CIVIL ENGINEERING 365 ALL ABOUT CIVIL ENGINEERING


  • 1.

    Cheng, M. L., Berger, M. F., Hyman, D. M. & Solit, D. B. Clinical tumour sequencing for precision oncology: time for a universal strategy. Nat. Rev. Cancer 18, 527–528 (2018).

    CAS 
    Article 

    Google Scholar
     

  • 2.

    Rusch, M. et al. Clinical cancer genomic profiling by three-platform sequencing of whole genome, whole exome and transcriptome. Nat. Commun. 9, 3962 (2018).

    Article 

    Google Scholar
     

  • 3.

    Kather, J. N., Halama, N. & Jaeger, D. Genomics and emerging biomarkers for immunotherapy of colorectal cancer. Semin. Cancer Biol. 52, 189–197 (2018).

    CAS 
    Article 

    Google Scholar
     

  • 4.

    Guinney, J. et al. The consensus molecular subtypes of colorectal cancer. Nat. Med. 21, 1350–1356 (2015).

    CAS 
    Article 

    Google Scholar
     

  • 5.

    Fontana, E., Eason, K., Cervantes, A., Salazar, R. & Sadanandam, A. Context matters—consensus molecular subtypes of colorectal cancer as biomarkers for clinical trials. Ann. Oncol. 30, 520–527 (2019).

    CAS 
    Article 

    Google Scholar
     

  • 6.

    Shia, J. et al. Morphological characterization of colorectal cancers in The Cancer Genome Atlas reveals distinct morphology–molecular associations: clinical and biological implications. Modern Pathol. 30, 599–609 (2017).

    CAS 
    Article 

    Google Scholar
     

  • 7.

    Greenson, J. K. et al. Pathologic predictors of microsatellite instability in colorectal cancer. Am. J. Surg. Pathol. 33, 126–133 (2009).

    Article 

    Google Scholar
     

  • 8.

    Le, D. T. et al. PD-1 blockade in tumors with mismatch-repair deficiency. N. Engl. J. Med. 372, 2509–2520 (2015).

    CAS 
    Article 

    Google Scholar
     

  • 9.

    Kather, J. N. et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat. Med. 25, 1054–1056 (2019).

    CAS 
    Article 

    Google Scholar
     

  • 10.

    Coudray, N. et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning. Nat. Med. 24, 1559–1567 (2018).

    CAS 
    Article 

    Google Scholar
     

  • 11.

    Sha, L. et al. Multi-field-of-view deep learning model predicts nonsmall cell lung cancer programmed death-ligand 1 status from whole-slide hematoxylin and eosin images. J. Pathol. Inform. 10, 24 (2019).

    Article 

    Google Scholar
     

  • 12.

    Schaumberg, A. J., Rubin, M. A. & Fuchs, T. J. H&E-stained whole slide image deep learning predicts SPOP mutation state in prostate cancer. Preprint at bioRxiv https://doi.org/10.1101/064279 (2018).

  • 13.

    Kather, J. N. et al. Deep learning detects virus presence in cancer histology. Preprint at bioRxiv https://doi.org/10.1101/690206 (2019).

  • 14.

    Zhang, H. et al. Predicting tumor mutational burden from liver cancer pathological images using convolutional neural network. In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 920–925 (Institute of Electrical and Electronics Engineers, 2019); https://doi.org/10.1109/BIBM47256.2019.8983139

  • 15.

    Campanella, G. et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25, 1301–1309 (2019).

    CAS 
    Article 

    Google Scholar
     

  • 16.

    Zhang, X., Zhou, X., Lin, M. & Sun, J. ShuffleNet: an extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 6848–6856 (Institute of Electrical and Electronics Engineers, 2018); https://doi.org/10.1109/CVPR.2018.00716

  • 17.

    Huang, G., Liu, Z., Van Der Maaten, L. & Weinberger, K. Q. Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 4700–4708 (Institute of Electrical and Electronics Engineers, 2017); https://doi.org/10.1109/CVPR.2017.243

  • 18.

    Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. & Wojna, Z. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2818–2826 (Institute of Electrical and Electronics Engineers, 2016); https://doi.org/10.1109/CVPR.2016.30

  • 19.

    He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 770–778 (Institute of Electrical and Electronics Engineers, 2016); https://doi.org/10.1109/CVPR.2016.90

  • 20.

    Srinidhi, C. L., Ciga, O. & Martel, A. L. Deep neural network models for computational histopathology: a survey. Preprint at https://arxiv.org/abs/1912.12378 (2019).

  • 21.

    Chen, P. C. et al. An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis. Nat. Med. 25, 1453–1457 (2019).

    CAS 
    Article 

    Google Scholar
     

  • 22.

    Muzny, D. M. et al. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012).

    CAS 
    Article 

    Google Scholar
     

  • 23.

    Fukamachi, H. et al. A subset of diffuse-type gastric cancer is susceptible to mTOR inhibitors and checkpoint inhibitors. J. Exp. Clin. Cancer Res. 38, 127 (2019).

    Article 

    Google Scholar
     

  • 24.

    The Cancer Genome Atlas Network et al. Comprehensive molecular profiling of lung adenocarcinoma. Nature 511, 543–550 (2014).

    Article 

    Google Scholar
     

  • 25.

    The Cancer Genome Atlas Networket al. Comprehensive molecular characterization of gastric adenocarcinoma. Nature 513, 202–209 (2014).

