Dose Metrics

In this example notebook we will compute Dose Volume Histograms (DVH) for our RTDOSE objects across structures found in RTSTRUCT objects in our dataset. We use HNSCC data from the Cancer Imaging Archive which has already been converted using PyDicer for demonstration purposes.

try:
    from pydicer import PyDicer
except ImportError:
    !pip install pydicer
    from pydicer import PyDicer

from pathlib import Path

from pydicer.utils import fetch_converted_test_data

Fetch data

HNSCC data prepared for this example are downloaded and stored into a testdata_hnscc directory. We will use this for our PyDicer working directory.

working_directory = fetch_converted_test_data("./testdata_hnscc", dataset="HNSCC")

Working directory %s aready exists, won't download test data.

Initialise PyDicer object

Using the working directory containing the test data.

pydicer = PyDicer(working_directory)

Compute DVH

Before we can extract dose metrics, we must compute Dose Volume Histograms for all dose objects and structure sets. This is done using the compute_dvh function.

pydicer.analyse.compute_dvh()

100%|██████████| 4/4 [00:45<00:00, 11.37s/objects, Compute DVH]

Inspect DVH

DVHs computed are stored in the respective dose object directories on the file system. Inspect a dose object directory (e.g. testdata_hnscc/data/HNSCC-01-0019/doses/309e1a). Here you will find a .png file which plots the DVH for each of the linked structures. In addition a .csv file stores the raw DVH values.

The DVHs can for this dataset can be loaded into a pandas DataFrame with the get_all_dvhs_for_dataset function.

df_dvh = pydicer.analyse.get_all_dvhs_for_dataset()
df_dvh.head()

	patient	struct_hash	dose_hash	label	cc	mean	0.0	0.1	0.2	0.3	...	77.2	77.3	77.4	77.5	77.6	77.7	77.8	77.9	78.0	78.1
0	HNSCC-01-0019	7cdcd9	309e1a	+1	4640.553474	29.679710	1.0	0.837845	0.835239	0.83267	...	0.000028	0.000020	0.000009	0.000002	6.165262e-07	0.0	0.0	0.0	0.0	0.0
1	HNSCC-01-0019	7cdcd9	309e1a	-.3	2689.948082	43.784290	1.0	1.000000	1.000000	1.00000	...	0.000048	0.000035	0.000016	0.000004	1.063598e-06	0.0	0.0	0.0	0.0	0.0
2	HNSCC-01-0019	7cdcd9	309e1a	Brain	549.576759	24.043829	1.0	1.000000	1.000000	1.00000	...	0.000000	0.000000	0.000000	0.000000	0.000000e+00	0.0	0.0	0.0	0.0	0.0
3	HNSCC-01-0019	7cdcd9	309e1a	Brainstem	47.636032	39.998913	1.0	1.000000	1.000000	1.00000	...	0.000000	0.000000	0.000000	0.000000	0.000000e+00	0.0	0.0	0.0	0.0	0.0
4	HNSCC-01-0019	7cdcd9	309e1a	CTV63	467.602730	71.274086	1.0	1.000000	1.000000	1.00000	...	0.000275	0.000202	0.000092	0.000024	6.118491e-06	0.0	0.0	0.0	0.0	0.0

5 rows × 788 columns

Compute Dose Metrics

The compute_dose_metrics function in the analyse module can compute D, V and Dcc metrics. Specify the points at which to compute those values. For example, the following cell computes the D95, D50, V5 and Dcc10.

df_dose_metrics = pydicer.analyse.compute_dose_metrics(
    d_point=[95, 50],
    v_point=[5],
    d_cc_point=[10]
)
df_dose_metrics.head()

	patient	struct_hash	dose_hash	label	cc	mean	D95	D50	V5	D10cc
0	HNSCC-01-0019	7cdcd9	309e1a	+1	4640.553474	29.679710	0.030835	25.358893	3558.228493	75.501190
1	HNSCC-01-0019	7cdcd9	309e1a	-.3	2689.948082	43.784290	11.384142	43.358867	2668.971062	75.501190
2	HNSCC-01-0019	7cdcd9	309e1a	Brain	549.576759	24.043829	7.657684	22.339093	544.220924	56.311918
3	HNSCC-01-0019	7cdcd9	309e1a	Brainstem	47.636032	39.998913	28.935185	39.202222	47.636032	46.232806
4	HNSCC-01-0019	7cdcd9	309e1a	CTV63	467.602730	71.274086	66.177108	71.589748	467.602730	75.494366