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[No title available]

AJNR. American journal of neuroradiology

Tiwari P, Madabhushi A.
PMID: 28860218
AJNR Am J Neuroradiol. 2017 Nov;38(11):E94. doi: 10.3174/ajnr.A5366. Epub 2017 Aug 31.

No abstract available.

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Tiwari P, Madabhushi A. . AJNR Am J Neuroradiol. 2017;38(11):E94doi: 10.3174/ajnr.A5366.
Tiwari, P., & Madabhushi, A. (2017). . AJNR. American journal of neuroradiology, 38(11), E94. https://doi.org/10.3174/ajnr.A5366
Tiwari, P, and Madabhushi, A. "." AJNR. American journal of neuroradiology vol. 38,11 (2017): E94. doi: https://doi.org/10.3174/ajnr.A5366
Tiwari P, Madabhushi A. . AJNR Am J Neuroradiol. 2017 Nov;38(11):E94. doi: 10.3174/ajnr.A5366. Epub 2017 Aug 31. PMID: 28860218; PMCID: PMC7963596.
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Association of computerized texture features on MRI with early treatment response following laser ablation for neuropathic cancer pain: preliminary findings.

Journal of medical imaging (Bellingham, Wash.)

Tiwari P, Danish SF, Jiang B, Madabhushi A.
PMID: 26870745
J Med Imaging (Bellingham). 2015 Oct;2(4):041008. doi: 10.1117/1.JMI.2.4.041008. Epub 2015 Sep 25.

Laser interstitial thermal therapy (LITT) has recently emerged as a new treatment modality for cancer pain management that targets the cingulum (pain center in the brain) and has shown promise over radio frequency (RF)-based ablation, due to magnetic resonance...

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Tiwari P, Danish SF, Jiang B, et al. Association of computerized texture features on MRI with early treatment response following laser ablation for neuropathic cancer pain: preliminary findings. J Med Imaging (Bellingham). 2015;2(4):041008doi: 10.1117/1.JMI.2.4.041008.
Tiwari, P., Danish, S. F., Jiang, B., & Madabhushi, A. (2015). Association of computerized texture features on MRI with early treatment response following laser ablation for neuropathic cancer pain: preliminary findings. Journal of medical imaging (Bellingham, Wash.), 2(4), 041008. https://doi.org/10.1117/1.JMI.2.4.041008
Tiwari, Pallavi, et al. "Association of computerized texture features on MRI with early treatment response following laser ablation for neuropathic cancer pain: preliminary findings." Journal of medical imaging (Bellingham, Wash.) vol. 2,4 (2015): 041008. doi: https://doi.org/10.1117/1.JMI.2.4.041008
Tiwari P, Danish SF, Jiang B, Madabhushi A. Association of computerized texture features on MRI with early treatment response following laser ablation for neuropathic cancer pain: preliminary findings. J Med Imaging (Bellingham). 2015 Oct;2(4):041008. doi: 10.1117/1.JMI.2.4.041008. Epub 2015 Sep 25. PMID: 26870745; PMCID: PMC4748210.
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Identifying MRI markers to evaluate early treatment related changes post laser ablation for cancer pain management.

Proceedings of SPIE--the International Society for Optical Engineering

Tiwari P, Danish S, Madabhushi A.
PMID: 25075271
Proc SPIE Int Soc Opt Eng. 2014 Mar 12;9036:90362L. doi: 10.1117/12.2043729.

Laser interstitial thermal therapy (LITT) has recently emerged as a new treatment modality for cancer pain management that targets the cingulum (pain center in the brain), and has shown promise over radio-frequency (RF) based ablation which is reported to...

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Tiwari P, Danish S, Madabhushi A. Identifying MRI markers to evaluate early treatment related changes post laser ablation for cancer pain management. Proc SPIE Int Soc Opt Eng. 2014;9036:90362Ldoi: 10.1117/12.2043729.
Tiwari, P., Danish, S., & Madabhushi, A. (2014). Identifying MRI markers to evaluate early treatment related changes post laser ablation for cancer pain management. Proceedings of SPIE--the International Society for Optical Engineering, 903690362L. https://doi.org/10.1117/12.2043729
Tiwari, Pallavi, et al. "Identifying MRI markers to evaluate early treatment related changes post laser ablation for cancer pain management." Proceedings of SPIE--the International Society for Optical Engineering vol. 9036 (2014): 90362L. doi: https://doi.org/10.1117/12.2043729
Tiwari P, Danish S, Madabhushi A. Identifying MRI markers to evaluate early treatment related changes post laser ablation for cancer pain management. Proc SPIE Int Soc Opt Eng. 2014 Mar 12;9036:90362L. doi: 10.1117/12.2043729. PMID: 25075271; PMCID: PMC4112118.
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The revolving door for AI and pathologists-docendo discimus?.

