scholarly journals Deep learning from HE slides predicts the clinical benefit from adjuvant chemotherapy in hormone receptor-positive breast cancer patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Soo Youn Cho ◽  
Jeong Hoon Lee ◽  
Jai Min Ryu ◽  
Jeong Eon Lee ◽  
Eun Yoon Cho ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Adjuvant Chemotherapy ◽  
Cancer Patients ◽  
Hormone Receptor ◽  
Oncotype Dx ◽  
Breast Cancer Patients ◽  
Hormone Receptor Positive ◽  
Predicted Risk ◽  
Positive Breast Cancer

AbstractWe hypothesized that a deep-learning algorithm using HE images might be capable of predicting the benefits of adjuvant chemotherapy in cancer patients. HE slides were retrospectively collected from 1343 de-identified breast cancer patients at the Samsung Medical Center and used to develop the Lunit SCOPE algorithm. Lunit SCOPE was trained to predict the recurrence using the 21-gene assay (Oncotype DX) and histological parameters. The risk prediction model predicted the Oncotype DX score > 25 and the recurrence survival of the prognosis validation cohort and TCGA cohorts. The most important predictive variable was the mitotic cells in the cancer epithelium. Of the 363 patients who did not receive adjuvant therapy, 104 predicted high risk had a significantly lower survival rate. The top-300 genes highly correlated with the predicted risk were enriched for cell cycle, nuclear division, and cell division. From the Oncotype DX genes, the predicted risk was positively correlated with proliferation-associated genes and negatively correlated with prognostic genes from the estrogen category. An integrative analysis using Lunit SCOPE predicted the risk of cancer recurrence and the early-stage hormone receptor-positive breast cancer patients who would benefit from adjuvant chemotherapy.

Survival advantage associated with neoadjuvant chemotherapy compared to neoadjuvant hormonal therapy in postmenopausal women with hormone receptor positive breast cancer: A National Cancer Data Base study.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e12119-e12119
Author(s):  
Alina Basnet ◽  
Dongliang Wang ◽  
Abirami Sivapiragasam
Keyword(s):  
Breast Cancer ◽  
Adjuvant Chemotherapy ◽  
Neoadjuvant Chemotherapy ◽  
Cancer Patients ◽  
Hormone Receptor ◽  
National Cancer Data Base ◽  
Breast Cancer Patients ◽  
Cancer Data ◽  
Hormone Receptor Positive ◽  
Positive Breast Cancer

e12119 Background: Neoadjuvant endocrine therapy (NET) and neoadjuvant chemotherapy (NCT) are both considered effective strategies in postmenopausal, hormone receptor positive breast cancer patients. Small prospective studies show comparable response rates and breast conservation rates. Using National Cancer Data Base (NCDB) we report overall survival (OS) differences between these two strategies with subgroup analysis by Estrogen Receptor (ER), Progesterone Receptor (PgR) status. Methods: We extracted data on hormone receptor positive breast cancer patients without metastasis in women aged ≥ 50 from the NCDB registry (2004-2014). We excluded patients who did not receive adjuvant endocrine therapy after NCT and patients who received adjuvant chemotherapy after NET as this could affect OS. We calculated OS using Kaplan Meier analysis with hazard ratio (HR) from cox regression model. Subgroup analysis was performed by ER, PgR status. Results: Out of 2,246,279 patients, 30,348 patients met our inclusion criteria. 7836 received NET and 22512 received NCT. OS rate was 70.8% vs 81.7% at 5 yrs and 42.5% vs 62.1% at 9 yrs for NET and NCT respectively with adjusted hazard ratio (HR) of 1.818; 95% CI (1.657-1.996). OS outcome for ER+/PgR+ group was 72.3% vs 83.5% at 5 yrs and 43.5% vs 64% at 9 yrs for NET and NCT respectively with adjusted HR of 1.807; 95% CI (1.624-2.010). OS for ER+/pgR- group was 62.9% vs 76.8% at 5 yrs and 33.1% vs 54.2% at 9 yrs for NET and NCT respectively with adjusted HR of 1.890; 95% CI (1.549-2.306). Our analysis also revealed that 5591 T1 patients received neoadjuvant therapy among which 2541 received NET and 3050 received NCT. Conclusions: We find a significant survival advantage in patients treated with NCT as opposed to NET. All subgroups showed imporved OS with NCT compared with NET. Limitations that should be considered in this registry based study are: not accounting for Her-2 status, differences in surgical technique, duration and choices of adjuvant chemotherapy and radiotherapy options.

scholarly journals Deep learning from HE slides to predict the clinical benefit from adjuvant chemotherapy in hormone receptor-positive breast cancer

2020 ◽  
Author(s):  
Soo Youn Cho ◽  
Jeong Hoon Lee ◽  
Jai Min Ryu ◽  
Jeong Eon Lee ◽  
Eun Yoon Cho ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
High Risk ◽  
Adjuvant Chemotherapy ◽  
Hormone Receptor ◽  
Early Stage ◽  
Oncotype Dx ◽  
Hormone Receptor Positive ◽  
Gene Assay ◽  
Positive Breast Cancer

Abstract Background: The predictive value of adjuvant chemotherapy for early-stage hormone receptor-positive breast cancer has been only validated by a 21-gene expression assay. We hypothesized that deep-learning prediction from HE images, called Lunit-SCOPE, is a potential prognostic and predictive biomarker of adjuvant chemotherapy.Methods: We retrospectively collected HE slides from 1153 de-identified breast cancer patients at the Samsung Medical Center (SMC) in order to develop a deep-learning algorithm called Lunit-SCOPE. The histological parameters from 255 patients, deciphered by Lunit-SCOPE, were trained to predict the recurrence score (RS) using the 21-gene assay from Oncotype DX. We validated the model’s performance using the recurrence survival of 898 patients and The Cancer Genome Atlas (TCGA) cohort, and examined related biological functions through RNA sequence data.Results: The histologic parameter-based RS prediction model predicted the oncotype DX score (R2=0.96) and the recurrence survival analysis on the validation (p<0.01) and TCGA cohort (p<0.01), where the most important variables were the nuclear grade and the mitotic cells in the cancer epithelium. Of the 85 patients classified as the high-risk group, 72 patients who received adjuvant therapy had a significantly better survival (p<0.01). The functions of the top 300 highly correlated genes with a predicted RS were enriched for cell cycle, nuclear division and cell division. Of the 21-genes from the Oncotype DX, the predicted RS had positive correlations with the proliferation category genes and was negatively correlated with the prognostic genes in the estrogen category.Conclusion: An integrative analysis using Lunit-SCOPE predicts a high risk of recurrence and those who would benefit from adjuvant chemotherapy for early-stage hormone-positive breast cancer.

Sign in / Sign up

Export Citation Format

Share Document