+
+
+
+# MEDTRACE
+
+### Longitudinal AI for brain-MRI disease evolution
+
+**An interactive, evidence-linked map of how a brain tumour changes over time.**
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+> [!WARNING]
+> **Research prototype. Not a medical device.**
+> MEDTRACE is not intended for diagnosis, treatment planning, or any clinical decision. It has
+> not been clinically validated. It reports **measured change only**, never a diagnosis, grade,
+> progression judgement, treatment recommendation, or prognosis. All clinical decisions remain
+> with qualified healthcare professionals. Built exclusively on public, de-identified research
+> data.
+
+
+> [!NOTE]
+> **What this hosted demo serves.** 28 glioblastoma patients from
+> [RHUH-GBM](https://doi.org/10.7937/4545-c905), 3 timepoints each, under CC BY 4.0. The figures
+> below describe the full local build across LUMIERE and BraTS 2023, which are covered by data use
+> agreements that grant use but not redistribution, so they are not published here.
+>
+> There is no server. This is a Static Space: the interface and its recorded responses are served
+> from this repository, imaging is range-fetched from the
+> [dataset repository](https://huggingface.co/datasets/AIOmarRehan/medtrace-rhuh-gbm-derived), and
+> both renderers run on your GPU. Nothing is computed on request.
+>
+> [Model](https://huggingface.co/AIOmarRehan/medtrace-brats-segresnet) Β·
+> [Dataset](https://huggingface.co/datasets/AIOmarRehan/medtrace-rhuh-gbm-derived) Β·
+> [Code](https://github.com/AIOmarRehan/medtrace)
+
+---
+
+
+
+---
+
+## The clinical problem
+
+A patient with a brain tumour is imaged repeatedly: before surgery, after surgery, during
+radiotherapy and chemotherapy, then at follow-up for years. **Clinical decisions are made by
+comparing these examinations, not by reading any one of them.**
+
+Today that comparison is largely manual:
+
+```
+radiologist opens prior study βββΊ reads previous report βββΊ scrolls both studies side by side
+ βββΊ re-measures the lesion by hand βββΊ mentally reconstructs the patient's history
+```
+
+Most medical-imaging AI does not help, because it analyses **one scan at one moment**.
+Longitudinal comparison is a recognised underdeveloped area of medical imaging AI, and it is
+exactly where the clinical decision actually happens.
+
+MEDTRACE exists to answer one question:
+
+
+
+### *What changed in this patient between examinations?*
+
+
+
+---
+
+## What MEDTRACE answers
+
+Every screen exists to serve one of five questions. Nothing else is in scope.
+
+| Question | How MEDTRACE answers it |
+|---|---|
+| **What changed?** | Quantified, with units and direction: volume, diameter, surface area, growth rate |
+| **Where did it change?** | Highlighted in the image, per lesion, in 2D and 3D |
+| **When did it change?** | Located on the disease timeline, with the interval in days |
+| **How confident are we?** | Per pipeline stage, with the reason in plain language |
+| **Why do you believe that?** | The evidence, one click away |
+
+**The signature interaction is the time scrubber.** Dragging it moves imaging, segmentation,
+measurements, clinical events, the 3D surface and the evidence panel together.
+
+> [!NOTE]
+> An observation without evidence **cannot exist** in MEDTRACE. This is enforced in the service
+> layer, not by convention. It is why the system can always answer "why".
+
+---
+
+## The workstation
+
+
+
+
+
+*The clinical workstation: patient timeline, synchronised prior/current comparison, measured
+findings, and the AI evidence strip.*
+
+
+
+
+
+
+
+### 2D comparison
+
+
+
+Prior and current study side by side, **linked by fractional depth** rather than world
+coordinates, because unregistered studies differ by tens of millimetres and copying a camera
+between them can place it outside the other volume entirely.
+
+
+
+*Scroll one pane and the other follows.*
+
+
+
+
+### Segmentation overlay
+
+
+
+The tumour mask drawn in the **series' own voxel grid**, reoriented from the NIfTI affines with
+no resampling. Outline by default, fill on demand.
+
+
+
+
+
+---
+
+## Analysis pipeline
+
+Deliberately a pipeline of specialised stages, not one large model. **Every stage emits its
+prediction, its confidence, its quality flags and its model version.**
+
+```mermaid
+%%{init: {"theme":"base","themeVariables":{"fontSize":"15px","textColor":"#1e293b","nodeTextColor":"#1e293b","lineColor":"#475569","edgeLabelBackground":"#ffffff"},"flowchart":{"nodeSpacing":34,"rankSpacing":50,"padding":10}}}%%
+flowchart LR
+ A[("MRI studies")] --> B["Quality control"]
+ B --> C["Segmentation"]
+ C --> D["Lesion detection"]
+ D --> E["Registration"]
+ E --> F["Lesion matching"]
+ F --> G["Change detection"]
+ G --> H[("DiseaseObservation")]
+ H --> I["Disease timeline"]
+ H --> J["3D evolution map"]
+ H --> K["Evidence engine"]
+
+ classDef src fill:#e0f2fe,stroke:#0284c7,stroke-width:1px,color:#0c4a6e
+ classDef stage fill:#ffffff,stroke:#64748b,stroke-width:1px,color:#1e293b
+ classDef core fill:#ccfbf1,stroke:#0d9488,stroke-width:2px,color:#134e4a
+ classDef view fill:#e0e7ff,stroke:#4f46e5,stroke-width:1px,color:#312e81
+
+ class A src
+ class B,C,D,E,F,G stage
+ class H core
+ class I,J,K view
+```
+
+
+What each stage actually does
+
+
+
+| Stage | Implementation | Output |
+|---|---|---|
+| **Quality control** | Rule-based validation of sequences, voxel spacing, orientation, protocol drift between timepoints | `QualityFlag[]` per study |
+| **Segmentation** | MONAI `SegResNet`, 18.8 M parameters, 4 sequences in β 3 overlapping compartments out | Tumour compartment masks |
+| **Lesion detection** | Connected-component extraction per compartment, with a measurability floor | `Lesion` candidates |
+| **Registration** | SimpleITK rigid then affine; atlas-space masks need none, native-space ones do | Transform + score |
+| **Lesion matching** | Similarity scoring over overlap (Dice), centroid distance, proximity and volume similarity | `Lesion` identity across time |
+| **Change detection** | Volumetric and morphological comparison with uncertainty propagation | Absolute + relative change, growth rate |
+| **Trajectory** | Longitudinal feature vectors per lesion across all timepoints | Per-lesion history |
+| **Evidence engine** | Structured findings β validated answer β linked evidence | `AIObservation` + `EvidenceItem[]` |
+
+
+
+---
+
+## Domain model: observations, not images
+
+The core domain object is **`DiseaseObservation`**, not `MRI`. The timeline, the analytics, the
+AI answers and the audit trail are all *views over observations*.
