A review of artificial intelligence in prostate cancer detection on imaging

A multitude of studies have explored the role of artificial intelligence (AI) in providing diagnostic support to radiologists, pathologists, and urologists in prostate cancer detection, risk-stratification, and management. This review provides a comprehensive overview of relevant literature regarding the use of AI models in (1) detecting prostate cancer on radiology images (magnetic resonance and ultrasound imaging), (2) detecting prostate cancer on histopathology images of prostate biopsy tissue, and (3) assisting in supporting tasks for prostate cancer detection (prostate gland segmentation, MRI-histopathology registration, MRI-ultrasound registration). We discuss both the potential of these AI models to assist in the clinical workflow of prostate cancer diagnosis, as well as the current limitations including variability in training data sets, algorithms, and evaluation criteria. We also discuss ongoing challenges and what is needed to bridge the gap between academic research on AI for prostate cancer and commercial solutions that improve routine clinical care

Comparison of [(11)C]choline positron emission tomography with T2- and diffusion-weighted magnetic resonance imaging for delineating malignant intraprostatic lesions

Purpose: To compare the accuracy of ¹¹C-choline (CHOL) positron emission tomography (PET) with the combination of T2-weighted (T2W) and diffusion-weighted (DW) magnetic resonance imaging (MRI) for delineating malignant intraprostatic lesions (IPLs) for guiding focal therapies and to investigate factors predicting the accuracy of CHOL-PET. Methods and Materials: This study included 21 patients who underwent CHOL-PET and T2W-/DW-MRI prior to radical prostatectomy. Two observers manually delineated IPL contours for each scan, and automatic IPL contours were generated on CHOL-PET based on varying proportions of the maximum standardized uptake value (SUV). IPLs identified on prostatectomy specimens defined the reference standard contours. The imaging-based contours were compared with the reference standard contours using Dice similarity coefficient (DSC), sensitivity and specificity. Factors that could potentially predict the DSC of the best contouring method were analyzed using linear models. Results: The best automatic contouring method, SUV60, had similar correlations (DSC 0.59) with the manual PET contours (DSC 0.52, P=0.127) and significantly better correlations than the manual MRI contours (DSC 0.37, P<0.001). The sensitivity and specificity values were 72% and 71% for SUV60; 53% and 86% for PET manual contouring; and 28% and 92% for MRI manual contouring. The tumor volume and transition zone pattern could independently predict the accuracy of CHOL-PET. Conclusions: CHOL-PET is superior to the combination of T2W- and DW-MRI for delineating IPLs. The accuracy of CHOL-PET is insufficient for gland-sparing focal therapies, 3 however may be accurate enough for focal boost therapies. The transition zone pattern is a new classification that may predict for how well CHOL-PET delineates IPLs.Joe H. Chang, Daryl Lim Joon, Ian D. Davis, Sze Ting Lee, Chee-Yan Hiew, Stephen Esler, Sylvia J. Gong, Morikatsu Wada, David Clouston, Richard O'Sullivan, Yin P. Goh, Damien Bolton, Andrew M. Scott, Vincent Kho

Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives

Artificial intelligence (AI) is the field of computer science that aims to build smart devices performing tasks that currently require human intelligence. Through machine learning (ML), the deep learning (DL) model is teaching computers to learn by example, something that human beings are doing naturally. AI is revolutionizing healthcare. Digital pathology is becoming highly assisted by AI to help researchers in analyzing larger data sets and providing faster and more accurate diagnoses of prostate cancer lesions. When applied to diagnostic imaging, AI has shown excellent accuracy in the detection of prostate lesions as well as in the prediction of patient outcomes in terms of survival and treatment response. The enormous quantity of data coming from the prostate tumor genome requires fast, reliable and accurate computing power provided by machine learning algorithms. Radiotherapy is an essential part of the treatment of prostate cancer and it is often difficult to predict its toxicity for the patients. Artificial intelligence could have a future potential role in predicting how a patient will react to the therapy side effects. These technologies could provide doctors with better insights on how to plan radiotherapy treatment. The extension of the capabilities of surgical robots for more autonomous tasks will allow them to use information from the surgical field, recognize issues and implement the proper actions without the need for human intervention

