A principled machine learning framework improves accuracy of stage II colorectal cancer prognosis

npj Digital Medicine

Volumn 1, Issue 1, 2018, Pages

  Dimitriou, Neofytos ^a   Arandjelović, Ognjen ^a   Harrison, David J ^b   Caie, Peter D ^b  

^a UNIVERSITY OF ST ANDREWS   (United Kingdom)

^b UNIVERSITY OF ST ANDREWS   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DIAGNOSIS; DISEASE CONTROL; DISEASES; PATIENT TREATMENT;

'CURRENT; CANCER PATIENTS; CANCER PROGNOSIS; CLINICAL GUIDELINE; DATA DRIVEN; INHERENT LIMITATIONS; LEARNING FRAMEWORKS; MACHINE-LEARNING; PERFORMANCE; REPORTING SYSTEMS;

MACHINE LEARNING;

ADULT; AGED; ARTICLE; CANCER MORTALITY; CANCER PATIENT; CANCER PROGNOSIS; CANCER SURGERY; CANCER SURVIVAL; CLASSIFIER; COHORT ANALYSIS; COLORECTAL CANCER; COLORECTAL SURGERY; DEEP LEARNING; FEATURE EXTRACTION; FEMALE; HIGH RISK PATIENT; HUMAN; HUMAN TISSUE; K NEAREST NEIGHBOR; LOW RISK PATIENT; MAJOR CLINICAL STUDY; MALE; PRACTICE GUIDELINE; PRIORITY JOURNAL; RANDOM FOREST; SUPPORT VECTOR MACHINE; TUMOR DIFFERENTIATION;

EID: 85108534486     PISSN: None     EISSN: 23986352     Source Type: Journal    
DOI: 10.1038/s41746-018-0057-x     Document Type: Article

References (57)

