Methods Inf Med 1994; 33(04): 397-401
DOI: 10.1055/s-0038-1635040
Original Article
Schattauer GmbH

Evaluation of a Decision-Support System for Inoperable Non-Small Cell Lung Cancer

T. Wigren
1   Tampere University, Department of Clinical Sciences, Tampere, Finland
,
P. Kolari
2   Technical Research Centre of Finland, Medical Engineering Laboratory, Tampere, Finland
› Author Affiliations
Further Information

Publication History

Publication Date:
08 February 2018 (online)

Abstract:

The purpose of this study was to find out whether a decision-support system is able to assist a clinician in predicting patient outcome and in selecting optimal treatment in oncology. The domain of the evaluated decision-support prototype was primary therapeutic decision making in inoperable non-smali cell lung cancer. The performance of the prototype was tested on retrospective material consisting of 112 patients treated by radiotherapy. Survival was the endpoint for examining whether the treatment decision proposed by the system was more accurate than the decision actually made by the clinician. Certain prognostic variables were used by the system to classify patients into two treatment groups, radical or palliative radiotherapy. The median survival times of these groups were 15 and 7 months, respectively, compared with 9 and 8 months in the corresponding groups classified by the clinician. Our results indicate that clinicians need support in treatment selection and that decision-support systems could be a potential answer.

 
  • REFERENCES

  • 1 Potthoff P, Rothemund M, Schwefel D, Engelbrecht R, van Eimeren W. Expert systems in medicine. Int J Technol Assessment in Health Care 1988; 04: 121-33.
  • 2 Wyatt J, Spiegelhalter D. Evaluating medical expert systems: what to test and how?. Med Inform 1990; 15: 205-17.
  • 3 Ardizzone E, Bonadonna F, Gaglio S. et al. Artificial intelligence techniques for cancer treatment planning. Med Inform 1988; 13: 199-210.
  • 4 Hyödynmaa S, Ojala A, Kolari P. et al. Decision support for treatment decision making and treatment result evaluation in radiation oncology. In: Minet P. eds. Impact of Personal Computers (PCs) on Radio-Oncology Departments. Commission Informatique of the Radiotherapy Section of the European Association of Radiology Workshop Geneva. Liege: 1990: 89-95.
  • 5 Kalet I, Paluszynski W. Knowledge-based computer systems for radiotherapy planning. Am J Clin Oncol 1990; 13: 344-51.
  • 6 Lhommé C, Giron A, Jan P. et al. Apport des systèmes experts dans la practique clinique: à propos de l’expérience Penelope, système expert d’aide au diagnostique et à la thérapeutique des adénocarninomes de l’ovaire. Bull Cancer 1992; 79: 1055-70.
  • 7 Maceratini R, Rafanelli M, Pisanelli M, Crollari S. Expert systems and the pancreatic cancer problems: decision support in the pre-operative diagnosis. J Biomed Eng 1989; 11: 487-510.
  • 8 Shortliffe EH, Scott AC, Bischoff MB, Campbell AB, Van Melle W, Jacobs CD. ONCOCIN: an expert system for oncology protocol management. In: Proceedings of the Seventh International J’oint Conference on Artificial Intelligence. Los Altos CA: Morgan-Kaufmann; 1981: 876-81.
  • 9 Hyödynmaa S, Saranummi N, Kolari P, Ojala A, Rantanen J. What is a knowledge based system for radiotherapy? A Scandinavian perspective. In: Joint U. S. Scand. Symp. on Future Directions of Computer-Aided Radiotherapy. San Antonio: NCI; 1990: 19-26.
  • 10 Kolari P, Yliaho J, Näriäinen K, Hyödynmaa S, Ojala A, Rantanen J, Saranummi N. Cartes - a prototype decision support system in oncology. In: Talmon J, Fox J. eds. Knowledge Based Systems in Medicine: Methods, Application and Evaluation (Proc. of the Workshop System Engineering in Medicine Maastricht). Heidelberg: Springer-Verlag; 1991: 148-58.
  • 11 Perez C. Non-small cell carcinoma of the lung: dose-time parameters. Cancer Treat Symp 1985; 02: 131-42.
  • 12 Wigren T, Kellokumpu-Lehtinen P, Ojala A. Radical radiotherapy of inoperable non-small cell lung cancer. Irradiation techniques and tumor characteristics in relation to local control and survival. Acta Oncol 1992; 31: 555-61.
  • 13 Bobrow DG, Stefik M. The LOOPS Manual. Xerox Corporation 1983; 124.
  • 14 Miller PL. Critiquing as a modality for explanation: Three systems. In: Proceedings ofAAMSI Congress 1984; 105-9.
  • 15 Miller PL. Expert Critiquing Systems. Practice Based Medical Consultation by Computer. New York: Springer Verlag; 1986
  • 16 Albain K, Crowley J, Le Blanc M, Livingston RB. Survival determinants in extensive-stage non-small-cell lung cancer: The Southwest Oncology Group experience. J Clin Oncol 1991; 09: 1618-26.
  • 17 Feinstein AR, Wells CK. A clinical-severity staging system for patients with lung cancer. Medicine 1990; 69: 1-33.
  • 18 Kaasa S, Mastekaasa A, Lund E. Prognostic factors for patients with inoperable non-small cell lung cancer, limited disease. Radiother Oncol 1989; 15: 235-42.
  • 19 Stanley K. Prognostic factors for survival in patients with inoperable lung cancer. J Natl Cancer Inst 1980; 65: 25-32.
  • 20 Thorogood J, Bulman AS, Collins T, Ash D. The use of discriminant analysis to guide palliative treatment for lung cancer patients. Clin Oncol 1992; 04: 22-6.
  • 21 Kent D, Shortliffe E, Carlson R, Bischoff M, Jacobs C. Improvements in data collection through physician use of a computer-based chemotherapy treatment consultant. J Clin Oncol 1985; 03: 1409-17.
  • 22 Rethans J-J, Höppener P, Wolfs G, Diederiks J. Do personal computers make doctors less personal?. BMJ 1988; 296: 1446-8.
  • 23 De Dombal FT. Computer-aided decision support in clinical medicine. Int J Biomed Comput 1989; 24: 9-16.