+
+```mermaid
+%%{init: {"theme":"base","themeVariables":{"fontSize":"15px","textColor":"#1e293b","nodeTextColor":"#1e293b","lineColor":"#475569","edgeLabelBackground":"#ffffff"}}}%%
+erDiagram
+ PATIENT ||--o{ STUDY : "timepoints"
+ PATIENT ||--o{ LESION : "identity"
+ PATIENT ||--o{ CLINICAL_EVENT : "treatment"
+ PATIENT ||--o{ AI_OBSERVATION : "answers"
+ STUDY ||--o{ SERIES : "sequences"
+ STUDY ||--o| STUDY_QUALITY : "validation"
+ STUDY ||--o{ SEGMENTATION : "masks"
+ STUDY ||--o{ LESION_OBSERVATION : "observed in"
+ LESION ||--o{ LESION_OBSERVATION : "observed at"
+ LESION_OBSERVATION ||--o{ MEASUREMENT : "quantified by"
+ LESION_OBSERVATION ||--o{ EVIDENCE_ITEM : "supports"
+ AI_OBSERVATION ||--o{ EVIDENCE_ITEM : "must cite"
+ MODEL_RUN ||--o{ LESION_OBSERVATION : "produced"
+ MODEL_RUN ||--o{ AI_OBSERVATION : "produced"
+```
+
+A **`Lesion` belongs to a patient, not a study**, and that is what makes a per-lesion trajectory
+possible, and it is precisely what a conventional viewer or segmentation tool does not provide.
+
+
+The 14-table schema
+
+
+
+`patients` Β· `studies` Β· `series` Β· `study_quality` Β· `registrations` Β· `segmentations` Β·
+`lesions` Β· `lesion_observations` Β· `measurements` Β· `clinical_events` Β· `model_runs` Β·
+`ai_observations` Β· `evidence_items` Β· `audit_events`
+
+Managed with SQLAlchemy + Alembic. PostgreSQL stores **metadata only**. Imaging stays on the
+filesystem and is served through a path-allowlisted endpoint, never by a database blob.
+
+
+
+---
+
+## AI segmentation
+
+A 3D `SegResNet` trained from scratch on BraTS 2023 GLI, on a Kaggle Tesla T4.
+
+```mermaid
+%%{init: {"theme":"base","themeVariables":{"fontSize":"15px","clusterBkg":"#f1f5f9","clusterBorder":"#94a3b8","textColor":"#1e293b","nodeTextColor":"#1e293b","lineColor":"#475569","edgeLabelBackground":"#ffffff"},"flowchart":{"nodeSpacing":34,"rankSpacing":58,"padding":10}}}%%
+flowchart LR
+ subgraph IN ["4 co-registered sequences"]
+ direction TB
+ A1["T1c"]
+ A2["T1n"]
+ A3["T2-FLAIR"]
+ A4["T2w"]
+ end
+
+ B["Normalise"]
+ C["Crop"]
+ D["SegResNet 3D"]
+ E["Sliding window"]
+
+ subgraph OUT ["3 overlapping compartments"]
+ direction TB
+ F1["TC"]
+ F2["WT"]
+ F3["ET"]
+ end
+
+ G["Threshold"]
+ H["BraTS labels"]
+
+ A1 --> B
+ A2 --> B
+ A3 --> B
+ A4 --> B
+ B --> C --> D --> E
+ E --> F1
+ E --> F2
+ E --> F3
+ F1 --> G
+ F2 --> G
+ F3 --> G
+ G --> H
+
+ classDef seq fill:#e0f2fe,stroke:#0284c7,stroke-width:1px,color:#0c4a6e
+ classDef step fill:#ffffff,stroke:#64748b,stroke-width:1px,color:#1e293b
+ classDef model fill:#ccfbf1,stroke:#0d9488,stroke-width:2px,color:#134e4a
+ classDef out fill:#ede9fe,stroke:#7c3aed,stroke-width:1px,color:#4c1d95
+
+ class A1,A2,A3,A4 seq
+ class B,C,E,G,H step
+ class D model
+ class F1,F2,F3 out
+```
+
+| Step | What happens |
+|---|---|
+| **T1c Β· T1n Β· T2-FLAIR Β· T2w** | Four sequences, in **this exact channel order** |
+| **Normalise** | Per case, per channel, zero mean unit variance over **non-zero voxels only** |
+| **Crop** | To the non-zero bounding box of the summed channels, 4-voxel margin |
+| **SegResNet 3D** | MONAI `SegResNet`, 18,798,627 parameters, 32 init filters, blocks down `[1,2,2,4]` |
+| **Sliding window** | 128Β³ patches, 0.5 overlap, gaussian blending |
+| **TC Β· WT Β· ET** | Independent sigmoid per channel, so the three compartments **overlap** rather than compete |
+| **Threshold** | 0.5, plus an enhancing-tumour floor of 200 voxels |
+| **BraTS labels** | Written WTβ2, then TCβ1, then ETβ3, in that order |
+
+> [!IMPORTANT]
+> **Channel order is not recoverable from the weights.** `[t1c, t1n, t2f, t2w]` is part of the
+> model contract, recorded in `ml/artifacts/model_card.json`. Wrong order β wrong output β *no
+> error*. This is the kind of silent failure the model card exists to prevent.
+
+### Held-out test performance
+
+**186 cases from 169 subjects never seen in training or tuning.** Splits are computed at
+**subject level**, because BraTS contains 1,251 cases from only 1,133 subjects, so a random split over
+cases would leak the same patient into train and test.
+
+| Compartment | Dice (mean) | Dice (median) | HD95 (median) | Sensitivity | Precision | Dice (mean) on a 0 to 1 scale |
+|---|---:|---:|---:|---:|---:|:---|
+| **Whole tumour** | **0.9216** | 0.9489 | 2.45 mm | 0.9248 | 0.9236 | β°β°β°β°β°β°β°β°β°β°β°β°β°β°β°β°β°β°β±β± |
+| **Tumour core** | **0.9078** | 0.9563 | 2.00 mm | 0.9166 | 0.9170 | β°β°β°β°β°β°β°β°β°β°β°β°β°β°β°β°β°β°β±β± |
+| **Enhancing tumour** | **0.8520** | 0.8984 | 1.41 mm | 0.8833 | 0.8487 | β°β°β°β°β°β°β°β°β°β°β°β°β°β°β°β°β°β±β±β± |
+| **Mean of the three** | **0.8938** | - | - | - | - | β°β°β°β°β°β°β°β°β°β°β°β°β°β°β°β°β°β°β±β± |
+
+> [!TIP]
+> **Test scored higher than validation** (0.8938 vs 0.8899 mean Dice). Since both epoch
+> selection and post-processing tuning used the validation split, the validation figures are
+> optimistic by construction, so the test figures are the honest ones, and they did not degrade.
