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Automated Ki-67 labeling index assessment in prostate cancer using artificial intelligence and multiplex fluorescence immunohistochemistry.
Blessin, Niclas C; Yang, Cheng; Mandelkow, Tim; Raedler, Jonas B; Li, Wenchao; Bady, Elena; Simon, Ronald; Vettorazzi, Eik; Lennartz, Maximilian; Bernreuther, Christian; Fraune, Christoph; Jacobsen, Frank; Krech, Till; Marx, Andreas; Lebok, Patrick; Minner, Sarah; Burandt, Eike; Clauditz, Till S; Wilczak, Waldemar; Sauter, Guido; Heinzer, Hans; Haese, Alexander; Schlomm, Thorsten; Graefen, Markus; Steurer, Stefan.
Afiliación
  • Blessin NC; Institute of Pathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Yang C; Institute of Pathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Mandelkow T; Institute of Pathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Raedler JB; Institute of Pathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Li W; College of Arts and Sciences, Boston University, Boston, MA, USA.
  • Bady E; Institute of Pathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Simon R; Department of Urology, Affiliated Zhongda Hospital of Southeast University, Nanjing, PR China.
  • Vettorazzi E; Institute of Pathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Lennartz M; Institute of Pathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Bernreuther C; Department of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Fraune C; Institute of Pathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Jacobsen F; Institute of Pathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Krech T; Institute of Pathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Marx A; Institute of Pathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Lebok P; Institute of Pathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Minner S; Institute of Pathology, Klinikum Fürth, Fürth, Germany.
  • Burandt E; Institute of Pathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Clauditz TS; Institute of Pathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Wilczak W; Institute of Pathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Sauter G; Institute of Pathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Heinzer H; Institute of Pathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Haese A; Institute of Pathology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Schlomm T; Martini-Clinic Prostate Cancer Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Graefen M; Martini-Clinic Prostate Cancer Center, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
  • Steurer S; Department of Urology, Charité - Universitätsmedizin Berlin, Berlin, Germany.
J Pathol ; 260(1): 5-16, 2023 05.
Article en En | MEDLINE | ID: mdl-36656126
ABSTRACT
The Ki-67 labeling index (Ki-67 LI) is a strong prognostic marker in prostate cancer, although its analysis requires cumbersome manual quantification of Ki-67 immunostaining in 200-500 tumor cells. To enable automated Ki-67 LI assessment in routine clinical practice, a framework for automated Ki-67 LI quantification, which comprises three different artificial intelligence analysis steps and an algorithm for cell-distance analysis of multiplex fluorescence immunohistochemistry (mfIHC) staining, was developed and validated in a cohort of 12,475 prostate cancers. The prognostic impact of the Ki-67 LI was tested on a tissue microarray (TMA) containing one 0.6 mm sample per patient. A 'heterogeneity TMA' containing three to six samples from different tumor areas in each patient was used to model Ki-67 analysis of multiple different biopsies, and 30 prostate biopsies were analyzed to compare a 'classical' bright field-based Ki-67 analysis with the mfIHC-based framework. The Ki-67 LI provided strong and independent prognostic information in 11,845 analyzed prostate cancers (p < 0.001 each), and excellent agreement was found between the framework for automated Ki-67 LI assessment and the manual quantification in prostate biopsies from routine clinical practice (intraclass correlation coefficient 0.94 [95% confidence interval 0.87-0.97]). The analysis of the heterogeneity TMA revealed that the Ki-67 LI of the sample with the highest Gleason score (area under the curve [AUC] 0.68) was as prognostic as the mean Ki-67 LI of all six foci (AUC 0.71 [p = 0.24]). The combined analysis of the Ki-67 LI and Gleason score obtained on identical tissue spots showed that the Ki-67 LI added significant additional prognostic information in case of classical International Society of Urological Pathology grades (AUC 0.82 [p = 0.002]) and quantitative Gleason score (AUC 0.83 [p = 0.018]). The Ki-67 LI is a powerful prognostic parameter in prostate cancer that is now applicable in routine clinical practice. In the case of multiple cancer-positive biopsies, the sole automated analysis of the worst biopsy was sufficient. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Neoplasias de la Próstata / Inteligencia Artificial Tipo de estudio: Diagnostic_studies / Guideline / Prognostic_studies Límite: Humans / Male Idioma: En Revista: J Pathol Año: 2023 Tipo del documento: Article País de afiliación: Alemania

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Neoplasias de la Próstata / Inteligencia Artificial Tipo de estudio: Diagnostic_studies / Guideline / Prognostic_studies Límite: Humans / Male Idioma: En Revista: J Pathol Año: 2023 Tipo del documento: Article País de afiliación: Alemania