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Quantification of Ki-67 labeling index in pediatric brain tumor immunohistochemistry images.

Code ↔ Paper

2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 2 matches
  1. [1] § RESULTS › Density maps ↔ summary_ratios.py, lines 106–146 · score 0.61 · QuPath, LI map, positive cell density, negative cell density, ratio, slide
  2. [2] § METHODS › Postprocessing in Python ↔ summary_ratios.py, lines 106–146 · score 0.60 · QuPath, LI map, cell density maps, ratio, negative cell, Ki

Paper

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The authors' code

Python · 389 lines · 16 KB · GPL-3.0 · 2 matches

  1. # %% IMPORTS
  2. import json
  3. import os
  4. import numpy as np
  5. import math
  6. import pandas as pd
  7. import argparse
  8. import matplotlib
  9. import matplotlib.pyplot as plt
  10. import seaborn as sns
  11. from PIL import Image
  12. from mpl_toolkits.axes_grid1 import make_axes_locatable
  14. # %% ARGUMENT PARSING
  15. parser = argparse.ArgumentParser(description='Create summary ratios and density maps.')
  16. parser.add_argument('--maps_dir', type=str, default=False, help='Directory of the cell density maps generated by QuPath.')
  17. parser.add_argument('--data_dir', type=str, default=False, help='Directory of the project data')
  18. parser.add_argument('--area_path', type=str, default=False, help='Path to Area.txt which is saved under the Results folder.')
  19. parser.add_argument('--csv_path', type=str, required=True, help='Path to the WSI dataframe CSV file')
  20. parser.add_argument('--WSIs_dir', type=str, default=False, help='Directory of the folder where the WSIs exist.')
  21. parser.add_argument('--norm_maps_dir', type=str, default=False, help='Directory to save the normalized cell density maps.')
  22. parser.add_argument('--result_dir', type=str, default=False, help='Directory to save the summary tables and graphs.')
  23. args = parser.parse_args()
  25. # %% DIRECTORIES & FILES NEEDED FOR THE ANALYSIS
  26. maps_dir = args.maps_dir
  28. data_dir = args.data_dir
  30. csv_path = args.csv_path
  31. # check if the CSV file exists
  32. if not os.path.isfile(csv_path):
  33. raise FileNotFoundError(f"The specified CSV file does not exist: {csv_path}")
  35. area_path = args.area_path
  36. # check if the Area.txt file exists
  37. if not os.path.isfile(area_path):
  38. raise FileNotFoundError(f"The specified Area.txt file does not exist: {area_path}")
  40. WSI_df = pd.read_csv(csv_path)
  42. WSIs_dir = args.WSIs_dir
  44. norm_maps_dir = args.norm_maps_dir
  45. # create the directory to save the normalised maps if it does not exist
  46. if not os.path.exists(norm_maps_dir):
  47. os.makedirs(norm_maps_dir)
  49. result_dir = args.result_dir
  50. # create the directory to save the results if it does not exist
  51. if not os.path.exists(result_dir):
  52. os.makedirs(result_dir)
  54. # get the area and total detections
  55. annotation_df = pd.read_csv(area_path,names=['name', 'detections', 'area'],delimiter=";",index_col=False)
  57. # %% RETRIEVE INFORMATION FROM JSON FILES
  58. file_list = [f for f in os.listdir(data_dir) if os.path.isdir(os.path.join(data_dir,f))]
  59. WSIs = os.listdir(WSIs_dir)
