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Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer.

ABSTRACT: The achievement of the pathologic complete response (pCR) has been considered a metric for the success of neoadjuvant chemotherapy (NAC) and a powerful surrogate indicator of the risk of recurrence and long-term survival. This study aimed to develop a multimodal deep learning model that combined clinical information and pretreatment MR images for predicting pCR to NAC in patients with breast cancer. The retrospective study cohort consisted of 536 patients with invasive breast cancer who underwent pre-operative NAC. We developed a deep learning model to fuse high-dimensional MR image features and the clinical information for the pretreatment prediction of pCR to NAC in breast cancer. The proposed deep learning model trained on all datasets as clinical information, T1-weighted subtraction images, and T2-weighted images shows better performance with area under the curve (AUC) of 0.888 as compared to the model using only clinical information (AUC = 0.827, P < 0.05). Our results demonstrate that the multimodal fusion approach using deep learning with both clinical information and MR images achieve higher prediction performance compared to the deep learning model without the fusion approach. Deep learning could integrate pretreatment MR images with clinical information to improve pCR prediction performance.

SUBMITTER: Joo S

PROVIDER: S-EPMC8458289 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer.

Joo Sunghoon S Ko Eun Sook ES Kwon Soonhwan S Jeon Eunjoo E Jung Hyungsik H Kim Ji-Yeon JY Chung Myung Jin MJ Im Young-Hyuck YH

Scientific reports 20210922 1

The achievement of the pathologic complete response (pCR) has been considered a metric for the success of neoadjuvant chemotherapy (NAC) and a powerful surrogate indicator of the risk of recurrence and long-term survival. This study aimed to develop a multimodal deep learning model that combined clinical information and pretreatment MR images for predicting pCR to NAC in patients with breast cancer. The retrospective study cohort consisted of 536 patients with invasive breast cancer who underwen ...[more]

PMID: 34552163

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Project description:PurposeThis study aims to investigate the diagnostic accuracy of various deep learning methods on DCE-MRI, in order to provide a simple and accessible tool for predicting pathologic response of NAC in breast cancer patients.MethodsIn this study, we enrolled 313 breast cancer patients who had complete DCE-MRI data and underwent NAC followed by breast surgery. According to Miller-Payne criteria, the efficacy of NAC was categorized into two groups: the patients achieved grade 1-3 of Miller-Payne criteria were classified as the non-responders, while patients achieved grade 4-5 of Miller-Payne criteria were classified as responders. Multiple deep learning frameworks, including ViT, VGG16, ShuffleNet_v2, ResNet18, MobileNet_v2, MnasNet-0.5, GoogleNet, DenseNet121, and AlexNet, were used for transfer learning of the classification model. The deep learning features were obtained from the final fully connected layer of the deep learning models, with 256 features extracted based on DCE-MRI data for each patient of each deep learning model. Various machine-learning techniques, including support vector machine (SVM), K-nearest neighbor (KNN), RandomForest, ExtraTrees, XGBoost, LightGBM, and multiple-layer perceptron (MLP), were employed to construct classification models.ResultsWe utilized various deep learning models to extract features and subsequently constructed machine learning models. Based on the performance of different machine learning models' AUC values, we selected the classifiers with the best performance. ResNet18 exhibited superior performance, with an AUC of 0.87 (95% CI: 0.82 - 0.91) and 0.87 (95% CI: 0.78 - 0.96) in the train and test cohorts, respectively.ConclusionsUsing pre-treatment DCE-MRI images, our study trained multiple deep models and developed the best-performing DLR model for predicting pathologic response of NAC in breast cancer patients. This prognostic tool provides a dependable and impartial basis for effectively identifying breast cancer patients who are most likely to benefit from NAC before its initiation. At the same time, it can also identify those patients who are insensitive to NAC, allowing them to proceed directly to surgical treatment and prevent the risk of losing the opportunity for surgery due to disease progression after NAC.