+
+
+Training configuration and honest limitations
+
+
+
+| Setting | Value |
+|---|---|
+| Architecture | `monai.networks.nets.SegResNet`, 3D, 32 init filters, blocks down `[1,2,2,4]` |
+| Loss | `DiceFocalLoss(sigmoid=True, squared_pred=True, batch=True)` |
+| Optimiser | AdamW, lr 2e-4, wd 1e-5, `CosineAnnealingLR` |
+| Precision | AMP float16 on Tesla T4 |
+| Patch sampling | 128Β³, 80% centred on whole tumour, 20% uniform |
+| Augmentation | Random axis flips, intensity scale Β±10%, intensity shift Β±10% |
+| Epochs | 37 of 60 completed (host RAM exhausted); **epoch 32 selected** on validation mean Dice |
+| Seed | `20260813` for both split and training |
+
+**Stated limitations** (from the model card):
+
+- Trained on **pre-operative** adult glioma only. Post-treatment appearances, such as resection
+ cavities, radiation change, are not represented.
+- Requires all four sequences. Behaviour with a missing sequence is untested.
+- Assumes BraTS preprocessing: skull-stripped, co-registered, 1 mm isotropic.
+- Measures **agreement with one annotation protocol on one dataset**. That is not a measure of
+ clinical accuracy.
+
+
+
+---
+
+## The training notebook
+
+The model was trained in a single notebook on a Kaggle Tesla T4, and it is in the repository with
+its outputs intact: [`ml/notebooks/brats_segmentation_training_output.ipynb`](ml/notebooks/brats_segmentation_training_output.ipynb).
+GitHub renders it, so every number below can be traced to the cell that printed it. The clean
+unexecuted version is [`brats_segmentation_training.ipynb`](ml/notebooks/brats_segmentation_training.ipynb).
+
+Nineteen numbered stages, from configuration through to verifying the exported weights actually
+load. Twenty-two code cells, twenty-one of them executed.
+
+### What the data looked like before any model existed
+
+
+
+
+
+
+
+Across 1,251 cases the whole tumour has a median volume of 89.3 cm3 and a range of 2.8 to 361.8
+cm3. The compartments are far smaller: enhancing tumour has a median of 17.3 cm3, and its minimum
+is **zero**, which is why the export applies a 200 voxel floor rather than reporting a
+one-voxel enhancing region as a finding.
+
+The single most consequential line the notebook printed:
+
+```
+tumour occupies 1.07% of all voxels
+-> uniform random patches would be almost pure background; sampling must be biased
+```
+
+That measurement is the reason patch sampling is 80% centred on the whole tumour and 20% uniform.
+It was not a hyperparameter guess.
+
+
+
+
+
+
+
+The four sequences for one case with the reference labels on T1C. Looking at the actual images is
+how the channel order was confirmed, and channel order is not recoverable from the weights: get it
+wrong and the model produces a plausible, wrong answer with no error anywhere.
+
+### Training
+
+
+
+
+
+
+
+Loss on the left, per-region validation Dice on the right, with the selected epoch marked. Thirty
+seven of a planned sixty epochs completed before host RAM was exhausted, and **epoch 32 was
+selected on mean validation Dice at 0.8867**. The curve is what justifies stopping there rather
+than at the last epoch: validation had flattened well before the run ended.
+
+### The test set, run once
+
+
+
+
+
+
+
+186 cases from 169 subjects, held out at subject level. Per-region Dice on the left, Dice against
+tumour size on the right, and that right-hand panel is the honest one: agreement collapses on the
+smallest tumours, where a few voxels of disagreement dominate the metric.
+
+| Split comparison | Validation | Test | Gap |
+|---|---:|---:|---:|
+| Mean Dice | 0.8899 | **0.8938** | **-0.0039** |
+
+The test score is marginally *higher* than validation. There is no overfitting to report, and the
+generalisation gap is smaller than the run-to-run noise.
+
+| Region | Cases below 0.5 Dice | Cases with empty ground truth |
+|---|---:|---:|
+| Whole tumour | 2 of 186 | 0 |
+| Tumour core | 5 of 186 | 1 |
+| Enhancing tumour | 8 of 186 | 5 |
+
+### The cases it got wrong
+
+Most projects show the best cases. The notebook prints the worst four, because those are the ones
+that say something.
+
+
+
+
+
+
+
+`BraTS-GLI-00675-001`, whole tumour Dice **0.000**, yet tumour core and enhancing tumour both
+**1.000**. A whole tumour score of zero alongside perfect compartments is a labelling edge case,
+not a model that cannot see the tumour.
+
+
+
+
+
+
+`BraTS-GLI-00493-000`, whole tumour Dice **0.184** on 34,451 labelled voxels, while core reaches
+0.924 and enhancing 0.883. The oedema boundary is the disagreement, which is the least
+reproducible boundary between human annotators too.
+
+
+
+
+
+> [!NOTE]
+> The worst case is where the enhancing floor and the quality gate earn their place. A study that
+> produces a result like this is flagged rather than reported as a confident measurement, and
+> nothing from this model reaches the measurement pipeline at all. See
+> [Validation & results](#validation--results) for why.
+
+Regenerate the figures from the notebook at any time:
+
+```bash
+make export-notebook-figures
+```
+
+## Validation & results
+
+### The decision not to ship the model into the measurement pipeline
+
+The trained model scores 0.894 mean Dice on held-out BraTS. It is **deliberately not** used for
+MEDTRACE's measurements, and the reason is measurement, not caution.
+
+Run against DeepBraTumIA on **12 randomly chosen real LUMIERE studies**
+(`ml/scripts/compare_on_lumiere.py`):
+
+| Region | Median Dice | Studies below 0.5 | Median Dice on a 0 to 1 scale |
+|---|---:|---:|:---|
+| Whole tumour | **0.923** | 0 of 12 | β°β°β°β°β°β°β°β°β°β°β°β°β°β°β°β°β°β°β±β± |
+| Tumour core | 0.816 | 1 of 12 | β°β°β°β°β°β°β°β°β°β°β°β°β°β°β°β°β±β±β±β± |
+| **Enhancing tumour** | **0.486** | **6 of 12** | β°β°β°β°β°β°β°β°β°β°β±β±β±β±β±β±β±β±β±β± |
+
+Splitting by how much enhancement is actually present shows this is not a uniform weakness:
+
+| DeepBraTumIA enhancing volume | n | Median ET Dice | Our volume vs theirs |
+|---|---:|---:|---:|
+| Bulky, β₯ 5 cmΒ³ | 5 | **0.861** | 1.03Γ |
+| Small, < 5 cmΒ³ | 7 | **0.193** | ~4Γ |
+
+**The cause was predicted before the comparison was run.** BraTS is pre-operative glioma, where
+enhancing tumour is a thick contrast-avid ring. LUMIERE is post-treatment: resection margins,
+radiation change and post-surgical enhancement all enhance, and none of it appears in BraTS. The
+model has never been shown a brain that has been operated on.
+
+> [!CAUTION]
+> Enhancing tumour is **the compartment MEDTRACE measures and reports change on**. Swapping the
+> pipeline over would inflate every enhancing volume, worst on exactly the small lesions where a
+> change of a few tenths of a cmΒ³ decides whether progression is reported. So the pipeline keeps
+> DeepBraTumIA's masks, and this comparison is documented as *agreement between two automated
+> tools*, not as accuracy.