  61. # data frame to store the results
  62. results_df = pd.DataFrame(columns = ['case_id', 'slide_id', 'label', 'Positive_Cell_Count', 'Positive_Cell_Density', 'Negative_Cell_Count', 'Negative_Cell_Density', 'Ki-67 LI', 'Cell_Density'])
  64. proc_l = len(file_list)
  65. proc = -1
  66. failed_list = []
  67. failed_ann = []
  68. for folder in file_list:
  69. proc += 1
  70. proc_perc = round(proc/proc_l*100, 2)
  71. print('Progress: ' + str(proc_perc) + '%')
  72. print('Processing image ' + str(proc) + ' of ' + str(proc_l))
  73. with open(data_dir + '/' + folder + '/server.json', 'r') as s_file:
  74. server_data = json.load(s_file)
  75. slide_id = server_data['builder']['metadata']['name'][:-4]
  76. case_id = slide_id.split('___')[0]
  78. # Convert slide_id to int to match CSV data type
  79. try:
  80. slide_id_lookup = int(slide_id)
  81. except ValueError:
  82. # If conversion fails, keep as string (for cases with non-numeric IDs)
  83. slide_id_lookup = slide_id
  85. label = WSI_df['label'].loc[WSI_df['slide_id']==slide_id_lookup].values[0]
  87. print('Reading data from image ' + slide_id + ':')
  88. with open(data_dir + '/' + folder + '/summary.json', 'r') as file:
  89. data = json.load(file)
  90. detection_data = data['hierarchy']['detectionClassificationCounts']
  91. if 'Positive' not in detection_data.keys() and 'Negative' not in detection_data.keys():
  92. print('The slide has not been properly segmented yet, skipping.')
  93. failed_list.append(slide_id)
  94. continue
  96. # calculate the Ki-67 LI
  97. pos_cell_count = detection_data.get('Positive', 0)
  98. neg_cell_count = detection_data.get('Negative', 0)
  100. # Check if there are any cells detected
  101. if pos_cell_count + neg_cell_count == 0:
  102. print('No cells detected in this slide, skipping.')
  103. failed_list.append(slide_id)
  104. continue
  106. # round ratio to 2 decimal places
  107. ki67_li = round(pos_cell_count / (pos_cell_count + neg_cell_count), 2)
  109. for WSI in WSIs:
  110. WSI_name = WSI.split('.')[0]
  111. if slide_id in WSI_name:
  112. slide_file_name = WSI
  113. print('Found the corresponding WSI file: ' + slide_file_name)
  114. break
  116. try:
  117. annAreamm = annotation_df['area'].loc[annotation_df['name'] == slide_file_name].values[0]*0.000001
  118. numdet = annotation_df['detections'].loc[annotation_df['name'] == slide_file_name].values[0]
  119. cells_smm = numdet/annAreamm
  120. pos_cell_dens = pos_cell_count/annAreamm
  121. neg_cell_dens = neg_cell_count/annAreamm
  122. except:
  123. print('Could not find any annotation data.')
  124. failed_ann.append(slide_file_name)
  125. else:
  126. print("Area of the segmented tissue: " + str(annAreamm))
  127. print("Total number of detections: " + str(numdet))
  128. print('Number of detections per mm^2: ' + str(cells_smm))
  130. results_df.loc[len(results_df)] = [case_id, slide_id, label, pos_cell_count, pos_cell_dens, neg_cell_count, neg_cell_dens, ki67_li*100, cells_smm]
  131. print('Positive cell count: ' + str(pos_cell_count))
  132. print('Negative cell count: ' + str(neg_cell_count))
  133. print('Positive to negative ratio: ' + str(ki67_li))
  135. # %% GENERATE THE NORMALISED AND RATIO CELL DENSITY MAPS
  136. # get the cell density maps generated by QuPath
