Deep learning-based prediction of response to HER2-targeted neoadjuvant chemotherapy from pre-treatment dynamic breast MRI: A multi-institutional validation study
Case Western Reserve University（凯斯西储大学）；University of Mons（蒙斯大学）；Cleveland Clinic（克利夫兰诊所）；Yale School of Medicine（耶鲁医学院）；Institut Jules Bordet（朱尔斯·博尔代研究所）；University Hospitals Cleveland Medical Center（大学医院克利夫兰医学中心）；Louis Stokes Cleveland VA Medical Center（路易斯·斯托克斯克利夫兰退伍军人事务医疗中心）
Nathaniel Braman, Mohammed El Adoui, Manasa Vulchi, Paulette Turk, Maryam Etesami, Pingfu Fu, Kaustav Bera, Stylianos Drisis, Vinay Varadan, Donna Plecha, Moham… 展开作者
Nathaniel Braman, Mohammed El Adoui, Manasa Vulchi, Paulette Turk, Maryam Etesami, Pingfu Fu, Kaustav Bera, Stylianos Drisis, Vinay Varadan, Donna Plecha, Mohammed Benjelloun, Jame Abraham, Anant Madabhushi
备注：
Braman and El Adoui contributed equally to this work. 33 pages, 3 figures in main text
Predicting response to neoadjuvant therapy is a vexing challenge in breast cancer. In this study, we evaluate the ability of deep learning to predict response to HER2-targeted neo-adjuvant chemotherapy (NAC) from pre-treatment dynamic contrast-enhanced (DCE) MRI acquired prior to treatment. In a retrospective study encompassing DCE-MRI data from a total of 157 HER2+ breast cancer patients from 5 institutions, we developed and validated a deep learning approach for predicting pathological complete response (pCR) to HER2-targeted NAC prior to treatment. 100 patients who received HER2-targeted neoadjuvant chemotherapy at a single institution were used to train (n=85) and tune (n=15) a convolutional neural network (CNN) to predict pCR. A multi-input CNN leveraging both pre-contrast and late post-contrast DCE-MRI acquisitions was identified to achieve optimal response prediction within the validation set (AUC=0.93). This model was then tested on two independent testing cohorts with pre-treatment DCE-MRI data. It achieved strong performance in a 28 patient testing set from a second institution (AUC=0.85, 95% CI 0.67-1.0, p=.0008) and a 29 patient multicenter trial including data from 3 additional institutions (AUC=0.77, 95% CI 0.58-0.97, p=0.006). Deep learning-based response prediction model was found to exceed a multivariable model incorporating predictive clinical variables (AUC < .65 in testing cohorts) and a model of semi-quantitative DCE-MRI pharmacokinetic measurements (AUC < .60 in testing cohorts). The results presented in this work across multiple sites suggest that with further validation deep learning could provide an effective and reliable tool to guide targeted therapy in breast cancer, thus reducing overtreatment among HER2+ patients.
Early prediction of pathological complete response (pCR) following neoadjuvant chemotherapy (NAC) for breast cancer plays a critical role in surgical planning and optimizing treatment strategies. Recently, machine and deep-learning based methods were suggested for early pCR prediction from multi-parametric MRI (mp-MRI) data including dynamic contrast-enhanced MRI and diffusion-weighted MRI (DWI) with moderate success. We introduce PD-DWI, a physiologically decomposed DWI machine-learning model to predict pCR from DWI and clinical data. Our model first decomposes the raw DWI data into the vario…
To deform or not: treatment-aware longitudinal registration for breast DCE-MRI during neoadjuvant chemotherapy via unsupervised keypoints detection
Radboud University Medical Centre（拉德堡德大学医学中心）；The Netherlands Cancer Institute（荷兰癌症研究所）；Macao Polytechnic University（澳门理工大学）；Maastricht University Medical Centre（马斯特里赫特大学医学中心）
Neoadjuvant chemotherapy has been used for breast cancer aiming at downgrading before surgery. In this article we propose a new quantitative analysis of the effects of the neoadjuvant therapy to obtain numerical, personalized, predictions on the shrinkage of the tumor size after the drug doses, by data assimilation of the individual patient. The algorithm has been validated by a sample of 37 patients with histological diagnosis of locally advanced primary breast carcinoma. The biopsy specimen, the initial tumor size and its reduction after each treatment were known for all patients. We find th…
Predicting Neoadjuvant Chemotherapy Response in Triple-Negative Breast Cancer Using Pre-Treatment Histopathologic Images
The Ohio State University（俄亥俄州立大学）；University of Rochester Medical Center（罗切斯特大学医学中心）；Warren Alpert Medical School of Brown University（布朗大学沃伦·阿尔珀特医学院）；Lifespan Academic Medical Center（Lifespan学术医学中心）；University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心影像科学研究所）；Department of Radiology, Antoni van Leeuwenhoek Hospital - The Netherlands Cancer Institute（荷兰癌症研究所安东尼·范·列文虎克医院放射科）