+
+---
+
+## Measurement, matching and change
+
+### Measurement chosen by measurement
+
+Surface area is computed by marching cubes over a **signed distance field**, not over the binary
+mask directly. The estimator was selected by comparison against analytic shapes: it degrades far
+more gracefully with anisotropic voxels, and this data spans **0.36 mm to 6.0 mm** slice spacing.
+
+The reported surface area **is** the area of the mesh shipped to the 3D viewer, verified to
+within 0.009% across 1,153 meshes, so the number in the findings panel and the surface on screen
+cannot disagree.
+
+### Cross-time lesion matching
+
+```mermaid
+%%{init: {"theme":"base","themeVariables":{"fontSize":"15px","textColor":"#1e293b","nodeTextColor":"#1e293b","lineColor":"#475569","edgeLabelBackground":"#ffffff"},"flowchart":{"nodeSpacing":40,"rankSpacing":55,"padding":10}}}%%
+flowchart LR
+ P["Prior lesions"] --> S{"Score each pair"}
+ C["Current lesions"] --> S
+
+ S --> O["Overlap Dice"]
+ S --> D["Centroid distance"]
+ S --> X["Proximity"]
+ S --> V["Volume similarity"]
+
+ O --> M["Optimal assignment"]
+ D --> M
+ X --> M
+ V --> M
+
+ M --> R1["Matched"]
+ M --> R2["New lesion"]
+ M --> R3["Disappeared"]
+ M --> R4["Uncertain"]
+
+ classDef inp fill:#e0f2fe,stroke:#0284c7,stroke-width:1px,color:#0c4a6e
+ classDef comp fill:#ffffff,stroke:#64748b,stroke-width:1px,color:#1e293b
+ classDef dec fill:#ccfbf1,stroke:#0d9488,stroke-width:2px,color:#134e4a
+ classDef good fill:#dcfce7,stroke:#16a34a,stroke-width:1px,color:#14532d
+ classDef warn fill:#ffedd5,stroke:#ea580c,stroke-width:2px,color:#7c2d12
+
+ class P,C inp
+ class O,D,X,V comp
+ class S,M dec
+ class R1,R2,R3 good
+ class R4 warn
+```
+
+| Outcome | Meaning |
+|---|---|
+| **Matched** | Keeps the same `Lesion` id, so the trajectory continues |
+| **New lesion** | An unmatched current lesion, so a new `Lesion` identity is created |
+| **Disappeared** | An unmatched prior lesion, recorded as an absence, not silently dropped |
+| **Uncertain** | `MATCH_UNCERTAIN`, `WEAK_SCORE` or `AMBIGUOUS_ALTERNATIVE`. The finding is de-emphasised and the 3D surface stays grey |
+
+A doubtful correspondence is **never presented as a confident one**. `MATCH_UNCERTAIN`,
+`WEAK_SCORE` and `AMBIGUOUS_ALTERNATIVE` de-emphasise the finding in the panel and force the 3D
+surface to render grey rather than in a change colour, because a colour that says "growing" is
+a claim, and it must not be made when the lesion it is compared against may be a different
+lesion.
+
+### Change with propagated uncertainty
+
+
+
+
+
+*Measured change per lesion, with confidence and the reason it is reduced.*
+
+
+
+Confidence is reported **per pipeline stage**, and an answer's confidence is the **minimum**
+across the observations it rests on, never an average. A confident segmentation combined with an
+uncertain registration produces an uncertain change measurement, and averaging would let the
+reliable measurement hide the unreliable one.
+
+Eleven quality flags feed this, each translated into plain language: *"the two studies used
+different acquisition protocols"*, *"slice thickness was large enough to affect volume
+measurement"*, *"another lesion scored almost as well as this correspondence"*.
+
+---
+
+## 3D disease evolution
+
+
+
+
+
+*Dragging the time scrubber: the lesion surface, the measurements and the evidence move together.*
+
+
+
+
+
+*Rotation follows the pointer, and the head stays solid from every angle.*
+
+
+
+### The head is volume-rendered, and that was a hard-won decision
+
+The brain context was originally a **surface** extracted from the skull-strip mask. It was
+reported as having holes five separate times. Each round found something real: open edges from a
+crop that borrowed its margin from the source volume, front-face culling that erased deep
+concavities, a camera whose view-up was parallel to its view direction, lesions left unlit by a
+single headlight. Each round fixed it, measured the rendered image as clean, and the report still stood.
+
+> [!NOTE]
+> A surface leaves only two options, and **each has a failure mode invisible to a software
+> rasteriser**. Translucent, and the result depends on blending order and multisample resolve,
+> which vary by driver. Opaque with the near wall culled, and it is a hollow bowl that hides the
+> anatomy it exists to show. The checks ran under SwiftShader; the defect lived on the GPU.
+
+The head is now the patient's own **skull-stripped contrast-enhanced T1, ray-cast as a volume**.
+There is no surface to close, no winding, no culling, no blending order, so *a gap in the anatomy is
+not expressible*. And it is the real anatomy at full 1 mm resolution rather than a smoothed
+approximation of its outer boundary.
+
+| Measurement | Measured on the Intel Iris Plus iGPU |
+|---|---|
+| Median frame | **16.7 ms β 59.9 fps** *(16.7 ms is the vsync interval, so the renderer is not the limit)* |
+| 95th percentile frame | 16.8 ms |
+| First frame after load | 4.0 s |
+| Main thread after a 60-step drag | 2 ms |
+
+Benchmarked on real hardware rather than in software, because software rendering is exactly how
+five rounds of a rendering defect stayed invisible. Run it yourself: `make benchmark-3d`.
+
+
+
+
+
+
+
+**Coloured by measured change.** Red > +25%, blue < β25%, green stable, **grey for a baseline or
+a doubtful match**. The prior timepoint is drawn as a wireframe.
+
+
+
+
+
+
+Selecting a lesion highlights it simultaneously in the 2D panes, the findings list and the 3D
+view. Lesion meshes are **never smoothed**, because that mesh is the source of the reported surface area.
+
+
+
+
+
+
+Why the lesions stay as surfaces while the head is a volume
+
+
+
+The lesions are the *measured* objects. Each needs its own colour for its own change, and a
+surface is the honest way to draw a boundary that came from a mask. They are extracted at full
+resolution (median 71 KB, max 715 KB per mesh) and shipped as binary PLY with per-vertex normals.
+
+`ml/scripts/check_mesh_integrity.py` verifies the shipped bytes rather than synthetic spheres:
+unit-length normals, outward orientation, triangle winding consistent with them, zero open edges,
+zero non-manifold edges, one connected component.
+
+
+
+---
+
+## The evidence engine
+
+Five fixed clinical questions, answered from measurements already in the database, validated
+before delivery, and stored with the evidence that supports them.
