Survival prediction using artificial neural networks in patients with uterine cervical cancer treated by radiation therapy alone

International Journal of Clinical Oncology

Volumn 7, Issue 5, 2002, Pages 294-300

  Ochi, T ^a   Murase, K ^a   Fujii, T ^a   Kawamura, M ^a   Ikezoe, J ^a  

^a EHIME UNIVERSITY SCHOOL OF MEDICINE   (Japan)

Author keywords

Artificial neural networks; Prognosis; Radiotherapy; Receiver operating characteristic (ROC) analysis; Uterine cervical cancer

Indexed keywords

ADULT; AGE; AGED; ARTICLE; ARTIFICIAL NEURAL NETWORK; CANCER RADIOTHERAPY; CANCER SURVIVAL; CONTROLLED STUDY; CYTOLOGY; FEMALE; HISTOPATHOLOGY; HUMAN; MAJOR CLINICAL STUDY; PATIENT SELECTION; PERFORMANCE; PREDICTION; PRIORITY JOURNAL; RADIATION DOSE; RADIATION RESPONSE; SURVIVAL TIME; UTERINE CERVIX BIOPSY; UTERINE CERVIX CANCER; UTERINE CERVIX CYTOLOGY;

EID: 0036808137     PISSN: 13419625     EISSN: None     Source Type: Journal    
DOI: 10.1007/s101470200043     Document Type: Article

References (24)

1. Perez CA, Kushe RR, Camel HM, et al. (1988) Analysis of pelvic tumor control and impact on survival in carcinoma of the uterine cervix treated with radiation therapy alone. Int J Radiat Oncol Biol Phys 14:613-621
2. Kim RY, Trotti A, Wu CJ, et al. (1989) Radiation alone in the treatment of cancer of the uterine cervix: Analysis of pelvic failure and dose response relationship. Int J Radiat Oncol Biol Phys 17:973-978
3. Kataoka M, Kawamura M, Nishiyama Y, et al. (1992) Results of the combination of external-beam and high-dose-rate intracavitary irradiation for patients with cervical carcinoma. Gynecol Oncol 44:48-52
4. Bruke TW, Hoskins WJ, Heller PB (1987) Clinical patterns of tumor recurrence after radical hysterectomy in stage IB cervical carcinoma. Obstet Gynecol 69:382-385
5. Fyles AW, Pintilie M, Kirkbride P, et al. (1995) Prognostic factors in patients with cervix cancer treated by radiation therapy: Results of a multiple regression analysis. Radiother Oncol 35:107-117
6. Stehman FB, Bundy BN, DiSaia PJ, et al. (1991) Carcinoma of the cervix treated with radiation therapy I. A multi-variate analysis of prognostic variables in the Gynecologic Oncology Group. Cancer 67:2776-2785
7. Bush M II. Dühmke E, Kuhn W, et al. (1991) Definitive radiation therapy in the treatment of carcinoma of the uterine cervix. Treatment results and prognostic factors. Strahlenther Onkol 167:628-637
8. Arai T, Nakano T, Oka K (1993) Local control prediction by histologic grading of radiation effects with drill biopsy specimens for uterine cervical cancer. Int J Radiat Oncol Biol Phys 27:811-816
9. Jacobs AJ, Faris C, Perez CA, et al. (1986) Short-term persistence of carcinoma of the uterine cervix after radiation. An indicator of long-term prognosis. Cancer 57:944-950
10. McLennan MT, McLennan CE (1975) Significance of cervicovaginal cytology after radiation therapy for cervical cancer. Am J Obstet Gynecol 121:96-100
11. Marchevsky AM, Patel S, Wiley KJ, et al. (1998) Artificial neural networks and logistic regression as tools for prediction of survival in patients with stages I and II non-small cell lung cancer. Mod Pathol 11:618-625
12. Bryce TJ, Dewhirst MW, Floyd CE, et al. (1998) Artificial neural network model of survival in patients treated with irradiation with and without concurrent chemotherapy for advanced carcinoma of the head and neck. Int J Radiat Oncol Biol Phys 41:339-345
13. Baxt WG (1995) Application of artificial neural networks to clinical medicine. Lancet 346:1135-1138
14. Lundin J (1998) Artificial neural networks in outcome prediction. Anns Chir Gynaecol 87:128-130
15. Kaplan EL, Meier P (1958) Non-parametric estimation from incomplete observations. J Am Statist Assoc 53:457-481
16. Shimosato Y, Oboshi S. Baba K (1971) Histological evaluation of effects of radiotherapy and chemotherapy for carcinomas. Jpn J Clin Oncol 1:19-35
17. Moriwaki S, Inohara T, Yoshida M (1971) Formularization of radiation effects of uterine cancer. Jpn J Cancer Clin 17:704-706
18. Peto R, Pike MC, Armitage P, et al. (1977) Design and analysis of randomized clinical trials requiring prolonged observation of each patient: II. Analysis and examples. Br J Cancer 35:1-39
19. Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323:533-536
20. Woods K (1997) Generating ROC curves for artificial neural networks. IEEE Trans Med Imag 16:329-337
21. Metz CE (1986) ROC methodology in radiologic imaging. Invest Radiol 21:720-733
22. Bottaci L, Drew PJ, Hartley JE, et al. (1997) Artificial neural networks applied to outcome prediction for colorectal cancer patients in separate institutions. Lancet 350:469-472
23. Burke HB. Goodman PH, Rosen DB, et al. (1997) Artificial neural networks improve the accuracy of cancer survival prediction. Cancer 79:857-862
24. Okamoto Y, Nakano H, Yoshikawa M, et al. (1994) Study on decision support system for the interpretation of laboratory data by an artificial neural network - With a special reference to estimation for histological diagnosis of liver diseases with laboratory data on liver function. Jpn J Clin Pathol 42:195-199