Journal of medical artificial intelligence

Van Es SL, Madabhushi A.
PMID: 31372599
J Med Artif Intell. 2019 Jun;2. doi: 10.21037/jmai.2019.05.02. Epub 2019 Jun 11.

No abstract available.

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Van Es SL, Madabhushi A. The revolving door for AI and pathologists-docendo discimus?. J Med Artif Intell. 2019;2doi: 10.21037/jmai.2019.05.02.
Van Es, S. L., & Madabhushi, A. (2019). The revolving door for AI and pathologists-docendo discimus?. Journal of medical artificial intelligence, 2. https://doi.org/10.21037/jmai.2019.05.02
Van Es, Simone L, and Madabhushi, Anant. "The revolving door for AI and pathologists-docendo discimus?." Journal of medical artificial intelligence vol. 2 (2019). doi: https://doi.org/10.21037/jmai.2019.05.02
Van Es SL, Madabhushi A. The revolving door for AI and pathologists-docendo discimus?. J Med Artif Intell. 2019 Jun;2. doi: 10.21037/jmai.2019.05.02. Epub 2019 Jun 11. PMID: 31372599; PMCID: PMC6675021.
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Training a cell-level classifier for detecting basal-cell carcinoma by combining human visual attention maps with low-level handcrafted features.

Journal of medical imaging (Bellingham, Wash.)

Corredor G, Whitney J, Arias V, Madabhushi A, Romero E.
PMID: 28382314
J Med Imaging (Bellingham). 2017 Apr;4(2):021105. doi: 10.1117/1.JMI.4.2.021105. Epub 2017 Mar 11.

Computational histomorphometric approaches typically use low-level image features for building machine learning classifiers. However, these approaches usually ignore high-level expert knowledge. A computational model (M_im) combines low-, mid-, and high-level image information to predict the likelihood of cancer in...

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Corredor G, Whitney J, Arias V, et al. Training a cell-level classifier for detecting basal-cell carcinoma by combining human visual attention maps with low-level handcrafted features. J Med Imaging (Bellingham). 2017;4(2):021105doi: 10.1117/1.JMI.4.2.021105.
Corredor, G., Whitney, J., Arias, V., Madabhushi, A., & Romero, E. (2017). Training a cell-level classifier for detecting basal-cell carcinoma by combining human visual attention maps with low-level handcrafted features. Journal of medical imaging (Bellingham, Wash.), 4(2), 021105. https://doi.org/10.1117/1.JMI.4.2.021105
Corredor, Germán, et al. "Training a cell-level classifier for detecting basal-cell carcinoma by combining human visual attention maps with low-level handcrafted features." Journal of medical imaging (Bellingham, Wash.) vol. 4,2 (2017): 021105. doi: https://doi.org/10.1117/1.JMI.4.2.021105
Corredor G, Whitney J, Arias V, Madabhushi A, Romero E. Training a cell-level classifier for detecting basal-cell carcinoma by combining human visual attention maps with low-level handcrafted features. J Med Imaging (Bellingham). 2017 Apr;4(2):021105. doi: 10.1117/1.JMI.4.2.021105. Epub 2017 Mar 11. PMID: 28382314; PMCID: PMC5363808.
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Radiomics-based convolutional neural network for brain tumor segmentation on multiparametric magnetic resonance imaging.

Journal of medical imaging (Bellingham, Wash.)

Prasanna P, Karnawat A, Ismail M, Madabhushi A, Tiwari P.
PMID: 31093517
J Med Imaging (Bellingham). 2019 Apr;6(2):024005. doi: 10.1117/1.JMI.6.2.024005. Epub 2019 May 07.

Accurate segmentation of gliomas on routine magnetic resonance image (MRI) scans plays an important role in disease diagnosis, prognosis, and patient treatment planning. We present a fully automated approach, radiomics-based convolutional neural network (RadCNN), for segmenting both high- and...