+
+```mermaid
+%%{init: {"theme":"base","themeVariables":{"fontSize":"15px","actorFontSize":"15px","noteFontSize":"14px","messageFontSize":"14px","textColor":"#1e293b","actorTextColor":"#1e293b","noteTextColor":"#1e293b","signalTextColor":"#1e293b","actorBkg":"#e0f2fe","actorBorder":"#0284c7","noteBkgColor":"#fef3c7","noteBorderColor":"#d97706","labelBoxBkgColor":"#e0f2fe","labelTextColor":"#1e293b"}}}%%
+sequenceDiagram
+ autonumber
+ actor U as Clinician
+ participant F as findings
+ participant A as answers
+ participant L as LLM
+ participant S as SafetyGuard
+ participant D as Database
+
+ U->>F: Ask one of five questions
+ F->>D: Read recorded measurements
+ D-->>F: Observations, confidence, flags
+ F->>F: Assemble structured findings
+ Note over F: No evidence means no observation
+ F->>A: Compose deterministic answer
+ A-->>S: Ground truth
+ F->>L: Same findings, ask for prose
+ L-->>S: Draft, or nothing at all
+ S->>S: Forbidden claim? Invented number?
+ S-->>U: Answer, evidence, confidence
+ S->>D: Persist and audit
+```
+
+> [!IMPORTANT]
+> **The model never sees pixels, never computes a number, and never has the last word.**
+> `answers.py` composes the answer from findings alone with no model involved, and that sentence is
+> the ground truth. A language model may make it *more readable*; it may not make it *different*.
+
+
+
+
+
+
+
+
+
+*Every answer carries its evidence, and every piece of evidence navigates to the study it came from.*
+
+
+
+An actual answer, generated from real measurements:
+
+> No measured enhancing volume changed by more than 25% between week-019-2 and week-033, 98 days
+> apart. 1 lesion changed by less: L06 measures 4.2 mmΒ³, decreased by 5.7%. 5 lesions had no
+> prior to compare against⦠**Confidence 0.64; slice thickness was large enough to affect volume
+> measurement.**
+
+### SafetyGuard
+
+Implements a fixed table of permitted and forbidden statements, and nothing beyond it. What a medical
+tool may and may not state is not an engineering decision.
+
+| Check | Outcome |
+|---|---|
+| Diagnosis, tumour type or grade | Draft discarded β measurement delivered |
+| Progression, response, improvement, recurrence | Draft discarded β measurement delivered |
+| Treatment recommendation | Draft discarded β measurement delivered |
+| Prognosis or survival | Draft discarded β measurement delivered |
+| Clinical urgency | Draft discarded β measurement delivered |
+| A number not present in the structured findings | Draft discarded β measurement delivered |
+| A lesion or study not in the evidence set | Draft discarded β measurement delivered |
+| **No linked evidence** | **Blocked, nothing delivered** |
+| Confidence below 0.6 | Delivered, marked low, reason in plain language |
+| Quality flags on the inputs | Delivered, flags surfaced alongside |
+
+The regular expressions are deliberately coarse and are **not** treated as a semantic filter. A
+pattern cannot understand a sentence, so the guarantee comes from the deterministic fallback, not
+from the cleverness of the patterns. A blocked draft is **kept**, because a block is a signal
+about the pipeline rather than just a filtered string.
+
+
+A worked example
+
+
+
+```
+LLM draft: "The tumour is malignant and has progressed."
+SafetyGuard: MODIFIED, diagnostic or grading claim; progression or response judgement
+Delivered: "The segmented enhancing volume increased from 12.2 cmΒ³ to 19.7 cmΒ³
+ (+61.5%) between 2025-06-12 and 2025-09-04."
+```
+
+Two false positives were found by measurement and fixed: a timepoint called `week-012` parses as
+the number β12, and a lesion called `L01` as 1, so identifiers are stripped before the numeric
+scan. Rounding is not fabrication. 19.7 written as 20 is accepted, 42 is not.
+
+
+
+### Reproducibility and audit
+
+`findings_hash` digests the question and the entire findings payload. Asking the same question
+about unchanged findings returns the **stored** answer rather than generating a second one, so a
+past statement stays reconstructable. Every generation writes an `AuditEvent` carrying the model
+version, the input hash, the output hash and the safety outcome, with the patient referenced by
+UUID and no findings in the detail.
+
+---
+
+## Architecture
+
+```mermaid
+%%{init: {"theme":"base","themeVariables":{"fontSize":"15px","clusterBkg":"#f8fafc","clusterBorder":"#94a3b8","textColor":"#1e293b","nodeTextColor":"#1e293b","lineColor":"#475569","edgeLabelBackground":"#ffffff"},"flowchart":{"nodeSpacing":40,"rankSpacing":62,"padding":12}}}%%
+flowchart TB
+ subgraph BROWSER ["Browser"]
+ W["Next.js Β· React"]
+ CS["Cornerstone3D"]
+ VTK["vtk.js"]
+ end
+
+ subgraph APILAYER ["FastAPI modular monolith"]
+ R["Routers"]
+ AN["Analysis"]
+ EV["Evidence engine"]
+ IG["Ingestion"]
+ end
+
+ subgraph MLLAYER ["ml, separate package"]
+ MM["medtrace_ml"]
+ ART["artifacts"]
+ end
+
+ subgraph SVC ["Docker Compose"]
+ PG[("PostgreSQL")]
+ MIO[("MinIO")]
+ ORT[("Orthanc")]
+ RD[("Redis")]
+ end
+
+ FS[("Filesystem")]
+
+ W --- CS
+ W --- VTK
+ W -->|"REST Β· PLY Β· NIfTI"| R
+ R --> AN
+ R --> EV
+ R --> IG
+ AN --> MM
+ EV --> MM
+ IG --> MM
+ MM --- ART
+ AN --> PG
+ IG --> PG
+ EV --> PG
+ IG --> ORT
+ AN --> MIO
+ R --> RD
+ R -->|"path allowlist"| FS
+
+ classDef ui fill:#e0f2fe,stroke:#0284c7,stroke-width:1px,color:#0c4a6e
+ classDef api fill:#ccfbf1,stroke:#0d9488,stroke-width:1px,color:#134e4a
+ classDef ml fill:#e0e7ff,stroke:#4f46e5,stroke-width:1px,color:#312e81
+ classDef svc fill:#f3e8ff,stroke:#9333ea,stroke-width:1px,color:#581c87
+ classDef fs fill:#fef3c7,stroke:#d97706,stroke-width:1px,color:#78350f
+
+ class W,CS,VTK ui
+ class R,AN,EV,IG api
+ class MM,ART ml
+ class PG,MIO,ORT,RD svc
+ class FS fs
+```
+
+
+
+**ML code stays out of application code.** The application depends on model *contracts*, never on
+training code. Two Python packages, `medtrace-api` and `medtrace-ml`, with the API importing the
+latter but never the reverse.