  137. HM_file_neg = maps_dir + '/' + slide_id + '_NegDMap.tif'
  138. HM_file_pos = maps_dir + '/' + slide_id + '_PosDMap.tif'
  140. failed_cell_density_list = []
  142. # check if both cell density map files exist before proceeding
  143. if os.path.isfile(HM_file_neg) and os.path.isfile(HM_file_pos):
  144. neg_pp_im = norm_maps_dir + '/' + slide_id + '_Neg_Cell_Dens_Map.png'
  145. pos_pp_im = norm_maps_dir + '/' + slide_id + '_Pos_Cell_Dens_Map.png'
  146. ratio_pp_im = norm_maps_dir + '/' + slide_id + '_Ki-67_LI_Map.png'
  148. # generate the normalised negative cell density map
  149. if not os.path.isfile(neg_pp_im):
  150. print("Generating normalized negative cell density map.")
  151. # get the raw grayscale negative cell density map
  152. im_neg = Image.open(HM_file_neg)
  153. width, height = im_neg.size
  155. # set color bar orientation depending on image dimensions:
  156. if width < height:
  157. orient = 'vertical'
  158. else:
  159. orient = 'horizontal'
  161. # normalize
  162. imarray_neg = np.array(im_neg)
  163. cm_lim_neg = np.max(imarray_neg)
  164. new_imarray_neg = imarray_neg/cm_lim_neg
  165. # keep values inside normalized range for a correct comparison between heat maps
  166. new_imarray_neg[new_imarray_neg>1] = 1
  168. # colorise the negative cell density map
  169. cmapQ = plt.get_cmap('jet')
  170. A_neg = cmapQ(new_imarray_neg)
  172. # convert dark blue background to white
  173. A_neg[new_imarray_neg == 0] = [1, 1, 1, 1]
  175. # plot and save normalised negative cell density map
  176. fig, ax = plt.subplots()
  177. im = ax.imshow(A_neg)
  178. ax.set_xticks([])
  179. ax.set_yticks([])
  180. ax.set_title('Negative Cell Density Map')
  181. divider = make_axes_locatable(ax)
  182. if orient == 'vertical':
  183. cax = divider.append_axes("right", size="5%", pad=0.20)
  184. else:
  185. cax = divider.append_axes("bottom", size="5%", pad=0.20)
  186. norm = matplotlib.colors.Normalize(vmin=0, vmax=neg_cell_dens)
  187. cb = plt.colorbar(plt.cm.ScalarMappable(norm=norm, cmap='jet'), cax=cax, orientation=orient, shrink=1.0)
  188. plt.title('Negative Cell Density Map')
  189. plt.savefig(neg_pp_im)
  190. cb.remove()
  191. plt.close("all")
  193. # mask of the negative cell density map
  194. A_neg_blue_mask = A_neg
  195. A_neg_blue_mask[np.any(A_neg_blue_mask[:, :, :3] != [1, 1, 1], axis=2)] = [0, 0, 1, 1]
  197. else:
  198. print("Normalized negative cell density map already exists, skipping.")
  200. # generate the normalised positive cell density map
  201. if not os.path.isfile(pos_pp_im):
  202. print("Generating normalized positive cell density map.")
  203. # get the raw grayscale positive cell density map
  204. im_pos = Image.open(HM_file_pos)
  206. width, height = im_pos.size
  207. # set color bar orientation depending on image dimensions:
  208. if width < height:
  209. orient = 'vertical'
  210. else:
  211. orient = 'horizontal'
  213. # normalise
  214. imarray_pos = np.array(im_pos)
  215. cm_lim_pos = np.max(imarray_pos)
  216. new_imarray_pos = imarray_pos/cm_lim_pos
  217. # keep values inside normalized range for a correct comparison between heat maps
  218. new_imarray_pos[new_imarray_pos>1] = 1
  220. # colorise
  221. cmapQ = plt.get_cmap('jet')