Machine Learning for Prostate Histopathology Assessment

Pathology reporting on radical prostatectomy (RP) specimens is essential to post-surgery patient care. However, current pathology interpretation of RP sections is typically qualitative and subject to intra- and inter-observer variability, which challenges quantitative and repeatable reporting of lesion grade, size, location, and spread. Therefore, we developed and validated a software platform that can automatically detect and grade cancerous regions on whole slide images (WSIs) of whole-mount RP sections to support quantitative and visual reporting. Our study used hæmatoxylin- and eosin-stained WSIs from 299 whole-mount RP sections from 71 patients, comprising 1.2 million 480μm×480μm regions-of-interest (ROIs) covering benign and cancerous tissues which contain all clinically relevant grade groups. Each cancerous region was annotated and graded by an expert genitourinary pathologist. We used a machine learning approach with 7 different classifiers (3 non-deep learning and 4 deep learning) to classify: 1) each ROI as cancerous vs. non-cancerous, and 2) each cancerous ROI as high- vs. low-grade. Since recent studies found some subtypes beyond Gleason grade to have independent prognostic value, we also used one deep learning method to classify each cancerous ROI from 87 RP sections of 25 patients as each of eight subtypes to support further clinical pathology research on this topic. We cross-validated each system against the expert annotations. To compensate for the staining variability across different WSIs from different patients, we computed the tissue component map (TCM) using our proposed adaptive thresholding algorithm to label nucleus pixels, global thresholding to label lumen pixels, and assigning the rest as stroma/other. Fine-tuning AlexNet with ROIs of the TCM yielded the best results for prostate cancer (PCa) detection and grading, with areas under the receiver operating characteristic curve (AUCs) of 0.98 and 0.93, respectively, followed by fine-tuned AlexNet with ROIs of the raw image. For subtype grading, fine-tuning AlexNet with ROIs of the raw image yielded AUCs ≥ 0.7 for seven of eight subtypes. To conclude, deep learning approaches outperformed non-deep learning approaches for PCa detection and grading. The TCMs provided the primary cues for PCa detection and grading. Machine learning can be used for subtype grading beyond the Gleason grading system

Prostate Tumor Volume Measurement on Digital Histopathology and Magnetic Resonance Imaging

An accurate assessment of prostate tumour burden supports appropriate treatment selection, ranging from active surveillance through focal therapy, to radical whole-prostate therapies. For selected patients, knowledge of the three-dimensional locations and sizes of prostate tumours on pre-procedural imaging supports planning of effective focal therapies that preferentially target tumours, while sparing surrounding healthy tissue. In the post-prostatectomy context, pathologic measurement of tumour burden in the surgical specimen may be an independent prognostic factor determining the need for potentially life-saving adjuvant therapy. An accurate and repeatable method for tumour volume assessment based on histology sections taken from the surgical specimen would be supportive both to the clinical workflow in the post-prostatectomy setting and to imaging validation studies correlating tumour burden measurements on pre-prostatectomy imaging with reference standard histologic tumour volume measurements. Digital histopathology imaging is enabling a transition to a more objective quantification of some surgical pathology assessments, such as tumour volume, that are currently visually estimated by pathologists and subject to inter-observer variability. Histologic tumour volume measurement is challenged by the traditional 3–5 mm sparse spacing of images acquired from sections of radical prostatectomy specimens. Tumour volume estimates may benefit from a well-motivated approach to inter-slide tumour boundary interpolation that crosses these large gaps in a smooth fashion. This thesis describes a new level set-based shape interpolation method that reconstructs smooth 3D shapes based on arbitrary 2D tumour contours on digital histology slides. We measured the accuracy of this approach and used it as a reference standard against which to compare previous approaches in the literature that are simpler to implement in a clinical workflow, with the aim of determining a method for histologic tumour volume estimation that is both accurate and amenable to widespread implementation. We also measured the effect of decreasing inter-slide spacing on the repeatability of histologic tumour volume estimation. Furthermore, we used this histologic reference standard for tumour volume to measure the accuracy, inter-observer variability, and inter-sequence variability of prostate tumour volume estimation based on radiologists’ contouring of multi-parametric magnetic resonance imaging (MPMRI). Our key findings were that (1) simple approaches to histologic tumour volume estimation that are based on 2- or 3-dimensional linear tumour measurements are more accurate than those based on 1-dimensional measurements; (2) although tumour shapes produced by smooth through-slide interpolation are qualitatively substantially different from those obtained from a planimetric approach normally used as a reference standard for histologic tumour volume, the volumes obtained were similar; (3) decreasing inter-slide spacing increases repeatability of histologic tumour volume estimates, and this repeatability decreases rapidly for inter-slide spacing values greater than 5 mm; (4) on MPMRI, observers consistently overestimated tumour volume as compared to the histologic reference standard; and (5) inter-sequence variability in MPMRI-based tumour volume estimation exceeded inter-observer variability