1. Ferlay, J. et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in globocan 2012. Int. J. Cancer 136, E359–E386 (2015)
2. Langner, C. & Schneider, N. Prognostic stratification of colorectal cancer patients: current perspectives. Cancer Manag Res 291 (2014). 10.2147/cmar.s38827
3. Loughrey, M. B., Quirke, P. & Shepherd, N. A. Dataset for colorectal cancer histopathology reports. The Royal College of Pathologists 343, 1–47 (2014)
4. Brenner, H., Kloor, M. & Pox, C. P. Colorectal cancer. Lancet 383, 1490–1502 (2013)
5. Edge, S. B. & Compton, C. C. The American Joint Committee on cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM. Ann. Surg. Oncol. 17, 1471–1474 (2010)
6. Fleming, M., Ravula, S., Tatishchev, S. F. & Wang, H. L. Colorectal carcinoma: pathologic aspects. J. Gastrointest. Oncol. 3, 153–173 (2012)
7. Compton, C. C. Optimal pathologic staging: defining stage II disease. Clin. Cancer Res. 13, 6862s–6870s (2007)
8. Nauta, R., Stablein, D. M. & Holyoke, D. Survival of patients with stage b2 colon carcinoma. Arch. Surg. 124, 180 (1989)
9. Barone, C. Adjuvant chemotherapy of colon cancer current strategies. Eur. J. Cancer Suppl. 6, 60–63 (2008)
10. Lombardi, L. et al. Adjuvant colon cancer chemotherapy: where we are and where well go. Cancer Treat. Rev. 36, S34–S41 (2010)
11. Lea, D., Håland, S., Hagland, H. R. & Søreide, K. Accuracy of TNM staging in colorectal cancer: a review of current culprits, the modern role of morphology and stepping-stones for improvements in the molecular era. Scand. J. Gastroenterol. 49, 1153–1163 (2014)
12. Maguire, A. Controversies in the pathological assessment of colorectal cancer. World J. Gastroenterol. 20, 9850 (2014)
13. von Karsa, L. et al. European guidelines for quality assurance in colorectal cancer screening and diagnosis: overview and introduction to the full supplement publication. Endoscopy 45, 51–59 (2012)
14. Lai, Y.-H. et al. Tumour budding is a reproducible index for risk stratification of patients with stage II colon cancer. Colorectal Dis. 16, 259–264 (2014)
15. Lugli, A., Karamitopoulou, E. & Zlobec, I. Tumour budding: a promising parameter in colorectal cancer. Br. J. Cancer 106, 1713–1717 (2012)
16. Lin, M. et al. Intratumoral as well as peritumoral lymphatic vessel invasion correlates with lymph node metastasis and unfavourable outcome in colorectal cancer. Clin. & Exp. Metastas-. 27, 123–132 (2010)
17. Kojima, M. et al. Pathological diagnostic criterion of blood and lymphatic vessel invasion in colorectal cancer: a framework for developing an objective pathological diagnostic system using the Delphi method, from the Pathology Working Group of the Japanese Society for Cancer of the Colon and Rectum. J. Clin. Pathol. 66, 551–558 (2013)
18. Zaorsky, N. G., Patil, D., Freedman, G. M. & Tuluc, M. Differentiating lymphovascular invasion from retraction artifact on histological specimen of breast carcinoma and their implications on prognosis. J. Breast Cancer 15, 478 (2012)
19. Korbar, B. et al. Deep-learning for classification of colorectal polyps on whole-slide images. Clin Orthop Relat Res. abs/1703.01550 (2017)
20. Vandenberghe, M. E. et al. Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer. Sci. Rep. 7, 45938 (2017)
21. Wang, X. et al. Prediction of recurrence in early stage non-small cell lung cancer using computer extracted nuclear features from digital H&E images. Sci Rep. 7 (2017). 10.1038/s41598-017-13773-7
22. Rao, A. et al. Identification of histological correlates of overall survival in lower grade gliomas using a bag-of-words paradigm: a preliminary analysis based on hematoxylin & eosin stained slides from the lower grade glioma cohort of the cancer genome atlas. J. Pathol. Inform. 8, 9 (2017)
23. Galon, J. et al. Towards the introduction of the ‘immunoscore’ in the classification of malignant tumours. J. Pathol. 232, 199–209 (2013)
24. Thakur, S. S. et al. The use of automated ki67 analysis to predict oncotype DX risk-of-recurrence categories in early-stage breast cancer. PLoS One 13, e0188983 (2018)
25. Bankhead, P. et al. Integrated tumor identification and automated scoring minimizes pathologist involvement and provides new insights to key biomarkers in breast cancer. Lab. Invest. 98, 15–26 (2017)
26. Caie, P. D., Zhou, Y., Turnbull, A. K., Oniscu, A. & Harrison, D. J. Novel histopathologic feature identified through image analysis augments stage II colorectal cancer clinical reporting. Oncotarget 7, 44381–44394 (2016)
27. Dunne, K., Cunningham, P. & Azuaje, F. Solutions to instability problems with sequential wrapper-based approaches to feature selection. J. Mach. Learn. Res., 1–22 (2002)
28. Horcic, M. et al. Tumor budding score based on 10 high-power fields is a promising basis for a standardized prognostic scoring system in stage ii colorectal cancer. Hum. Pathol. 44, 697–705 (2013)
29. Cacchi, C. et al. Clinical significance of lymph vessel density in T3 colorectal carcinoma. Int. J. Colorectal Dis. 27, 721–726 (2012)
30. Sugai, T. et al. Vascular invasion and stromal s100a4 expression at the invasive front of colorectal cancer are novel determinants and tumor prognostic markers. J. Cancer 8, 1552–1561 (2017)
