of neoadjuvant chemotherapy responses in patients with breast cancer.
Duan J; Zhao Y; Sun Q; Liang D; Liu Z; Chen X; Li ZC
Cancer Med; 2023 Dec; 12(23):21256-21269. PubMed ID: 37962087
[TBL] [Abstract][Full Text] [Related]
29. 18F-fluorodeoxyglucose (FDG) PET/CT after two cycles of neoadjuvant therapy may predict response in HER2-negative, but not in HER2-positive breast cancer.
Cheng J; Wang Y; Mo M; Bao X; Zhang Y; Liu G; Zhang J; Geng D
Oncotarget; 2015 Oct; 6(30):29388-95. PubMed ID: 26336821
[TBL] [Abstract][Full Text] [Related]
30. [Value of fused 18F-FDG PET/CT images in predicting efficacy of neoadjuvant chemotherapy on breast cancer].
Li D; Chen JH; Wang J; Ling R; Yao Q; Wang L
Ai Zheng; 2007 Aug; 26(8):900-4. PubMed ID: 17697556
[TBL] [Abstract][Full Text] [Related]
31. Enhancing skin lesion classification with advanced deep learning ensemble models: a path towards accurate medical diagnostics.
Munuswamy Selvaraj K; Gnanagurusubbiah S; Roby Roy RR; John Peter JH; Balu S
Curr Probl Cancer; 2024 Apr; 49():101077. PubMed ID: 38480028
[TBL] [Abstract][Full Text] [Related]
32. Deep Learning Prediction of Pathologic Complete Response in Breast Cancer Using MRI and Other Clinical Data: A Systematic Review.
Khan N; Adam R; Huang P; Maldjian T; Duong TQ
Tomography; 2022 Nov; 8(6):2784-2795. PubMed ID: 36412691
[TBL] [Abstract][Full Text] [Related]
33. LDDNet: A Deep Learning Framework for the Diagnosis of Infectious Lung Diseases.
Podder P; Das SR; Mondal MRH; Bharati S; Maliha A; Hasan MJ; Piltan F
Sensors (Basel); 2023 Jan; 23(1):. PubMed ID: 36617076
[TBL] [Abstract][Full Text] [Related]
34. [Texture analysis based on contrast-enhanced MRI can predict treatment response to neoadjuvant chemotherapy of breast cancer].
Sun SH; Zhou CW; Zhao LY; Zhang RZ; Ouyang H
Zhonghua Zhong Liu Za Zhi; 2017 May; 39(5):344-349. PubMed ID: 28535650
[No Abstract] [Full Text] [Related]
35. FDG-PET/CT and MRI for Evaluation of Pathologic Response to Neoadjuvant Chemotherapy in Patients With Breast Cancer: A Meta-Analysis of Diagnostic Accuracy Studies.
Sheikhbahaei S; Trahan TJ; Xiao J; Taghipour M; Mena E; Connolly RM; Subramaniam RM
Oncologist; 2016 Aug; 21(8):931-9. PubMed ID: 27401897
[TBL] [Abstract][Full Text] [Related]
36. MRI-based response patterns during neoadjuvant chemotherapy can predict pathological (complete) response in patients with breast cancer.
Goorts B; Dreuning KMA; Houwers JB; Kooreman LFS; Boerma EG; Mann RM; Lobbes MBI; Smidt ML
Breast Cancer Res; 2018 Apr; 20(1):34. PubMed ID: 29669584
[TBL] [Abstract][Full Text] [Related]
37. [Prediction of Response to Neoadjuvant Chemotherapy for Breast Cancer Based on Histomorphology Analysis of Needle Biopsy Images].
Xu CY; Xie JW; Yang CX; Jiang YN; Zhang ZH; Xu J
Sichuan Da Xue Xue Bao Yi Xue Ban; 2021 Mar; 52(2):279-285. PubMed ID: 33829703
[TBL] [Abstract][Full Text] [Related]
38. Utility of
Lee IH; Lee SJ; Lee J; Jung JH; Park HY; Jeong SY; Lee SW; Chae YS
BMC Cancer; 2020 Nov; 20(1):1106. PubMed ID: 33198673
[TBL] [Abstract][Full Text] [Related]
39. A New Method of Identifying Pathologic Complete Response After Neoadjuvant Chemotherapy for Breast Cancer Patients Using a Population-Based Electronic Medical Record System.
Wu G; Cheligeer C; Brisson AM; Quan ML; Cheung WY; Brenner D; Lupichuk S; Teman C; Basmadjian RB; Popwich B; Xu Y
Ann Surg Oncol; 2023 Apr; 30(4):2095-2103. PubMed ID: 36542249
[TBL] [Abstract][Full Text] [Related]
40. A spatial attention guided deep learning system for prediction of pathological complete response using breast cancer histopathology images.
Duanmu H; Bhattarai S; Li H; Shi Z; Wang F; Teodoro G; Gogineni K; Subhedar P; Kiraz U; Janssen EAM; Aneja R; Kong J
Bioinformatics; 2022 Sep; 38(19):4605-4612. PubMed ID: 35962988
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]