An oral cavity squamous cell carcinoma quantitative histomorphometric-based image classifier of nuclear morphology can risk stratify patients for disease-specific survival

Modern Pathology

Volumn 30, Issue 12, 2017, Pages 1655-1665

  Lu, Cheng ^a,b   Lewis, James S ^c,d,e,f   Dupont, William D ^d   Plummer, W Dale ^d   Janowczyk, Andrew ^b   Madabhushi, Anant ^b  

^a SHAANXI NORMAL UNIVERSITY   (China)

^b Case Western Reserve University   (United States)

^c VANDERBILT UNIVERSITY SCHOOL OF MEDICINE   (United States)

^d VANDERBILT UNIVERSITY MEDICAL CENTER   (United States)

^e WASHINGTON UNIVERSITY   (United States)

^f WASHINGTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; ARTICLE; CANCER PROGNOSIS; CANCER STAGING; CLINICAL FEATURE; COHORT ANALYSIS; DIGITAL IMAGING; DISEASE SPECIFIC SURVIVAL; FEMALE; HIGH RISK PATIENT; HUMAN; IMAGE ANALYSIS; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; MORPHOLOGY; MORPHOMETRY; MOUTH CARCINOMA; MOUTH SQUAMOUS CELL CARCINOMA; NUCLEAR SHAPE; PREDICTIVE VALUE; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; RANK SUM TEST; RECEIVER OPERATING CHARACTERISTIC; RISK ASSESSMENT; SURGICAL MARGIN; SURVIVAL ANALYSIS; TISSUE MICROARRAY; AGED; CELL NUCLEUS; CLASSIFICATION; COMPUTER ASSISTED DIAGNOSIS; DISEASE FREE SURVIVAL; HEAD AND NECK TUMOR; KAPLAN MEIER METHOD; MIDDLE AGED; MORTALITY; MOUTH TUMOR; PATHOLOGY; PROCEDURES; PROGNOSIS; SQUAMOUS CELL CARCINOMA;

ADULT; AGED; CARCINOMA, SQUAMOUS CELL; CELL NUCLEUS; DISEASE-FREE SURVIVAL; FEMALE; HEAD AND NECK NEOPLASMS; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; KAPLAN-MEIER ESTIMATE; MACHINE LEARNING; MALE; MIDDLE AGED; MOUTH NEOPLASMS; NEOPLASM STAGING; PROGNOSIS;

EID: 85037041276     PISSN: 08933952     EISSN: 15300285     Source Type: Journal    
DOI: 10.1038/modpathol.2017.98     Document Type: Article

References (40)

