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Items: 1 to 50 of 214

1.

Deep-learning approaches for Gleason grading of prostate biopsies.

Madabhushi A, Feldman MD, Leo P.

Lancet Oncol. 2020 Jan 8. pii: S1470-2045(19)30793-4. doi: 10.1016/S1470-2045(19)30793-4. [Epub ahead of print] No abstract available.

PMID:
31926804
2.

Changes in CT Radiomic Features Associated with Lymphocyte Distribution Predict Overall Survival and Response to Immunotherapy in Non-Small Cell Lung Cancer.

Khorrami M, Prasanna P, Gupta A, Patil P, Velu PD, Thawani R, Corredor G, Alilou M, Bera K, Fu P, Feldman M, Velcheti V, Madabhushi A.

Cancer Immunol Res. 2020 Jan;8(1):108-119. doi: 10.1158/2326-6066.CIR-19-0476. Epub 2019 Nov 12.

PMID:
31719058
3.

Repeatability of radiomics and machine learning for DWI: Short-term repeatability study of 112 patients with prostate cancer.

Merisaari H, Taimen P, Shiradkar R, Ettala O, Pesola M, Saunavaara J, Boström PJ, Madabhushi A, Aronen HJ, Jambor I.

Magn Reson Med. 2019 Nov 8. doi: 10.1002/mrm.28058. [Epub ahead of print]

PMID:
31703155
4.

Author Correction: 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. 2019 Oct 29;9(1):15873. doi: 10.1038/s41598-019-52008-9.

5.

Innovations in risk-stratification and treatment of Veterans with oropharynx cancer; roadmap of the 2019 Field Based Meeting.

Sandulache VC, Lei YL, Heasley LE, Chang M, Amos CI, Sturgis EM, Graboyes E, Chiao EY, Rogus-Pulia N, Lewis J, Madabhushi A, Frederick MJ, Sabichi A, Ittmann M, Yarbrough WG, Chung CH, Ferrarotto R, Mai W, Skinner HD, Duvvuri U, Gerngross P, Sikora AG.

Oral Oncol. 2019 Oct 21:104440. doi: 10.1016/j.oraloncology.2019.104440. [Epub ahead of print] No abstract available.

PMID:
31648864
6.

Quantitative nuclear histomorphometric features are predictive of Oncotype DX risk categories in ductal carcinoma in situ: preliminary findings.

Li H, Whitney J, Bera K, Gilmore H, Thorat MA, Badve S, Madabhushi A.

Breast Cancer Res. 2019 Oct 17;21(1):114. doi: 10.1186/s13058-019-1200-6.

7.

Corrigendum to "Predicting pathologic response to neoadjuvant chemoradiation in resectable stage III non-small cell lung cancer patients using computed tomography radiomic features" [Lung Cancer 135 (September) (2019) 1-9].

Khorrami M, Jain P, Bera K, Alilou M, Thawani R, Patil P, Ahmad U, Murthy S, Stephans K, Fu P, Velcheti V, Madabhushi A.

Lung Cancer. 2019 Oct;136:156. doi: 10.1016/j.lungcan.2019.08.012. Epub 2019 Aug 21. No abstract available.

PMID:
31564290
8.

Predicting pathologic response to neoadjuvant chemoradiation in resectable stage III non-small cell lung cancer patients using computed tomography radiomic features.

Khorrami M, Jain P, Bera K, Alilou M, Thawani R, Patil P, Ahmad U, Murthy S, Stephans K, Fu P, Velcheti V, Madabhushi A.

Lung Cancer. 2019 Sep;135:1-9. doi: 10.1016/j.lungcan.2019.06.020. Epub 2019 Jul 5. Erratum in: Lung Cancer. 2019 Oct;136:156.

PMID:
31446979
9.

Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology.

Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A.

Nat Rev Clin Oncol. 2019 Nov;16(11):703-715. doi: 10.1038/s41571-019-0252-y. Epub 2019 Aug 9. Review.

10.

The revolving door for AI and pathologists-docendo discimus?

Van Es SL, Madabhushi A.