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Prasanna P, Karnawat A, Ismail M, et al. Radiomics-based convolutional neural network for brain tumor segmentation on multiparametric magnetic resonance imaging. J Med Imaging (Bellingham). 2019;6(2):024005doi: 10.1117/1.JMI.6.2.024005.
Prasanna, P., Karnawat, A., Ismail, M., Madabhushi, A., & Tiwari, P. (2019). Radiomics-based convolutional neural network for brain tumor segmentation on multiparametric magnetic resonance imaging. Journal of medical imaging (Bellingham, Wash.), 6(2), 024005. https://doi.org/10.1117/1.JMI.6.2.024005
Prasanna, Prateek, et al. "Radiomics-based convolutional neural network for brain tumor segmentation on multiparametric magnetic resonance imaging." Journal of medical imaging (Bellingham, Wash.) vol. 6,2 (2019): 024005. doi: https://doi.org/10.1117/1.JMI.6.2.024005
Prasanna P, Karnawat A, Ismail M, Madabhushi A, Tiwari P. Radiomics-based convolutional neural network for brain tumor segmentation on multiparametric magnetic resonance imaging. J Med Imaging (Bellingham). 2019 Apr;6(2):024005. doi: 10.1117/1.JMI.6.2.024005. Epub 2019 May 07. PMID: 31093517; PMCID: PMC6503346.
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Collagen fiber orientation disorder from H&E images is prognostic for early stage breast cancer: clinical trial validation.

NPJ breast cancer

Li H, Bera K, Toro P, Fu P, Zhang Z, Lu C, Feldman M, Ganesan S, Goldstein LJ, Davidson NE, Glasgow A, Harbhajanka A, Gilmore H, Madabhushi A.
PMID: 34362928
NPJ Breast Cancer. 2021 Aug 06;7(1):104. doi: 10.1038/s41523-021-00310-z.

Collagen fiber organization has been found to be implicated in breast cancer prognosis. In this study, we evaluated whether computerized features of Collagen Fiber Orientation Disorder in Tumor-associated Stroma (CFOD-TS) on Hematoxylin & Eosin (H&E) slide images were prognostic...

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Li H, Bera K, Toro P, et al. Collagen fiber orientation disorder from H&E images is prognostic for early stage breast cancer: clinical trial validation. NPJ Breast Cancer. 2021;7(1):104doi: 10.1038/s41523-021-00310-z.
Li, H., Bera, K., Toro, P., Fu, P., Zhang, Z., Lu, C., Feldman, M., Ganesan, S., Goldstein, L. J., Davidson, N. E., Glasgow, A., Harbhajanka, A., Gilmore, H., & Madabhushi, A. (2021). Collagen fiber orientation disorder from H&E images is prognostic for early stage breast cancer: clinical trial validation. NPJ breast cancer, 7(1), 104. https://doi.org/10.1038/s41523-021-00310-z
Li, Haojia, et al. "Collagen fiber orientation disorder from H&E images is prognostic for early stage breast cancer: clinical trial validation." NPJ breast cancer vol. 7,1 (2021): 104. doi: https://doi.org/10.1038/s41523-021-00310-z
Li H, Bera K, Toro P, Fu P, Zhang Z, Lu C, Feldman M, Ganesan S, Goldstein LJ, Davidson NE, Glasgow A, Harbhajanka A, Gilmore H, Madabhushi A. Collagen fiber orientation disorder from H&E images is prognostic for early stage breast cancer: clinical trial validation. NPJ Breast Cancer. 2021 Aug 06;7(1):104. doi: 10.1038/s41523-021-00310-z. PMID: 34362928; PMCID: PMC8346522.
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Heterogeneity in treatment effects across diverse populations.

Pharmaceutical statistics

Nugent BM, Madabushi R, Buch B, Peiris V, Crentsil V, Miller VM, Bull J, R Jenkins M.
PMID: 34396690
Pharm Stat. 2021 Sep;20(5):929-938. doi: 10.1002/pst.2161. Epub 2021 Aug 16.

Differences in patient characteristics, including age, sex, and race influence the safety and effectiveness of drugs, biologic products, and medical devices. Here we provide a summary of the topics discussed during the opening panel at the 2018 Johns Hopkins...

