tumor_volumetrics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CBTN learning lab for multi-modal imaging\n",
    "## Tumor size assessment over time using volumetric segmentations\n",
    "#### October 10, 2024\n",
    "\n",
    "This notebook illustrates how to extract tumor volumetrics to assess changes in tumor size over time. Assuming 3D volumetric segmentations have been generated such as via the `d3b-ped-auto-seg-nnunet` or `brats2024-peds-awssme` deep learning-based segmentation gears, and if necessary manually edited for accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import flywheel\n",
    "import os\n",
    "\n",
    "your_api_key = 'enter-your-api-key'\n",
    "fw = flywheel.Client(your_api_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "fw = flywheel.Client(os.getenv('FW_LAB_API_KEY'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get the project container\n",
    "proj_container = fw.lookup('cbtn-summit/CBTN_proc_data')\n",
    "\n",
    "# set up a local folder to store the data files in\n",
    "def maybe_make_dir(dir_path):\n",
    "    if not os.path.exists(dir_path):\n",
    "        os.makedirs(dir_path)\n",
    "\n",
    "local_data_dir = 'data'\n",
    "maybe_make_dir(local_data_dir)\n",
    "\n",
    "# loop through all files in the project\n",
    "# if an edited tumor seg file exists then download it to the local folder\n",
    "for session in proj_container.sessions.iter():\n",
    "    sub_label = session.subject.label\n",
    "    ses_label = session.label\n",
    "    for acquisition in session.acquisitions.iter():\n",
    "        for file_ in acquisition.files:\n",
    "            if ('ManualSegmentation' in file_.name):\n",
    "                local_session_dir = f'{local_data_dir}/{sub_label}/{ses_label}/'\n",
    "                maybe_make_dir(local_session_dir)\n",
    "                local_file_path = f'{local_session_dir}/{file_.name}'\n",
    "                if not os.path.exists(local_file_path):\n",
    "                    acquisition.download_file(file_.name, local_file_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nibabel as nib\n",
    "import numpy as np\n",
    "\n",
    "# initialize volume calculator\n",
    "def calculate_whole_tumor_size(seg_filename):\n",
    "    mask = nib.load(seg_filename).get_fdata()\n",
    "    mask = np.rint(mask)\n",
    "\n",
    "    seg_voxels = mask[mask!=0]\n",
    "    return len(seg_voxels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glob import glob\n",
    "import pandas as pd\n",
    "\n",
    "file_list = glob(f'{local_data_dir}/*/*/*.nii.gz')\n",
    "\n",
    "# initialize our results lists\n",
    "results = []\n",
    "\n",
    "for seg_file_path in file_list:\n",
    "    sub_label = seg_file_path.split('/')[1]\n",
    "    ses_label = seg_file_path.split('/')[2]\n",
    "    size_in_vox = calculate_whole_tumor_size(seg_file_path)\n",
    "    results.append([sub_label, ses_label, size_in_vox])\n",
    "\n",
    "# convert our list into a pandas dataframe\n",
    "df = pd.DataFrame(results, columns=['subject','session','tumor_size_vox'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>session</th>\n",
       "      <th>tumor_size_vox</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C2479803</td>\n",
       "      <td>6046</td>\n",
       "      <td>11591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C2479803</td>\n",
       "      <td>6681</td>\n",
       "      <td>4065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C2479803</td>\n",
       "      <td>6229</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C2479803</td>\n",
       "      <td>6611</td>\n",
       "      <td>2933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C2479803</td>\n",
       "      <td>6322</td>\n",
       "      <td>4146</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    subject session  tumor_size_vox\n",
       "0  C2479803    6046           11591\n",
       "1  C2479803    6681            4065\n",
       "2  C2479803    6229             164\n",
       "3  C2479803    6611            2933\n",
       "4  C2479803    6322            4146"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['session'] = df['session'].astype(int)\n",