  222. A_pos = cmapQ(new_imarray_pos)
  224. # convert dark blue background to white
  225. A_pos[new_imarray_pos == 0] = [1, 1, 1, 1]
  227. new_A_pos = A_pos
  228. white_mask = np.all(new_A_pos[:, :, :3] == [1, 1, 1], axis=2)
  229. new_A_pos[white_mask] = A_neg_blue_mask[white_mask]
  231. # plot and save normalised positive cell density map
  232. fig, ax = plt.subplots()
  233. im = ax.imshow(new_A_pos)
  234. ax.set_xticks([])
  235. ax.set_yticks([])
  236. ax.set_title('Positive Cell Density Map')
  237. divider = make_axes_locatable(ax)
  238. if orient == 'vertical':
  239. cax = divider.append_axes("right", size="5%", pad=0.20)
  240. else:
  241. cax = divider.append_axes("bottom", size="5%", pad=0.20)
  242. norm = matplotlib.colors.Normalize(vmin=0, vmax=pos_cell_dens)
  243. cb = plt.colorbar(plt.cm.ScalarMappable(norm=norm, cmap='jet'), cax=cax, orientation=orient, shrink=1.0)
  244. plt.savefig(pos_pp_im)
  245. cb.remove()
  246. plt.close("all")
  247. else:
  248. print("Normalized positive cell density map already exists, skipping.")
  250. # generate the Ki-67 LI map
  251. if not os.path.isfile(ratio_pp_im):
  252. print("Generating ratio cell density map.")
  253. neg_im = Image.open(HM_file_neg)
  254. width, height = neg_im.size
  255. # set color bar orientation depending on image dimensions:
  256. if width < height:
  257. orient = 'vertical'
  258. else:
  259. orient = 'horizontal'
  260. neg_imarray = np.array(neg_im)
  261. pos_im = Image.open(HM_file_pos)
  262. pos_imarray = np.array(pos_im)
  263. for k in range(np.shape(neg_imarray)[0]):
  264. for l in range(np.shape(neg_imarray)[1]):
  265. denominator = neg_imarray[k, l] + pos_imarray[k, l]
  266. # if denominator is 0, assign a default value
  267. if denominator == 0:
  268. neg_imarray[k, l] = 0.0001
  269. else:
  270. neg_imarray[k, l] = pos_imarray[k, l] / denominator
  271. if neg_imarray[k, l] < 0.0001:
  272. neg_imarray[k, l] = 0.0001
  273. elif neg_imarray[k, l] > 1:
  274. neg_imarray[k, l] = 1
  275. elif math.isnan(neg_imarray[k, l]):
  276. neg_imarray[k, l] = 0.0001
  278. # only take into account foreground pixels
  279. relevant_values = neg_imarray[neg_imarray > 0]
  280. n_relval = len(relevant_values)
  281. adjusted_n_relval = round(n_relval*0.95)
  282. sorted_relval = sorted(relevant_values)
  283. min_cm_lim = 0.03
  284. cm_lim = max(max(sorted_relval[:adjusted_n_relval]), min_cm_lim)
  285. # normalize with obtained value
  286. neg_imarray = neg_imarray/cm_lim
  287. # keep values inside normalized range for a correct comparison between heat maps
  288. neg_imarray[neg_imarray>1] = 1
  290. # colorise
  291. cmapQ = plt.get_cmap('jet')
  292. A_ki67_li = cmapQ(neg_imarray)
  294. # convert dark blue background to white
  295. dark_blue = [0, 0, 0.5, 1] # RGBA values for dark blue
  296. white = [1, 1, 1, 1] # RGBA values for white
  297. A_ki67_li[
  298. (A_ki67_li[:, :, 0] == dark_blue[0]) &
  299. (A_ki67_li[:, :, 1] == dark_blue[1]) &
  300. (A_ki67_li[:, :, 2] == dark_blue[2]) &
  301. (A_ki67_li[:, :, 3] == dark_blue[3])
  302. ] = white
  304. new_A_ki67_li = A_ki67_li
  305. white_mask = np.all(new_A_ki67_li[:, :, :3] == [1, 1, 1], axis=2)
  306. new_A_ki67_li[white_mask] = A_neg_blue_mask[white_mask]