+
+
+
+
+
+*Every endpoint is typed end to end: Pydantic v2 on the server, generated TypeScript contracts in
+the browser.*
+
+
+
+
+Repository layout
+
+
+
+```
+medtrace/
+βββ apps/
+β βββ api/ FastAPI modular monolith
+β β βββ alembic/versions/ schema migrations
+β β βββ medtrace/
+β β β βββ analysis/ the measurement pipeline
+β β β βββ domain/ models.py, enums.py
+β β β βββ evidence/ findings Β· answers Β· llm Β· safety Β· service
+β β β βββ ingestion/ BraTS and LUMIERE readers, quality control
+β β β βββ routers/
+β β βββ scripts/ one-off data operations
+β β βββ tests/
+β βββ web/ Next.js clinical workstation
+β βββ scripts/ browser verification and the 3D benchmark
+β βββ src/{app,components,lib,store}
+βββ ml/
+β βββ medtrace_ml/ measure Β· lesions Β· matching Β· change Β· trajectory
+β β registration Β· mesh Β· volume Β· labels
+β βββ notebooks/ training notebook, its generator and its checks
+β βββ scripts/ dataset preparation, model and mesh verification
+β βββ artifacts/ trained weights, TorchScript, model card
+β βββ reports/ evaluation and agreement CSVs
+βββ packages/ reserved for shared contracts
+βββ infrastructure/ MLflow image, database init
+βββ data/
+β βββ raw/ the datasets
+β βββ derived/ generated meshes and atlas volumes
+β βββ kaggle/ upload staging, manifest and splits kept
+βββ docs/screenshots/ interface captures used in this README
+```
+
+
+
+---
+
+## Technology stack
+
+
+
+> [!NOTE]
+> **On the LLM:** the default configuration is `MEDTRACE_LLM_PROVIDER=none`, and that is a
+> *working* configuration rather than a disabled one. MedGemma-27B does not fit on the
+> development GPU, and the five clinical questions must be answerable regardless, so the
+> deterministic composer answers them. Point `MEDTRACE_LLM_BASE_URL` at a vLLM, Ollama or
+> llama.cpp endpoint and it will rephrase; if that endpoint is unreachable you lose wording, not
+> correctness.
+
+---
+
+## Datasets
+
+Public, de-identified research data only. No real hospital patient data enters this repository
+under any circumstances.
+
+| Dataset | Role | Scale ingested |
+|---|---|---|
+| **BraTS 2023 GLI** | Segmentation training and held-out evaluation | 1,251 cases from 1,133 subjects Β· 118 with two labelled timepoints |
+| **LUMIERE** | The longitudinal dataset: timeline, matching, trajectory | 91 patients Β· 638 timepoints Β· 2,487 series |
+
+LUMIERE's acquisition metadata records **three field strengths, 21 scanner models and slice
+thickness from 0.8 mm to 6.0 mm**, so quality control and confidence reporting are built against
+measured heterogeneity rather than imagined inputs.
+
+
+Data we hold and deliberately will not use
+
+
+
+LUMIERE ships **survival time in weeks, IDH status and MGMT methylation** for all 91 patients.
+That makes outcome and molecular prediction technically possible with the data already on disk.
+
+We are not building it. Prognosis is forbidden by the safety policy this project holds itself to, and a
+measurement system must be trustworthy before prediction built on top of it means anything. This
+is recorded as a deliberate decision rather than an oversight, so the temptation is resolved once
+instead of repeatedly.
+
+LUMIERE's **expert RANO ratings for 616 timepoints** are likewise used only as a reference
+standard for evaluation. Never a training target, never surfaced as a MEDTRACE output.
+
+
+
+---
+
+## Verification
+
+Nothing here is asserted without measurement. **696 automated checks.**
+
+| Suite | Checks | What it proves |
+|---|---:|---|
+| `pytest` API | **155** | Contracts, ingestion, quality control, pipeline output, SafetyGuard |
+| `pytest` ML | **168** | Measurement, matching, change, meshing, volume windowing, label mappings |
+| `vitest` web | **17** | Camera conventions, hole detection geometry |
+| `verify-study-linkage` | **189** | Every pane displays the study it claims, at every timepoint |
+| `verify-evidence` | **42** | Answers carry evidence, cite only measured numbers, and navigate |
+| `verify-evolution` | **37** | The 3D view draws the right surfaces, in one coordinate frame |
+| `verify-workstation` | **19** | The clinical shell, the scrubber, and the intended-use notice |
+| `verify-brain-shell` | **15** | No holes in the rendered head, at five viewing angles |
+| `verify-overlay` | **15** | The 2D tumour overlay draws the right mask |
+| `verify-slice-sync` | **14** | The two panes really scroll together, and stop when unlinked |
+| `verify-findings` | **8** | The UI shows real measured findings, not seeded numbers |
+
+
+Why the browser checks exist at all
+
+
+
+Because instrumentation has been wrong more than once, and each time it was wrong in a way that
+*passed*:
+
+- A hole detector that counted the gaps between the legend's **text glyphs**, because an element
+ screenshot captures whatever is drawn over the element.
+- The same check running on the patient the app opens on, which has a single 96-pixel lesion, and
+ and passing with zero holes while the defect was obvious on a patient with twelve.
+- A watertightness test that only ever ran at stride 1, while the shipped meshes used stride 2.
+- A "fit view" check that measured the brain instead of the lesion.
+
+Expected values are taken **from the API**, not from the page. The hole detector now lives in its
+own module with its own unit tests, because three wrong versions of a check is enough.
+
+
+
+---
+
+## Getting started
+
+### Prerequisites
+
+| Requirement | Needed |
+|---|---|
+| Docker Desktop | Running, with the Linux engine |
+| Python | β₯ 3.11 *(resolved against 3.14)* |
+| Node.js | β₯ 20 *(tested on 24)* |
+| Disk | ~50 GB for both datasets |
+
+### First-time setup
+
+```bash
+cp .env.example .env
+
+make up # PostgreSQL, Redis, MinIO, Orthanc, MLflow
+make install # API venv + ML venv + web dependencies
+make migrate # create the schema
+make seed # synthetic demonstration patient (labelled synthetic in the UI)
+```
+
+Then ingest and analyse. This is the slow part:
+
+```bash
+make ingest-lumiere # 91 patients, 638 timepoints
+make ingest-brats # 118 subjects with two labelled timepoints
+make analyse # the full measurement pipeline (~20 min)
+make link-masks # reorient each series' mask for the 2D overlay
+```
+
+### Running it
+
+Three terminals, in this order:
+
+```bash
+# 1. services
+docker compose up -d
+
+# 2. API
+cd apps/api
+.venv/Scripts/python.exe -m uvicorn medtrace.main:app --host 127.0.0.1 --port 8000
+
+# 3. web (production build, see the note below)
+cd apps/web
+npm run start
+```
+
+| Service | Address |
+|---|---|
+| **Workstation** | **http://localhost:3000** |
+| API documentation | http://localhost:8000/docs |
+| Health check | http://localhost:8000/health |
+| Orthanc | http://localhost:8042 |
+| MinIO console | http://localhost:9101 |
+
+**Stop cleanly:** `Ctrl+C` in the web and API terminals, then `docker compose stop`.
+
+> [!TIP]
+> Use `npm run start`, not `npm run dev`. Volume rendering is the heaviest part of MEDTRACE, and
+> development mode's HMR, source maps and double-invoked effects consume memory the renderer
+> needs. Never run `npm run build` while a dev server is running, because they share `.next`.
