A review of artificial intelligence in prostate cancer detection on imaging

Bhattacharya, Indrani; Khandwala, Yash S.; Vesal, Sulaiman; Shao, Wei; Yang, Qianye; Soerensen, Simon John Christoph; Fan, Richard E.; Ghanouni, Pejman; Kunder, Christian A.; Brooks, James D.; Hu, Yipeng; Rusu, Mirabela; Sonn, Geoffrey A.

doi:10.1177/17562872221128791

Cited by 23 publications

(17 citation statements)

References 208 publications

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“…Especially when considering the much more homogenous group of confirmed tumours in the second work. Finally, an excellent review of the application of AI techniques in classification of prostate lesions among different MRI modalities is given in [61]. The authors reported a mean sensitivity of 0.80, which is close to our results.…”

Section: Discussionsupporting

confidence: 87%

Textural Analysis Supports Prostate MR Diagnosis in PIRADS Protocol

Gibała,

Obuchowicz,

Lasek

et al. 2023

Applied Sciences

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Prostate cancer is one of the most common cancers in the world. Due to the ageing of society and the extended life of the population, early diagnosis is a great challenge for healthcare. Unfortunately, the currently available diagnostic methods, in which magnetic resonance imaging (MRI) using the PIRADS protocol plays an increasingly important role, are imperfect, mostly in the inability to visualise small cancer foci and misinterpretation of the imagery data. Therefore, there is a great need to improve the methods currently applied and look for even better ones for the early detection of prostate cancer. In the presented research, anonymised MRI scans of 92 patients with evaluation in the PIRADS protocol were selected from the data routinely scanned for prostate cancer. Suspicious tissues were depicted manually under medical supervision. The texture features in the marked regions were calculated using the qMaZda software. The multiple-instance learning approach based on the SVM classifier allowed recognising between healthy and ill prostate tissue. The best F1 score equal to 0.77 with a very high recall equal to 0.70 and precision equal to 0.85 was recorded for the texture features describing the central zone. The research showed that the use of texture analysis in prostate MRI may allow for automation of the assessment of PIRADS scores.

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Section: Discussionsupporting

confidence: 87%

Textural Analysis Supports Prostate MR Diagnosis in PIRADS Protocol

Gibała,

Obuchowicz,

Lasek

et al. 2023

Applied Sciences

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“…Insufficient data in the development of algorithms leads to errors in performance. 16,[19][20][21][22][23][24][25][26][27][28][29][30] Underrepresentation of disease entities or population groups is likely to result in some entities not being identified correctly by the algorithm, meaning it is likely to underperform in such groups. This creates bias and an increased risk of errors in these subgroups.…”

Section: Missing Data Leading To Bias and Hidden Stratificationmentioning

confidence: 99%

Why do errors arise in artificial intelligence diagnostic tools in histopathology and how can we minimize them?

Evans,

Snead

2023

Histopathology

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Artificial intelligence (AI)‐based diagnostic tools can offer numerous benefits to the field of histopathology, including improved diagnostic accuracy, efficiency and productivity. As a result, such tools are likely to have an increasing role in routine practice. However, all AI tools are prone to errors, and these AI‐associated errors have been identified as a major risk in the introduction of AI into healthcare. The errors made by AI tools are different, in terms of both cause and nature, to the errors made by human pathologists. As highlighted by the National Institute for Health and Care Excellence, it is imperative that practising pathologists understand the potential limitations of AI tools, including the errors made. Pathologists are in a unique position to be gatekeepers of AI tool use, maximizing patient benefit while minimizing harm. Furthermore, their pathological knowledge is essential to understanding when, and why, errors have occurred and so to developing safer future algorithms. This paper summarises the literature on errors made by AI diagnostic tools in histopathology. These include erroneous errors, data concerns (data bias, hidden stratification, data imbalances, distributional shift, and lack of generalisability), reinforcement of outdated practices, unsafe failure mode, automation bias, and insensitivity to impact. Methods to reduce errors in both tool design and clinical use are discussed, and the practical roles for pathologists in error minimisation are highlighted. This aims to inform and empower pathologists to move safely through this seismic change in practice and help ensure that novel AI tools are adopted safely.

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“…Machine learning methods to detect aggressive/clinically significant prostate cancer on MRI can help standardize radiologist interpretations, which currently suffer from up to 34% missed aggressive prostate cancers, falsepositive rates >35%, and high inter-reader variability (κ = 0.46-0.78). [1][2][3][4] Most existing MRI-based prostate cancer detection systems [5][6][7][8][9][10][11][12][13][14] consider MRI features in a vacuum, and ignore histopathology image information. Histopathology images acquired via surgery or biopsy contain definitive information about cancer presence and aggressiveness.…”

Section: Introductionmentioning

confidence: 99%

ArtHiFy: artificial histopathology-style features for improving MRI-based prostate cancer detection

Bhattacharya,

Shao,

et al. 2024

Medical Imaging 2024: Computer-Aided Diagnosis

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Automated detection and aggressiveness classification of prostate cancer on Magnetic Resonance Imaging (MRI) can help standardize radiologist interpretations, and guide MRI-Ultrasound fusion biopsies. Existing automated methods rely on MRI features alone, disregarding histopathology image information. Histopathology images contain definitive information about the presence, extent, and aggressiveness of cancer. We present a two-step radiology-pathology fusion model, ArtHiFy, Artificial Histopathology-style Features for improving MRI-based prostate cancer detection, that leverages generative models in a multimodal co-learning strategy, enabling learning from resource-rich histopathology, but prediction using resource-poor MRI alone. In the first step, ArtHiFy generates artificial low-resolution histopathology-style features from MRI using a modified Geometry-consistent Generative Adversarial Network (GcGAN). The generated low-resolution histopathology-style features emphasize cancer regions as having less texture variations, mimicking densely packed nuclei in real histopathology images. In the second step, ArtHiFy uses these generated artificial histopathology-style features in addition to MR images in a convolutional neural network architecture to detect and localize aggressive and indolent prostate cancer on MRI. ArtHiFy does not require spatial alignment between MRI and histopathology images during training, and it does not require histopathology images at all during inference, making it clinically relevant for MRI-based prostate cancer diagnosis in new patients. We trained ArtHiFy using prostate cancer patients who underwent radical prostatectomy, and evaluated it on patients with and without prostate cancer. Our experiments showed that ArtHiFy improved prostate cancer detection performance over existing top performing prostate cancer detection models, with statistically significant differences.

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A review of artificial intelligence in prostate cancer detection on imaging

Cited by 23 publications

References 208 publications

Textural Analysis Supports Prostate MR Diagnosis in PIRADS Protocol

Textural Analysis Supports Prostate MR Diagnosis in PIRADS Protocol

Why do errors arise in artificial intelligence diagnostic tools in histopathology and how can we minimize them?

ArtHiFy: artificial histopathology-style features for improving MRI-based prostate cancer detection

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