J Med Artif Intell. 2019 Jun;2. pii: 12. doi: 10.21037/jmai.2019.05.02. Epub 2019 Jun 11. No abstract available.

11.

Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI.

Chirra P, Leo P, Yim M, Bloch BN, Rastinehad AR, Purysko A, Rosen M, Madabhushi A, Viswanath SE.

J Med Imaging (Bellingham). 2019 Apr;6(2):024502. doi: 10.1117/1.JMI.6.2.024502. Epub 2019 Jun 14.

PMID:
31259199
12.

Machine Learning Prediction of Response to Cardiac Resynchronization Therapy.

Feeny AK, Rickard J, Patel D, Toro S, Trulock KM, Park CJ, LaBarbera MA, Varma N, Niebauer MJ, Sinha S, Gorodeski EZ, Grimm RA, Ji X, Barnard J, Madabhushi A, Spragg DD, Chung MK.

Circ Arrhythm Electrophysiol. 2019 Jul;12(7):e007316. doi: 10.1161/CIRCEP.119.007316. Epub 2019 Jun 20.

PMID:
31216884
13.

Radiomics-based convolutional neural network for brain tumor segmentation on multiparametric magnetic resonance imaging.

Prasanna P, Karnawat A, Ismail M, Madabhushi A, Tiwari P.

J Med Imaging (Bellingham). 2019 Apr;6(2):024005. doi: 10.1117/1.JMI.6.2.024005. Epub 2019 May 7.

PMID:
31093517
14.

Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2)-Positive Breast Cancer.

Braman N, Prasanna P, Whitney J, Singh S, Beig N, Etesami M, Bates DDB, Gallagher K, Bloch BN, Vulchi M, Turk P, Bera K, Abraham J, Sikov WM, Somlo G, Harris LN, Gilmore H, Plecha D, Varadan V, Madabhushi A.

JAMA Netw Open. 2019 Apr 5;2(4):e192561. doi: 10.1001/jamanetworkopen.2019.2561.

15.

HistoQC: An Open-Source Quality Control Tool for Digital Pathology Slides.

Janowczyk A, Zuo R, Gilmore H, Feldman M, Madabhushi A.

JCO Clin Cancer Inform. 2019 Apr;3:1-7. doi: 10.1200/CCI.18.00157.

16.

Applications of machine learning in drug discovery and development.

Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, Li B, Madabhushi A, Shah P, Spitzer M, Zhao S.

Nat Rev Drug Discov. 2019 Jun;18(6):463-477. doi: 10.1038/s41573-019-0024-5. Review.

17.

Risk factors and epidemiologic predictors of blood stream infections with New Delhi Metallo-b-lactamase (NDM-1) producing Enterobacteriaceae.

Snyder BM, Montague BT, Anandan S, Madabhushi AG, Pragasam AK, Verghese VP, Balaji V, Simões EAF.

Epidemiol Infect. 2019 Jan;147:e137. doi: 10.1017/S0950268819000256.

18.

Correlation between MRI phenotypes and a genomic classifier of prostate cancer: preliminary findings.

Purysko AS, Magi-Galluzzi C, Mian OY, Sittenfeld S, Davicioni E, du Plessis M, Buerki C, Bullen J, Li L, Madabhushi A, Stephenson A, Klein EA.

Eur Radiol. 2019 Sep;29(9):4861-4870. doi: 10.1007/s00330-019-06114-x. Epub 2019 Mar 7.

19.

Convolutional neural network initialized active contour model with adaptive ellipse fitting for nuclear segmentation on breast histopathological images.

Xu J, Gong L, Wang G, Lu C, Gilmore H, Zhang S, Madabhushi A.

J Med Imaging (Bellingham). 2019 Jan;6(1):017501. doi: 10.1117/1.JMI.6.1.017501. Epub 2019 Feb 8.

PMID:
30840729
20.

Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-site study.

Viswanath SE, Chirra PV, Yim MC, Rofsky NM, Purysko AS, Rosen MA, Bloch BN, Madabhushi A.

