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Items: 1 to 20 of 699

1.

Early tumor response prediction for lung cancer patients using novel longitudinal pattern features from sequential PET/CT image scans.

Buizza G, Toma-Dasu I, Lazzeroni M, Paganelli C, Riboldi M, Chang Y, Smedby Ö, Wang C.

Phys Med. 2018 Oct;54:21-29. doi: 10.1016/j.ejmp.2018.09.003. Epub 2018 Sep 22.

PMID:
30337006
2.

State of the Art: Machine Learning Applications in Glioma Imaging.

Lotan E, Jain R, Razavian N, Fatterpekar GM, Lui YW.

AJR Am J Roentgenol. 2018 Oct 17:1-12. doi: 10.2214/AJR.18.20218. [Epub ahead of print]

PMID:
30332296
3.

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. 2018 Oct 16. doi: 10.1007/s10549-018-4990-9. [Epub ahead of print]

PMID:
30328048
4.

Quantitative vessel tortuosity: A potential CT imaging biomarker for distinguishing lung granulomas from adenocarcinomas.

Alilou M, Orooji M, Beig N, Prasanna P, Rajiah P, Donatelli C, Velcheti V, Rakshit S, Yang M, Jacono F, Gilkeson R, Linden P, Madabhushi A.

Sci Rep. 2018 Oct 16;8(1):15290. doi: 10.1038/s41598-018-33473-0.

5.

Biliary Tract Cancer at CT: A Radiomics-based Model to Predict Lymph Node Metastasis and Survival Outcomes.

Ji GW, Zhang YD, Zhang H, Zhu FP, Wang K, Xia YX, Zhang YD, Jiang WJ, Li XC, Wang XH.

Radiology. 2018 Oct 16:181408. doi: 10.1148/radiol.2018181408. [Epub ahead of print]

PMID:
30325283
6.

CT-based Radiomics for Biliary Tract Cancer: A Possible Solution for Predicting Lymph Node Metastases.

Laghi A, Voena C.

Radiology. 2018 Oct 16:182158. doi: 10.1148/radiol.2018182158. [Epub ahead of print]

PMID:
30325279
7.

Novel Quantitative PET Techniques for Clinical Decision Support in Oncology.

Zaidi H, Alavi A, Naqa IE.

Semin Nucl Med. 2018 Nov;48(6):548-564. doi: 10.1053/j.semnuclmed.2018.07.003. Epub 2018 Sep 10. Review.

PMID:
30322481
8.

Radiomic Features of Hippocampal Subregions in Alzheimer's Disease and Amnestic Mild Cognitive Impairment.

Feng F, Wang P, Zhao K, Zhou B, Yao H, Meng Q, Wang L, Zhang Z, Ding Y, Wang L, An N, Zhang X, Liu Y.

Front Aging Neurosci. 2018 Sep 25;10:290. doi: 10.3389/fnagi.2018.00290. eCollection 2018.

9.

Effects of MRI scanner parameters on breast cancer radiomics.

Saha A, Yu X, Sahoo D, Mazurowski MA.

Expert Syst Appl. 2017 Nov 30;87:384-391. doi: 10.1016/j.eswa.2017.06.029. Epub 2017 Jun 20.

PMID:
30319179
10.

Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour.

Su C, Jiang J, Zhang S, Shi J, Xu K, Shen N, Zhang J, Li L, Zhao L, Zhang J, Qin Y, Liu Y, Zhu W.

Eur Radiol. 2018 Oct 12. doi: 10.1007/s00330-018-5704-8. [Epub ahead of print]

PMID:
30315419
11.

Radiomics features on non-contrast-enhanced CT scan can precisely classify AVM-related hematomas from other spontaneous intraparenchymal hematoma types.

Zhang Y, Zhang B, Liang F, Liang S, Zhang Y, Yan P, Ma C, Liu A, Guo F, Jiang C.

Eur Radiol. 2018 Oct 10. doi: 10.1007/s00330-018-5747-x. [Epub ahead of print]

PMID:
30306329
12.

A Visually Interpretable, Dictionary-Based Approach to Imaging-Genomic Modeling, With Low-Grade Glioma as a Case Study.

Kuthuru S, Deaderick W, Bai H, Su C, Vu T, Monga V, Rao A.

Cancer Inform. 2018 Oct 5;17:1176935118802796. doi: 10.1177/1176935118802796. eCollection 2018.

13.

A biomarker basing on radiomics for the prediction of overall survival in non-small cell lung cancer patients.

He B, Zhao W, Pi JY, Han D, Jiang YM, Zhang ZG, Zhao W.

Respir Res. 2018 Oct 10;19(1):199. doi: 10.1186/s12931-018-0887-8.

14.

Advances in Imaging and Automated Quantification of Malignant Pulmonary Diseases: A State-of-the-Art Review.

Hochhegger B, Zanon M, Altmayer S, Pacini GS, Balbinot F, Francisco MZ, Dalla Costa R, Watte G, Santos MK, Barros MC, Penha D, Irion K, Marchiori E.

Lung. 2018 Oct 9. doi: 10.1007/s00408-018-0156-0. [Epub ahead of print] Review.

PMID:
30302536
15.

Differentiation of focal organising pneumonia and peripheral adenocarcinoma in solid lung lesions using thin-section CT-based radiomics.

Zhang T, Yuan M, Zhong Y, Zhang YD, Li H, Wu JF, Yu TF.

Clin Radiol. 2018 Oct 4. pii: S0009-9260(18)30520-8. doi: 10.1016/j.crad.2018.08.014. [Epub ahead of print]

PMID:
30293800
16.

Towards a modular decision support system for radiomics: A case study on rectal cancer.

Gatta R, Vallati M, Dinapoli N, Masciocchi C, Lenkowicz J, Cusumano D, Casá C, Farchione A, Damiani A, van Soest J, Dekker A, Valentini V.

Artif Intell Med. 2018 Oct 3. pii: S0933-3657(17)30593-6. doi: 10.1016/j.artmed.2018.09.003. [Epub ahead of print]

PMID:
30292538
17.

PET/MRI in Breast Cancer.

Pujara AC, Kim E, Axelrod D, Melsaether AN.

J Magn Reson Imaging. 2018 Oct 6. doi: 10.1002/jmri.26298. [Epub ahead of print]

PMID:
30291656
18.

Effect of machine learning methods on predicting NSCLC overall survival time based on Radiomics analysis.

Sun W, Jiang M, Dang J, Chang P, Yin FF.

Radiat Oncol. 2018 Oct 5;13(1):197. doi: 10.1186/s13014-018-1140-9.

19.

Lung tumor segmentation methods: Impact on the uncertainty of radiomics features for non-small cell lung cancer.

Owens CA, Peterson CB, Tang C, Koay EJ, Yu W, Mackin DS, Li J, Salehpour MR, Fuentes DT, Court LE, Yang J.

PLoS One. 2018 Oct 4;13(10):e0205003. doi: 10.1371/journal.pone.0205003. eCollection 2018.

20.

Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features.

Yin P, Mao N, Zhao C, Wu J, Sun C, Chen L, Hong N.

Eur Radiol. 2018 Oct 2. doi: 10.1007/s00330-018-5730-6. [Epub ahead of print]

PMID:
30280245

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