31. Heindl, A., Nawaz, S. & Yuan, Y. Mapping spatial heterogeneity in the tumor microenvironment: a new era for digital pathology. Lab. Invest. 95, 377–384 (2015)
32. Isella, C. et al. Stromal contribution to the colorectal cancer transcriptome. Nat. Genet. 47, 312–319 (2015)
33. Bhangu, A. et al. Epithelial mesenchymal transition in colorectal cancer: seminal role in promoting disease progression and resistance to neoadjuvant therapy. Surg. Oncol. 21, 316–323 (2012)
34. Nakashima, Y. et al. Nuclear atypia grading score is a useful prognostic factor in papillary gastric adenocarcinoma. Histopathology 59, 841–849 (2011)
35. Eynard, H. G., Soria, E. A., Cuestas, E., Rovasio, R. A. & Eynard, A. R. Assessment of colorectal cancer prognosis through nuclear morphometry. J. Surg. Res. 154, 345–348 (2009)
36. Barresi, V., Bonetti, L. R., Ieni, A., Caruso, R. A. & Tuccari, G. Poorly differentiated clusters: clinical impact in colorectal cancer. Clin. Colorectal Cancer 16, 9–15 (2017)
37. Hynes, S. O. et al. Back to the future: routine morphological assessment of the tumour microenvironment is prognostic in stage II/III colon cancer in a large population-based study. Histopathology 71, 12–26 (2017)
38. Rajaganeshan, R. et al. The influence of invasive growth pattern and microvessel density on prognosis in colorectal cancer and colorectal liver metastases. Br. J. Cancer 96, 1112–1117 (2007)
39. Arandjelović, O. A new framework for interpreting the outcomes of imperfectly blinded controlled clinical trials. PLoS One 7, e48984 (2012)
40. Osuala, R. & Arandjelović, O. Visualization of patient specific disease risk. In Proc. IEEE International Conference on Biomedical and Health Informatics 241–244, Orlando, Florida, USA (2017)
41. Li, J. & Arandjelović, O. Intuitive and interpretable visual communication of a complex statistical model of disease progression and risk. In Proc. International Conference of the IEEE Engineering in Medicine and Biology Society 4199–4202, (2017)
42. O’Neil, M. & Damjanov, I. Histopathology of colorectal cancer after neoadjuvant chemoradiation therapy. Open Pathol. J. 3, 91–98 (2009)
43. Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th International Joint Conference on Artificial Intelligence—Volume 2, IJCAI'95, 1137–1143 (Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1995)
44. Barracliffe, L., Arandjelović, O. & Humphris, G. Can machine learning predict healthcare professionals’ responses to patient emotions? In Proc. International Conference on Bioinformatics and Computational Biology 101–106 (Honolulu, Hawaii, USA 2017)
45. Karsten, J. & Arandjelović, O. Automatic vertebrae localization from CT scans using volumetric descriptors. In Proc. International Conference of the IEEE Engineering in Medicine and Biology Society 576–579, (2017)
46. Nigri, E. & Arandjelović, O. Light curve analysis from Kepler spacecraft collected data. In Proc. ACM International Conference on Multimedia Retrieval 93–98, Bucharest, Romania (2017)
47. Beykikhoshk, A., Arandjelović, O., Phung, D., Venkatesh, S. & Caelli, T. Using Twitter to learn about the autism community. Social. Netw. Anal. Min. 5, 5–22 (2015)
48. Birkett, C., Arandjelović, O. & Humphris, G. Towards objective and reproducible study of patient-doctor interaction: automatic text analysis based VR-CoDES annotation of consultation transcripts. In Proc. International Conference of the IEEE Engineering in Medicine and Biology Society 2638–2641, (2017)
49. Ling, C. X., Huang, J. & Zhang, H. Auc: A statistically consistent and more discriminating measure than accuracy. In Proceedings of the 18th International Joint Conference on Artificial Intelligence, IJCAI'03, 519–524 (Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2003)
50. Bergstra, J. & Bengio, Y. Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281–305 (2012)
51. Bergstra, J. S., Bardenet, R., Bengio, Y. & Kégl, B. Algorithms for hyper-parameter optimization. In Shawe-Taylor, J., Zemel, R. S., Bartlett, P. L., Pereira, F. & Weinberger, K. Q. (eds.) Advances in Neural Information Processing Systems 24, 2546–2554 (Curran Associates, Inc., 2011)
52. Hutter, F., Hoos, H. H. & Leyton-Brown, K. Sequential model-based optimization for general algorithm configuration. In Proceedings of the 5th International Conference on Learning and Intelligent Optimization, 507–523 (Springer-Verlag, 2011). 10.1007/978-3-642-25566-3_40
53. Guyon, I. & Elisseeff, A. An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)
54. Chandrashekar, G. & Sahin, F. A survey on feature selection methods. Comput. & Electr. Eng. 40, 16–28 (2014)
55. Gurcan, M. N. et al. Histopathological image analysis: a review. Ieee. Rev. Biomed. Eng. 2, 147–171 (2009)
56. Pudil, P., Novovičová, J. & Kittler, J. Floating search methods in feature selection. Pattern Recognit. Lett. 15, 1119–1125 (1994)
57. Jain, A. & Zongker, D. Feature selection: evaluation, application, and small sample performance. IEEE. Trans. Pattern Anal. Mach. Intell. 19, 153–158 (1997)