Datasets:

Taylor658
/

stereotactic-radiosurgery-k1-with-segmentation

@@ -1,97 +1,174 @@
 ---
-datasets:
-  - name: Stereotactic Radiosurgery Dataset (SRS)
-  - description: "A comprehensive dataset designed for developing AI models in Stereotactic Radiosurgery (SRS). This dataset includes clinical, imaging, tumor segmentation, and treatment planning data to support research in automated contouring, dose prediction, and treatment optimization."
-  - license: "CC BY-NC 4.0"
-  - tags:
-    - medical-imaging
-    - radiotherapy
-    - tumor-segmentation
-    - dose-optimization
-    - AI-healthcare
-  - languages:
-    - en
 ---
-## 🎯 **Stereotactic Radiosurgery Dataset (SRS)**
-## 🎯 **Overview**
-The **Stereotactic Radiosurgery Dataset** is tailored for research in advanced AI applications for Stereotactic Radiosurgery (SRS).
 ---
-## 📊 **Dataset Summary**
-| **Feature**              | **Details**                                   |
-|--------------------------|-----------------------------------------------|
-| 🏥 **Clinical Data**       | 400 patient records with demographic and medical history information. |
-| 🧠 **Imaging Data**        | High-resolution CT, MRI, and PET scans with isotropic and anisotropic voxel sizes. |
-| 🎯 **Tumor Segmentations** | Segmentation paths for GTV, CTV, and PTV with inter-observer variability. |
-| ⚙️ **Treatment Plans**      | Beam arrangements, dose distributions, DVHs, and optimization objectives. |
-| 📂 **File Formats**        | Metadata in CSV and images/segmentations in NIfTI. |
 ---
-## 💡 **Features**
-### **1. Clinical Data**
-- Patient demographics: Age, gender, weight, height.
-- Medical history: Comorbidities, previous treatments.
-- Tumor details: Histology, grade, and stage.
-### **2. Imaging Data**
-- Modalities: CT, MRI, and PET.
-- Imaging protocols: Contrast-enhanced and non-contrast-enhanced scans.
-- Scanner metadata: Manufacturer and model.
-### **3. Tumor Segmentation**
-- Gross Tumor Volume (GTV), Clinical Target Volume (CTV), and Planning Target Volume (PTV).
-- Segmentation paths stored in NIfTI format.
-### **4. Treatment Plans**
-- Dose distributions, beam arrangements, and dose-volume histograms (DVHs).
-- Optimization goals: Tumor dose maximization and organ sparing.
 ---
-## 🔍 **Usage**
-This dataset supports multiple applications:
-- **Automated Contouring:** Train models for accurate tumor volume delineation.
-- **Treatment Optimization:** Develop algorithms for optimized treatment plans.
-- **Patient Outcome Prediction:** Research predictive analytics for treatment response.
 ---
-## 🛠️ **File Organization**
-- **Main CSV:** Contains metadata for all patient cases.
-- **Synthetic Images:** Paths to synthetic CT, MRI, and PET scans.
-- **Tumor Segmentations:** Paths to NIfTI files for GTV, CTV, and PTV segmentations.
-- **Simulated Paths:** Placeholder paths simulate real-world usage scenarios.
 ---
-## 🎨 **Visual Example**
-Below is an example row from the dataset:
-| **Feature**               | **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 Arrangements         | `Single`                                   |
-| Dose Distributions (Gy)   | `35.04`                                    |
 ---
-## 📜 **Citations**
-If you use this dataset, please cite it as follows:
 ---
-## 💬 **Contact**
-For questions, reach out to the dataset maintainer: **A Taylor @ hf.co/taylor658**
 ---
-## 🔒 **Licensing**
-This dataset is licensed under **apache-2.0**.

 ---
+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)