+>
+> The viewer caps its cache at **500 MB and six resident volumes**, shows its footprint in the
+> toolbar, and offers a `reset viewer` control if the graphics context is ever lost.
+
+> [!WARNING]
+> **No authentication.** The API is unauthenticated and binds to `127.0.0.1` only, with
+> development credentials from `.env`. It is for local use. Do not expose it.
+
+
+Optional commands
+
+
+
+```bash
+# Tests
+cd apps/api && .venv/Scripts/python.exe -m pytest -q # 155
+cd ml && ../apps/api/.venv/Scripts/python.exe -m pytest -q # 168
+cd apps/web && npm run test # 22 unit
+
+# Browser verification (needs both servers running)
+make verify # every browser suite
+make verify-evidence # the AI evidence engine
+make verify-brain-shell # no holes in the rendered head
+make benchmark-3d # 3D frame rate on the real GPU
+
+# Quality
+make lint # Ruff + ESLint
+make typecheck # tsc --noEmit
+
+# Data and model inspection
+make inspect p=Patient-006 # per-lesion trajectories
+make quality-report # what quality control found
+make check-mesh-integrity # normals, winding, watertightness of shipped meshes
+make check-volume-pockets # no enclosed pockets of air inside the 3D display volumes
+make verify-meshes # every stored mesh against its observation
+make check-readme-diagrams # render this README's diagrams, check no label overflows
+```
+
+Ports are shifted off the defaults, **PostgreSQL on 5433 and MinIO console on 9101**, so the stack
+does not collide with locally installed services.
+
+
+
+---
+
+## Build status
+
+| Stage | Scope | State |
+|---|---|:---:|
+| **0** | Clinical problem, scope, safety boundaries, evaluation plan, architecture, dataset cards | Done |
+| **1** | Docker Compose infrastructure, FastAPI, 14-table observation model, clinical workstation | Done |
+| **2** | Dataset ingestion, quality control, Cornerstone3D medical viewer, slice synchronisation | Done |
+| **3** | Measurement, lesion extraction, registration, cross-time matching, change detection, trajectory | Done |
+| **4** | 3D disease evolution, volume rendering, surface extraction tied to the time scrubber | Done |
+| **5** | Evidence engine, SafetyGuard, five clinical questions, audit trail | Done |
+
+**Deliberately not built:** MLOps automation (DVC, MLflow registry integration, Great
+Expectations), authentication and role-based access, deformable registration, progression
+classification, prognosis. The first is a project decision; the rest are scoped to later versions.
+
+---
+
+## Honesty as a design principle
+
+MEDTRACE is presented as a prototype, because that is what it is. Understanding why clinical
+validation matters, and saying so plainly, is a strength when talking to clinicians, not a
+weakness to hide behind confident language.
+
+Every number in this README is measured and reproducible from the repository. Where a result is
+unflattering, it is stated: the model's enhancing-tumour agreement on post-treatment data is
+poor, and that is why it is not in the measurement pipeline.
+
+---
+
+
+
+**Datasets:** BraTS 2023 GLI and LUMIERE, used under their respective research licences.
+LUMIERE is non-commercial.
+
+
+
+*MEDTRACE reports measured change. It does not diagnose.*
+
+
diff --git a/api/patients.json b/api/patients.json
new file mode 100644
index 0000000000000000000000000000000000000000..0f91c3b37479b93753c722511c4a24e0ed761255
--- /dev/null
+++ b/api/patients.json
@@ -0,0 +1 @@
+[{"age_at_baseline_years":65,"external_id":"RHUH-0001","id":"eb1d080b-101f-45ce-8455-cf9f4967dd55","is_synthetic":false,"lesion_count":2,"sex":"male","source_dataset":"RHUH_GBM","study_count":3},{"age_at_baseline_years":65,"external_id":"RHUH-0002","id":"4709627e-6d9f-467a-9c36-76e45ed24071","is_synthetic":false,"lesion_count":5,"sex":"female","source_dataset":"RHUH_GBM","study_count":3},{"age_at_baseline_years":75,"external_id":"RHUH-0003","id":"37ab76a9-f8d9-4ccb-8ae7-5b617bc333f8","is_synthetic":false,"lesion_count":4,"sex":"male","source_dataset":"RHUH_GBM","study_count":3},{"age_at_baseline_years":64,"external_id":"RHUH-0004","id":"0e36f014-cba2-4b67-a60a-d5c720f13384","is_synthetic":false,"lesion_count":2,"sex":"female","source_dataset":"RHUH_GBM","study_count":3},{"age_at_baseline_years":52,"external_id":"RHUH-0005","id":"a2cf6c6a-db6d-493a-9f48-ada357b32b83","is_synthetic":false,"lesion_count":2,"sex":"male","source_dataset":"RHUH_GBM","study_count":3},{"age_at_baseline_years":72,"external_id":"RHUH-0006","id":"1644cc40-79e6-47bf-a4e4-51c8248a7ad9","is_synthetic":false,"lesion_count":3,"sex":"male","source_dataset":"RHUH_GBM","study_count":3},{"age_at_baseline_years":51,"external_id":"RHUH-0007","id":"6bc843a9-e804-46d5-9d7d-d387a1f9c1ed","is_synthetic":false,"lesion_count":3,"sex":"female","source_dataset":"RHUH_GBM","study_count":3},{"age_at_baseline_years":69,"external_id":"RHUH-0008","id":"e6fe336a-7b59-43a7-a419-3f01b51196bb","is_synthetic":false,"lesion_count":2,"sex":"male","source_dataset":"RHUH_GBM","study_count":3},{"age_at_baseline_years":68,"external_id":"RHUH-0009","id":"8180e98b-ffa6-4f09-b73b-b35f9937a765","is_synthetic":false,"lesion_count":2,"sex":"male","source_dataset":"RHUH_GBM","study_count":3},{"age_at_baseline_years":66,"external_id":"RHUH-0010","id":"75835d9b-32d4-4c9a-8cf9-8f57b066084b","is_synthetic":false,"lesion_count":2,"sex":"male","source_dataset":"RHUH_GBM","study_count":3},{"age_at_baseline_years":71,"external_id":"RHUH-0011","id":"2043be8c-57f4-4845-88d0-cadadd106f19","is_synthetic":false,"lesion_count":3,"sex":"female","source_dataset":"RHUH_GBM","study_count":3},{"age_at_baseline_years":64,"external_id":"RHUH-0012","id":"041c2146-8e58-4c2d-b5dd-6c888ff2447e","is_synthetic":false,"lesion_count":2,"sex":"female","source_dataset":"RHUH_GBM","study_count":3},{"age_at_baseline_years":54,"external_id":"RHUH-0013","id":"31d3fa6c-d577-4972-9845-fd45c6d03801","is_synthetic":false,"lesion_count":4,"sex":"male","source_dataset":"RHUH_GBM","study_count":3},{"age_at_baseline_years":78,"external_id":"RHUH-0014","id":"65c97660-6722-4a04-8a81-840ce91c1a8d","is_synthetic":false,"lesion_count":11,"sex":"male","source_dataset":"RHUH_GBM","study_count":3},{"age_at_baseline_years":60,"external_id":"RHUH-0015","id":"92f0ea5a-eacc-42a8-a3c6-2345fc9e3722","is_synthetic":false,"lesion_count":4,"sex":"male","