Project description:BackgroundFor patients with breast cancer undergoing neoadjuvant chemotherapy (NACT), most of the existing prediction models of pathologic complete response (pCR) using clinicopathological features were based on standard statistical models like logistic regression, while models based on machine learning mostly utilized imaging data and/or gene expression data. This study aims to develop a robust and accessible machine learning model to predict pCR using clinicopathological features alone, which can be used to facilitate clinical decision-making in diverse settings.MethodsThe model was developed and validated within the National Cancer Data Base (NCDB, 2018-2020) and an external cohort at the University of Chicago (2010-2020). We compared logistic regression and machine learning models, and examined whether incorporating quantitative clinicopathological features improved model performance. Decision curve analysis was conducted to assess the model's clinical utility.ResultsWe identified 56,209 NCDB patients receiving NACT (pCR rate: 34.0%). The machine learning model incorporating quantitative clinicopathological features showed the best discrimination performance among all the fitted models [area under the receiver operating characteristic curve (AUC): 0.785, 95% confidence interval (CI): 0.778-0.792], along with outstanding calibration performance. The model performed best among patients with hormone receptor positive/human epidermal growth factor receptor 2 negative (HR+/HER2-) breast cancer (AUC: 0.817, 95% CI: 0.802-0.832); and by adopting a 7% prediction threshold, the model achieved 90.5% sensitivity and 48.8% specificity, with decision curve analysis finding a 23.1% net reduction in chemotherapy use. In the external testing set of 584 patients (pCR rate: 33.4%), the model maintained robust performance both overall (AUC: 0.711, 95% CI: 0.668-0.753) and in the HR+/HER2- subgroup (AUC: 0.810, 95% CI: 0.742-0.878).ConclusionsThe study developed a machine learning model ( https://huolab.cri.uchicago.edu/sample-apps/pcrmodel ) to predict pCR in breast cancer patients undergoing NACT that demonstrated robust discrimination and calibration performance. The model performed particularly well among patients with HR+/HER2- breast cancer, having the potential to identify patients who are less likely to achieve pCR and can consider alternative treatment strategies over chemotherapy. The model can also serve as a robust baseline model that can be integrated with smaller datasets containing additional granular features in future research.

Project description:BackgroundTo investigate the association of axillary pathologic complete response (pCR) rate among breast cancer patients with pCR after neoadjuvant chemotherapy (NCT).MethodsThe retrospective clinical data of 1,903 patients who were treated with NCT between March, 2010 and December, 2018, were collected from one Chinese database and analyzed. The correlations between clinicopathological characteristics and breast pCR with axillary pCR were calculated by χ2 test. Binary logistic regression analysis was used for multivariate analysis. The relative risk of positive axillary nodes after NCT in patients with and without breast pCR was analyzed using a Cochran-Mantel-Haenszel (CMH) test stratified by initial N stage and tumor subtype.ResultsThe rate of axillary pCR was increased in the cases with initial cN0, Ki67 high expression, HR+HER2+/HR-HER2+/TN subtypes, and breast pCR. After NCT, the relative risk of nodal disease burden was 4.81 in patients without breast pCR compared with patients with breast pCR. The relative risk of positive nodal status in patients with cN0, cN1, cN2, and cN3 disease without vs. with breast pCR was 6.45, 4.88, 5.69 and 6.24, respectively. The relative risk of positive nodal status in patients with HR+HER2-, HR+HER2+, HR-HER2+, and TN disease was 4.02, 4.50, 3.82 and 4.18, respectively. Of cN0 patients with breast pCR, only 4 out of 44 (9%) with HER2-positive disease had 1 or 2 axillary lymph node metastases at final surgical pathology compared to 30 out of 98 (31%) of those without breast pCR. There was no evidence of positive nodal residue among all 21 patients (100%) with TN disease compared to 65% (36 of 55) of patients without breast pCR.ConclusionsNodal status is strongly correlated with breast pCR after NCT. Patients with initial cN0/1 TN/HER2 positive disease who achieve breast pCR at surgery have a low risk of nodal metastasis. These results suggest that the failure rate of missing positive lymph nodes among those patients was very low and that it is safe for such patients to undergo sentinel lymph node biopsy (SLNB) after NCT. This study also provides a theoretical basis for clinical trials focused on the avoidance of axillary surgery in selected patients.

Dataset Information

Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer.

Publications

Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer.

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