scholarly journals A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy

2020 ◽  
Vol 22 (1) ◽  
Author(s):  
Elizabeth J. Sutton ◽  
Natsuko Onishi ◽  
Duc A. Fehr ◽  
Brittany Z. Dashevsky ◽  
Meredith Sadinski ◽  
...  
2021 ◽  
Author(s):  
Ji-Yeon Kim ◽  
Eunjoo Jeon ◽  
Soonhwan Kwon ◽  
Hyungsik Jung ◽  
Sunghoon Joo ◽  
...  

Abstract BackgroundThe aim of this study was to develop a machine learning(ML) based model to accurately predict pathologic complete response(pCR) to neoadjuvant chemotherapy(NAC) using pretreatment clinical and pathological characteristics of electronic medical record(EMR) data in breast cancer(BC).Methods The EMR data from patients diagnosed with early and locally advanced BC and who received NAC followed by curative surgery were reviewed. A total of 16 clinical and pathological characteristics was selected to develop ML model. We practiced six ML models using default settings for multivariate analysis with extracted variables. ResultsIn total, 2,065 patients were included in this analysis. Overall, 30.6% (n=632) of patients achieved pCR. Among six ML models, the LightGBM had the highest area under the curve (AUC) for pCR prediction. After hyper-parameter tuning with Bayesian optimization, AUC was 0.810. Performance of pCR prediction models in different histology-based subtypes was compared. The AUC was highest in HR+HER2- subgroup and lowest in HR-/HER2- subgroup (HR+/HER2- 0.841, HR+/HER2+ 0.716, HR-/HER2 0.753, HR-/HER2- 0.653).ConclusionsA ML based pCR prediction model using pre-treatment clinical and pathological characteristics provided useful information to predict pCR during NAC. This prediction model would help to determine treatment strategy in patients with BC planned NAC.


Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 608
Author(s):  
Toshiaki Iwase ◽  
Aaroh Parikh ◽  
Seyedeh S. Dibaj ◽  
Yu Shen ◽  
Tushaar Vishal Shrimanker ◽  
...  

Our previous study indicated that a high amount of visceral adipose tissue was associated with poor survival outcomes in patients with early breast cancer who received neoadjuvant chemotherapy. However, inconsistency was observed in the prognostic role of body composition in breast cancer treatment outcomes. In the present study, we aimed to validate our previous research by performing a comprehensive body composition analysis in patients with a standardized clinical background. We included 198 patients with stage III breast cancer who underwent neoadjuvant chemotherapy between January 2007 and June 2015. The impact of body composition on pathologic complete response and survival outcomes was determined. Body composition measurements had no significant effect on pathologic complete response. Survival analysis showed a low ratio of total visceral adipose tissue to subcutaneous adipose tissue (V/S ratio ≤ 34) was associated with shorter overall survival. A changepoint method determined that a V/S ratio cutoff of 34 maximized the difference in overall survival. Our study indicated the prognostic effect of body composition measurements in patients with locally advanced breast cancer compared to those with early breast cancer. Further investigation will be needed to clarify the biological mechanism underlying the association of V/S ratio with prognosis in locally advanced breast cancer.


Oncotarget ◽  
2018 ◽  
Vol 9 (41) ◽  
pp. 26406-26416 ◽  
Author(s):  
Angela Santonja ◽  
Alfonso Sánchez-Muñoz ◽  
Ana Lluch ◽  
Maria Rosario Chica-Parrado ◽  
Joan Albanell ◽  
...  

2005 ◽  
Vol 23 (28) ◽  
pp. 7098-7104 ◽  
Author(s):  
Ana M. Gonzalez-Angulo ◽  
Sean E. McGuire ◽  
Thomas A. Buchholz ◽  
Susan L. Tucker ◽  
Henry M. Kuerer ◽  
...  

Purpose To identify clinicopathological factors predictive of distant metastasis in patients who had a pathologic complete response (pCR) after neoadjuvant chemotherapy (NC). Methods Retrospective review of 226 patients at our institution identified as having a pCR was performed. Clinical stage at diagnosis was I (2%), II (36%), IIIA (27%), IIIB (23%), and IIIC (12%). Eleven percent of all patients were inflammatory breast cancers (IBC). Ninety-five percent received anthracycline-based chemotherapy; 42% also received taxane-based therapy. The relationship of distant metastasis with clinicopathologic factors was evaluated, and Cox regression analysis was performed to identify independent predictors of development of distant metastasis. Results Median follow-up was 63 months. There were 31 distant metastases. Ten-year distant metastasis-free rate was 82%. Multivariate Cox regression analysis using combined stage revealed that clinical stages IIIB, IIIC, and IBC (hazard ratio [HR], 4.24; 95% CI, 1.96 to 9.18; P < .0001), identification of ≤ 10 lymph nodes (HR, 2.94; 95% CI, 1.40 to 6.15; P = .004), and premenopausal status (HR, 3.08; 95% CI, 1.25 to 7.59; P = .015) predicted for distant metastasis. Freedom from distant metastasis at 10 years was 97% for no factors, 88% for one factor, 77% for two factors, and 31% for three factors (P < .0001). Conclusion A small percentage of breast cancer patients with pCR experience recurrence. We identified factors that independently predicted for distant metastasis development. Our data suggest that premenopausal patients with advanced local disease and suboptimal axillary node evaluation may be candidates for clinical trials to determine whether more aggressive or investigational adjuvant therapy will be of benefit.


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