Novel histopathologic feature identified through image analysis augments stage II colorectal cancer clinical reporting

Oncotarget

Volumn 7, Issue 28, 2016, Pages 44381-44394

  Caie, Peter D ^a,b   Zhou, Ying ^c   Turnbull, Arran K ^c   Oniscu, Anca ^a,b   Harrison, David J ^a,b  

^a UNIVERSITY OF ST ANDREWS   (United Kingdom)

^b ROYAL INFIRMARY OF EDINBURGH   (United Kingdom)

^c UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

Big data; Data mining; Digital pathology; Poorly differentiated clusters; Tumor buds

Indexed keywords

AGED; ALGORITHM; ARTICLE; CANCER PROGNOSIS; CANCER STAGING; CLINICAL FEATURE; COHORT ANALYSIS; COLORECTAL CANCER; CONTROLLED STUDY; DISEASE ASSOCIATION; FEMALE; HISTOPATHOLOGY; HUMAN; IMAGE ANALYSIS; IMAGE ANALYSIS ALGORITHM; MAJOR CLINICAL STUDY; MALE; PROCESS DEVELOPMENT; SENSITIVITY AND SPECIFICITY; TUMOR DIFFERENTIATION; VALIDATION PROCESS; WORKFLOW; COLORECTAL TUMOR; DATA MINING; PATHOLOGY; PROGNOSIS; SURVIVAL ANALYSIS; VERY ELDERLY;

AGED; AGED, 80 AND OVER; COLORECTAL NEOPLASMS; DATA MINING; FEMALE; HUMANS; MALE; NEOPLASM STAGING; PROGNOSIS; SURVIVAL ANALYSIS;

EID: 84978771247     PISSN: None     EISSN: 19492553     Source Type: Journal    
DOI: 10.18632/oncotarget.10053     Document Type: Article

References (35)

