---
pretty_name: "Stereotactic Radiosurgery Dataset (SRS)"
license: cc-by-nc-4.0
language:
  - en
size_categories:
  - n<1K
task_categories:
  - tabular-classification
  - tabular-regression
tags:
  - medical-imaging
  - radiotherapy
  - radiosurgery
  - tumor-segmentation
  - dose-optimization
  - AI-healthcare
  - synthetic
  - tabular
  - metadata-only
---

# 🎯 Stereotactic Radiosurgery Dataset (SRS)

> 🏥 **400 synthetic patient records** describing the clinical, imaging, segmentation, and treatment-planning metadata of a stereotactic radiosurgery workflow, delivered as a single CSV with placeholder file paths.

> ⚠️ **Disclaimer**: This is a **metadata-only synthetic dataset**. It contains **no real patients**, **no image files**, and **no segmentation files**. Every record is generated; paths in the imaging and segmentation columns are placeholders that do not resolve to any file. **Not for clinical use, treatment planning, or any patient-facing decision.**

---

## 📋 At a Glance

| | |
|---|---|
| 🔢 **Rows** | 400 synthetic patient records |
| 📁 **Files** | One CSV (`stereotactic-radiosurgery-k1-with-segmentation.csv`) |
| 🧠 **Imaging** | Metadata only; no CT, MRI, or PET files included |
| 🎯 **Segmentation** | Metadata only; GTV, CTV, and PTV paths are placeholders |
| 🧬 **Generation** | Synthetic, template-based |
| 🌍 **Language** | English |
| 📄 **License** | CC BY-NC 4.0 |
| 🏷️ **Version** | 1.0 |

---

## 📝 Overview

The dataset models the **shape** of an SRS data pipeline, from patient intake through imaging, target delineation, and plan parameters, so that tooling and models can be prototyped against a realistic schema before access to protected clinical data is arranged. It is intended for research and development of AI methods in SRS, not as a source of clinical ground truth.

---

## 📊 Dataset Summary

| Feature | Details |
|---|---|
| 🏥 **Clinical Metadata** | 400 records with synthetic demographics, medical history, and tumor descriptors |
| 🧠 **Imaging Metadata** | Modality, contrast protocol, voxel geometry, and scanner descriptors for CT, MRI, and PET; no image files |
| 🎯 **Segmentation Metadata** | Placeholder paths and descriptors for GTV, CTV, and PTV, including a synthetic inter-observer variability field |
| ⚙️ **Treatment Plan Metadata** | Beam arrangement, prescribed dose, DVH summary fields, and optimization objective labels |
| 📂 **File Format** | CSV; path columns follow NIfTI naming conventions but point to no file |

---

## 💡 Fields

### 🏥 Clinical
- Patient demographics: age, sex, weight, height (synthetic)
- Medical history: comorbidities, previous treatments (synthetic labels)
- Tumor details: histology, grade, and stage (synthetic labels)

### 🧠 Imaging Metadata
- Modality: CT, MRI, or PET
- Protocol: contrast-enhanced or non-contrast
- Scanner descriptors: manufacturer and model (synthetic labels)
- Voxel geometry: isotropic or anisotropic descriptors

### 🎯 Segmentation Metadata
- Gross Tumor Volume (GTV), Clinical Target Volume (CTV), Planning Target Volume (PTV)
- Placeholder NIfTI-style paths (e.g., `/simulated/path/SIM-0001_GTV_segmentation.nii`)
- Synthetic inter-observer variability descriptor

### ⚙️ Treatment Plan Metadata
- Beam arrangement label, prescribed dose (Gy), DVH summary fields
- Optimization objective labels (target coverage, organ-at-risk sparing)

---

## 🔍 Example Record

| Field | Example Value |
|---|---|
| `Patient_ID` | `SIM-0001` |
| `Age` | `36` |
| `Imaging_Modality` | `CT` |
| `Tumor_Histology` | `Meningioma` |
| `GTV_Segmentation_Path` | `/simulated/path/SIM-0001_GTV_segmentation.nii` |
| `Beam_Arrangement` | `Single` |
| `Prescribed_Dose_Gy` | `35.04` |

> 📌 **Note**: The `SIM-` prefix on every identifier marks the record as synthetic. Column names above are illustrative; see the CSV header for the exact schema.

---

## 🚀 Usage

### Load the Dataset

```python
from datasets import load_dataset

dataset = load_dataset("Taylor658/stereotactic-radiosurgery-k1-with-segmentation", split="train")
print(dataset[0])
```

### 🎯 Intended Usage

- 🧪 **Pipeline prototyping**: build and test ingestion, validation, and cohort-selection code against an SRS-shaped schema before connecting to a protected clinical source
- 🏷️ **Tabular modeling exercises**: classification or regression on synthetic metadata fields (e.g., beam arrangement from histology and stage) to develop and debug training code
- 📐 **Schema design**: a starting point for defining the metadata a real SRS dataset should carry

### 🚫 Out of Scope

- Training or validating any model intended for clinical contouring, dose prediction, or treatment optimization; no image or dose data is present
- Patient outcome prediction; outcomes in the file are synthetic labels with no relationship to real treatment response
- Any use that treats the records as representing real people

---

## ⚠️ Limitations

### 🧬 Synthetic Content
- Every field is generated; there is no underlying patient, scan, contour, or plan
- Distributions of age, histology, dose, and other fields are template choices, not epidemiological or clinical distributions

### 📁 No Imaging or Segmentation Files
- Path columns are placeholders and do not resolve
- The dataset cannot support image-based tasks

### ⚙️ Plan Fields Are Labels
- Dose, beam, and DVH fields are summary values, not the output of a treatment planning system
- No dose distribution, structure set, or plan file is included

### 🏥 Not for Clinical Use
- Nothing in this dataset should inform patient care, planning, or QA

---

## 📜 License

Released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). You may share and adapt the material for **non-commercial** purposes with attribution.

---

## 🙌 Citation

```bibtex
@misc{srs_synthetic_metadata_2026,
  title  = {Stereotactic Radiosurgery Dataset (SRS): Synthetic Metadata},
  author = {Taylor, A.},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/datasets/Taylor658/stereotactic-radiosurgery-k1-with-segmentation}},
  note   = {Metadata-only synthetic dataset with placeholder imaging and segmentation paths.}
}
```

---

## 🧑‍💻 Contributing

- 💬 Open a Discussion to propose additional metadata fields, histology categories, or plan descriptors

---

**👨‍🚀 Author:** A Taylor · **🤗 Hugging Face:** [hf.co/Taylor658](https://huggingface.co/Taylor658) · **🐙 GitHub:** [ATaylorAerospace](https://github.com/ATaylorAerospace)