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Nugent BM, Madabushi R, Buch B, et al. Heterogeneity in treatment effects across diverse populations. Pharm Stat. 2021;20(5):929-938doi: 10.1002/pst.2161.
Nugent, B. M., Madabushi, R., Buch, B., Peiris, V., Crentsil, V., Miller, V. M., Bull, J., & R Jenkins, M. (2021). Heterogeneity in treatment effects across diverse populations. Pharmaceutical statistics, 20(5), 929-938. https://doi.org/10.1002/pst.2161
Nugent, Bridget M, et al. "Heterogeneity in treatment effects across diverse populations." Pharmaceutical statistics vol. 20,5 (2021): 929-938. doi: https://doi.org/10.1002/pst.2161
Nugent BM, Madabushi R, Buch B, Peiris V, Crentsil V, Miller VM, Bull J, R Jenkins M. Heterogeneity in treatment effects across diverse populations. Pharm Stat. 2021 Sep;20(5):929-938. doi: 10.1002/pst.2161. Epub 2021 Aug 16. PMID: 34396690.
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Computer extracted features of nuclear morphology in hematoxylin and eosin images distinguish Stage II and IV colon tumors.

The Journal of pathology

Kumar N, Verma R, Chen C, Lu C, Fu P, Willis J, Madabhushi A.
PMID: 35007352
J Pathol. 2022 Jan 10; doi: 10.1002/path.5864. Epub 2022 Jan 10.

We assessed the utility of quantitative features of colon cancer nuclei, extracted from digitized hematoxylin and eosin-stained whole slide images (WSIs), to distinguish between Stage II from Stage IV colon cancers. Our discovery cohort comprised 100 Stage II and...

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Kumar N, Verma R, Chen C, et al. Computer extracted features of nuclear morphology in hematoxylin and eosin images distinguish Stage II and IV colon tumors. J Pathol. 2022;doi: 10.1002/path.5864.
Kumar, N., Verma, R., Chen, C., Lu, C., Fu, P., Willis, J., & Madabhushi, A. (2022). Computer extracted features of nuclear morphology in hematoxylin and eosin images distinguish Stage II and IV colon tumors. The Journal of pathology, . https://doi.org/10.1002/path.5864
Kumar, Neeraj, et al. "Computer extracted features of nuclear morphology in hematoxylin and eosin images distinguish Stage II and IV colon tumors." The Journal of pathology vol. (2022). doi: https://doi.org/10.1002/path.5864
Kumar N, Verma R, Chen C, Lu C, Fu P, Willis J, Madabhushi A. Computer extracted features of nuclear morphology in hematoxylin and eosin images distinguish Stage II and IV colon tumors. J Pathol. 2022 Jan 10; doi: 10.1002/path.5864. Epub 2022 Jan 10. PMID: 35007352.
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New Radiomic Markers of Pulmonary Vein Morphology Associated With Post-Ablation Recurrence of Atrial Fibrillation.

IEEE journal of translational engineering in health and medicine

Labarbera MA, Atta-Fosu T, Feeny AK, Firouznia M, Mchale M, Cantlay C, Roach T, Axtell A, Schoenhagen P, Barnard J, Smith JD, Van Wagoner DR, Madabhushi A, Chung MK.
PMID: 34976444
IEEE J Transl Eng Health Med. 2021 Dec 09;10:1800209. doi: 10.1109/JTEHM.2021.3134160. eCollection 2022.

No abstract available.

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Labarbera MA, Atta-Fosu T, Feeny AK, et al. New Radiomic Markers of Pulmonary Vein Morphology Associated With Post-Ablation Recurrence of Atrial Fibrillation. IEEE J Transl Eng Health Med. 2021;10:1800209doi: 10.1109/JTEHM.2021.3134160.
Labarbera, M. A., Atta-Fosu, T., Feeny, A. K., Firouznia, M., Mchale, M., Cantlay, C., Roach, T., Axtell, A., Schoenhagen, P., Barnard, J., Smith, J. D., Van Wagoner, D. R., Madabhushi, A., & Chung, M. K. (2022). New Radiomic Markers of Pulmonary Vein Morphology Associated With Post-Ablation Recurrence of Atrial Fibrillation. IEEE journal of translational engineering in health and medicine, 101800209. https://doi.org/10.1109/JTEHM.2021.3134160
Labarbera, Michael A, et al. "New Radiomic Markers of Pulmonary Vein Morphology Associated With Post-Ablation Recurrence of Atrial Fibrillation." IEEE journal of translational engineering in health and medicine vol. 10 (2022): 1800209. doi: https://doi.org/10.1109/JTEHM.2021.3134160
Labarbera MA, Atta-Fosu T, Feeny AK, Firouznia M, Mchale M, Cantlay C, Roach T, Axtell A, Schoenhagen P, Barnard J, Smith JD, Van Wagoner DR, Madabhushi A, Chung MK. New Radiomic Markers of Pulmonary Vein Morphology Associated With Post-Ablation Recurrence of Atrial Fibrillation. IEEE J Transl Eng Health Med. 2021 Dec 09;10:1800209. doi: 10.1109/JTEHM.2021.3134160. eCollection 2022. PMID: 34976444; PMCID: PMC8716081.
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Prostate cancer risk stratification via non-destructive 3D pathology with deep learning-assisted gland analysis.

