|
1
|
Woodard GA, Ray KM, Joe BN and Price ER:
Qualitative Radiogenomics: Association between Oncotype DX Test
Recurrence Score and BI-RADS Mammographic and Breast MR Imaging
Features. Radiology. 286:60–70. 2018. View Article : Google Scholar
|
|
2
|
Gevaert O, Echegaray S, Khuong A, Hoang
CD, Shrager JB, Jensen KC, Berry GJ, Guo HH, Lau C, Plevritis SK,
et al: Predictive radiogenomics modeling of EGFR mutation status in
lung cancer. Sci Rep. 7:416742017. View Article : Google Scholar : PubMed/NCBI
|
|
3
|
Zhou M, Leung A, Echegaray S, Gentles A,
Shrager JB, Jensen KC, Berry GJ, Plevritis SK, Rubin DL, Napel S,
et al: Non-small cell lung cancer radiogenomics map identifies
relationships between molecular and imaging phenotypes with
prognostic implications. Radiology. 286:307–315. 2018. View Article : Google Scholar
|
|
4
|
Hussein S, Green A, Watane A, Reiter D,
Chen X, Papadakis GZ, Wood B, Cypess A, Osman M and Bagci U:
Automatic segmentation and quantification of white and brown
adipose tissues from PET/CT scans. IEEE Trans Med Imaging.
36:734–744. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
5
|
Nitipir C, Niculae D, Orlov C, Barbu MA,
Popescu B, Popa AM, Pantea AMS, Stanciu AE, Galateanu B, Ginghina
O, et al: Update on radionuclide therapy in oncology. Oncol Lett.
14:7011–7015. 2017.
|
|
6
|
El-Maouche D, Sadowski SM, Papadakis GZ,
Guthrie L, Cottle-Delisle C, Merkel R, Millo C, Chen CC, Kebebew E
and Collins MT: 68Ga-DOTATATE for tumor localization in
tumor-induced osteomalacia. J Clin Endocrinol Metab. 101:3575–3581.
2016. View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Jansen RW, van Amstel P, Martens RM, Kooi
IE, Wesseling P, de Langen AJ, Menke-Van der Houven van Oordt CW,
Jansen BHE, Moll AC, Dorsman JC, et al: Non-invasive tumor
genotyping using radiogenomic biomarkers, a systematic review and
oncology-wide pathway analysis. Oncotarget. 9:20134–20155. 2018.
View Article : Google Scholar : PubMed/NCBI
|
|
8
|
Ypsilantis PP, Siddique M, Sohn HM, Davies
A, Cook G, Goh V and Montana G: Predicting response to neoadjuvant
chemotherapy with PET imaging using convolutional neural networks.
PLoS One. 10:e01370362015. View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Fujishima H, Fumoto S, Shibata T, Nishiki
K, Tsukamoto Y, Etoh T, Moriyama M, Shiraishi N and Inomata M: A
17-molecule set as a predictor of complete response to neoadjuvant
chemotherapy with docetaxel, cisplatin, and 5-fluorouracil in
esophageal cancer. PLoS One. 12:e01880982017. View Article : Google Scholar : PubMed/NCBI
|
|
10
|
Bownes RJ, Turnbull AK, Martinez-Perez C,
Cameron DA, Sims AH and Oikonomidou O: On-treatment biomarkers can
improve prediction of response to neoadjuvant chemotherapy in
breast cancer. Breast Cancer Res. 21:732019. View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Angus L, Smid M, Wilting SM, van Riet J,
Van Hoeck A, Nguyen L, Nik-Zainal S, Steenbruggen TG, Tjan-Heijnen
VCG, Labots M, et al: The genomic landscape of metastatic breast
cancer highlights changes in mutation and signature frequencies.
Nat Genet. 51:1450–1458. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Trivizakis E, Manikis GC, Nikiforaki K,
Drevelegas K, Constantinides M, Drevelegas A and Marias K:
Extending 2D convolutional neural networks to 3D for advancing deep
learning cancer classification with application to MRI liver tumor
differentiation. IEEE J Biomed Health Inform. 23:923–930. 2018.
View Article : Google Scholar
|
|
13
|
Lecun Y, Bottou L, Bengio Y and Haffner P:
Gradient-based learning applied to document recognition.
