Image analysis-based tumor infiltrating lymphocytes measurement predicts breast cancer pathologic complete response in SWOG S0800 neoadjuvant chemotherapy trial.
Kristina A FanucciYalai BaiVasiliki PelekanouZeina A NahlehSaba ShafiSneha BurelaWilliam E BarlowPriyanka SharmaAlastair M ThompsonAndrew K GodwinDavid L RimmGabriel N HortobagyiYihan LiuLeona WangWei WeiLajos PusztaiKim R M BlenmanPublished in: NPJ breast cancer (2023)
We assessed the predictive value of an image analysis-based tumor-infiltrating lymphocytes (TILs) score for pathologic complete response (pCR) and event-free survival in breast cancer (BC). About 113 pretreatment samples were analyzed from patients with stage IIB-IIIC HER-2-negative BC randomized to neoadjuvant chemotherapy ± bevacizumab. TILs quantification was performed on full sections using QuPath open-source software with a convolutional neural network cell classifier (CNN11). We used easTILs% as a digital metric of TILs score defined as [sum of lymphocytes area (mm 2 )/stromal area(mm 2 )] × 100. Pathologist-read stromal TILs score (sTILs%) was determined following published guidelines. Mean pretreatment easTILs% was significantly higher in cases with pCR compared to residual disease (median 36.1 vs.14.8%, p < 0.001). We observed a strong positive correlation (r = 0.606, p < 0.0001) between easTILs% and sTILs%. The area under the prediction curve (AUC) was higher for easTILs% than sTILs%, 0.709 and 0.627, respectively. Image analysis-based TILs quantification is predictive of pCR in BC and had better response discrimination than pathologist-read sTILs%.
Keyphrases
- neoadjuvant chemotherapy
- convolutional neural network
- locally advanced
- free survival
- lymph node
- peripheral blood
- sentinel lymph node
- phase iii
- bone marrow
- deep learning
- phase ii
- open label
- double blind
- radiation therapy
- clinical trial
- real time pcr
- study protocol
- squamous cell carcinoma
- cell therapy
- randomized controlled trial
- placebo controlled
- mesenchymal stem cells
- systematic review
- machine learning
- clinical practice
- early stage