adapted query

Author: DanielaSchacherer

notebooks/pathomics/getting_started_with_digital_pathology.ipynb

@@ -925,7 +925,7 @@
         "id": "GXdR9lJvQ5Tf"
       },
       "source": [
-        "In case of DICOM-derived attributes the attributes' naming is corresponding directly to the name of the respective DICOM attribute plus a suffix, which indicates whether the column contains `CodeSchemeDesignator` and `CodeValue`, or `CodeMeaning`. If this is new to you, a brief explanation on the three-value based coding scheme in DICOM can be found at https://learn.canceridc.dev/dicom/coding-schemes.\n",
+        "A description for each of the metadata attributes can be found [here]() as part of `idc-index`s documentation. In case of DICOM-derived attributes the attributes' naming is corresponding directly to the name of the respective DICOM attribute plus a suffix, which indicates whether the column contains `CodeSchemeDesignator` and `CodeValue`, or `CodeMeaning`. If this is new to you, a brief explanation on the three-value based coding scheme in DICOM can be found at https://learn.canceridc.dev/dicom/coding-schemes.\n",
         "Note that many columns have the datatype \"object\" due to missing (None Type) data, but also the fact that they might contain a list. In case of `sm_instance_index` this is, because slide preparation, e.g. staining, might consist of multiple steps, such as Eosin stain first, followed by Hematoxylin stain (H&E). In case of `sm_index`, all values of the instances belonging to the respective DICOM `Series` are listed.\n",
         "\n",
         "However, with the index tables being simply pandas DataFrames, the easiest way to get an overview of the values in the index tables are basic pandas operations:"
@@ -1396,7 +1396,7 @@
         "id": "3eN9444WE2ew"
       },
       "source": [
-        "We now check all unique values available in `primaryAnatomicStructureModifier_CodeMeaning`, which tells us more about the tissue type (tumor, normal or other):   "
+        "We now check all unique values available in `primaryAnatomicStructureModifier_CodeMeaning`, which tells us more about additional characteristics of the slide, such as whether it is tumor or normal tissue: "
       ]
     },
     {
@@ -1477,12 +1477,10 @@
       "source": [
         "query = '''\n",
         "SELECT\n",
-        "    index.SeriesInstanceUID as digital_slide_id,\n",
-        "    index.StudyInstanceUID as case_id,\n",
-        "    -- The 'cancer_subtype' can be easily infered from the collection_id.\n",
-        "    (REPLACE (REPLACE(index.collection_id, 'cptac_luad', 'luad'), 'cptac_lscc', 'lscc')) AS cancer_subtype,\n",
-        "    -- The 'tissue_types' indicates whether a slides contains normal, tumor or other tisse.\n",
-        "    sm_index.primaryAnatomicStructureModifier_CodeMeaning as tissue_type\n",
+        "    index.SeriesInstanceUID,\n",
+        "    index.StudyInstanceUID,\n",
+        "    index.collection_id,\n",
+        "    sm_index.primaryAnatomicStructureModifier_CodeMeaning\n",
         "FROM\n",
         "    index\n",
         "JOIN\n",
@@ -1535,7 +1533,7 @@
         "os.makedirs(example_slides_dir, exist_ok=True)\n",
         "\n",
         "idc_client.download_from_selection(downloadDir=example_slides_dir,\n",
-        "                                   seriesInstanceUID=selection['digital_slide_id'].tolist(), dirTemplate='%SeriesInstanceUID')"
+        "                                   seriesInstanceUID=selection['SeriesInstanceUID'].tolist(), dirTemplate='%SeriesInstanceUID')"
       ]
     },
     {
@@ -1944,7 +1942,7 @@
       },
       "outputs": [],
       "source": [
-        "viewer_url = idc_client.get_viewer_URL(seriesInstanceUID=slides_metadata.iloc[0]['digital_slide_id'])\n",
+        "viewer_url = idc_client.get_viewer_URL(seriesInstanceUID=selection.iloc[0]['SeriesInstanceUID'])\n",
         "IFrame(viewer_url, width=1000, height=500)"
       ]
     },
@@ -1996,7 +1994,7 @@
         "\n",
         "# Open slide with wsidicom\n",
         "slide = WsiDicom.open(slides_metadata.iloc[0]['local_path'])\n",
-        "print('Tissue type: ', slides_metadata.iloc[0]['reference_class_label'])\n",
+        "print('Tissue type: ', selection.iloc[0]['primaryAnatomicStructureModifier_CodeMeaning'])\n",
         "# Check which levels are available\n",
         "print('Available levels: ')\n",
         "for level in slide.levels:\n",
@@ -2055,7 +2053,7 @@
         "# Open slide with openslide\n",
         "file_path = os.path.join(slides_metadata.iloc[0]['local_path'], os.listdir(slides_metadata.iloc[0]['local_path'])[0])\n",
         "slide = OpenSlide(file_path)\n",
-        "print('Tissue type: ', slides_metadata.iloc[0]['reference_class_label'])\n",
+        "print('Tissue type: ', selection.iloc[0]['primaryAnatomicStructureModifier_CodeMeaning'])\n",
         "# Check which levels are available\n",
         "print(f'Number of available levels: {slide.level_count}')\n",
         "print(f'Dimensions of available levels: {slide.level_dimensions}')\n",