Proceedings of the IEEE. 86:2278–2324. 1998. View Article : Google Scholar
|
|
14
|
Sabour S, Frosst N and Hinton GE: Dynamic
routing between capsules. In: Proceedings of the 31st International
Conference on Neural Information Processing Systems; Curran
Associates Inc; Red Hook, NY. pp. 3856–3866. 2017
|
|
15
|
Szegedy C, Vanhoucke V, Ioffe S, Shlens J
and Wojna Z: Rethinking the inception architecture for computer
vision. In: Proceedings of the Conference on Computer Vision and
Pattern Recognition (CVPR); Las Vegas, NV. pp. 2818–2826. 2016
|
|
16
|
Huang G, Liu Z, van der Maaten L and
Weinberger KQ: Densely connected convolutional networks. In:
Proceedings of the Conference on Computer Vision and Pattern
Recognition (CVPR); Honolulu, HI. pp. 2261–2269. 2017
|
|
17
|
He K, Gkioxari G, Dollar P and Girshick R:
Mask R-CNN. In: Proceedings of the IEEE International Conference on
Computer Vision. IEEE International Conference on Computer Vision
(ICCV); pp. 2980–2988. 2017
|
|
18
|
Chang K, Bai HX, Zhou H, Su C, Bi WL,
Agbodza E, Kavouridis VK, Senders JT, Boaro A, Beers A, et al:
Residual convolutional neural network for the determination of IDH
status in low- and high-grade gliomas from mr imaging. Clin Cancer
Res. 24:1073–1081. 2018. View Article : Google Scholar
|
|
19
|
Chang P, Grinband J, Weinberg BD, Bardis
M, Khy M, Cadena G, Su MY, Cha S, Filippi CG, Bota D, et al:
Deep-learning convolutional neural networks accurately classify
genetic mutations in gliomas. AJNR Am J Neuroradiol. 39:1201–1207.
2018. View Article : Google Scholar : PubMed/NCBI
|
|
20
|
Li Z, Wang Y, Yu J, Guo Y and Cao W: Deep
Learning based Radiomics (DLR) and its usage in noninvasive IDH1
prediction for low grade glioma. Sci Rep. 7:54672017. View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Liang S, Zhang R, Liang D, Song T, Ai T,
Xia C, Xia L and Wang Y: Multimodal 3D densenet for IDH genotype
prediction in gliomas. Genes (Basel). 9:1–17. 2018. View Article : Google Scholar
|
|
22
|
Korfiatis P, Kline TL, Lachance DH, Parney
IF, Buckner JC and Erickson BJ: Residual deep convolutional neural
network predicts MGMT methylation status. J Digit Imaging.
30:622–628. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
23
|
Akkus Z, Ali I, Sedlar J, Kline TL,
Agrawal JP, Parney IF, Giannini C and Erickson BJ: Predicting 1p19q
chromosomal deletion of low-grade gliomas from MR Images using deep
learning. arXiv:1611.06939. Accessed November 21, 2016.
|
|
24
|
Gupta T and Sarin R: Poor-prognosis
high-grade gliomas: Evolving an evidence-based standard of care.
Lancet Oncol. 3:557–564. 2002. View Article : Google Scholar : PubMed/NCBI
|
|
25
|
Decuyper M, Bonte S and van Holen R:
Binary glioma grading: Radiomics versus pre-trained CNN features.
In: Proceedings of the 21st International Conference. Part III;
Granada, Spain. pp. 498–505. 2018
|
|
26
|
Smedley NF and Hsu W: Using deep neural
networks for radiogenomic analysis. In: Proceedings of the 15th
International Symposium on Biomedical Imaging (ISBI 2018);
Washington, DC. pp. 1529–1533. 2018
|
|
27
|
Zhou Y, Li Z, Zhu H, Chen C, Gao M, Xu K
and Xu J: Holistic brain tumor screening and classification based
on densenet and recurrent neural network. In: Proceedings of the
International MICCAI Brainlesion Workshop; Springer; Cham. pp.
208–217. 2018
|
|
28
|
Momeni A, Thibault M and Gevaert O:
Dropout-enabled ensemble learning for multi-scale biomedical data.
In: Proceedings of the International MICCAI Brainlesion Workshop;
Springer; Cham: pp. 407–415. 2018
|
|
29
|
Afshar P, Mohammadi A and Plataniotis KN:
Brain tumor type classification via capsule networks. In:
Proceedings of the International Conference on Image Processing,
ICIP; IEEE; pp. 3129–3133. 2018
|
|
30
|
Molina JR, Yang P, Cassivi SD, Schild SE
and Adjei AA: Non-small cell lung cancer: Epidemiology, risk
factors, treatment, and survivorship. Mayo Clin Proc. 83:584–594.