  308. # plot and save the ki-67 li map
  309. fig, ax = plt.subplots()
  310. im = ax.imshow(new_A_ki67_li)
  311. ax.set_xticks([])
  312. ax.set_yticks([])
  313. ax.set_title('Ki-67 LI Map')
  314. divider = make_axes_locatable(ax)
  315. if orient == 'vertical':
  316. cax = divider.append_axes("right", size="5%", pad=0.20)
  317. else:
  318. cax = divider.append_axes("bottom", size="5%", pad=0.20)
  319. norm = matplotlib.colors.Normalize(vmin=0, vmax=ki67_li)
  320. cb = plt.colorbar(plt.cm.ScalarMappable(norm=norm, cmap='jet'), cax=cax, orientation=orient, shrink=1.0)
  321. plt.savefig(ratio_pp_im)
  322. cb.remove()
  323. plt.close("all")
  324. else:
  325. print("Normalized Ki-67 LI map already exists, skipping.")
  326. else:
  327. print(f"Required cell density map files not found for slide ID: {slide_id}. Skipping.")
  329. # %% CREATE SUMMARY STATISTICS
  330. # save the results to a csv file
  331. results_df.to_csv(os.path.join(result_dir, 'summary_results.csv'), index=False)
  333. statistics_df = pd.DataFrame(columns = ['variable', 'label', 'mean', 'sd', 'median', 'minimum', 'maximum'])
  335. summary_points = ['Positive_Cell_Count', 'Positive_Cell_Density', 'Negative_Cell_Count', 'Negative_Cell_Density', 'Ki-67 LI', 'Cell_Density']
  337. y_units = {'Positive_Cell_Count': 'Number of positive cells',
  338. 'Positive_Cell_Density': 'Number of positive cells per mm^2',
  339. 'Negative_Cell_Count': 'Number of negative cells',
  340. 'Negative_Cell_Density': 'Number of negative cells per mm^2',
  341. 'Ki-67 LI': 'Ki-67 LI',
  342. 'Cell_Density': 'Number of cells per mm^2'}
  344. res_df = pd.read_csv(os.path.join(result_dir, 'summary_results.csv'))
  345. label_list = np.unique(res_df['label'].tolist())
  347. # summary table and boxplots
  348. for sp in summary_points:
  349. box_data = []
  350. for label in label_list:
  351. sp_array = res_df[sp].loc[res_df['label'] == label]
  352. sp_array = sp_array[~np.isnan(sp_array)]
  353. box_data.append(sp_array)
  354. sp_mean = round(np.mean(sp_array), 2)
  355. sp_sd = round(np.std(sp_array), 2)
  356. sp_median = round(np.median(sp_array), 2)
  357. sp_min = round(np.min(sp_array), 2)
  358. sp_max = round(np.max(sp_array), 2)
  359. # summary statistics
  360. statistics_df.loc[len(statistics_df)] = [sp, label, sp_mean, sp_sd, sp_median, sp_min, sp_max]
  362. # boxplots
  363. palette = sns.color_palette("deep", 10)
  364. custom_colors = [palette[9]]
  365. palette = sns.color_palette(custom_colors * len(label_list))
  367. sns.set_style("whitegrid", {'axes.grid': False})
  368. plt.figure(figsize=(10, 6))
  369. plt.gca().set_facecolor('white')
  370. flierprops = dict(marker='D', markerfacecolor='darkgrey', markersize=5, linestyle='none')
  371. boxplot = sns.boxplot(
  372. x='label',
  373. y=sp,
  374. data=res_df,
  375. flierprops=flierprops,
  376. palette=palette,
  377. linewidth=2
  378. )
  379. plt.title(f'{sp} across all diagnoses')
  380. plt.xlabel('Diagnosis')
  381. plt.ylabel(y_units[sp])
  382. plt.tight_layout()
  383. plt.savefig(os.path.join(result_dir,sp + '_BoxPlot.png'))
  384. plt.close()
  386. # summary statistics
  387. statistics_df.to_csv(os.path.join(result_dir, 'summary_statistics.csv'), index=False)
  389. # %%