BMC Med Imaging. 2019 Feb 28;19(1):22. doi: 10.1186/s12880-019-0308-6.

21.

Disorder in Pixel-Level Edge Directions on T1WI Is Associated with the Degree of Radiation Necrosis in Primary and Metastatic Brain Tumors: Preliminary Findings.

Prasanna P, Rogers L, Lam TC, Cohen M, Siddalingappa A, Wolansky L, Pinho M, Gupta A, Hatanpaa KJ, Madabhushi A, Tiwari P.

AJNR Am J Neuroradiol. 2019 Mar;40(3):412-417. doi: 10.3174/ajnr.A5958. Epub 2019 Feb 7. Erratum in: AJNR Am J Neuroradiol. 2019 Jun;40(6):E33.

22.

Mass Effect Deformation Heterogeneity (MEDH) on Gadolinium-contrast T1-weighted MRI is associated with decreased survival in patients with right cerebral hemisphere Glioblastoma: A feasibility study.

Prasanna P, Mitra J, Beig N, Nayate A, Patel J, Ghose S, Thawani R, Partovi S, Madabhushi A, Tiwari P.

Sci Rep. 2019 Feb 4;9(1):1145. doi: 10.1038/s41598-018-37615-2.

23.

Quantitative Image Analysis of Human Epidermal Growth Factor Receptor 2 Immunohistochemistry for Breast Cancer: Guideline From the College of American Pathologists.

Bui MM, Riben MW, Allison KH, Chlipala E, Colasacco C, Kahn AG, Lacchetti C, Madabhushi A, Pantanowitz L, Salama ME, Stewart RL, Thomas NE, Tomaszewski JE, Hammond ME.

Arch Pathol Lab Med. 2019 Oct;143(10):1180-1195. doi: 10.5858/arpa.2018-0378-CP. Epub 2019 Jan 15.

24.

Computer-Aided Laser Dissection: A Microdissection Workflow Leveraging Image Analysis Tools.

Hipp JD, Johann DJ, Chen Y, Madabhushi A, Monaco J, Cheng J, Rodriguez-Canales J, Stumpe MC, Riedlinger G, Rosenberg AZ, Hanson JC, Kunju LP, Emmert-Buck MR, Balis UJ, Tangrea MA.

J Pathol Inform. 2018 Dec 11;9:45. doi: 10.4103/jpi.jpi_60_18. eCollection 2018.

25.

Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas.

Beig N, Khorrami M, Alilou M, Prasanna P, Braman N, Orooji M, Rakshit S, Bera K, Rajiah P, Ginsberg J, Donatelli C, Thawani R, Yang M, Jacono F, Tiwari P, Velcheti V, Gilkeson R, Linden P, Madabhushi A.

Radiology. 2019 Mar;290(3):783-792. doi: 10.1148/radiol.2018180910. Epub 2018 Dec 18.

26.

Shape Features of the Lesion Habitat to Differentiate Brain Tumor Progression from Pseudoprogression on Routine Multiparametric MRI: A Multisite Study.

Ismail M, Hill V, Statsevych V, Huang R, Prasanna P, Correa R, Singh G, Bera K, Beig N, Thawani R, Madabhushi A, Aahluwalia M, Tiwari P.

AJNR Am J Neuroradiol. 2018 Dec;39(12):2187-2193. doi: 10.3174/ajnr.A5858. Epub 2018 Nov 1.

27.

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. Erratum in: Sci Rep. 2019 Oct 29;9(1):15873.

28.

Stable and discriminating features are predictive of cancer presence and Gleason grade in radical prostatectomy specimens: a multi-site study.

Leo P, Elliott R, Shih NNC, Gupta S, Feldman M, Madabhushi A.

Sci Rep. 2018 Oct 8;8(1):14918. doi: 10.1038/s41598-018-33026-5.

29.

Novel Quantitative Imaging for Predicting Response to Therapy: Techniques and Clinical Applications.

Bera K, Velcheti V, Madabhushi A.

Am Soc Clin Oncol Educ Book. 2018 May 23;38:1008-1018. doi: 10.1200/EDBK_199747. Review.

