ArticleBreast cancer (Tokyo, Japan)2025
Clinical utility of tumor-infiltrating lymphocyte evaluation by two different methods in breast cancer patients treated with neoadjuvant chemotherapy.
Article in Breast cancer (Tokyo, Japan), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- QuTILs: Open-Source Image-Based Infiltrating Immune Cell Detection for Research Application.Research square · 2026Article
- Prognostic significance of peripheral myeloid-derived suppressor cells in advanced breast cancer.Breast cancer (Tokyo, Japan) · 2026Article
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17 authors.
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Abstract
purposeThe aim of this study was to examine the clinical utility of tumor-infiltrating lymphocytes (TILs) evaluated by "average" and "hot-spot" methods in breast cancer patients.
methodsWe examined 367 breast cancer patients without neoadjuvant chemotherapy (NAC) by average and hot-spot methods to determine the consistency of TIL scores between biopsy and surgical specimens. TIL scores before NAC were also compared with the pathological complete response (pCR) rate and clinical outcomes in 144 breast cancer patients that received NAC. TIL scores evaluated by the two methods were predicted from clinicopathological data using random forest regression.
resultsSurgical specimens showed higher TIL scores than biopsy specimens using the hot-spot method (p < 0.001), while biopsy and surgical specimens showed similar TIL scores using the average method. There was a linear relationship between the pCR rate and TIL scores determined using hot-spot (p < 0.001) and average methods (p = 0.001). Patients without pCR and low TILs by the average method had significantly worse overall survival compared to other patients (p = 0.02). The root mean squared errors of the predicted TIL score for the test set were 19.662 (hot-spot) and 10.955 (average).
conclusionThe average method may have an advantage for breast cancer patients receiving NAC, since the TIL score using this method is more consistent between biopsy and surgical specimens, and it associates better with clinical outcomes. Our exploratory study showed that machine learning from clinicopathological data may better predict TIL scores assessed by the average, rather than hot-spot, method.
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