Cancer research

Xie W, Reder NP, Koyuncu CF, Leo P, Hawley S, Huang H, Mao C, Postupna N, Kang S, Serafin R, Gao G, Han Q, Bishop KW, Barner LA, Fu P, Wright JL, Keene CD, Vaughan JC, Janowczyk A, Glaser AK, Madabhushi A, True LD, Liu JT.
PMID: 34853071
Cancer Res. 2021 Dec 01; doi: 10.1158/0008-5472.CAN-21-2843. Epub 2021 Dec 01.

Prostate cancer treatment planning is largely dependent upon examination of core-needle biopsies. The microscopic architecture of the prostate glands forms the basis for prognostic grading by pathologists. Interpretation of these convoluted 3D glandular structures via visual inspection of a...

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Xie W, Reder NP, Koyuncu CF, et al. Prostate cancer risk stratification via non-destructive 3D pathology with deep learning-assisted gland analysis. Cancer Res. 2021;doi: 10.1158/0008-5472.CAN-21-2843.
Xie, W., Reder, N. P., Koyuncu, C. F., Leo, P., Hawley, S., Huang, H., Mao, C., Postupna, N., Kang, S., Serafin, R., Gao, G., Han, Q., Bishop, K. W., Barner, L. A., Fu, P., Wright, J. L., Keene, C. D., Vaughan, J. C., Janowczyk, A., Glaser, A. K., Madabhushi, A., True, L. D., & Liu, J. T. (2021). Prostate cancer risk stratification via non-destructive 3D pathology with deep learning-assisted gland analysis. Cancer research, . https://doi.org/10.1158/0008-5472.CAN-21-2843
Xie, Weisi, et al. "Prostate cancer risk stratification via non-destructive 3D pathology with deep learning-assisted gland analysis." Cancer research vol. (2021). doi: https://doi.org/10.1158/0008-5472.CAN-21-2843
Xie W, Reder NP, Koyuncu CF, Leo P, Hawley S, Huang H, Mao C, Postupna N, Kang S, Serafin R, Gao G, Han Q, Bishop KW, Barner LA, Fu P, Wright JL, Keene CD, Vaughan JC, Janowczyk A, Glaser AK, Madabhushi A, True LD, Liu JT. Prostate cancer risk stratification via non-destructive 3D pathology with deep learning-assisted gland analysis. Cancer Res. 2021 Dec 01; doi: 10.1158/0008-5472.CAN-21-2843. Epub 2021 Dec 01. PMID: 34853071.
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A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans.

Cancers

Alilou M, Prasanna P, Bera K, Gupta A, Rajiah P, Yang M, Jacono F, Velcheti V, Gilkeson R, Linden P, Madabhushi A.
PMID: 34205005
Cancers (Basel). 2021 Jun 03;13(11). doi: 10.3390/cancers13112781.

The aim of this study is to evaluate whether NIS radiomics can distinguish lung adenocarcinomas from granulomas on non-contrast CT scans, and also to improve the performance of Lung-RADS by reclassifying benign nodules that were initially assessed as suspicious....

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Alilou M, Prasanna P, Bera K, et al. A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans. Cancers (Basel). 2021;13(11)doi: 10.3390/cancers13112781.
Alilou, M., Prasanna, P., Bera, K., Gupta, A., Rajiah, P., Yang, M., Jacono, F., Velcheti, V., Gilkeson, R., Linden, P., & Madabhushi, A. (2021). A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans. Cancers, 13(11), . https://doi.org/10.3390/cancers13112781
Alilou, Mehdi, et al. "A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans." Cancers vol. 13,11 (2021). doi: https://doi.org/10.3390/cancers13112781
Alilou M, Prasanna P, Bera K, Gupta A, Rajiah P, Yang M, Jacono F, Velcheti V, Gilkeson R, Linden P, Madabhushi A. A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans. Cancers (Basel). 2021 Jun 03;13(11). doi: 10.3390/cancers13112781. PMID: 34205005; PMCID: PMC8199879.
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