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Pattern of care in radiotherapy at a University Hospital in Spain: the RENORT project

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Clinical and Translational Oncology Aims and scope Submit manuscript

Abstract

Purpose

RENORT is a novel data mining application developed to extract relevant clinical data from oncology information systems (OIS; ARIA and Mosaiq) used in radiation oncology (RO).

Methods/patients

We used RENORT to extract demographic and clinical data from the OIS of all patients treated at the RO Department at the General Hospital of Valencia during the year 2019.

Results

A total of 1158 treatments were performed. The female/male ratio was 39.3%/60.7%, with a mean age of 66 years. The mean waiting time between the treatment decision/proposal to the first visit was 10.1 days. Mean duration of the treatment preparation process was 21 days. Most patients (90.4%) completed treatment within the prescribed time ± 7 days. The most common sites/treatment types were: metastatic/palliative treatments (n = 300; 25.9%), breast (209; 18.0%), genitourinary (195; 16.8%), digestive (116; 10.0%), thoracic (104; 9.0%), head and neck (62; 5.4%), and skin cancer (51; 4.4%). The distribution according to treatment intent was as follows: palliative (n = 266; 23.0%), adjuvant curative (335; 28.9%), radical without adjuvant treatment (229; 19.8%), radical with concomitant treatment (188; 16.2%), curative neoadjuvant (70; 6.0%), salvage radiotherapy (61; 5.3%); and reirradiation (9; 0.8%). The most common treatment techniques were IMRT/VMAT with IGRT (n = 468; 40.4%), 3D-CRT with IGRT (421; 36.4%), SBRT (127; 11.0%), 2DRT (57; 4.9%), and SFRT (56; 4.8%). A mean of 15.9 fractions were administered per treatment. Hypofractionated schemes were used in 100% of radical intent breast and prostate cancer treatments.

Conclusions

The RENORT application facilitates data retrieval from oncology information systems to allow for a comprehensive determination of the real role of radiotherapy in the treatment of cancer patients. This application is valuable to identify patterns of care and to assess treatment efficacy.

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Acknowledgements

We wish to thank the following companies and individuals for their contributions to this project: Varian and Elekta for their support for the project, especially the development of the RENORT application for their information systems ARIA and Mosaiq, respectively; Joao Pedro Alves and Ludger Diederich for their direct involvement in the development of the application; Francisco Vázquez (Elekta) and Jaime Calderón (Varian) for believing in the project and supporting it; Astellas Pharma Spain for their support in the data mining work of both applications, and especially Emilio Pedrosa for trusting in the need for the project for Radiation Oncology; Nunsys and their analytics team for their work on data integration of all services, data mining, dashboards, and reports; Pilar Samper, Amalia Palacios, and Marta Lloret for their invaluable help throughout the project; the SEOR Boards chaired by Pedro Lara, Carlos Ferrer, and Jorge Contreras for their support in continuing the project; my colleagues from the Department of Radiation Oncology-Ascires of the General Hospital of Valencia for their dedication in perfecting the introduction of data and in refining it to improve the utility of the application. Finally, we thank Bradley Londres for professional English language editing of this manuscript.

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Correspondence to J. López-Torrecilla.

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López-Torrecilla, J., González Sanchis, D., Granero Cabañero, D. et al. Pattern of care in radiotherapy at a University Hospital in Spain: the RENORT project. Clin Transl Oncol 23, 1657–1665 (2021). https://doi.org/10.1007/s12094-021-02564-2

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