A Systematic Review of Artificial Intelligence in Prostate Cancer

Figures & data

Figure 1 Schematic depicting the architecture of an artificial neural network.

Table 1 Summary of All Articles Included in the Systematic Review

AI Type	AI Name	Summary	Author/Reference
ANN + risk classification	PRODIGE	Proposed Umbrella Protocol that standardizes data and procedures to create a consistent dataset useful to elaborate Decision Support Systems. Thus this tool supports personalized decision making from multifactorial data sources.	Alitto et al^Citation38
ANN + MRI dx		Tested machine learning classifiers for transition zone and peripheral zone in MRI to classify prostate tumors with or without a Gleason 4 component. Classifiers trained within each zone had higher performance than the subjected option of pathologists.	Antonelli et al^Citation21
ANN + patient interaction	askMUSIC	Clinical registry to help patients interact with treatment decisions with similar characteristics. Newly diagnosed patients can explore treatment options and compare their recommended treatments with other patients with similar treatments.	Auffenberg et al^Citation37
ANN + histopathologic dx		Automated image analysis using techniques similar to facial recognition to capture architectural differences between benign epithelium and various Gleason grades. This automated method showed concordance with trained pathologists and showed promise in differentiating G3+4 and G4+3 grades.	Bhele et al^Citation14
ANN + PSA dx		Statistical prediction of early prostate cancer by using PSA levels. The ANN accuracy level was higher than conventional PSA parameters and multivariate analysis.	Djavan et al^Citation8
ANN + MRI dx		Multiparametric MRI image processing combining the apparent diffusion coefficient and T2-weighted texture features. Results suggest texture features together with simple data augmentation offer reasonably accurate classification of Gleason patterns.	Fehr et al^Citation20
ANN + PSA dx		Multivariate algorithms based on clinical characteristics could reduce rate of false-positives in prostate screening more than free PSA alone. The logistic regression model showed higher accuracy and sensitivity when compared to free PSA showing promise to reduce the number of unnecessary prostate biopsies.	Finne et al^Citation9
ANN + PSA dx		Logistic regression and ANN diagnostic performance comparison. When aligned both algorithms showed no significant difference between the 2 results.	Ge et al^Citation10
ANN + biomarker dx		Predictive biomarkers for outcome in KI67 and DLX2. Both showed value to be able to inform clinical decision making in patients for active surveillance.	Green et al^Citation35
ANN + biomarker dx		Targeted proteomics to discover robust proteomic signatures for prostate cancer. Computationally guided proteomics can be used to discover highly accurate non-invasive biomarkers.	Kim et al^Citation36
ANN + risk stratification		Using H&E slides, image processing used to be able to identify recurrence. Two level CNN has high accuracy when applied to 30 recurrent cases and 30 non-recurrent cases. This can possibly be applied to choose treatment options based on slides.	Kumar et al^Citation39
ANN + histopathologic dx		Using a polychotomous logistic regression model and ANN for predicting biopsy results. Comparison of models showed no statistical difference between the two.	Lawrentschuk et al^Citation15
ANN + biomarker dx		Comparing statistical methods with ANN using conventional and experimental biomarkers. Study showed the promise for both methods in evaluating prognostic markers and potential to use the technology to evaluate new markers.	Naguib et al^Citation40
ANN + PSA dx		Using ANN to predict recurrence based on free/total PSA, PSA density, and other clinical characteristics. The ANN found a pattern of prostate cancer with those patients that had a negative initial biopsy, and thus the ANN reduced unnecessary repeat biopsies.	Remzi et al^Citation17
ANN + PSA dx		Used ANN to evaluate diagnostic value of % free PSA in men with total PSA levels between 2 and 20 μg/L. The ANN showed enhanced accuracy when included with digital rectal examination and prostate volume measurements when compared to % free PSA alone.	Stephan et al^Citation11
ANN + PSA dx		Develop a classification and regression tree that could identify patients with significance prostate cancer based on patients with abnormal PSA, digital rectal examination findings, or both. The analysis showed provided net benefit when compared to a logistic regression model, PSA density, and biopsying all patients.	Stojadinovic et al^Citation9
ANN + histopathologic dx		The study evaluated the capacity of ANNs to assess prostate biopsies based on the presence, extent, and Gleason grade of the malignant tissue in comparison to experienced urological pathologists. Results showed that an ANN is able to be trained to detect and assess prostate biopsies with similar accuracy to urological pathologists.	Ström et al^Citation2
ANN + MRI dx		Classifier system for prediction of prostate cancer Gleason score using texture features of T2-weighted imaging in MRI images. Texture feature analysis showed good classification performance for GS in prostate cancer.	Toivonen et al^Citation23
ANN + histopathologic dx		Stratification of adenocarcinomas based on histological examination of tumor structure. Complex patterns were stratified and then classified showing a reduction in intraobserver variability giving a better choice for patients to be recommended active surveillance.	Waliszewski et al^Citation14

Figure 2 Flow diagram representing different phases of the systematic literature research according to PRISMA criteria.