2008. View Article : Google Scholar : PubMed/NCBI
|
|
31
|
Yu D, Zhou M, Yang F, Dong D, Gevaert O,
Liu Z, Shi J and Tian J: Convolutional neural networks for
predicting molecular profiles of non-small cell lung cancer. In:
Proceedings of the 14th International Symposium on Biomedical
Imaging (ISBI 2017); Melbourne, VIC. pp. 569–572. 2017
|
|
32
|
Wang S, Shi J, Ye Z, Dong D, Yu D, Zhou M,
Liu Y, Gevaert O, Wang K, Zhu Y, et al: Predicting EGFR mutation
status in lung adenocarcinoma on CT image using deep learning. Eur
Respir J. 53:18009862019. View Article : Google Scholar
|
|
33
|
Lingle W, Erickson BJ, Zuley ML, Jarosz R,
Bonaccio E, Filippini J and Gruszauskas N: Radiology data from the
cancer genome atlas breast invasive carcinoma [TCGA-BRCA]
collection. Cancer Imaging Arch. View Article : Google Scholar
|
|
34
|
Zhu Z, Albadawy E, Saha A, Zhang J,
Harowicz MR and Mazurowski MA: Deep learning for identifying
radiogenomic associations in breast cancer. Comput Biol Med.
109:85–90. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
35
|
Ha R, Mutasa S, Karcich J, Gupta N,
Pascual Van Sant E, Nemer J, Sun M, Chang P, Liu MZ and
Jambawalikar S: Predicting breast cancer molecular subtype with MRI
dataset utilizing convolutional neural network algorithm. J Digit
Imaging. 32:276–282. 2019. View Article : Google Scholar : PubMed/NCBI
|
|
36
|
Yoon H, Ramanathan A, Alamudun F and
Tourassi G: Deep radiogenomics for predicting clinical phenotypes
in invasive breast cancer. In: Proceedings of the 14th
International Workshop on Breast Imaging (IWBI 2018); pp.
752018
|
|
37
|
Zhu Z, Harowicz M, Zhang J, Saha A, Grimm
LJ, Hwang ES and Mazurowski MA: Deep learning analysis of breast
MRIs for prediction of occult invasive disease in ductal carcinoma
in situ. Comput Biol Med. 115:1034982019. View Article : Google Scholar : PubMed/NCBI
|
|
38
|
Bibault JE, Giraud P, Durdux C, Taieb J,
Berger A, Coriat R, Chaussade S, Dousset B, Nordlinger B and Burgun
A: Deep Learning and Radiomics predict complete response after
neo-adjuvant chemoradiation for locally advanced rectal cancer. Sci
Rep. 8:1–8. 2018.
|
|
39
|
Chen W, Ji H, Feng J, Liu R, Yu Y, Zhou R
and Zhou J: Classification of pancreatic cystic neoplasms based on
multimodality images. In: Proceedings of the International Workshop
on Machine Learning in Medical Imaging (MLMI 2018); pp. 161–169.
2018
|
|
40
|
Cha KH, Hadjiiski LM, Chan H-P, Samala RK,
Cohan RH, Caoili EM, Paramagul C, Alva A and Weizer AZ: Bladder
cancer treatment response assessment using deep learning in CT with
transfer learning. In: Proceedings Volume 10134, Medical Imaging
2017: Computer-Aided Diagnosis; pp. 10134042017
|
|
41
|
Cha KH, Hadjiiski L, Chan HP, Weizer AZ,
Alva A, Cohan RH, Caoili EM, Paramagul C and Samala RK: Bladder
cancer treatment response assessment in CT using radiomics with
deep-learning. Sci Rep. 7:87382017. View Article : Google Scholar : PubMed/NCBI
|
|
42
|
Banerjee I, Crawley A, Bhethanabotla M,
Daldrup-Link HE and Rubin DL: Transfer learning on fused
multiparametric MR images for classifying histopathological
subtypes of rhabdomyosarcoma. Comput Med Imaging Graph. 65:167–175.
2018. View Article : Google Scholar
|
|
43
|
Zhou Z, Chen L, Sher D, Zhang Q, Shah J,
Pham NL, Jiang S and Wang J: Predicting lymph node metastasis in
head and neck cancer by combining many-objective radiomics and
3-dimensioal convolutional neural network through evidential
reasoning. arXiv180507021. Accessed May 18, 2018.
|
|
44
|
Pedano N, Flanders AE, Scarpace L,
Mikkelsen T, Eschbacher JM, Hermes B and Ostrom Q: Radiology data
from The Cancer Genome Atlas Low Grade Glioma [TCGA-LGG]
Collection. Cancer Imaging Arch. View Article : Google Scholar
|
|
45
|
Newitt D and Hylton N: Multi-center breast
DCE-MRI data and segmentations from patients in the I-SPY 1/ACRIN
6657 trials. Cancer Imaging Arch. View Article : Google Scholar
|
|
46
|
London AJ: Artificial intelligence and
black-box medical decisions: Accuracy versus explainability.
Hastings Cent Rep. 49:15–21. 2019. View Article : Google Scholar : PubMed/NCBI
|
