Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study

Abstract

Better understanding of the dose-toxicity relationship is critical for safe dose escalation to improve local control in late-stage cervical cancer radiotherapy. In this study, we introduced a convolutional neural network (CNN) model to analyze rectum dose distribution and predict rectum toxicity. Forty-two cervical cancer patients treated with combined external beam radiotherapy (EBRT) and brachytherapy (BT) were retrospectively collected, including twelve toxicity patients and thirty non-toxicity patients. We adopted a transfer learning strategy to overcome the limited patient data issue. A 16-layers CNN developed by the visual geometry group (VGG-16) of the University of Oxford was pre-trained on a large-scale natural image database, ImageNet, and fine-tuned with patient rectum surface dose maps (RSDMs), which were accumulated EBRT + BT doses on the unfolded rectum surface. We used the adaptive synthetic sampling approach and the data augmentation method to address the two challenges, data imbalance and data scarcity. The gradient-weighted class activation maps (Grad-CAM) were also generated to highlight the discriminative regions on the RSDM along with the prediction model. We compare different CNN coefficients fine-tuning strategies, and compare the predictive performance using the traditional dose volume parameters, e.g. D , and the texture features extracted from the RSDM. Satisfactory prediction performance was achieved with the proposed scheme, and we found that the mean Grad-CAM over the toxicity patient group has geometric consistence of distribution with the statistical analysis result, which indicates possible rectum toxicity location. The evaluation results have demonstrated the feasibility of building a CNN-based rectum dose-toxicity prediction model with transfer learning for cervical cancer radiotherapy.

Introduction to machine and deep learning for medical physicists

“…This approach has been successfully applied to medical image segmentation problems by transferring knowledge from natural image applications (e.g., Google ImageNet database) 70 . This idea can be extended to other tasks, where Zhen et al demonstrated a CNN for predicting rectal toxicity in cervical cancer radiotherapy by fine‐tuning a pretrained network (VGG‐16) on the natural images from ImageNet 71 …”

Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis

“…This rapid development is partly stimulated by its many important applications, one of which is drug toxicity prediction in silico [88,127,158]. Together with “Big Data” science [159], machine learning techniques may provide much more information about toxicity than ever before.…”

Radiation Oncology in the Era of Big Data and Machine Learning for Precision Medicine

“…Many research scientists [89][90][91][92][93][94][95] have investigated the application of ML in radiotherapy treatment response and outcome predictions. Lee et al [89] studied utilizing of Bayesian network ensemble to predict radiation pneumonitis risk for NSCLC patients whom received curative 3D conformal radiotherapy.…”

“…Their findings indicated that prediction of treatment response can be improved by utilizing data mining approaches, which were able to unravel important non-linear complex interactions among model variables and have the capacity to predict on unseen data for prospective clinical applications. Zhen et al [91] introduced a CNN model to analyze the rectum dose distribution and predict rectum toxicity. The evaluation results demonstrated the feasibility of building a CNN-based rectum dose-toxicity prediction model with transfer learning for cervical cancer radiotherapy.…”