summary_ratios.py at commit d1efa7c, under GPL-3.0 · at the source

Overview

Authors: Christoforos Spyretos1,2, Juan Manuel Pardo Ladino1, Hakon Andersen Blomstrand3,4, Per Nyman2,5,6, Oscar Snödahl2,6,7, Alia Shamikh8,9, Nils Elander4,10, Neda Haj-Hosseini1,2
ORCID iDs: Christoforos Spyretos, Hakon Andersen Blomstrand, Per Nyman, Oscar Snödahl, Nils Elander, Neda Haj-Hosseini
  1. Department of Biomedical Engineering, Linköping University, Linköping, Sweden
  2. Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
  3. Clinical Department of Clinical Pathology, Region Östergötland, Linköping, Sweden
  4. Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
  5. Crown Princess Victoria Children's Hospital, Region Östergötland, Linköping, Sweden
  6. Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
  7. Clinical Department of Radiology in Linköping, Region Östergötland, Linköping, Sweden
  8. Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Solna, Sweden
  9. Department of Oncology-Pathology, Karolinska Institute, Solna, Sweden
  10. Clinical Department of Oncology in Linköping, Region Östergötland, Linköping, Sweden
Institutions: Linköping University (Sweden); Region Östergötland; Karolinska University Hospital (Sweden); Karolinska Institutet (Sweden)
Journal: Journal of neuropathology and experimental neurology, volume 85, issue 5, pages 475-486
Dates: published online 10 March 2026; in print May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1093/jnen/nlaf163 · PMID 41806389 · PMCID PMC13127889 · OpenAlex W7134976525
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), human (organism), other condition (population)
Methods: Connectivity, Statistics
Keywords: pediatric brain tumor, histopathology, immunohistochemistry, Ki-67 labeling index, deep learning, QuPath, cell segmentation, cell classification, cell density map
MeSH: Brain Neoplasms*, Ki-67 Antigen*, Biomarkers, Tumor, Child, Child, Preschool, Female, Glioma, Humans, Immunohistochemistry, Infant, Male (* major topic)
Topic: Glioma Diagnosis and Treatment (Genetics, Medicine), according to OpenAlex
Funding: Linköping University's Cancer Strength Area (2024), and the Medical Research Council of Southeast Sweden (FORSS-1011571); Children's Brain Tumor Network; Children’s Brain Tumor Tissue Consortium; Medical Research Council of Southeast Sweden (FORSS-1011571); Joanna Cocozza's Foundation (2025-2026); Swedish Childhood Cancer Foundation (MT2021-0011, MT2022-0013); Linköping University’s Cancer Strength Area; Children's Brain Tumor Tissue Consortium/the Children's Brain Tumor Network
Citations: cited by 1 paper (Europe PMC); 40 references in the paper

Abstract

Quantification of the Kiel 67 (Ki-67) labeling index (LI) is critical for assessing proliferation and prognosis in tumors but manual scoring remains a common practice. We present an automated framework for Ki-67 scoring in whole slide images (WSIs) developed for research settings using an Apache Groovy code script for QuPath and complemented by a Python postprocessing script that provides cell density maps and summary tables. Tissue segmentation is performed by pixel classifiers and cell segmentation is conducted using StarDist, a deep learning model, followed by adaptive thresholding to classify Ki-67 positive and negative nuclei. The pipeline was applied to a cohort of 632 pediatric brain tumor cases with 734 Ki-67 WSIs from the Children’s Brain Tumor Network. Medulloblastomas showed the highest Ki-67 LI (median: 19.84), followed by atypical teratoid rhabdoid tumors (median: 19.36), brainstem glioma-diffuse intrinsic pontine gliomas (median: 11.50), high-grade gliomas (grades 3, 4) (median: 9.50), and ependymomas (median: 5.88). Lower indices were found in meningiomas (median: 1.84) and the lowest were seen in low-grade gliomas (grades 1, 2) (median: 0.85), dysembryoplastic neuroepithelial tumors (median: 0.63), and gangliogliomas (median: 0.50). The results demonstrate a significant correlation (P < .05) in Ki-67 LI across most of the tumor families/types aligning with neuro-oncology and neuropathology consensus.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

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Christoforos-Spyretos/QuPath-Automatic-Cell-Detection-for-Ki-67-WSIs

License: GPL-3.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: d1efa7cff94c115107a0e4d9790d606b0eba0134, 21 May 2026
Languages: Python (1)
Size: 13 files, 1 script
Software Heritage: not archived
Found in: “DATA AVAILABILITY”
Holds: README, license file, environment (requirements.txt)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: Matplotlib (1 file), NumPy (1 file), pandas (1 file), Pillow (1 file), seaborn (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
3 files
At the source: github.com/Christoforos-Spyretos/QuPath-Automatic-Cell-Detection-for-Ki-67-WSIs

The paper's code and data availability statement is in the Data section.

