AI Analysis
Final verdict: SAFE
The package shows minimal risks across all evaluated categories, with no network calls, shell executions, obfuscations, or credential harvesting attempts detected. The only concern is incomplete author metadata.
- Incomplete author metadata
- Low risk in all other evaluated categories
Per-check LLM notes
- Network: No network calls detected, which is normal for a DICOM processing tool unless it requires external services.
- Shell: No shell execution detected, which is expected for a pure Python package focused on DICOM RT file handling.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The author's information is incomplete, indicating potential lack of transparency or newness to the platform.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
No shell execution patterns detected
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: health.ucsd.edu>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository brianmanderson/Dicom_RT_and_Images_to_Mask appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with DicomRTTool
Create a medical imaging analysis tool using the Python package 'DicomRTTool'. This tool will allow radiologists and medical researchers to analyze DICOM images and RT structures more effectively. Here are the steps and features for your application: 1. **Project Setup**: Begin by setting up a virtual environment and installing the necessary packages including DicomRTTool, NumPy, and any other dependencies required for handling DICOM files. 2. **DICOM File Importation**: Implement functionality to import DICOM image series from a specified directory. Use DicomRTTool to read and display basic information about the imported DICOM files such as patient ID, study date, and modality. 3. **RT Structure Analysis**: Extend the tool to support importing RT structure files associated with the DICOM images. Utilize DicomRTTool to parse these files and visualize the ROI (Region of Interest) contours on the DICOM slices. 4. **Dose Distribution Visualization**: If available, integrate support for importing dose distribution files and visualize them alongside the DICOM images and RT structures. Ensure users can manipulate the visualization settings, such as color maps and opacity. 5. **Prediction Mask Conversion**: Develop a feature where users can input their own segmentation predictions (as numpy arrays) and convert them into RT structure format compatible with DICOM standards. This feature should leverage DicomRTTool's conversion utilities to accurately map the prediction masks onto the original DICOM slices. 6. **User Interface**: Design a simple yet intuitive GUI using libraries like PyQt or Tkinter to facilitate user interaction with the application. The interface should allow easy navigation through DICOM images, manipulation of visualization options, and seamless integration with all the above functionalities. 7. **Documentation & Testing**: Provide comprehensive documentation explaining how to use each feature of the application. Additionally, implement unit tests to ensure the reliability of the DICOM file handling, RT structure parsing, and prediction mask conversion processes. This project aims to streamline the workflow for medical professionals dealing with complex DICOM datasets, making it easier to interpret and analyze radiological data.