Evaluating Quality Indicators of Glioblastoma Care: Audit Results From an Indian Tertiary Care Cancer Center.

PURPOSE: There are limited reports of quality metrics in glioblastoma. We audited our adherence to quality indicators as proposed in the PRIME Quality Improvement study. METHODS: This is a retrospective audit of patients treated between 2017 and 2020. After postsurgical integrated diagnosis, patients received radiotherapy (RT) with concurrent and adjuvant temozolomide (TMZ). Multiparametric magnetic resonance imaging at predefined times guided management. Numbers with proportions for indices were calculated. Survival was estimated using the Kaplan-Meier method. RESULTS: One hundred six patients were consecutively treated. The median age was 55 years (interquartile range of 47-61 years) with a male preponderance (68%). Ninety-six (90.6%) patients underwent subtotal resection, and 10 (9.4%) biopsy alone. Isocitrate dehydrogenase was wild-type in 96 (91%), and O6-methylguanine-DNA methyltransferase was unmethylated in 70 (66.0%) patients. Telomerase reverse transcriptase promoter was mutated in 64 (60.4%), and TP53 was mutated in 22 (20.8%). Concurrent radiation and TMZ were planned for 104 (98.1%), and radiation alone for 2 (1.9%). The median time to concurrent RT-TMZ was 36 days (interquartile range 30-44 days). All patients planned for RT-TMZ completed treatment, but only 81 (76%) completed adjuvant TMZ. Sixty-three (59%) completed six cycles, 18 (17%) received less than six cycles, and 25 (24%) did not receive adjuvant TMZ. At a median follow-up of 24 months (range 21-31 months), the median (95% CI) progression-free survival and overall survival were 11 (95% CI, 9.4 to 13.0) and 20.0 (95% CI, 15 to 26) months, respectively. CONCLUSION: Our patients met quality indices in most domains; outcomes are comparable with global results. Metrics will be periodically evaluated to include new standards and assess continuous service appropriateness.

Research Goal-Driven Data Model and Harmonization for De-Identifying Patient Data in Radiomics.

There are various efforts in de-identifying patient's radiation oncology data for their uses in the advancement of research in medicine. Though the task of de-identification needs to be defined in the context of research goals and objectives, existing systems lack the flexibility of modeling data and normalization of names of attributes for accomplishing them. In this work, we describe a de-identification process of radiation and clinical oncology data, which is guided by a data model and a schema of dynamically capturing domain ontology and normalization of terminologies, defined in tune with the research goals in this area. The radiological images are obtained in DICOM format. It consists of diagnostic, radiation therapy (RT) treatment planning, RT verification, and RT response images. During the DICOM de-identification, a few crucial pieces of information are taken about the dataset. The proposed model is generic in organizing information modeling in sync with the de-identification of a patient's clinical information. The treatment and clinical data are provided in the comma-separated values (CSV) format, which follows a predefined data structure. The de-identified data is harmonized throughout the entire process. We have presented four specific case studies on four different types of cancers, namely glioblastoma multiforme, head-neck, breast, and lung. We also present experimental validation on a few patients' data in these four areas. A few aspects are taken care of during de-identification, such as preservation of longitudinal date changes (LDC), incremental de-identification, referential data integrity between the clinical and image data, de-identified data harmonization, and transformation of the data to an underlined database schema.