# Dosimetric Awareness of Radiation Oncology Professionals

## 3: Predicting the Impact of Target Volume Contouring Variations on the Organ at Risk Dose: Results of a Qualitative Survey

This work was [published](https://doi.org/10.1016/j.radonc.2025.110999) in [Radiotherapy and Oncology](https://www.thegreenjournal.com/) (the "Green Journal"). More details about this work is included in [Chapter 3](chapter3.md).

## 4: Comparing the Performance of Radiation Oncologists versus a Deep Learning Dose Predictor to Estimate Dosimetric Impact of Segmentation Variations for Radiotherapy

This work was presented as an [oral talk](https://proceedings.mlr.press/v250/kamath24a.html) at [MIDL 2024](https://2024.midl.io/scientific-program). More details about this work is included in [Chapter 4](chapter4.md).

## 5: AutoDoseRank: Automated Dosimetry-Informed Segmentation Ranking for Radiotherapy

This work was presented as an [oral talk](https://link.springer.com/chapter/10.1007/978-3-031-73376-5_21) at the [CaPTion Workshop at MICCAI 2024](https://caption-workshop.github.io/#Workshop%20sessions). More details about this work is included in [Chapter 5](chapter5.md).

**Ongoing connection:** These studies motivate [personalized contour review and correction](https://amithjkamath.github.io/projects/Theme-Correction/), combining models that represent observer variability with workflows that help clinicians resolve consequential differences.


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