source_dataset":"RHUH_GBM","study_count":3},{"age_at_baseline_years":68,"external_id":"RHUH-0016","id":"904c26dc-10ec-4454-9767-9758b9dc9789","is_synthetic":false,"lesion_count":3,"sex":"female","source_dataset":"RHUH_GBM","study_count":3},{"age_at_baseline_years":47,"external_id":"RHUH-0017","id":"2be8a602-8102-4bf4-a6a0-87df5f84f3c1","is_synthetic":false,"lesion_count":4,"sex":"male","source_dataset":"RHUH_GBM","study_count":3},{"age_at_baseline_years":57,"external_id":"RHUH-0018","id":"9bf9a4c8-66f6-470a-a03a-e425c4f5f438","is_synthetic":false,"lesion_count":8,"sex":"male","source_dataset":"RHUH_GBM","study_count":3},{"age_at_baseline_years":61,"external_id":"RHUH-0019","id":"05e8f381-fc6b-4afa-a8ae-e1a8888b7be3","is_synthetic":false,"lesion_count":4,"sex":"male","source_dataset":"RHUH_GBM","study_count":3},{"age_at_baseline_years":55,"external_id":"RHUH-0020","id":"add7899e-4dcb-4587-9733-ec0a4068cfa5","is_synthetic":false,"lesion_count":7,"sex":"male","source_dataset":"RHUH_GBM","study_count":3},{"age_at_baseline_years":56,"external_id":"RHUH-0021","id":"8280c78d-5068-4b5a-ac27-d33c0839b167","is_synthetic":false,"lesion_count":1,"sex":"male","source_dataset":"RHUH_GBM","study_count":3},{"age_at_baseline_years":78,"external_id":"RHUH-0022","id":"4ec108df-781d-4553-b5e0-56aa6c18c94a","is_synthetic":false,"lesion_count":1,"sex":"female","source_dataset":"RHUH_GBM","study_count":3},{"age_at_baseline_years":59,"external_id":"RHUH-0023","id":"69ea08eb-e7e9-4728-b9f5-4eaeac564c4a","is_synthetic":false,"lesion_count":5,"sex":"male","source_dataset":"RHUH_GBM","study_count":3},{"age_at_baseline_years":50,"external_id":"RHUH-0024","id":"5cbd85a6-01b0-40aa-b6ba-eb8a5dd942c3","is_synthetic":false,"lesion_count":3,"sex":"male","source_dataset":"RHUH_GBM","study_count":3},{"age_at_baseline_years":49,"external_id":"RHUH-0025","id":"0a1ba0ca-23d8-46ed-9974-f86a08bf7027","is_synthetic":false,"lesion_count":1,"sex":"male","source_dataset":"RHUH_GBM","study_count":3},{"age_at_baseline_years":63,"external_id":"RHUH-0026","id":"d84b4c77-418f-4934-8b0b-257a47d73d31","is_synthetic":false,"lesion_count"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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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diff --git a/api/study/06f843bf-75d5-45a7-8630-56ea34a8acc0.json b/api/study/06f843bf-75d5-45a7-8630-56ea34a8acc0.json
new file mode 100644
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new file mode 100644
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diff --git a/api/study/0a15d8e4-145f-4cab-9167-8b13a9ff444f.json b/api/study/0a15d8e4-145f-4cab-9167-8b13a9ff444f.json
new file mode 100644
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diff --git a/api/study/0cd019eb-859f-4687-8c74-fa0b7a36e4e4.json b/api/study/0cd019eb-859f-4687-8c74-fa0b7a36e4e4.json
new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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diff --git a/api/study/cb048904-2924-40f0-b911-37a207859135.json b/api/study/cb048904-2924-40f0-b911-37a207859135.json
new file mode 100644
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new file mode 100644
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diff --git a/api/study/d0b8c25d-19c6-4fce-8837-049d2b4e77a7.json b/api/study/d0b8c25d-19c6-4fce-8837-049d2b4e77a7.json
new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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new file mode 100644
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diff --git a/api/study/f55bdf14-0e4c-4ef4-8f7a-82d7f0e76bbc.json b/api/study/f55bdf14-0e4c-4ef4-8f7a-82d7f0e76bbc.json
new file mode 100644
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new file mode 100644
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-
-
-
-
-
- My static Space
-
-
-
-
-
Welcome to your static Space!
-
You can modify this app directly by editing index.html in the Files and versions tab.
Not for diagnosis or treatment. All clinical decisions remain with qualified healthcare professionals.
checking
Loading patient
\ No newline at end of file
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+11:"$c:metadata"
+
+*The clinical workstation: patient timeline, synchronised prior/current comparison, measured
+findings, and the AI evidence strip.*
+
+
+
+Prior and current study side by side, **linked by fractional depth** rather than world
+coordinates, because unregistered studies differ by tens of millimetres and copying a camera
+between them can place it outside the other volume entirely.
+
+
+
+*Scroll one pane and the other follows.*
+
+
+
+The tumour mask drawn in the **series' own voxel grid**, reoriented from the NIfTI affines with
+no resampling. Outline by default, fill on demand.
+
+
+
+
+
+
+
+
+
+
+
+`BraTS-GLI-00675-001`, whole tumour Dice **0.000**, yet tumour core and enhancing tumour both
+**1.000**. A whole tumour score of zero alongside perfect compartments is a labelling edge case,
+not a model that cannot see the tumour.
+
+
+
+`BraTS-GLI-00493-000`, whole tumour Dice **0.184** on 34,451 labelled voxels, while core reaches
+0.924 and enhancing 0.883. The oedema boundary is the disagreement, which is the least
+reproducible boundary between human annotators too.
+
+
+
+*Measured change per lesion, with confidence and the reason it is reduced.*
+
+
+
+*Dragging the time scrubber: the lesion surface, the measurements and the evidence move together.*
+
+
+
+*Rotation follows the pointer, and the head stays solid from every angle.*
+
+
+
+**Coloured by measured change.** Red > +25%, blue < β25%, green stable, **grey for a baseline or
+a doubtful match**. The prior timepoint is drawn as a wireframe.
+
+
+
+Selecting a lesion highlights it simultaneously in the 2D panes, the findings list and the 3D
+view. Lesion meshes are **never smoothed**, because that mesh is the source of the reported surface area.
+
+
+
+
+
+*Every answer carries its evidence, and every piece of evidence navigates to the study it came from.*
+
+
+
+*Every endpoint is typed end to end: Pydantic v2 on the server, generated TypeScript contracts in
+the browser.*
+
+
+
+# MEDTRACE
+
+### Longitudinal AI for brain-MRI disease evolution
+
+**An interactive, evidence-linked map of how a brain tumour changes over time.**
+
+