Deep Learning Role in Early Diagnosis of Prostate Cancer

Abstract

The objective of this work is to develop a computer-aided diagnostic system for early diagnosis of prostate cancer. The presented system integrates both clinical biomarkers (prostate-specific antigen) and extracted features from diffusion-weighted magnetic resonance imaging collected at multiple b values. The presented system performs 3 major processing steps. First, prostate delineation using a hybrid approach that combines a level-set model with nonnegative matrix factorization. Second, estimation and normalization of diffusion parameters, which are the apparent diffusion coefficients of the delineated prostate volumes at different b values followed by refinement of those apparent diffusion coefficients using a generalized Gaussian Markov random field model. Then, construction of the cumulative distribution functions of the processed apparent diffusion coefficients at multiple b values. In parallel, a K-nearest neighbor classifier is employed to transform the prostate-specific antigen results into diagnostic probabilities. Finally, those prostate-specific antigen–based probabilities are integrated with the initial diagnostic probabilities obtained using stacked nonnegativity constraint sparse autoencoders that employ apparent diffusion coefficient–cumulative distribution functions for better diagnostic accuracy. Experiments conducted on 18 diffusion-weighted magnetic resonance imaging data sets achieved 94.4% diagnosis accuracy (sensitivity = 88.9% and specificity = 100%), which indicate the promising results of the presented computer-aided diagnostic system.

Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines

“…The complexity of the non-imaging data in multimodal fusion work was limited, particularly in the context of available featurerich and time-series data in the EHR. Instead, most studies focused primarily on basic demographic information such as age and gender 25,27,39 , a limited range of categorical clinical history such as hypertension or smoking status 32,34 or disease-specific clinical features known to be strongly associated with the disease of interest such as APOE4 for Alzheimer's 25,28,33,36 or PSA blood test for prediction of prostate cancer 40 . While selecting features known to be associated with disease is meaningful, future work may further benefit from utilizing large volumes of feature-rich data, as seen in fields outside medicine such as autonomous driving 44,45 .…”

“…Yoo et al 38 took the mean of the predicted probabilities from two single modality models as the final prediction. Reda et al 40 built another classifier using the single modality models' prediction probabilities as inputs. Qiu et al 41 trained three independent imaging models that took as input a single MRI slice, each from a specific anatomical location.…”

“…This is because late fusion employs separate models for separate modalities, and aggregation functions such as majority voting and averaging can be applied even when predictions from a modality is missing. When the different input modalities have very different numbers of features, predictions might be overly influenced by the most feature-rich modality (e.g., Reda et al 40 ). Late fusion is favorable in this scenario as it considers each modality separately.…”

“…As the included studies investigate different objectives, use different input modalities, report different performance metrics, and not all papers provide confidence bounds, we are not able to aggregate or statistically compare the performance gains in a meta-analysis. In addition, the reported metrics cannot always be considered valid, since some studies didn't use an independent test-set for an unbiased performance estimate 29,40 . The limited number of studies per medical field and the heterogeneity of each study also makes it difficult to compare the studies qualitatively.…”

Deep learning‐based quantification of PET/CT prostate gland uptake: association with overall survival

“…Deep learning in prostatic malignancies has previously been applied in magnetic resonance imaging (MRI) Reda et al, 2018), whereas maximum standardized uptake value (SUVmax) by 18 F-FDG PET/CT appears to be of relevance in predicting overall survival in metastasizing prostate cancer (Jadvar et al, 2013). Deep learning in prostatic malignancies has previously been applied in magnetic resonance imaging (MRI) Reda et al, 2018), whereas maximum standardized uptake value (SUVmax) by 18 F-FDG PET/CT appears to be of relevance in predicting overall survival in metastasizing prostate cancer (Jadvar et al, 2013).…”