    Article 

    Google Scholar
     

  • 26.

    André, F. et al. Alpelisib for PIK3CA-mutated, hormone receptor-positive advanced breast cancer. N. Engl. J. Med. 380, 1929–1940 (2019).

    Article 

    Google Scholar
     

  • 27.

    The Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 490, 61–70 (2012).

  • 28.

    Xue, Z. et al. MAP3K1 and MAP2K4 mutations are associated with sensitivity to MEK inhibitors in multiple cancer models. Cell Res. 28, 719–729 (2018).

    CAS 
    Article 

    Google Scholar
     

  • 29.

    The Cancer Genome Atlas Network. Genomic classification of cutaneous melanoma. Cell 161, 1681–1696 (2015).

  • 30.

    The Cancer Genome Atlas Research Network. The molecular taxonomy of primary prostate cancer. Cell 163, 1011–1025 (2015).

  • 31.

    Cancer Genome Atlas Research Network. Integrated genomic characterization of pancreatic ductal adenocarcinoma. Cancer Cell 32, 185–203.e13 (2017).

  • 32.

    Hammerman, P. S. et al. Comprehensive genomic characterization of squamous cell lung cancers. Nature 489, 519–525 (2012).

    CAS 
    Article 

    Google Scholar
     

  • 33.

    The Cancer Genome Atlas Research Network. Comprehensive and integrative genomic characterization of hepatocellular carcinoma. Cell 169, 1327–1341.e23 (2017).

  • 34.

    Khalaf, A. M. et al. Role of Wnt/β-catenin signaling in hepatocellular carcinoma, pathogenesis, and clinical significance. J. Hepatocell. Carcinoma 5, 61–73 (2018).

    CAS 
    Article 

    Google Scholar
     

  • 35.

    Linehan, W. M. et al. Comprehensive molecular characterization of papillary renal-cell carcinoma. N. Engl. J. Med. 374, 135–145 (2016).

    Article 

    Google Scholar
     

  • 36.

    Creighton, C. J. et al. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 499, 43–49 (2013).

    CAS 
    Article 

    Google Scholar
     

  • 37.

    Davis, C. F. et al. The somatic genomic landscape of chromophobe renal cell carcinoma. Cancer Cell 26, 319–330 (2014).

    CAS 
    Article 

    Google Scholar
     

  • 38.

    The Cancer Genome Atlas Network. Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature 517, 576–582 (2015).

  • 39.

    Li, C., Egloff, A. M., Sen, M., Grandis, J. R. & Johnson, D. E. Caspase-8 mutations in head and neck cancer confer resistance to death receptor-mediated apoptosis and enhance migration, invasion, and tumor growth. Mol. Oncol. 8, 1220–1230 (2014).

    CAS 
    Article 

    Google Scholar
     

  • 40.

    Burk, R. D. et al. Integrated genomic and molecular characterization of cervical cancer. Nature 543, 378–384 (2017).

    CAS 
    Article 

    Google Scholar
     

  • 41.

    Thorsson, V. et al. The immune landscape of cancer. Immunity 48, 812–830.e14 (2018).

    CAS 
    Article 

    Google Scholar
     

  • 42.

    Liu, Y. et al. Comparative molecular analysis of gastrointestinal adenocarcinomas. Cancer Cell 33, 721–735.e8 (2018).

    CAS 
    Article 

    Google Scholar
     

  • 43.

    Macenko, M. et al. A method for normalizing histology slides for quantitative analysis. In 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro 1107–1110 (Institute of Electrical and Electronics Engineers, 2009); https://doi.org/10.1109/ISBI.2009.5193250

  • 44.

    Barresi, V., Bonetti, L. R. & Bettelli, S. KRAS, NRAS, BRAF mutations and high counts of poorly differentiated clusters of neoplastic cells in colorectal cancer: observational analysis of 175 cases. Pathology 47, 551–556 (2015).

    CAS 
    Article 

    Google Scholar
     

  • 45.

    Hoffmeister, M. et al. Statin use and survival after colorectal cancer: the importance of comprehensive confounder adjustment. J. Natl Cancer Inst. 107, djv045 (2015).

    Article 

    Google Scholar
     

  • 46.

    Brenner, H., Chang-Claude, J., Seiler, C. M. & Hoffmeister, M. Long-term risk of colorectal cancer after negative colonoscopy. J. Clin. Oncol. 29, 3761–3767 (2011).

    Article 

    Google Scholar
     

  • 47.

    Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).

    Article 

    Google Scholar
     

  • 48.

    Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal 6, pl1 (2013).

    Article 

    Google Scholar
     

  • 49.

    Bailey, M. H. et al. Comprehensive characterization of cancer driver genes and mutations. Cell 173, 371–385.e18 (2018).

    CAS 
    Article 

    Google Scholar
     

  • 50.

    Berger, A. C. et al. A comprehensive pan-cancer molecular study of gynecologic and breast cancers. Cancer Cell 33, 690–705.e9 (2018).

    CAS 
    Article 

    Google Scholar
     

  • 51.

    Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).

    Article 

    Google Scholar
     

  • 52.

    Bianconi, F., Kather, J. N. & Reyes-Aldasoro, C. C. Evaluation of colour pre-processing on patch-based classification of H&E-stained images. In European Congress on Digital Pathology (eds. Reyes-Aldasoro, C. et al.) 56–64 (Lecture Notes in Computer Science Volume 11435, Springer, 2019).



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