1. Chaturvedi AK, Anderson WF, Lortet-Tieulent J, et al. Worldwide trends in incidence rates for oral cavity and oropharyngeal cancers. J Clin Oncol Off J Am Soc Clin Oncol 2013;31:4550-4559.
2. Gupta B, Johnson NW, Kumar N. Global epidemiology of head and neck cancers: A continuing challenge. Oncology 2016;91:13-23.
3. D'Souza G, Kreimer AR, Viscidi R, et al. Case-control study of human papillomavirus and oropharyngeal cancer. N Engl J Med 2007;356:1944-1956.
4. Sturgis EM, Cinciripini PM. Trends in head and neck cancer incidence in relation to smoking prevalence: An emerging epidemic of human papillomavirusassociated cancers Cancer 2007;110:1429-1435.
5. Hutcheson KA, Lewin JS. Functional outcomes after chemoradiotherapy of laryngeal and pharyngeal cancers. Curr Oncol Rep 2012;14:158-165.
6. Group, E. B. C. T. C. Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: An overview of the randomised trials. Lancet 2005;365:1687-1717.
7. Li Y, Bai S, Carroll W, et al. Validation of the risk model: high-risk classification and tumor pattern of invasion predict outcome for patients with low-stage oral cavity squamous cell carcinoma. Head Neck Pathol 2013;7:211-223.
8. Thomas B, Stedman M, Davies L. Grade as a prognostic factor in oral squamous cell carcinoma: A populationbased analysis of the data: early oral cancer-grade matters. Laryngoscope 2014;124:688-694.
9. Brandwein-Gensler M, Smith RV, Wang B, et al. Validation of the histologic risk model in a new cohort of patients with head and neck squamous cell carcinoma. Am J Surg Pathol 2010;34:676-688.
10. Sinha P, Mehrad M, Chernock RD, et al. Histologic and systemic prognosticators for local control and survival in margin-negative transoral laser microsurgery treated oral cavity squamous cell carcinoma: systemic and histologic prognosticators in transoral laser microsurgery-treated oral cavity cancers. Head Neck 2015;37:52-63.
11. Ali S, Lewis J, Madabhushi A. Spatially aware cell cluster(spACC1) graphs: predicting outcome in oropharyngeal pl6+ tumors. Med Image Comput Comput-Assist Interv 2013;16:412-419.
12. Doyle S, Hwang M, Shah K et al. Automated grading of prostate cancer using architectural and textural image features. In Proceedings of the 4th IEEE International Symposium on Biomedical Imaging 2007; 1284-1287.
13. Beck AH, Sangoi AR, Leung S, et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci Transl Med 2011;3:108ra113.
14. Lewis JSJ, Ali S, Luo J, et al. A quantitative histomorphometric classifier (QuHbIC) identifies aggressive versus indolent p16-positive oropharyngeal squamous cell carcinoma. Am J Surg Pathol 2014;38:128-137.
15. Basavanhally A, Ganesan S, Feldman MD, et al. Multi-field-of-view framework for distinguishing tumor grade in ER+ breast cancer from entire histopathology slides. IEEE Trans Biomed Eng 2013;60:2089-2099.
16. Yu K-H, Zhang C, Berry GJ, et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun 2016;7: 12474.
17. Romo-Bucheli D, Janowczyk A, Romero A, et al. Automated tubule nuclei quantification and correlation with oncotype DX risk categories in ER+ breast cancer whole slide images. Sci Rep 2016;6:32706.
18. Bose P, Brockton NT, Guggisberg K, et al. Fractal analysis of nuclear histology integrates tumor and stromal features into a single prognostic factor of the oral cancer microenvironment. BMC Cancer 2015;15: 409-415.
19. Madabhushi A, Lee G. Image analysis and machine learning in digital pathology: challenges and opportunities. Med Image Anal 2016;33:170-175.
20. Bhargava R, Madabhushi A. Emerging themes in image informatics and molecular analysis for digital pathology. Annu Rev Biomed Eng 2016;18:387-412.
21. Xu H, Lu C, Berendt R, et al. Automatic nuclei detection based on generalized Laplacian of Gaussian filters. IEEE J Biomed Health Inform 2016;21:826-837.
22. Veta M, van Diest PJ, Kornegoor R, et al. Automatic nuclei segmentation in H&E stained breast cancer histopathology images. PloS One 2013;8:e70221.
23. Lu C, Mahmood M, Jha N, et al. A robust automatic nuclei segmentation technique for quantitative histopathological image analysis. Anal Quant Cytopathol Histopathol 2012;34:296-308.
24. Cheng Lu, Mandal M. Toward automatic mitotic cell detection and segmentation in multispectral histopathological images. IEEE J Biomed Health Inform 2014;18:594-605.
25. Peng H, Long F, Ding C. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 2005;27:1226-1238.
26. Breiman L. Random forests. Mach Learn 2001;45:5-32.
27. Bobdey S, Balasubramaniam G, Mishra P. Nomogram prediction for survival of patients with oral cavity squamous cell carcinoma: nomogram prediction for survival of patients with oral cancer. Head Neck 2016;38:1826-1831.
28. Marchiano E, Patel TD, Eloy JA, et al. Impact of nodal level distribution on survival in oral cavity squamous cell carcinoma: A population-based study. Otolaryngol-Head Neck Surg 2016;155:99-105.
29. Zhang XC, Xu C, Mitchell RM, et al. Tumor evolution and intratumor heterogeneity of an oropharyngeal squamous cell carcinoma revealed by whole-genome sequencing. Neoplasia N Y N 2013;15:1371-1378.
30. Mroz EA, Rocco JW. MATH, a novel measure of intratumor genetic heterogeneity, is high in pooroutcome classes of head and neck squamous cell carcinoma. Oral Oncol 2013;49:211-215.
31. Mroz EA, Tward AD, Pickering CR, et al. High intratumor genetic heterogeneity is related to worse outcome in patients with head and neck squamous cell carcinoma. Cancer 2013;119:3034-3042.
32. Gerlinger M, Rowan AJ, Horswell S, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 2012;366:883-892.
33. Singh RR, Murugan P, Patel LR, et al. Intratumoral morphologic and molecular heterogeneity of rhabdoid renal cell carcinoma: challenges for personalized therapy. Mod Pathol 2015;28:1225-1235.
34. Sciallis AP, Bedroske PP, Schoolmeester JK, et al. Highgrade endometrial stromal sarcomas: A clinicopathologic study of a group of tumors with heterogenous morphologic and genetic features. Am J Surg Pathol 2014;38:1161-1172.
35. Mafficini A, Amato E, Fassan M, et al. Reporting tumor molecular heterogeneity in histopathological diagnosis. PloS One 2014;9:e104979.
36. Vedam VKV, Boaz K, Natarajan S. Prognostic efficacy of nuclear morphometry at invasive front of oral squamous cell carcinoma: An image analysis microscopic study. Anal Cell Pathol 2014;1:1-9.
37. Mroz EA, Tward AM, Hammon RJ, et al. Intra-tumor genetic heterogeneity and mortality in head and neck cancer: Analysis of data from The Cancer Genome Atlas. PLOS Med 2015;12:e1001786.
38. Chang Y-C, Nieh S, Chen SF, et al. Invasive pattern grading score designed as an independent prognostic indicator in oral squamous cell carcinoma: Invasive pattern grading score in OSCC. Histopathology 2010;57:295-303.
39. Chapter 3: lip and oral cavity. In: Edge S, Byrd DR, Compton CC, et al. (eds). AJCC Cancer Staging Manual, 7th edn. Springer: New York, 2010, pp 29-40.
40. Ridge JA, Lydiatt WM, Patel SG, et al. Chapter 7: lip and oral cavity. In: Amin MB, Edge S, Greene F, et al. (eds). AJCC Cancer Staging Manual, 8th edn. Springer: New York, 2016, pp 113-121.