Notes: PRISMA figure adapted from Liberati A, Altman D, Tetzlaff J, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. Journal of clinical epidemiology. 2009;62(10). Creative Commons.⁴³

Auffenberg GB, Ghani KR, Ramani S, et al. Urological Surgery Improvement, askMUSIC: leveraging a clinical registry to develop a new machine learning model to inform patients of prostate cancer treatments chosen by similar men. Eur Urol. 2019;75:901–907. doi:10.1016/j.eururo.2018.09.050

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Fehr D, Veeraraghavan H, Wibmer A, et al. Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proc Natl Acad Sci U S A. 2015;112:E6265–6273. doi:10.1073/pnas.1505935112

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Kim Y, Jeon J, Mejia S, et al. Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer. Nat Commun. 2016;7:11906.

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Waliszewski P, Wagenlehner F, Gattenlöhner S, Weidner W. On the relationship between tumor structure and complexity of the spatial distribution of cancer cell nuclei: a fractal geometrical model of prostate carcinoma. Prostate. 2015;75:399–414. doi:10.1002/pros.22926

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Djavan B, Remzi M, Zlotta A, Seitz C, Snow P, Marberger M. Novel artificial neural network for early detection of prostate cancer. J Clin Oncol. 2002;20:921–929. doi:10.1200/JCO.2002.20.4.921

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Sato C, Naganawa S, Nakamura T, et al. Differentiation of noncancerous tissue and cancer lesions by apparent diffusion coefficient values in transition and peripheral zones of the prostate. J Magn Reson Imaging. 2005;21:258–262. doi:10.1002/jmri.20251

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Stojadinovic M, Stojadinovic M, Pantic D. Decision tree analysis for prostate cancer prediction. Srp Arh Celok Lek. 2018;147:39.

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Finne P, Finne R, Auvinen A, et al. Predicting the outcome of prostate biopsy in screen-positive men by a multilayer perceptron network. Urology. 2000;56:418–422. doi:10.1016/S0090-4295(00)00672-5

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Fruge AD, Smith KS, Bail JR, Rais-Bahrami S, Demark-Wahnefried W. Biomarkers associated with tumor Ki67 and Cathepsin L gene expression in prostate cancer patients participating in a presurgical weight loss trial. Front Oncol. 2020;10:544201. doi:10.3389/fonc.2020.544201

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Green WJ, Ball G, Hulman G, et al. KI67 and DLX2 predict increased risk of metastasis formation in prostate cancer – a targeted molecular approach. Br J Cancer. 2016;115:236–242. doi:10.1038/bjc.2016.169

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Alitto AR, Gatta R, Vanneste B, et al. PRODIGE: pRediction models in prOstate cancer for personalized meDIcine challenGE. Future Oncol. 2017;13:2171–2181. doi:10.2217/fon-2017-0142

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Bhele S, Ma Z, Mohanty S, et al. A machine learning tool to complement Gleason grading of prostate carcinoma. 2014.

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Neeraj K, Ruchika V, Ashish A, et al. Convolutional neural networks for prostate cancer recurrence prediction. Proc SPIE. 2017.

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Rodvold DM, McLeod DG, Brandt JM, Snow PB, Murphy GP. Introduction to artificial neural networks for physicians: taking the lid off the black box. Prostate. 2001;46:39–44. doi:10.1002/1097-0045(200101)46:1<39::AID-PROS1006>3.0.CO;2-M

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Ge P, Gao F, Chen G. Predictive models for prostate cancer based on logistic regression and artificial neural network. 2015 IEEE International Conference on Mechatronics and Automation (ICMA). 2015;1472–1477.

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Strom P, Kartasalo K, Olsson H, et al. Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based. Diagnostic Study, Lancet Oncol. 2020;21:222–232. doi:10.1016/S1470-2045(19)30738-7

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Toivonen J, Montoya Perez I, Movahedi P, et al. Radiomics and machine learning of multisequence multiparametric prostate MRI: towards improved non-invasive prostate cancer characterization. PLoS One. 2019;14:e0217702. doi:10.1371/journal.pone.0217702

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Liberati A, Altman D, Tetzlaff J, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration. Journal of clinical epidemiology. 2009;62:(10).

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