Artificial Intelligence-assisted Prostate Cancer Diagnosis: Radiologic-Pathologic Correlation

PCa-RadHop: A transparent and lightweight feed-forward method for clinically significant prostate cancer segmentation

Prostate Cancer is one of the most frequently occurring cancers in men, with a low survival rate if not early diagnosed. PI-RADS reading has a high false positive rate, thus increasing the diagnostic incurred costs and patient discomfort. Deep learning (DL) models achieve a high segmentation performance, although require a large model size and complexity. Also, DL models lack of feature interpretability and are perceived as "black-boxes" in the medical field. PCa-RadHop pipeline is proposed in this work, aiming to provide a more transparent feature extraction process using a linear model. It adopts the recently introduced Green Learning (GL) paradigm, which offers a small model size and low complexity. PCa-RadHop consists of two stages: Stage-1 extracts data-driven radiomics features from the bi-parametric Magnetic Resonance Imaging (bp-MRI) input and predicts an initial heatmap. To reduce the false positive rate, a subsequent stage-2 is introduced to refine the predictions by including more contextual information and radiomics features from each already detected Region of Interest (ROI). Experiments on the largest publicly available dataset, PI-CAI, show a competitive performance standing of the proposed method among other deep DL models, achieving an area under the curve (AUC) of 0.807 among a cohort of 1,000 patients. Moreover, PCa-RadHop maintains orders of magnitude smaller model size and complexity.

Advances in radiology and pathology of prostate cancer: a review for the pathologist

Multiparametric magnetic resonance imaging (mpMRI) has improved systematic prostate biopsy procedures in the diagnosis of clinically significant prostate cancer (csPCa) by reducing the number of unnecessary biopsies; numerous level one evidence studies have confirmed the accuracy of MRI-targeted biopsy, but, still today, systematic prostate biopsy is recommended to reduce the 15-20% false negative rate of mpMRI. New advanced imaging has been proposed to detect suspicious lesions and perform targeted biopsies especially when mpMRI cannot be performed. Transrectal ultrasound (TRUS) modalities are emerging as methods with greater sensitivity and specificity for the detection of PCa compared to the traditional TRUS; these techniques include elastography and contrast-enhanced ultrasound, as well as improved B-mode and Doppler techniques. These modalities can be combined to define a novel ultrasound approach: multiparametric ultrasound (mpUS). More recently, micro-ultrasound (MicroUS) and prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) have demonstrated to be sensitive for the detection of primary prostatic lesions resulting highly correlated with the aggressiveness of the primary prostatic tumor. In parallel, artificial intelligence is advancing and is set out to deeply change both radiology and pathology. In this study we address the role, advantages and shortcomings of novel imaging techniques for Pca, and discuss future directions including the applications of artificial intelligence-based techniques to imaging as well as histology. The significance of these findings for the practicing pathologist is discussed.

Effects of gut microbiota on prostatic cancer: a two-sample Mendelian randomization study

Aim

Recent observational and small-sample case-control studies have shown a relationship between gut microbiota composition and prostatic cancer (PCa). Nevertheless, the causal association between gut microbiota and PCa is still unclear. Herein, we used the Mendelian randomization (MR) method to explore the potential causal relationship between gut microbiota and PCa.

Methods

In this two-sample MR study, data were extracted from the summary statistics of gut microbiota from the largest available genome-wide association study meta-analysis conducted by the MiBioGen consortium (n = 14,306) and the Dutch Microbiome Project (n = 8,208). Summary statistics for PCa were obtained from the FinnGen consortium release data (n = 95,213). Inverse variance weighted (IVW), MR-Egger, strength test (F), and MR-PRESSO were used to examine the potential causal association between gut microbiota and PCa. Cochran's Q statistics were used to quantify the heterogeneity of instrumental variables.

Results

IVW estimates suggested that the relative abundance of Akkermansia muciniphila (odds ratio [OR] = 0.7926, 95% confidence interval [CI]: 0.6655-0.9440) and Bacteroides salyersiae (OR = 0.9023, 95% CI: 0.8262-0.9853) were negatively associated with the odds of PCa, while that of Eubacterium biforme (OR = 1.1629, 95% CI: 1.0110-1.3376) was positively associated with the odds of PCa. In addition, we explored these relationships among patients without other cancers and similarly found that the relative abundance of Akkermansia muciniphila, Bacteroides salyersiae, and Eubacterium biforme were linked to PCa (all P < 0.05).

Conclusion

Gut microbiota potentially influenced the occurrence of PCa. Our findings may provide some new ideas for researching the methods of PCa prevention. In addition, further studies are needed to explore the causal association and specific underlying mechanisms between gut microbiota and PCa.

A Review of the Clinical Applications of Artificial Intelligence in Abdominal Imaging

Artificial intelligence (AI) has been a topic of substantial interest for radiologists in recent years. Although many of the first clinical applications were in the neuro, cardiothoracic, and breast imaging subspecialties, the number of investigated and real-world applications of body imaging has been increasing, with more than 30 FDA-approved algorithms now available for applications in the abdomen and pelvis. In this manuscript, we explore some of the fundamentals of artificial intelligence and machine learning, review major functions that AI algorithms may perform, introduce current and potential future applications of AI in abdominal imaging, provide a basic understanding of the pathways by which AI algorithms can receive FDA approval, and explore some of the challenges with the implementation of AI in clinical practice.

