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A Deep Learning-Based Assay for Programmed Death Ligand 1 Immunohistochemistry Scoring in Non-Small Cell Lung Carcinoma: Does it Help Pathologists Score?
Ito, Hiroaki; Yoshizawa, Akihiko; Terada, Kazuhiro; Nakakura, Akiyoshi; Rokutan-Kurata, Mariyo; Sugimoto, Tatsuhiko; Nishimura, Kazuya; Nakajima, Naoki; Sumiyoshi, Shinji; Hamaji, Masatsugu; Menju, Toshi; Date, Hiroshi; Morita, Satoshi; Bise, Ryoma; Haga, Hironori.
Afiliación
  • Ito H; Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan.
  • Yoshizawa A; Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan; Department of Diagnostic Pathology, Nara Medical University, Nara, Japan. Electronic address: akyoshi@kuhp.kyoto-u.ac.jp.
  • Terada K; Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan.
  • Nakakura A; Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan.
  • Rokutan-Kurata M; Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan.
  • Sugimoto T; Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan.
  • Nishimura K; Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan.
  • Nakajima N; Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan; Department of Diagnostic Pathology, Toyooka Hospital, Hyogo, Japan.
  • Sumiyoshi S; Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan; Department of Diagnostic Pathology, Tenri Hospital, Nara, Japan.
  • Hamaji M; Department of Thoracic Surgery, Kyoto University Hospital, Kyoto, Japan.
  • Menju T; Department of Thoracic Surgery, Kyoto University Hospital, Kyoto, Japan.
  • Date H; Department of Thoracic Surgery, Kyoto University Hospital, Kyoto, Japan.
  • Morita S; Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan.
  • Bise R; Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan.
  • Haga H; Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan.
Mod Pathol ; 37(6): 100485, 2024 Jun.
Article en En | MEDLINE | ID: mdl-38588885
ABSTRACT
Several studies have developed various artificial intelligence (AI) models for immunohistochemical analysis of programmed death ligand 1 (PD-L1) in patients with non-small cell lung carcinoma; however, none have focused on specific ways by which AI-assisted systems could help pathologists determine the tumor proportion score (TPS). In this study, we developed an AI model to calculate the TPS of the PD-L1 22C3 assay and evaluated whether and how this AI-assisted system could help pathologists determine the TPS and analyze how AI-assisted systems could affect pathologists' assessment accuracy. We assessed the 4 methods of the AI-assisted system (1 and 2) pathologists first assessed and then referred to automated AI scoring results (1, positive tumor cell percentage; 2, positive tumor cell percentage and visualized overlay image) for final confirmation, and (3 and 4) pathologists referred to the automated AI scoring results (3, positive tumor cell percentage; 4, positive tumor cell percentage and visualized overlay image) while determining TPS. Mixed-model analysis was used to calculate the odds ratios (ORs) with 95% CI for AI-assisted TPS methods 1 to 4 compared with pathologists' scoring. For all 584 samples of the tissue microarray, the OR for AI-assisted TPS methods 1 to 4 was 0.94 to 1.07 and not statistically significant. Of them, we found 332 discordant cases, on which the pathologists' judgments were inconsistent; the ORs for AI-assisted TPS methods 1, 2, 3, and 4 were 1.28 (1.06-1.54; P = .012), 1.29 (1.06-1.55; P = .010), 1.28 (1.06-1.54; P = .012), and 1.29 (1.06-1.55; P = .010), respectively, which were statistically significant. For discordant cases, the OR for each AI-assisted TPS method compared with the others was 0.99 to 1.01 and not statistically significant. This study emphasized the usefulness of the AI-assisted system for cases in which pathologists had difficulty determining the PD-L1 TPS.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inmunohistoquímica / Biomarcadores de Tumor / Carcinoma de Pulmón de Células no Pequeñas / Antígeno B7-H1 / Patólogos / Aprendizaje Profundo / Neoplasias Pulmonares Límite: Female / Humans / Male Idioma: En Revista: Mod Pathol Asunto de la revista: PATOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Japón Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inmunohistoquímica / Biomarcadores de Tumor / Carcinoma de Pulmón de Células no Pequeñas / Antígeno B7-H1 / Patólogos / Aprendizaje Profundo / Neoplasias Pulmonares Límite: Female / Humans / Male Idioma: En Revista: Mod Pathol Asunto de la revista: PATOLOGIA Año: 2024 Tipo del documento: Article País de afiliación: Japón Pais de publicación: Estados Unidos