Whole tumor section quantitative image analysis maximizes between-pathologists' reproducibility for clinical immunohistochemistry-based biomarkers.

Pathologists have had increasing responsibility for quantitating immunohistochemistry (IHC) biomarkers with the expectation of high between-reader reproducibility due to clinical decision-making especially for patient therapy. Digital imaging-based quantitation of IHC clinical slides offers a potential aid for improvement; however, its clinical adoption is limited potentially due to a conventional field-of-view annotation approach. In this study, we implemented a novel solely morphology-based whole tumor section annotation strategy to maximize image analysis quantitation results between readers. We first compare the field-of-view image analysis annotation approach to digital and manual-based modalities across multiple clinical studies (~120 cases per study) and biomarkers (ER, PR, HER2, Ki-67, and p53 IHC) and then compare a subset of the same cases (~40 cases each from the ER, PR, HER2, and Ki-67 studies) using whole tumor section annotation approach to understand incremental value of all modalities. Between-reader results for each biomarker in relation to conventional scoring modalities showed similar concordance as manual read: ER field-of-view image analysis: 95.3% (95% CI 92.0-98.2%) vs digital read: 92.0% (87.8-95.8%) vs manual read: 94.9% (91.4-97.8%); PR field-of-view image analysis: 94.1% (90.3-97.2%) vs digital read: 94.0% (90.2-97.1%) vs manual read: 94.4% (90.9-97.2%); Ki-67 field-of-view image analysis: 86.8% (82.1-91.4%) vs digital read: 76.6% (70.9-82.2%) vs manual read: 85.6% (80.4-90.4%); p53 field-of-view image analysis: 81.7% (76.4-86.8%) vs digital read: 80.6% (75.0-86.0%) vs manual read: 78.8% (72.2-83.3%); and HER2 field-of-view image analysis: 93.8% (90.0-97.2%) vs digital read: 91.0 (86.6-94.9%) vs manual read: 87.2% (82.1-91.9%). Subset implementation and analysis on the same cases using whole tumor section image analysis approach showed significant improvement between pathologists over field-of-view image analysis and manual read (HER2 100% (97-100%), P=0.013 field-of-view image analysis and 0.013 manual read; Ki-67 100% (96.9-100%), P=0.040 and 0.012; ER 98.3% (94.1-99.5%), p=0.232 and 0.181; and PR 96.6% (91.5-98.7%), p=0.012 and 0.257). Overall, whole tumor section image analysis significantly improves between-pathologist's reproducibility and is the optimal approach for clinical-based image analysis algorithms.