Medical imaging and multimodal artificial intelligence models for streamlining and enhancing cancer care: opportunities and challenges

INTRODUCTION

Artificial intelligence (AI) has the potential to transform oncologic care. There have been significant developments in AI applications in medical imaging and increasing interest in multimodal models. These are likely to enable improved oncologic care through more precise diagnosis, increasingly in a more personalized and less invasive manner. In this review, we provide an overview of the current state and challenges that clinicians, administrative personnel and policy makers need to be aware of and mitigate for the technology to reach its full potential.

AREAS COVERED

The article provides a brief targeted overview of AI, a high-level review of the current state and future potential AI applications in diagnostic radiology and to a lesser extent digital pathology, focusing on oncologic applications. This is followed by a discussion of emerging approaches, including multimodal models. The article concludes with a discussion of technical, regulatory challenges and infrastructure needs for AI to realize its full potential.

EXPERT OPINION

There is a large volume of promising research, and steadily increasing commercially available tools using AI. For the most advanced and promising precision diagnostic applications of AI to be used clinically, robust and comprehensive quality monitoring systems and informatics platforms will likely be required.

Transverse prostate maximum sectional area can predict clinically significant prostate cancer in PI-RADS 3 lesions at multiparametric magnetic resonance imaging

Background

To evaluate multiparametric magnetic resonance imaging (mpMRI) parameters, such as TransPA (transverse prostate maximum sectional area), TransCGA (transverse central gland sectional area), TransPZA (transverse peripheral zone sectional area), and TransPAI (TransPZA/TransCGA ratio) in predicting prostate cancer (PCa) in prostate imaging reporting and data system (PI-RADS) 3 lesions.

Methods

Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV), the area under the receiver operating characteristic curve (AUC), and the best cut-off, were calculated. Univariate and multivariate analyses were carried out to evaluate the capability to predict PCa.

Results

Out of 120 PI-RADS 3 lesions, 54 (45.0%) were PCa with 34 (28.3%) csPCas. Median TransPA, TransCGA, TransPZA and TransPAI were 15.4cm², 9.1cm², 5.5cm² and 0.57, respectively. At multivariate analysis, location in the transition zone (OR=7.92, 95% CI: 2.70-23.29, P<0.001) and TransPA (OR=0.83, 95% CI: 0.76-0.92, P<0.001) were independent predictors of PCa. The TransPA (OR=0.90, 95% CI: 0.082-0.99, P=0.022) was an independent predictor of csPCa. The best cut-off of TransPA for csPCa was 18 (Sensitivity 88.2%, Specificity 37.2%, PPV 35.7%, NPV 88.9%). The discrimination (AUC) of the multivariate model was 0.627 (95% CI: 0.519-0.734, P<0.031).

Conclusions

In PI-RADS 3 lesions, the TransPA could be useful in selecting patients requiring biopsy.

An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS)

Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation.

Artificial Intelligence for Clinical Diagnosis and Treatment of Prostate Cancer

As medical science and technology progress towards the era of "big data", a multi-dimensional dataset pertaining to medical diagnosis and treatment is becoming accessible for mathematical modelling. However, these datasets are frequently inconsistent, noisy, and often characterized by a significant degree of redundancy. Thus, extensive data processing is widely advised to clean the dataset before feeding it into the mathematical model. In this context, Artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms based on artificial neural networks (ANNs) and their types, are being used to produce a precise and cross-sectional illustration of clinical data. For prostate cancer patients, datasets derived from the prostate-specific antigen (PSA), MRI-guided biopsies, genetic biomarkers, and the Gleason grading are primarily used for diagnosis, risk stratification, and patient monitoring. However, recording diagnoses and further stratifying risks based on such diagnostic data frequently involves much subjectivity. Thus, implementing an AI algorithm on a PC's diagnostic data can reduce the subjectivity of the process and assist in decision making. In addition, AI is used to cut down the processing time and help with early detection, which provides a superior outcome in critical cases of prostate cancer. Furthermore, this also facilitates offering the service at a lower cost by reducing the amount of human labor. Herein, the prime objective of this review is to provide a deep analysis encompassing the existing AI algorithms that are being deployed in the field of prostate cancer (PC) for diagnosis and treatment. Based on the available literature, AI-powered technology has the potential for extensive growth and penetration in PC diagnosis and treatment to ease and expedite the existing medical process.

Quality in MR reporting of the prostate – improving acquisition, the role of AI and future perspectives

The high quality of MRI reporting of the prostate is the most critical component of the service provided by a radiologist. Prostate MRI structured reporting with PI-RADS v. 2.1 has been proven to improve consistency, quality, guideline-based care in the management of prostate cancer. There is room for improved accuracy of prostate mpMRI reporting, particularly as PI-RADS core criteria are subjective for radiologists. The application of artificial intelligence may support radiologists in interpreting MRI scans. This review addresses the quality of prostate multiparametric MRI (mpMRI) structured reporting (include improvements in acquisition using artificial intelligence) in terms of size of prostate gland, imaging quality, lesion location, lesion size, TNM staging, sector map, and discusses the future prospects of quality in MR reporting.