Deep Learning Model for Predicting the Pathological Complete Response to Neoadjuvant Chemoradiotherapy of Locally Advanced Rectal Cancer

Objective This study aimed to develop an artificial intelligence model for predicting the pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) of locally advanced rectal cancer (LARC) using digital pathological images. Background nCRT followed by total mesorectal excision (TME) is a standard treatment strategy for patients with LARC. Predicting the PCR to nCRT of LARC remine difficulty. Methods 842 LARC patients treated with standard nCRT from three medical centers were retrospectively recruited and subgrouped into the training, testing and external validation sets. Treatment response was classified as pCR and non-pCR based on the pathological diagnosis after surgery as the ground truth. The hematoxylin & eosin (H&E)-stained biopsy slides were manually annotated and used to develop a deep pathological complete response (DeepPCR) prediction model by deep learning. Results The proposed DeepPCR model achieved an AUC-ROC of 0.710 (95% CI: 0.595, 0.808) in the testing cohort. Similarly, in the external validation cohort, the DeepPCR model achieved an AUC-ROC of 0.723 (95% CI: 0.591, 0.844). The sensitivity and specificity of the DeepPCR model were 72.6% and 46.9% in the testing set and 72.5% and 62.7% in the external validation cohort, respectively. Multivariate logistic regression analysis showed that the DeepPCR model was an independent predictive factor of nCRT (P=0.008 and P=0.004 for the testing set and external validation set, respectively). Conclusions The DeepPCR model showed high accuracy in predicting pCR and served as an independent predictive factor for pCR. The model can be used to assist in clinical treatment decision making before surgery.

[1]  Tang,et al.  Development and interpretation of a pathomics-based model for the prediction of microsatellite instability in Colorectal Cancer , 2020, Theranostics.

[2]  D. Feier,et al.  Pre-Treatment T2-WI Based Radiomics Features for Prediction of Locally Advanced Rectal Cancer Non-Response to Neoadjuvant Chemoradiotherapy: A Preliminary Study , 2020, Cancers.

[3]  Jasnit S. Makkar,et al.  Radiomics of MRI for pretreatment prediction of pathologic complete response, tumor regression grade, and neoadjuvant rectal score in patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiation: an international multicenter study , 2020, European Radiology.

[4]  Xiaoyan Zhang,et al.  Predicting Rectal Cancer Response to Neoadjuvant Chemoradiotherapy Using Deep Learning of Diffusion Kurtosis MRI. , 2020, Radiology.

[5]  Marco Novelli,et al.  Deep learning for prediction of colorectal cancer outcome: a discovery and validation study , 2020, The Lancet.

[6]  K. Sung,et al.  Deep learning-based radiomic features for improving neoadjuvant chemoradiation response prediction in locally advanced rectal cancer , 2019, Physics in medicine and biology.

[7]  Junzhou Huang,et al.  Deep Multi-instance Learning for Survival Prediction from Whole Slide Images , 2019, MICCAI.

[8]  G. Wainrib,et al.  Deep learning-based classification of mesothelioma improves prediction of patient outcome , 2019, Nature Medicine.

[9]  S. P. Shayesteh,et al.  Prediction of Response to Neoadjuvant Chemoradiotherapy by MRI-Based Machine Learning Texture Analysis in Rectal Cancer Patients , 2019, Journal of Gastrointestinal Cancer.

[10]  Thomas J. Fuchs,et al.  Clinical-grade computational pathology using weakly supervised deep learning on whole slide images , 2019, Nature Medicine.

[11]  Yuqiang Li,et al.  MRI-Based Radiomics Predicts Tumor Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer , 2019, Front. Oncol..

[12]  Jakob Nikolas Kather,et al.  Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer , 2019, Nature Medicine.

[13]  Tiffany C. Kwong,et al.  Machine learning for prediction of chemoradiation therapy response in rectal cancer using pre-treatment and mid-radiation multi-parametric MRI. , 2019, Magnetic resonance imaging.

[14]  Gregory S. Corrado,et al.  Detecting Anemia from Retinal Fundus Images , 2019, ArXiv.

[15]  Y. Huang,et al.  Local environment in biopsy better predict the pathological response to neoadjuvant chemoradiotherapy in rectal cancer , 2019, Bioscience reports.

[16]  Luiz Eduardo Soares de Oliveira,et al.  Multiple instance learning for histopathological breast cancer image classification , 2019, Expert Syst. Appl..

[17]  Shaoqun Zeng,et al.  From Detection of Individual Metastases to Classification of Lymph Node Status at the Patient Level: The CAMELYON17 Challenge , 2019, IEEE Transactions on Medical Imaging.

[18]  Mita Nasipuri,et al.  Patch-based system for Classification of Breast Histology images using deep learning , 2019, Comput. Medical Imaging Graph..

[19]  Mohammad Mansouri,et al.  An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets , 2018, Nature Biomedical Engineering.

[20]  A. Jemal,et al.  Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries , 2018, CA: a cancer journal for clinicians.

[21]  Abhijit Guha Roy,et al.  Multiple instance learning of deep convolutional neural networks for breast histopathology whole slide classification , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[22]  Peter H. N. de With,et al.  Cancer detection in histopathology whole-slide images using conditional random fields on deep embedded spaces , 2018, Medical Imaging.

[23]  Linda G. Shapiro,et al.  Multi-Instance Multi-Label Learning for Multi-Class Classification of Whole Slide Breast Histopathology Images , 2018, IEEE Transactions on Medical Imaging.

[24]  Andrew H. Beck,et al.  Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer , 2017, JAMA.

[25]  Jeffrey A. Golden,et al.  Deep Learning Algorithms for Detection of Lymph Node Metastases From Breast Cancer: Helping Artificial Intelligence Be Seen. , 2017, JAMA.

[26]  Xin Wang,et al.  Cancer Metastasis Detection via Spatially Structured Deep Network , 2017, IPMI.

[27]  Jianfei Cai,et al.  MIML-FCN+: Multi-Instance Multi-Label Learning via Fully Convolutional Networks with Privileged Information , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[28]  Abhishek Das,et al.  Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).

[29]  Xiaohui Xie,et al.  Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification , 2016, bioRxiv.

[30]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[31]  Joel H. Saltz,et al.  Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  R. Vather,et al.  Clinical tumour size and nodal status predict pathologic complete response following neoadjuvant chemoradiotherapy for rectal cancer , 2014, International Journal of Colorectal Disease.

[33]  D. Hanahan,et al.  Hallmarks of Cancer: The Next Generation , 2011, Cell.

[34]  Jun Sang Kim,et al.  Pathologic Complete Response of Primary Tumor Following Preoperative Chemoradiotherapy for Locally Advanced Rectal Cancer: Long-term Outcomes and Prognostic Significance of Pathologic Nodal Status (KROG 09-01) , 2010, Annals of surgery.

[35]  Karin Haustermans,et al.  Long-term outcome in patients with a pathological complete response after chemoradiation for rectal cancer: a pooled analysis of individual patient data. , 2010, The Lancet. Oncology.

[36]  N. Petrelli,et al.  Preoperative multimodality therapy improves disease-free survival in patients with carcinoma of the rectum: NSABP R-03. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[37]  P. Hewett,et al.  Complete pathologic response after preoperative rectal cancer chemoradiotherapy , 2009, ANZ journal of surgery.

[38]  C. Capirci,et al.  Prognostic value of pathologic complete response after neoadjuvant therapy in locally advanced rectal cancer: long-term analysis of 566 ypCR patients. , 2008, International journal of radiation oncology, biology, physics.

[39]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .