Modeling and analyzing predictive monthly survival in females diagnosed with gynecological cancers

Timothy Samec, Raed Seetan

Abstract


Cancer ranks as a leading cause of death worldwide; an estimated 1.7 million new diagnoses were reported in 2021. Ovarian cancer, the most lethal of gynecological malignancies, has no effective screening with over 70% of patients being diagnosed in an advanced stage. The aim of this study is to determine the most statistically significant contributing factors through a multivariate regression into the severity of female gynecological cancers. Data from the SEER cancer database were utilized in this study. Several attempted multivariate linear regressions were implemented with further reduced models; however, a linear model could not be properly fit to the data. Because of unmet assumptions, nonparametric moving, local regression, LOESS, was performed. After smoothing factors were included to reduced models, residual information was minimized although few conclusions can be drawn from the resulting statistics. These issues were prevalent mainly because of the massive variability in the data and inherent lack of linearity. This can be a significant issue with clinical data that does not dive deeper into cancer-dependent factors including genetic expression and cell surface receptor overexpression. General patient demographic data and diagnostic information alone does not provide enough detail to make a definite conclusion or prediction on patient survivability.

References


REFERENCES

R. L. Siegel and K. D. Miller, “Cancer Statistics , 2021,” vol. 71, no. 1, pp. 7–33, 2021.

J. G. Tate et al., “COSMIC: The Catalogue Of Somatic Mutations In Cancer,” Nucleic Acids Res., vol. 47, no. D1, pp. D941–D947, 2019.

S. S. Faubion, K. L. Maclaughlin, M. E. Long, S. Pruthi, and P. M. Casey, “Surveillance and Care of the Gynecologic Cancer Survivor,” J. Women’s Heal., vol. 24, no. 11, pp. 899–906, 2015.

S. K. Srivastava et al., “Racial health disparities in ovarian cancer: not just black and white,” J. Ovarian Res., vol. 10, no. 1, p. 58, 2017.

R. L. Siegel, K. D. Miller, and A. Jemal, “Cancer statistics,” CA Cancer J Clin, vol. 66, no. 1, pp. 7–30, 2016.

N. Howlader, L. A. G. Ries, A. B. Mariotto, M. E. Reichman, J. Ruhl, and K. A. Cronin, “Improved estimates of cancer-specific survival rates from population-based data,” J. Natl. Cancer Inst., vol. 102, no. 20, pp. 1584–1598, 2010.

F. F. Costa, “Big data in biomedicine,” Drug Discov. Today, vol. 19, no. 4, pp. 433–440, 2014.

Y. Collins, K. Holcomb, E. Chapman-Davis, D. Khabele, and J. H. Farley, “Gynecologic cancer disparities: A report from the Health Disparities Taskforce of the Society of Gynecologic Oncology,” Gynecol. Oncol., vol. 133, no. 2, pp. 353–361, 2014.

G. Taglang and D. B. Jackson, “Use of big data in drug discovery and clinical trials,” Gynecol. Oncol., vol. 141, no. 1, pp. 17–23, 2016.

Y. Shimada, F. Sato, K. Shimizu, G. Tsujimoto, and K. Tsukada, “CDNA microarray analysis of esophageal cancer: Discoveries and prospects,” Gen. Thorac. Cardiovasc. Surg., vol. 57, no. 7, pp. 347–356, 2009.

M. Kolasa, R. Wojtyna, and W. Długosz Rafałand Jóźwicki, “Application of Artificial Neural Network to Predict Survival Time for Patients with Bladder Cancer,” in Computers in Medical Activity, E. Kkacki, M. Rudnicki, and J. Stempczyńska, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 113–122.

K. Xiao, T. Lin, K. Lam, and Y. Li, “A facile strategy for fine-tuning the stability and drug release of stimuli-responsive cross-linked micellar nanoparticles toward precision drug delivery,” Nanoscale, vol. 9, no. 23, pp. 7765–7770, 2017.

F. M. Drawnel et al., “Molecular Phenotyping Combines Molecular Information, Biological Relevance, and Patient Data to Improve Productivity of Early Drug Discovery,” Cell Chem. Biol., vol. 24, no. 5, pp. 624-634.e3, 2017.

E. D. Karagiannis, C. A. Alabi, and D. G. Anderson, “Rationally designed tumor-penetrating nanocomplexes,” ACS Nano, vol. 6, no. 10, pp. 8484–8487, 2012.

National Cancer Institute and DCCPS, “Surveillance, Epidemiology, and End Results (SEER) Program.” [Online]. Available: www.seer.cancer.gov.

“Surveillance, Epidemiology, and End Results (SEER) Program (www.seer.cancer.gov) Research Data (1975-2016), National Cancer Institute, DCCPS, Surveillance Research Program, released April 2019, based on the November 2018 submission.” .

H. G. Welch and W. C. Black, “Are deaths within 1 month of cancer-directed surgery attributed to cancer?,” J. Natl. Cancer Inst., vol. 94, no. 14, pp. 1066–1070, 2002.

D. Sarfati, T. Blakely, and N. Pearce, “Measuring cancer survival in populations: Relative survival vs cancer-specific survival,” Int. J. Epidemiol., vol. 39, no. 2, pp. 598–610, 2010.

M. D. Pineda, E. White, A. R. Kristal, and V. Taylor, “Asian breast cancer survival in the US: A comparison between Asian immigrants, US-born Asian Americans and Caucasians,” International Journal of Epidemiology, vol. 30, no. 5. pp. 976–982, 2001.

D. M. Parkin and M. Khlat, “Studies of cancer in migrants: Rationale and methodology,” Eur. J. Cancer, vol. 32, no. 5, pp. 761–771, 1996.

M. D. Ganggayah, N. A. Taib, Y. C. Har, P. Lio, and S. K. Dhillon, “Predicting factors for survival of breast cancer patients using machine learning techniques,” BMC Med. Inform. Decis. Mak., vol. 19, no. 1, pp. 1–17, 2019.

K. Matsuo et al., “Survival outcome prediction in cervical cancer: Cox models vs deep-learning model,” Am. J. Obs. Gynecol, vol. 220, no. 4, pp. 1–22, 2019.

K. Kourou, T. P. Exarchos, K. P. Exarchos, M. V Karamouzis, and D. I. Fotiadis, “Machine learning applications in cancer prognosis and prediction,” vol. 13, pp. 8–17, 2015.

Y. Ueda et al., “Serum biomarkers for early detection of gynecologic cancers,” Cancers (Basel)., vol. 2, no. 2, pp. 1312–1327, 2010.

A. Rodriguez, Z. Blanchard, K. Maurer, and J. Gertz, “Estrogen Signaling in Endometrial Cancer: a Key Oncogenic Pathway with Several Open Questions,” Horm Cancer, vol. 10, no. 2–3, pp. 51–63, 2019.

Y. J. Song, S. H. Shin, J. S. Cho, M. H. Park, J. H. Yoon, and Y. J. Jegal, “The role of lymphovascular invasion as a prognostic factor in patients with lymph node-positive operable invasive breast cancer,” Journal of Breast Cancer, vol. 14, no. 3. pp. 198–203, 2011.

J. E. Dancey, P. L. Bedard, N. Onetto, and T. J. Hudson, “The genetic basis for cancer treatment decisions,” Cell, vol. 148, no. 3. pp. 409–420, 2012.




DOI: http://doi.org/10.11591/ijphs.v10i4.20936

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