Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI

Abstract

Objectives-To develop and validate a proof-of-concept convolutional neural network (CNN)based deep learning system (DLS) that classifies common hepatic lesions on multi-phasic MRI.Methods-A custom CNN was engineered by iteratively optimizing the network architecture and training cases, finally consisting of three convolutional layers with associated rectified linear units, two maximum pooling layers, and two fully connected layers. Four hundred ninety-four hepatic lesions with typical imaging features from six categories were utilized, divided into training (n = 434) and test (n = 60) sets. Established augmentation techniques were used to generate 43,400 training samples. An Adam optimizer was used for training. Monte Carlo cross-validation was performed. After model engineering was finalized, classification accuracy for the final CNN was compared with two board-certified radiologists on an identical unseen test set.

Supervised Machine Learning in Oncology: A Clinician's Guide

“…When applied to the same task, a machine learning model achieved a 92% accuracy, 92% sensitivity, and 98% specificity. 11 In this study, quantifying and reporting the performance of expert physicians allowed the authors to conclude that their constructed models were comparable to physician assessment, which increases the likelihood that these models can be brought into clinical practice with confidence in their clinical utility.…”

“…One technique is to split an image into multiple patches and use each patch as a data point in a training dataset. 11 While data augmentation can increase the size of a training cohort, it does not enable a training dataset to become more representative of a general patient population. Carefully examining the selection process of the training dataset will ensure that clinicians do not overestimate the external validity of a machine learning model; a common mistake is to presume that the efficacy of the proposed model will be equivalent when applied to a significantly different patient population than the training patient set.…”

“…In the aforementioned liver lesion study, validation was performed including two board-certified radiologists. 11 In addition to the average radiologist performance, important metrics such as the standard deviation, range, and distribution of physician performance should be reported when possible. These metrics are understandably difficult to capture, but given the heterogeneity of clinician judgment, quantifying the spread of physician performance can only further help demonstrate the clinical benefit of a consistent and reproducible model.…”

“…[8][9][10] More recently, machine learning has been used to help clinicians recommend cancer treatment modalities when evidence-driven guidelines are lacking, inconsistent, or impractical. 11 For example, the Liver Imaging Reporting and Data System (LI-RADS), which is used to standardize the diagnosis of hepatocellular carcinoma, is complex and requires manual assessment of too many variables to be easily implemented in practice. 12 Additionally, machine learning models can efficiently predict responses to various treatment options based on a patient's individual clinical presentation, helping clinicians determine which treatment course is most appropriate for each patient.…”

Radiomics and Deep Learning: Hepatic Applications

“…Schmauch et al (62) also described the technical feasibility of applying deep learning to the detection of focal liver lesions using ultrasound images. The potential utility of deep learning for the classification of focal hepatic lesions has now been evaluated in several studies, all of which devised deep learning algorithms to classify liver lesions into five to six predefined categories based on manually cropped CT or MR images containing these lesions (63,64). Yasaka et al (63) developed an algorithm for classifying liver masses using multi-phasic CT images and reported an accuracy of 84% in the test dataset.…”