[Baltasar Llopis Mínguez (1934-1990). A pioneer in research on bladder cancer and introduction of computing in Urology].

OBJECTIVE

To recall the figure of a great Valencian urologist, to emphasize his great personality and humanity, and to draw particular attention to his significant contribution to the study of prognostic factors in urology and estimation of individual oncological risk, as well as to introduction of computing in urology.

METHOD

His work, the testimony of colleagues who treated him, and data obtained from his close relatives, as well as our own personal knowledge, are reviewed. Result. Baltasar Llopis was born in Valencia, and obtained his degree and doctorate in Medicine at the Valencia University. He specialized in urology with Dr. Tramoyeres Cases, for whom he acted as assistant surgeon and with whom he shared work at La Fe Hospital, where he carried out his complete urological activity, since its inception. Dr. Llopis opted for oncological research, with a special focus on urothelial tumors. He pioneered diagnosis of these tumors using tumor markers and the study of prognostic factors to assess the individual risk of relapse and to implement a specific chemotherapeutic treatment, which he introduced in clinical practice at La Fe Hospital. He thus demonstrated the two essential components of his personality, his investigative and human sides.

CONCLUSION

A multi-faceted person with great skills and intelligence, Dr. Llopis eagerly devoted himself to research aimed at understanding the biological behavior of cancer, particularly urothelial tumors. In the early 80s he pioneered worldwide the development of specific markers, estimations of individual oncological risk, and prognostic factors useful for planning treatment. He was 20 years ahead of the era of predictive nomograms and their clinical

INTRODUCTION

In addition to being a forerunner of computing applications in Urology, he designed a database for registration of superficial bladder tumors, which allowed him to perform statistical and multivariate analyses using multiple regression models to predict the risk of relapse.