Response of breast cancer to neoadjuvant chemotherapy (NAC) can be monitored using the change in visible tumor on magnetic resonance imaging (MRI). In our current workflow, seed points are manually placed in areas of enhancement likely to contain cancer. A constrained volume growing method uses these manually placed seed points as input and generates a tumor segmentation. This method is rigorously validated using complete pathological embedding. In this study, we propose to exploit deep learning for fast and automatic seed point detection, replacing manual seed point placement in our existing …
Neoadjuvant chemotherapy (NAC) is a standard-of-care treatment for locally advanced triple negative breast cancer (TNBC) before surgery. The early assessment of TNBC response to NAC would enable an oncologist to adapt the therapeutic plan of a non-responding patient, thereby improving treatment outcomes while preventing unnecessary toxicities. To this end, a promising approach consists of obtaining \textsl{in silico} personalized forecasts of tumor response to NAC \textsl{via} computer simulation of mechanistic models constrained with patient-specific magnetic resonance imaging (MRI) data acqu…
NACNet: A Histology Context-aware Transformer Graph Convolution Network for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple Negative Breast Cancer
Georgia State University（佐治亚州立大学）；Federal University of Minas Gerais（米纳斯吉拉斯联邦大学）；Winship Cancer Institute（温希普癌症研究所）；Emory University（埃默里大学）
Background and Objective: Non-invasively predicting the risk of cancer metastasis before surgery plays an essential role in determining optimal treatment methods for cancer patients (including who can benefit from neoadjuvant chemotherapy). Although developing radiomics based machine learning (ML) models has attracted broad research interest for this purpose, it often faces a challenge of how to build a highly performed and robust ML model using small and imbalanced image datasets. Methods: In this study, we explore a new approach to build an optimal ML model. A retrospective dataset involving…
Graph Representation Learning of Longitudinal Medical Imaging Trajectories for Treatment Response Prediction
Technical University of Munich（慕尼黑工业大学）；TUM University Hospital Rechts der Isar（慕尼黑工业大学伊萨尔河右岸大学医院）；Helmholtz Munich（慕尼黑亥姆霍兹中心）；Universitat de Barcelona（巴塞罗那大学）；Medical University of Vienna（维也纳医科大学）；King’s College London（伦敦国王学院）；Munich Center for Machine Learning（慕尼黑机器学习中心）
A Large Model for Non-invasive and Personalized Management of Breast Cancer from Multiparametric MRI
The Hong Kong University of Science and Technology（香港科技大学）；PLA Middle Military Command General Hospital（中国人民解放军中部战区总医院）；The University of Hong Kong（香港大学）；The Chinese University of Hong Kong（香港中文大学）；Yunnan Cancer Hospital（云南省肿瘤医院）；th Medical Center of Chinese PLA General Hospital（中国人民解放军总医院第五医学中心）；Southern Medical University（南方医科大学）
TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIs
State University of New York at Stony Brook（纽约州立大学石溪分校）；Newark Beth Israel Medical Center（纽瓦克贝斯以色列医疗中心）；Columbia University Irving Medical Center（哥伦比亚大学欧文医疗中心）；Emory University School of Medicine（埃默里大学医学院）；Georgia Institute of Technology（佐治亚理工学院）
Characterization of breast parenchyma on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity of underlying tissue structures. Current quantitative approaches, including radiomics and deep learning models, do not explicitly capture the complex and subtle parenchymal structures, such as fibroglandular tissue. In this paper, we propose a novel method to direct a neural network's attention to a dedicated set of voxels surrounding biologically relevant tissue structures. By extracting multi-dimensional topological structures with high salienc…
Tumors are extremely heterogeneous and comprise of a number of intratumor microenvironments or sub-regions. These tumor microenvironments may interact with eac based on complex high-level relationships, which could provide important insight into the organizational structure of the tumor network. To that end, we developed a tumor connectomics framework (TCF) to understand and model the complex functional and morphological interactions within the tumor. Then, we demonstrate the TCF's potential in predicting treatment response in breast cancer patients being treated with neoadjuvant chemotherapy.…
