Dataset Information

Prediction of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer by using a deep learning model with 18F-FDG PET/CT.

ABSTRACT:

Objectives

The aim of the study is 18F-FDG PET/CT imaging by using deep learning method are predictive for pathological complete response pCR after Neoadjuvant chemotherapy (NAC) in locally advanced breast cancer (LABC).

Introduction

NAC is the standard treatment for locally advanced breast cancer (LABC). Pathological complete response (pCR) after NAC is considered a good predictor of disease-free survival (DFS) and overall survival (OS).Therefore, there is a need to develop methods that can predict the pCR at the time of diagnosis.

Methods

This article was designed as a retrospective chart study.For the convolutional neural network model, a total of 355 PET/CT images of 31 patients were used. All patients had primary breast surgery after completing NAC.

Results

Pathological complete response was obtained in a total of 9 patients. The study results show that our proposed deep convolutional neural networks model achieved a remarkable success with an accuracy of 84.79% to predict pathological complete response.

Conclusion

It was concluded that deep learning methods can predict breast cancer treatment.

SUBMITTER: Bulut G

PROVIDER: S-EPMC10501592 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Prediction of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer by using a deep learning model with 18F-FDG PET/CT.

Bulut Gülcan G Atilgan Hasan Ikbal HI Çınarer Gökalp G Kılıç Kazım K Yıkar Deniz D Parlar Tuba T

PloS one 20230914 9

<h4>Objectives</h4>The aim of the study is 18F-FDG PET/CT imaging by using deep learning method are predictive for pathological complete response pCR after Neoadjuvant chemotherapy (NAC) in locally advanced breast cancer (LABC).<h4>Introduction</h4>NAC is the standard treatment for locally advanced breast cancer (LABC). Pathological complete response (pCR) after NAC is considered a good predictor of disease-free survival (DFS) and overall survival (OS).Therefore, there is a need to develop metho ...[more]

PMID: 37708209

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Project description:PurposeWe aimed to determine the rate of pathological complete response (pCR), clinicopathological factors associated with pCR, and clinical outcomes following neoadjuvant chemotherapy in locally advanced breast cancer.MethodsMedical records of patients who had undergone neoadjuvant chemotherapy for breast cancer between January 2007 and September 2011 were retrospectively reviewed, and the pCR rates were calculated according to three sets of criteria: the National Surgical Adjuvant Breast and Bowel Project (NSABP), the MD Anderson Cancer Center (MDACC), and the German Breast Group (GBG). Tumors were classified as luminal A like, luminal B like, human epidermal growth factor receptor 2 (HER2), or triple-negative. pCR and clinical outcome, including overall survival (OS) and disease-free survival (DFS) rates were analyzed at the median follow-up of 54.2 months.ResultsOf a total of 179 patients who had received neoadjuvant chemotherapy, 167 patients (93.3%) had locally advanced breast cancer and 12 patients (6.7%) had early-stage breast cancer. The majority of patients (152 patients, 89.4%) received anthracycline-based neoadjuvant chemotherapy. The objective clinical response rate was 61.5%, comprising clinical partial response in 5.5% and clinical complete response in 3.9% of patients. Twenty-one (11.7%), 20 (11.2%), and 17 patients (9.5%) achieved pCR according to NSABP, MDACC, and GBG definitions, respectively. pCR rates, as defined by NSABP, according to breast cancer subtype were 4.4%, 9.7%, 24.2%, and 19.2% in luminal A like, luminal B like, HER2, and triple-negative subtypes, respectively. Patients who achieved pCR had significantly better DFS (5-year DFS rates, 80% vs. 53%, p=0.030) and OS (5-year OS rates, 86% vs. 54%, p=0.042) than those who did not.ConclusionThe pCR rate following neoadjuvant chemotherapy for breast cancer in Thai women attending our institution was 11.7%; pCR was more frequently observed in HER2 and triple-negative breast tumor subtypes. Patients who achieved pCR had significantly improved survival.

Dataset Information

Prediction of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer by using a deep learning model with 18F-FDG PET/CT.

Objectives

Introduction

Methods

Results

Conclusion

Publications

Prediction of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer by using a deep learning model with 18F-FDG PET/CT.

Similar Datasets

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