Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients

Dodington, David W.; Lagree, Andrew; Tabbarah, Sami; Mohebpour, Majid; Sadeghi‐Naini, Ali; Tran, William T.; Lu, Fang-I

doi:10.1007/s10549-020-06093-4

Cited by 28 publications

(15 citation statements)

References 36 publications

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“…mean pixel intensity and correlation of the gray-level co-occurrence matrix). By the multivariate binary logistic regression, it achieves an accuracy of 0.79 in a dataset with 58 patients (33). All these studies using conventional machine learning methods present a limited prediction success, largely due to limited sources of information and human-defined engineering features insufficient for prediction support.…”

Section: Discussionmentioning

confidence: 99%

A Spatial Attention Guided Deep Learning System for Prediction of Pathological Complete Response Using Breast Cancer Histopathology Images

Duanmu

Bhattarai

et al. 2022

Preprint

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Predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) patients accurately is direly needed for clinical decision making. pCR is also regarded as a strong predictor of overall survival. In this work, we propose a deep learning system to predict pCR to NAC based on serial pathology images stained with hematoxylin and eosin (H&E) and two immunohistochemical biomarkers (Ki67 and PHH3). To support human prior domain knowledge based guidance and enhance interpretability of the deep learning system, we introduce a human knowledge derived spatial attention mechanism to inform deep learning models of informative tissue areas of interest. For each patient, three serial breast tumor tissue sections from biopsy blocks were sectioned, stained in three different stains, and integrated. The resulting comprehensive attention information from the image triplets is used to guide our prediction system for prognostic tissue regions. The experimental dataset consists of 26,419 pathology image patches of 1,000 × 1,000 pixels from 73 TNBC patients treated with NAC. Image patches from randomly selected 43 patients are used as a training dataset and images patches from the rest 30 are used as a testing dataset. By the maximum voting from patch-level results, our proposed model achieves a 93% patient-level accuracy, outperforming baselines and other state-of-the-art systems, suggesting its high potential for clinical decision making. The codes, the documentation, and example data are available on an open source at: https://github.com/jkonglab/PCR_Prediction_Serial_WSIs_biomarkers

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Section: Discussionmentioning

confidence: 99%

A Spatial Attention Guided Deep Learning System for Prediction of Pathological Complete Response Using Breast Cancer Histopathology Images

Duanmu

Bhattarai

et al. 2022

Preprint

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“…In one study of 58 breast cancer patients, Dodington et al . [ 43 ] focused on the nuclear level after segmentation was used to extract a limited set of nuclear features for analyses; the nuclear intensity and gray-level co-occurrence matrix (GLCM-COR) of tumor nuclear features were found to be related to pCR in univariate analysis ( P = 0.035, P = 0.039). Differently, our learning process with CNN II was guided simply by the assessment results of the treatment response instead of focusing on specific features of tumor nuclear morphology, which allowed us to explore a wider range of image information values.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based predictive biomarker of pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer

Yang

Wei

et al. 2021

J Transl Med

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Background Pathological complete response (pCR) is considered a surrogate endpoint for favorable survival in breast cancer patients treated with neoadjuvant chemotherapy (NAC). Predictive biomarkers of treatment response are crucial for guiding treatment decisions. With the hypothesis that histological information on tumor biopsy images could predict NAC response in breast cancer, we proposed a novel deep learning (DL)-based biomarker that predicts pCR from images of hematoxylin and eosin (H&E)-stained tissue and evaluated its predictive performance. Methods In total, 540 breast cancer patients receiving standard NAC were enrolled. Based on H&E-stained images, DL methods were employed to automatically identify tumor epithelium and predict pCR by scoring the identified tumor epithelium to produce a histopathological biomarker, the pCR-score. The predictive performance of the pCR-score was assessed and compared with that of conventional biomarkers including stromal tumor-infiltrating lymphocytes (sTILs) and subtype. Results The pCR-score derived from H&E staining achieved an area under the curve (AUC) of 0.847 in predicting pCR directly, and achieved accuracy, F1 score, and AUC of 0.853, 0.503, and 0.822 processed by the logistic regression method, respectively, higher than either sTILs or subtype; a prediction model of pCR constructed by integrating sTILs, subtype and pCR-score yielded a mean AUC of 0.890, outperforming the baseline sTIL-subtype model by 0.051 (0.839, P = 0.001). Conclusion The DL-based pCR-score from histological images is predictive of pCR better than sTILs and subtype, and holds the great potentials for a more accurate stratification of patients for NAC.

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“…A CNN was implemented to identify the tumor bed of each section ( Figure 1 a(iii)). The CNN, outlined in a previous study [ 33 ], took H&E input images of 750 × 750 pixels and returned a vector, which contained the probability of the tile belonging to the tumor bed. The probabilities were then used to re-build the original WSI, outlining the location of the tumor bed ( Figure 1 a(iv)).…”

Section: Methodsmentioning

confidence: 99%

“…Overall, these studies demonstrate the ongoing interest to enhance automation for breast cancer diagnosis. In this present study, we build on our previous work [ 31 , 32 , 33 ] to develop CADs for pathology and propose a computational pipeline for histologic grading.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade

et al. 2021

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Background: Evaluating histologic grade for breast cancer diagnosis is standard and associated with prognostic outcomes. Current challenges include the time required for manual microscopic evaluation and interobserver variability. This study proposes a computer-aided diagnostic (CAD) pipeline for grading tumors using artificial intelligence. Methods: There were 138 patients included in this retrospective study. Breast core biopsy slides were prepared using standard laboratory techniques, digitized, and pre-processed for analysis. Deep convolutional neural networks (CNNs) were developed to identify the regions of interest containing malignant cells and to segment tumor nuclei. Imaging-based features associated with spatial parameters were extracted from the segmented regions of interest (ROIs). Clinical datasets and pathologic biomarkers (estrogen receptor, progesterone receptor, and human epidermal growth factor 2) were collected from all study subjects. Pathologic, clinical, and imaging-based features were input into machine learning (ML) models to classify histologic grade, and model performances were tested against ground-truth labels at the patient-level. Classification performances were evaluated using receiver-operating characteristic (ROC) analysis. Results: Multiparametric feature sets, containing both clinical and imaging-based features, demonstrated high classification performance. Using imaging-derived markers alone, the classification performance demonstrated an area under the curve (AUC) of 0.745, while modeling these features with other pathologic biomarkers yielded an AUC of 0.836. Conclusion: These results demonstrate an association between tumor nuclear spatial features and tumor grade. If further validated, these systems may be implemented into pathology CADs and can assist pathologists to expeditiously grade tumors at the time of diagnosis and to help guide clinical decisions.

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Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients

Cited by 28 publications

References 36 publications

A Spatial Attention Guided Deep Learning System for Prediction of Pathological Complete Response Using Breast Cancer Histopathology Images

A Spatial Attention Guided Deep Learning System for Prediction of Pathological Complete Response Using Breast Cancer Histopathology Images

Deep learning-based predictive biomarker of pathological complete response to neoadjuvant chemotherapy from histological images in breast cancer

Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade

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