Details

Autor(en) / Beteiligte

• Chen, Yuli
• Li, Haojia
• Janowczyk, Andrew
• Toro, Paula
• Corredor, Germán
• Whitney, Jon
• Lu, Cheng
• Koyuncu, Can F.
• Mokhtari, Mojgan
• Buzzy, Christina
• Ganesan, Shridar
• Feldman, Michael D.
• Fu, Pingfu
• Corbin, Haley
• Harbhajanka, Aparna
• Gilmore, Hannah
• Goldstein, Lori J.
• Davidson, Nancy E.
• Desai, Sangeeta
• Parmar, Vani
• Madabhushi, Anant

Titel

Computational pathology improves risk stratification of a multi-gene assay for early stage ER+ breast cancer

Ist Teil von

• NPJ breast cancer, 2023-05, Vol.9 (1), p.40-40, Article 40

Ort / Verlag

London: Nature Publishing Group UK

Erscheinungsjahr

2023

Quelle

Electronic Journals Library

Beschreibungen/Notizen

• Prognostic markers currently utilized in clinical practice for estrogen receptor-positive (ER+) and lymph node-negative (LN−) invasive breast cancer (IBC) patients include the Nottingham grading system and Oncotype Dx (ODx). However, these biomarkers are not always optimal and remain subject to inter-/intra-observer variability and high cost. In this study, we evaluated the association between computationally derived image features from H&E images and disease-free survival (DFS) in ER+ and LN− IBC. H&E images from a total of n  = 321 patients with ER+ and LN− IBC from three cohorts were employed for this study (Training set: D1 ( n  = 116), Validation sets: D2 ( n  = 121) and D3 ( n  = 84)). A total of 343 features relating to nuclear morphology, mitotic activity, and tubule formation were computationally extracted from each slide image. A Cox regression model (IbRiS) was trained to identify significant predictors of DFS and predict a high/low-risk category using D1 and was validated on independent testing sets D2 and D3 as well as within each ODx risk category. IbRiS was significantly prognostic of DFS with a hazard ratio (HR) of 2.33 (95% confidence interval (95% CI) = 1.02–5.32, p  = 0.045) on D2 and a HR of 2.94 (95% CI = 1.18–7.35, p  = 0.0208) on D3. In addition, IbRiS yielded significant risk stratification within high ODx risk categories (D1 + D2: HR = 10.35, 95% CI = 1.20–89.18, p  = 0.0106; D1: p  = 0.0238; D2: p  = 0.0389), potentially providing more granular risk stratification than offered by ODx alone.