Models of Temporal Enhanced Ultrasound Data for Prostate Cancer Diagnosis: The Impact of Time-Series Order

Recent studies have shown the value of Temporal Enhanced Ultrasound (TeUS) imaging for tissue characterization in transrectal ultrasound-guided prostate biopsies. Here, we present results of experiments designed to study the impact of temporal order of the data in TeUS signals. We assess the impact of variations in temporal order on the ability to automatically distinguish benign prostate-tissue from malignant tissue. We have previously used Hidden Markov Models (HMMs) to model TeUS data, as HMMs capture temporal order in time series. In the work presented here, we use HMMs to model malignant and benign tissues; the models are trained and tested on TeUS signals while introducing variation to their temporal order. We first model the signals in their original temporal order, followed by modeling the same signals under various time rearrangements. We compare the performance of these models for tissue characterization. Our results show that models trained over the original order-preserving signals perform statistically significantly better for distinguishing between malignant and benign tissues, than those trained on rearranged signals. The performance degrades as the amount of temporal-variation increases. Specifically, accuracy of tissue characterization decreases from 85% using models trained on original signals to 62% using models trained and tested on signals that are completely temporally-rearranged. These results indicate the importance of order in characterization of tissue malignancy from TeUS data

Automatic high-grade cancer detection on prostatectomy histopathology images

Automatic cancer grading and high-grade cancer detection for radical prostatectomy (RP) specimens can benefit pathological assessment for prognosis and post-surgery treatment decision making. We developed and validated an automatic system which grades cancerous tissue as high-grade (Gleason grade 4 and higher) vs. low-grade (Gleason grade 3) on digital histopathology whole-slide images (WSIs). We combined this grading system with our previouslyreported cancer detection system to build a high-grade cancer detection system which automatically finds high-grade cancerous foci on WSIs. The system was tuned on a 3-patient data set and cross-validated against expert-drawn contours on a separate 68-patient data set comprising 286 mid-gland whole-slide images of RP specimens. The system uses machine learning techniques to classify each region of interest (ROI) on the slide as cancer or non-cancer and each cancerous ROI as high-grade or low-grade cancer. We used leave-one-patient-out cross-validation to measure the performance of cancer grading for classified ROIs with three different classifiers and the performance of the high-grade cancer detection system on a per tumor focus basis. The best performing (Fisher) classifier yielded an area under the receiver-operating characteristic curve of 0.87 for cancer grading. The system yielded error rates of 19.5% and 23.4% for pure high-grade (Gleason 4+4, 5+5) and high-grade (Gleason Score ≥ 7) cancer detection, respectively. The system demonstrated potential for practical computation speeds. Upon successful multi-centre validation, this system has the potential to assist the pathologist to find high-grade cancer more efficiently, which benefits the selection and guidance of adjuvant therapy and prognosis post RP

Development of a computer aided diagnosis model for prostate cancer classification on multi-parametric MRI

Multi-parametric MRI (mp-MRI) is becoming a standard in contemporary prostate cancer screening and diagnosis, and has shown to aid physicians in cancer detection. It offers many advantages over traditional systematic biopsy, which has shown to have very high clinical false-negative rates of up to 23% at all stages of the disease. However beneficial, mp-MRI is relatively complex to interpret and suffers from inter-observer variability in lesion localization and grading. Computer-aided diagnosis (CAD) systems have been developed as a solution as they have the power to perform deterministic quantitative image analysis. We measured the accuracy of such a system validated using accurately co-registered whole-mount digitized histology. We trained a logistic linear classifier (LOGLC), support vector machine (SVC), k-nearest neighbour (KNN) and random forest classifier (RFC) in a four part ROI based experiment against: 1) cancer vs. non-cancer, 2) high-grade (Gleason score ≥4+3) vs. low-grade cancer (Gleason score \u3c4+3), 3) high-grade vs. other tissue components and 4) high-grade vs. benign tissue by selecting the classifier with the highest AUC using 1-10 features from forward feature selection. The CAD model was able to classify malignant vs. benign tissue and detect high-grade cancer with high accuracy. Once fully validated, this work will form the basis for a tool that enhances the radiologist\u27s ability to detect malignancies, potentially improving biopsy guidance, treatment selection, and focal therapy for prostate cancer patients, maximizing the potential for cure and increasing quality of life

