7. Clinical Data (Excel)

Clinical data may be provided together with the DICOM data using the current ZODIAC clinical data template. The clinical file is processed locally in the user’s browser during Step 1, using the rules defined in the selected anonymization profile.

The clinical file must include the required matching fields Patient_ID and Study_ID. These values must correspond to the patient and study identifiers present in the source DICOM files before anonymization. During anonymization, these fields are pseudonymized consistently with the DICOM metadata so that the anonymized clinical records can still be matched to the anonymized DICOM studies.

The same Study_ID may appear for different patients. This is supported because ZODIAC matches clinical data to DICOM data using the combination of Patient_ID + Study_ID, not Study_ID alone.

A supplemental Excel template, for example supplemental_template_sample.xlsx, is provided to standardize data submission. Institutes should use the current template and keep the column names unchanged, because each column name corresponds to a predefined anonymization rule in the selected profile.

The source clinical file is not uploaded to the ZODIAC platform. It is processed locally in the browser, and only the anonymized clinical output file is used for upload together with the anonymized DICOM data.

Clinical fields are handled according to the selected anonymization profile. Identifier fields such as Patient_ID and Study_ID are pseudonymized. Other fields may be retained, removed, generalized, or transformed depending on the profile rules and the scientific purpose of the dataset.

Date and demographic fields are also handled according to the selected profile. For example, birth date may be removed or generalized, age may be retained or grouped, and other date fields may be retained, cleared, generalized, or transformed depending on the configured rules. The View Rules link in the interface shows the exact rules applied by the selected profile.

In the example above, the repeated Study_ID value is valid because the Patient_ID values are different. After anonymization, both Patient_ID and Study_ID are transformed consistently with the corresponding DICOM metadata.