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Data

Datasets cited

Data availability

The open-access dataset used in this study was obtained from the Children’s Brain Tumor Network (https://cbtn.org). The dataset for neuroendocrine and testicular seminoma are open-access and are available at https://github.com/cialab/neuroendocrine_ and https://zenodo.org/records/11218961, respectively. The code and detailed instructions for execution linked to this study are available in the GitHub repository: https://github.com/Christoforos-Spyretos/QuPath-Automatic-Cell-Detection-for-Ki-67-WSIs

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 8 authors, 9 keywords, 11 MeSH terms, 8 funders, 29 references.

Cite

This paper

Spyretos, C., Pardo Ladino, J. M., Andersen Blomstrand, H., Nyman, P., Snödahl, O., Shamikh, A., Elander, N., & Haj-Hosseini, N. (2026). Quantification of Ki-67 labeling index in pediatric brain tumor immunohistochemistry images. Journal of neuropathology and experimental neurology, 85(5), 475-486. https://doi.org/10.1093/jnen/nlaf163

BibTeX

@article{spyretos2026quantification,
author = {Spyretos, Christoforos and Pardo Ladino, Juan Manuel and Andersen Blomstrand, Hakon and Nyman, Per and Snödahl, Oscar and Shamikh, Alia and Elander, Nils and Haj-Hosseini, Neda},
title = {{Quantification of Ki-67 labeling index in pediatric brain tumor immunohistochemistry images}},
journal = {Journal of neuropathology and experimental neurology},
year = {2026},
month = may,
volume = {85},
number = {5},
pages = {475--486},
publisher = {Oxford University Press},
issn = {0022-3069},
doi = {10.1093/jnen/nlaf163},
url = {https://doi.org/10.1093/jnen/nlaf163},
pmid = {41806389},
pmcid = {PMC13127889}
}

RIS

TY - JOUR
AU - Spyretos, Christoforos
AU - Pardo Ladino, Juan Manuel
AU - Andersen Blomstrand, Hakon
AU - Nyman, Per
AU - Snödahl, Oscar
AU - Shamikh, Alia
AU - Elander, Nils
AU - Haj-Hosseini, Neda
TI - Quantification of Ki-67 labeling index in pediatric brain tumor immunohistochemistry images
T2 - Journal of neuropathology and experimental neurology
J2 - J Neuropathol Exp Neurol
PY - 2026
DA - 2026/05/01
VL - 85
IS - 5
SP - 475
EP - 486
SN - 0022-3069
PB - Oxford University Press
DO - 10.1093/jnen/nlaf163
UR - https://doi.org/10.1093/jnen/nlaf163
LA - en
ER -

CSL-JSON

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"id": "10.1093/jnen/nlaf163",
"type": "article-journal",
"title": "Quantification of Ki-67 labeling index in pediatric brain tumor immunohistochemistry images",
"container-title": "Journal of neuropathology and experimental neurology",
"author": [
{
"family": "Spyretos",
"given": "Christoforos"
},
{
"family": "Pardo Ladino",
"given": "Juan Manuel"
},
{
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"given": "Hakon"
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{
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},
{
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"given": "Oscar"
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{
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"given": "Alia"
},
{
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},
{
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}
],
"container-title-short": "J Neuropathol Exp Neurol",
"volume": "85",
"issue": "5",
"page": "475-486",
"DOI": "10.1093/jnen/nlaf163",
"PMID": "41806389",
"PMCID": "PMC13127889",
"ISSN": "0022-3069",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/jnen/nlaf163",
"language": "en",
"issued": {
"date-parts": [
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2026,
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1
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]
}
}

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