30.

Spatial Architecture and Arrangement of Tumor-Infiltrating Lymphocytes for Predicting Likelihood of Recurrence in Early-Stage Non-Small Cell Lung Cancer.

Corredor G, Wang X, Zhou Y, Lu C, Fu P, Syrigos K, Rimm DL, Yang M, Romero E, Schalper KA, Velcheti V, Madabhushi A.

Clin Cancer Res. 2019 Mar 1;25(5):1526-1534. doi: 10.1158/1078-0432.CCR-18-2013. Epub 2018 Sep 10.

PMID:
30201760
31.

Identifying the morphologic basis for radiomic features in distinguishing different Gleason grades of prostate cancer on MRI: Preliminary findings.

Penzias G, Singanamalli A, Elliott R, Gollamudi J, Shih N, Feldman M, Stricker PD, Delprado W, Tiwari S, Böhm M, Haynes AM, Ponsky L, Fu P, Tiwari P, Viswanath S, Madabhushi A.

PLoS One. 2018 Aug 31;13(8):e0200730. doi: 10.1371/journal.pone.0200730. eCollection 2018.

32.

Nuclear shape and orientation features from H&E images predict survival in early-stage estrogen receptor-positive breast cancers.

Lu C, Romo-Bucheli D, Wang X, Janowczyk A, Ganesan S, Gilmore H, Rimm D, Madabhushi A.

Lab Invest. 2018 Nov;98(11):1438-1448. doi: 10.1038/s41374-018-0095-7. Epub 2018 Jun 29.

33.

Advances in the computational and molecular understanding of the prostate cancer cell nucleus.

Carleton NM, Lee G, Madabhushi A, Veltri RW.

J Cell Biochem. 2018 Sep;119(9):7127-7142. doi: 10.1002/jcb.27156. Epub 2018 Jun 20. Review.

34.

Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer.

Whitney J, Corredor G, Janowczyk A, Ganesan S, Doyle S, Tomaszewski J, Feldman M, Gilmore H, Madabhushi A.

BMC Cancer. 2018 May 30;18(1):610. doi: 10.1186/s12885-018-4448-9.

35.

High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: Application to invasive breast cancer detection.

Cruz-Roa A, Gilmore H, Basavanhally A, Feldman M, Ganesan S, Shih N, Tomaszewski J, Madabhushi A, González F.

PLoS One. 2018 May 24;13(5):e0196828. doi: 10.1371/journal.pone.0196828. eCollection 2018.

36.

Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings.

Shiradkar R, Ghose S, Jambor I, Taimen P, Ettala O, Purysko AS, Madabhushi A.

J Magn Reson Imaging. 2018 Dec;48(6):1626-1636. doi: 10.1002/jmri.26178. Epub 2018 May 7.

37.

A resolution adaptive deep hierarchical (RADHicaL) learning scheme applied to nuclear segmentation of digital pathology images.

Janowczyk A, Doyle S, Gilmore H, Madabhushi A.

Comput Methods Biomech Biomed Eng Imaging Vis. 2018;6(3):270-276. doi: 10.1080/21681163.2016.1141063. Epub 2016 Apr 28.

38.

Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography.

Orooji M, Alilou M, Rakshit S, Beig N, Khorrami MH, Rajiah P, Thawani R, Ginsberg J, Donatelli C, Yang M, Jacono F, Gilkeson R, Velcheti V, Linden P, Madabhushi A.

J Med Imaging (Bellingham). 2018 Apr;5(2):024501. doi: 10.1117/1.JMI.5.2.024501. Epub 2018 Apr 18.

39.

A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue.

Nirschl JJ, Janowczyk A, Peyster EG, Frank R, Margulies KB, Feldman MD, Madabhushi A.

PLoS One. 2018 Apr 3;13(4):e0192726. doi: 10.1371/journal.pone.0192726. eCollection 2018.

40.

Advanced Morphologic Analysis for Diagnosing Allograft Rejection: The Case of Cardiac Transplant Rejection.

Peyster EG, Madabhushi A, Margulies KB.