    "df = df.sort_values(by='session', axis=0).reset_index(drop=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subject</th>\n",
       "      <th>session</th>\n",
       "      <th>tumor_size_vox</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C1173420</td>\n",
       "      <td>697</td>\n",
       "      <td>1869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C1173420</td>\n",
       "      <td>791</td>\n",
       "      <td>3333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C1277724</td>\n",
       "      <td>4762</td>\n",
       "      <td>31416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C1277724</td>\n",
       "      <td>4921</td>\n",
       "      <td>2791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C1277724</td>\n",
       "      <td>5005</td>\n",
       "      <td>7890</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    subject  session  tumor_size_vox\n",
       "0  C1173420      697            1869\n",
       "1  C1173420      791            3333\n",
       "2  C1277724     4762           31416\n",
       "3  C1277724     4921            2791\n",
       "4  C1277724     5005            7890"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# for each unique subject, plot their tumor volume over time\n",
    "for sub in df['subject'].unique().tolist():\n",
    "    sub_df = df[df['subject']==sub]\n",
    "    plt.plot(sub_df['session'], sub_df['tumor_size_vox'],'.-')\n",
    "    plt.xlabel('Session (age in days)')\n",
    "    plt.ylabel('Tumor Size (Number of Voxels)')\n",
    "    plt.title(sub)\n",
    "    plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def calculate_region_tumor_size(seg_filename, n_values):\n",
    "    mask = nib.load(seg_filename).get_fdata()\n",
    "    mask = np.rint(mask)\n",
    "    \n",
    "    results = []\n",
    "    for vox_value in range(1, n_values+1):\n",
    "        seg_voxels = mask[mask==vox_value]\n",
    "        region_size = len(seg_voxels)\n",
    "        results.append(region_size)\n",
    "    \n",
    "    return results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize our results lists\n",
    "results = []\n",
    "\n",
    "for seg_file_path in file_list:\n",
    "    sub_label = seg_file_path.split('/')[1]\n",
    "    ses_label = seg_file_path.split('/')[2]\n",
    "    region_sizes = calculate_region_tumor_size(seg_file_path, 4)\n",
    "    results.append([sub_label, ses_label] + region_sizes)\n",
    "\n",
    "# convert our list into a pandas dataframe\n",
    "region_df = pd.DataFrame(results, columns=['subject','session',\\\n",
    "                                    'enhancing',\\\n",
    "                                    'non-enhancing',\\\n",
    "                                    'cyst',\\\n",
    "                                    'edema'])\n",
    "\n",
    "\n",
    "\n",
    "# enhancing_mask = np.logical_and(mask >= 1, mask <= 1)\n",
    "# nonenhancing_mask = np.logical_and(mask >= 2, mask <= 2)\n",
    "# cyst_mask = np.logical_and(mask >= 3, mask <= 3)\n",
    "# edema_mask = np.logical_and(mask >= 4, mask <= 4)\n",
    "# wt_mask = np.logical_and(mask >= 1, mask <= 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "region_df['session'] = region_df['session'].astype(int)\n",
    "region_df = region_df.sort_values(by='session', axis=0).reset_index(drop=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "region_labels = ['enhancing','non-enhancing','cyst','edema']\n",
    "for sub in region_df['subject'].unique().tolist():\n",
    "    sub_df = region_df[region_df['subject']==sub]\n",
    "    size_values = []\n",
    "    region_values=[]\n",
    "    for region in region_labels:\n",
    "        sizes_this_region = sub_df[region].tolist()\n",
    "        size_values = size_values + sizes_this_region\n",
    "        region_values = region_values + ([region] * len(sizes_this_region))\n",
    "    plot_df = pd.DataFrame()\n",
    "    plot_df['size'] = size_values\n",
    "    plot_df['region'] = region_values\n",
    "    plot_df['session'] = sub_df['session'].tolist() * int(len(plot_df) / len(sub_df['session'].tolist()))\n",
    "    plt.figure()\n",
    "    sns.barplot(plot_df, x=\"session\", y=\"size\", hue=\"region\")\n",
    "    plt.title(sub)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}