1. Beck AH, Sangoi AR, Leung S, Marinelli RJ, Nielsen TO, van de Vijver MJ, West RB, van de Rijn M, Koller D. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci Transl Med. 2011; 3:108ra113.
2. Nawaz S, Heindl A, Koelble K, Yuan Y. Beyond immune density: critical role of spatial heterogeneity in estrogen receptor-negative breast cancer. Mod Pathol. 2015; 28:766-777.
3. Caie PD, Turnbull AK, Farrington SM, Oniscu A, Harrison DJ. Quantification of tumour budding, lymphatic vessel density and invasion through image analysis in colorectal cancer. J Transl Med. 2014; 12:156.
4. Ueno H, Mochizuki H, Akagi Y, Kusumi T, Yamada K, Ikegami M, Kawachi H, Kameoka S, Ohkura Y, Masaki T, Kushima R, Takahashi K, Ajioka Y, et al. Optimal Colorectal Cancer Staging Criteria in TNM Classification. J Clin Oncol. 2012.
5. Compton CC. Colorectal carcinoma: diagnostic, prognostic, and molecular features. Mod Pathol. 2003; 16:376-388.
6. Stoffel EM, Boland CR. Genetics and Genetic Testing in Hereditary Colorectal Cancer. Gastroenterology. 2015.
7. Nagtegaal ID, Quirke P, Schmoll HJ. Has the new TNM classification for colorectal cancer improved care? Nat Rev Clin Oncol.2012; 9:119-123.
8. Compton CC. Optimal pathologic staging: defining stage II disease. Clin Cancer Res. 2007; 13:6862s-6870s.
9. Morris EJ, Maughan NJ, Forman D, Quirke P. Who to treat with adjuvant therapy in Dukes B/stage II colorectal cancer? The need for high quality pathology. Gut. 2007; 56:1419-1425.
10. Jass JR, Love SB, Northover JM. A new prognostic classification of rectal cancer. Lancet. 1987; 1:1303-1306.
11. Ueno H, Hase K, Hashiguchi Y, Shimazaki H, Tanaka M, Miyake O, Masaki T, Shimada Y, Kinugasa Y, Mori Y, Kishimoto M, Kameoka S, Sato Y, et al. Site-specific tumor grading system in colorectal cancer: multicenter pathologic review of the value of quantifying poorly differentiated clusters. Am J Surg Pathol. 2014; 38:197-204.
12. Roxburgh CS, McMillan DC, Richards CH, Atwan M, Anderson JH, Harvey T, Horgan PG, Foulis AK. The clinical utility of the combination of T stage and venous invasion to predict survival in patients undergoing surgery for colorectal cancer. Ann Surg. 2014; 259:1156-1165.
13. Deans GT, Heatley M, Anderson N, Patterson CC, Rowlands BJ, Parks TG, Spence RA. Jass' classification revisited. J Am Coll Surg. 1994; 179:11-17.
14. Puppa G, Senore C, Sheahan K, Vieth M, Lugli A, Zlobec I, Pecori S, Wang LM, Langner C, Mitomi H, Nakamura T, Watanabe M, Ueno H, et al. Diagnostic reproducibility of tumour budding in colorectal cancer: a multicentre, multinational study using virtual microscopy. Histopathology. 2012; 61:562-575.
15. Kingston EF, Goulding H, Bateman AC. Vascular invasion is underrecognized in colorectal cancer using conventional hematoxylin and eosin staining. Dis Colon Rectum. 2007; 50:1867-1872.
16. Lugli A, Karamitopoulou E, Zlobec I. Tumour budding: a promising parameter in colorectal cancer. Br J Cancer. 2012; 106:1713-1717.
17. Ueno H, Murphy J, Jass JR, Mochizuki H, Talbot IC. Tumour 'budding' as an index to estimate the potential of aggressiveness in rectal cancer. Histopathology. 2002; 40:127-132.
18. Kim JW, Shin MK, Kim BC. Clinicopathologic impacts of poorly differentiated cluster-based grading system in colorectal carcinoma. J Korean Med Sc. 2015; 30:16-23.
19. Barresi V, Reggiani Bonetti L, Vitarelli E, Di Gregorio C, Ponz de Leon M, Barresi G. Immunohistochemical assessment of lymphovascular invasion in stage I colorectal carcinoma: prognostic relevance and correlation with nodal micrometastases. Am J Surg Pathol. 2012; 36:66-72.
20. Matsumoto K, Nakayama Y, Inoue Y, Minagawa N, Katsuki T, Shibao K, Tsurudome Y, Hirata K, Nagata N, Itoh H. Lymphatic microvessel density is an independent prognostic factor in colorectal cancer. Dis Colon Rectum. 2007; 50:308-314.
21. Cacchi C, Arnholdt HM, Jahnig H, Anthuber M, Probst A, Oruzio DV, Markl B. Clinical significance of lymph vessel density in T3 colorectal carcinoma. Int J Colorectal Dis. 2012; 27:721-726.
22. Huijbers A, Tollenaar RA, Pelt GW, Zeestraten EC, Dutton S, McConkey CC, Domingo E, Smit VT, Midgley R, Warren BF, Johnstone EC, Kerr DJ, Mesker WE. The proportion of tumor-stroma as a strong prognosticator for stage II and III colon cancer patients: validation in the VICTOR trial. Ann Oncol. 2013; 24:179-185.
23. Park JH, Richards CH, McMillan DC, Horgan PG, Roxburgh CS. The relationship between tumour stroma percentage, the tumour microenvironment and survival in patients with primary operable colorectal cancer. Ann Oncol. 2014; 25:644-651.
24. Royston P, Altman DG, Sauerbrei W. Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med. 2006; 25:127-141.
25. Angell HK, Gray N, Womack C, Pritchard DI, Wilkinson RW, Cumberbatch M. Digital pattern recognition-based image analysis quantifies immune infiltrates in distinct tissue regions of colorectal cancer and identifies a metastatic phenotype. Br J Cancer. 2013; 109:1618-1624.
26. Breiman L. Random forests. Machine learning. 2001; 45:5-32.
27. Chou PA. Optimal partitioning for classification and regression trees. IEEE Trans Pattern Anal Mach Intell. 1991; 340-354.
28. Ueno H, Kajiwara Y, Shimazaki H, Shinto E, Hashiguchi Y, Nakanishi K, Maekawa K, Katsurada Y, Nakamura T, Mochizuki H, Yamamoto J, Hase K. New criteria for histologic grading of colorectal cancer. Am J Surg Pathol. 2012; 36:193-201.
29. Brachtel E, Yagi Y. Digital imaging in pathology-current applications and challenges. J Biophotonics. 2012; 5:327-335.
30. Kopetz S, Tabernero J, Rosenberg R, Jiang ZQ, Moreno V, Bachleitner-Hofmann T, Lanza G, Stork-Sloots L, Maru D, Simon I, Capella G, Salazar R. Genomic Classifier ColoPrint Predicts Recurrence in Stage II Colorectal Cancer Patients More Accurately Than Clinical Factors. The oncologist. 2015; 20:127-133.
31. Yuan Y. Modelling the spatial heterogeneity and molecular correlates of lymphocytic infiltration in triple-negative breast cancer. J R Soc Interface. 2015; 12.
32. Maurice B Loughrey PQ, Neil A Shepherd. Final Standards and datasets for reporting cancers Dataset for colorectal cancer histopathology reports July 2014. Dataset for colorectal cancer histopathology reports.
33. Quirke P, Williams GT, Ectors N, Ensari A, Piard F, Nagtegaal I. The future of the TNM staging system in colorectal cancer: time for a debate? The Lancet Oncology. 2007; 8:651-657.
34. Camp RL, Dolled-Filhart M, Rimm DL. X-tile: a new bioinformatics tool for biomarker assessment and outcomebased cut-point optimization. Clin Cancer Res. 2004; 10:7252-7259.
35. Lubbock AL, Katz E, Harrison DJ, Overton IM. TMA Navigator: Network inference, patient stratification and survival analysis with tissue microarray data. Nucleic Acids Res. 2013; 41:W562-568.