Transplantation. 2018 Aug;102(8):1230-1239. doi: 10.1097/TP.0000000000002189. Review.

41.

Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings.

Algohary A, Viswanath S, Shiradkar R, Ghose S, Pahwa S, Moses D, Jambor I, Shnier R, Böhm M, Haynes AM, Brenner P, Delprado W, Thompson J, Pulbrock M, Purysko AS, Verma S, Ponsky L, Stricker P, Madabhushi A.

J Magn Reson Imaging. 2018 Feb 22. doi: 10.1002/jmri.25983. [Epub ahead of print]

42.

Coregistration of Preoperative MRI with Ex Vivo Mesorectal Pathology Specimens to Spatially Map Post-treatment Changes in Rectal Cancer Onto In Vivo Imaging: Preliminary Findings.

Antunes J, Viswanath S, Brady JT, Crawshaw B, Ros P, Steele S, Delaney CP, Paspulati R, Willis J, Madabhushi A.

Acad Radiol. 2018 Jul;25(7):833-841. doi: 10.1016/j.acra.2017.12.006. Epub 2018 Jan 19.

43.

Radiogenomic analysis of hypoxia pathway is predictive of overall survival in Glioblastoma.

Beig N, Patel J, Prasanna P, Hill V, Gupta A, Correa R, Bera K, Singh S, Partovi S, Varadan V, Ahluwalia M, Madabhushi A, Tiwari P.

Sci Rep. 2018 Jan 8;8(1):7. doi: 10.1038/s41598-017-18310-0.

44.

Radiomics and radiogenomics in lung cancer: A review for the clinician.

Thawani R, McLane M, Beig N, Ghose S, Prasanna P, Velcheti V, Madabhushi A.

Lung Cancer. 2018 Jan;115:34-41. doi: 10.1016/j.lungcan.2017.10.015. Epub 2017 Nov 8. Review.

PMID:
29290259
45.

Prostate shapes on pre-treatment MRI between prostate cancer patients who do and do not undergo biochemical recurrence are different: Preliminary Findings.

Ghose S, Shiradkar R, Rusu M, Mitra J, Thawani R, Feldman M, Gupta AC, Purysko AS, Ponsky L, Madabhushi A.

Sci Rep. 2017 Nov 20;7(1):15829. doi: 10.1038/s41598-017-13443-8.

46.

An Image Analysis Resource for Cancer Research: PIIP-Pathology Image Informatics Platform for Visualization, Analysis, and Management.

Martel AL, Hosseinzadeh D, Senaras C, Zhou Y, Yazdanpanah A, Shojaii R, Patterson ES, Madabhushi A, Gurcan MN.

Cancer Res. 2017 Nov 1;77(21):e83-e86. doi: 10.1158/0008-5472.CAN-17-0323.

47.

Prediction of recurrence in early stage non-small cell lung cancer using computer extracted nuclear features from digital H&E images.

Wang X, Janowczyk A, Zhou Y, Thawani R, Fu P, Schalper K, Velcheti V, Madabhushi A.

Sci Rep. 2017 Oct 19;7(1):13543. doi: 10.1038/s41598-017-13773-7.

48.

Discriminative Scale Learning (DiScrn): Applications to Prostate Cancer Detection from MRI and Needle Biopsies.

Wang H, Viswanath S, Madabhushi A.

Sci Rep. 2017 Sep 28;7(1):12375. doi: 10.1038/s41598-017-12569-z.

49.

Reply.

Tiwari P, Madabhushi A.

AJNR Am J Neuroradiol. 2017 Nov;38(11):E94. doi: 10.3174/ajnr.A5366. Epub 2017 Aug 31. No abstract available.

50.

Co-Registration of ex vivo Surgical Histopathology and in vivo T2 weighted MRI of the Prostate via multi-scale spectral embedding representation.

Li L, Pahwa S, Penzias G, Rusu M, Gollamudi J, Viswanath S, Madabhushi A.

Sci Rep. 2017 Aug 18;7(1):8717. doi: 10.1038/s41598-017-08969-w.

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