Automatic cancer detection and localization on prostatectomy histopathology images

Automatic localization of cancer on whole-slide histology images from radical prostatectomy specimens would support quantitative graphical pathology reporting and research studies validating in vivo imaging against gold-standard histopathology. There is an unmet need for such a system that is robust to staining variability, is sufficiently fast and parallelizable as to be integrated into the clinical pathology workflow, and is validated using whole-slide images. We developed and validated such a system, with tuning occurring on an 8-patient data set and cross-validation occurring on a separate 41-patient data set comprising 703,745 480μm × 480μm sub-images from 166 whole-slide images. Our system computes tissue component maps from pixel data using a technique that is robust to staining variability, showing the loci of nuclei, luminal areas, and areas containing other tissue including stroma. Our system then computes first-and second-order texture features from the tissue component maps and uses machine learning techniques to classify each sub-image on the slide as cancer or non-cancer. The system was validated against expert-drawn contours that were verified by a genitourinary pathologist. We used leave-one-patient-out, 5-fold, and 2-fold cross-validation to measure performance with three different classifiers. The best performing support vector machine classifier yielded an area under the receiver operating characteristic curve of 0.95 from leave-one-out cross-validation. The system demonstrated potential for practically useful computation speeds, with further optimization and parallelization of the implementation. Upon successful multi-centre validation, this system has the potential to enable quantitative surgical pathology reporting and accelerate imaging validation studies using histopathologic reference standards

Texture-based prostate cancer classification on MRI: How does inter-class size mismatch affect measured system performance?

Multi-parametric MRI (mp-MRI) has shown to be useful in contemporary prostate biopsy procedures. Unfortunately, mp-MRI is relatively complex to interpret and suffers from inter-observer variability in lesion localization and grading. Computer-aided diagnosis (CAD) systems have been developed as a potential solution and have been shown to boost diagnostic accuracy. We measured the accuracy of a CAD model using a systematic sampling algorithm to remove any spatial bias present in our input. We trained four classifiers with 1-10 features chosen by forward feature selection for each and reported the system with the highest AUC in both the peripheral zone and central gland. Furthermore, we investigated the effect on system performance by varying the minimum tumour size threshold and by varying the average difference in area between malignant and healthy tissue samples. The CAD model was able to classify malignant vs. benign tissue with accuracies competitive with those reported in the literature. Eroding healthy tissue ROIs positively biased the system\u27s performance for the PZ, but no such trend was found in the CG. Once fully validated, this system has the potential to imp

Evaluating texture-based prostate cancer classification on multi-parametric magnetic resonance imaging and prostate specific membrane antigen positron emission tomography

In-vivo imaging of the prostate has shown to be useful for prostate cancer (PCa) localization especially during biopsy procedures. Multi-parametric MRI (mp-MRI) is gaining rapid popularity amongst clinicians but is complex and difficult to interpret by even expert radiologists. Prostate specific membrane antigen positron emission tomography (PSMA PET) is emerging as a new tool for PCa detection and has shown promising results towards lesion identification. Both imaging procedures suffer from intra- and inter- observer variability in PCa detection. Computer-aided diagnosis (CAD) systems have been developed as a solution to mitigate observer variability and have shown to boost diagnostic accuracy. There are currently no studies published that assessed the benefit of incorporating PSMA PET imaging and mp-MRI into a CAD system for PCa detection. We compared the accuracy of CAD models trained and tested on features from mp-MRI+PSMA PET, mp-MRI and PSMA PET by training on 1-10 features chosen from three feature selection methods for 10 different classifiers for each of the three experiments. We found that models trained on mp-MRI provided lower overall error and greater specificity, and models trained on mp-MRI+PSMA PET and PSMA PET provided greater sensitivity to lesions in the central gland, which is a known area of difficulty for mp-MRI. Further validation using a larger dataset is required to prove the added benefit of PSMA PET imaging as a second modality to PCa CAD systems. Once fully validated, these results will demonstrate the added benefit of incorporating PSMA PET imaging into CAD models towards PCa detection